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Posted to commits@kafka.apache.org by ij...@apache.org on 2017/11/01 12:58:41 UTC
[01/14] kafka-site git commit: Update site for 1.0.0 release
Repository: kafka-site
Updated Branches:
refs/heads/asf-site e977ecca1 -> cb024c135
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/upgrade.html
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<script><!--#include virtual="js/templateData.js" --></script>
+
+<h4><a id="upgrade_1_0_0" href="#upgrade_1_0_0">Upgrading from 0.8.x, 0.9.x, 0.10.0.x, 0.10.1.x, 0.10.2.x or 0.11.0.x to 1.0.0</a></h4>
+<p>Kafka 1.0.0 introduces wire protocol changes. By following the recommended rolling upgrade plan below,
+ you guarantee no downtime during the upgrade. However, please review the <a href="#upgrade_100_notable">notable changes in 1.0.0</a> before upgrading.
+</p>
+
+<p><b>For a rolling upgrade:</b></p>
+
+<ol>
+ <li> Update server.properties on all brokers and add the following properties. CURRENT_KAFKA_VERSION refers to the version you
+ are upgrading from. CURRENT_MESSAGE_FORMAT_VERSION refers to the current message format version currently in use. If you have
+ not overridden the message format previously, then CURRENT_MESSAGE_FORMAT_VERSION should be set to match CURRENT_KAFKA_VERSION.
+ <ul>
+ <li>inter.broker.protocol.version=CURRENT_KAFKA_VERSION (e.g. 0.8.2, 0.9.0, 0.10.0, 0.10.1, 0.10.2, 0.11.0).</li>
+ </ul>
+ </li>
+ <li> Upgrade the brokers one at a time: shut down the broker, update the code, and restart it. </li>
+ <li> Once the entire cluster is upgraded, bump the protocol version by editing <code>inter.broker.protocol.version</code> and setting it to 1.0.
+ <li> Restart the brokers one by one for the new protocol version to take effect. </li>
+</ol>
+
+<p><b>Additional Upgrade Notes:</b></p>
+
+<ol>
+ <li>If you are willing to accept downtime, you can simply take all the brokers down, update the code and start them back up. They will start
+ with the new protocol by default.</li>
+ <li>Bumping the protocol version and restarting can be done any time after the brokers are upgraded. It does not have to be immediately after.
+ Similarly for the message format version.</li>
+</ol>
+
+<h5><a id="upgrade_100_notable" href="#upgrade_100_notable">Notable changes in 1.0.0</a></h5>
+<ul>
+ <li>Topic deletion is now enabled by default, since the functionality is now stable. Users who wish to
+ to retain the previous behavior should set the broker config <code>delete.topic.enable</code> to <code>false</code>. Keep in mind that topic deletion removes data and the operation is not reversible (i.e. there is no "undelete" operation)</li>
+ <li>For topics that support timestamp search if no offset can be found for a partition, that partition is now included in the search result with a null offset value. Previously, the partition was not included in the map.
+ This change was made to make the search behavior consistent with the case of topics not supporting timestamp search.
+ <li>If the <code>inter.broker.protocol.version</code> is 1.0 or later, a broker will now stay online to serve replicas
+ on live log directories even if there are offline log directories. A log directory may become offline due to IOException
+ caused by hardware failure. Users need to monitor the per-broker metric <code>offlineLogDirectoryCount</code> to check
+ whether there is offline log directory. </li>
+ <li>Added KafkaStorageException which is a retriable exception. KafkaStorageException will be converted to NotLeaderForPartitionException in the response
+ if the version of client's FetchRequest or ProducerRequest does not support KafkaStorageException. </li>
+ <li>-XX:+DisableExplicitGC was replaced by -XX:+ExplicitGCInvokesConcurrent in the default JVM settings. This helps
+ avoid out of memory exceptions during allocation of native memory by direct buffers in some cases.</li>
+ <li>The overridden <code>handleError</code> method implementations have been removed from the following deprecated classes in
+ the <code>kafka.api</code> package: <code>FetchRequest</code>, <code>GroupCoordinatorRequest</code>, <code>OffsetCommitRequest</code>,
+ <code>OffsetFetchRequest</code>, <code>OffsetRequest</code>, <code>ProducerRequest</code>, and <code>TopicMetadataRequest</code>.
+ This was only intended for use on the broker, but it is no longer in use and the implementations have not been maintained.
+ A stub implementation has been retained for binary compatibility.</li>
+ <li>The Java clients and tools now accept any string as a client-id.</li>
+ <li>The deprecated tool <code>kafka-consumer-offset-checker.sh</code> has been removed. Use <code>kafka-consumer-groups.sh</code> to get consumer group details.</li>
+ <li>SimpleAclAuthorizer now logs access denials to the authorizer log by default.</li>
+ <li>Authentication failures are now reported to clients as one of the subclasses of <code>AuthenticationException</code>.
+ No retries will be performed if a client connection fails authentication.</li>
+ <li>Custom <code>SaslServer</code> implementations may throw <code>SaslAuthenticationException</code> to provide an error
+ message to return to clients indicating the reason for authentication failure. Implementors should take care not to include
+ any security-critical information in the exception message that should not be leaked to unauthenticated clients.</li>
+ <li>The <code>app-info</code> mbean registered with JMX to provide version and commit id will be deprecated and replaced with
+ metrics providing these attributes.</li>
+ <li>Kafka metrics may now contain non-numeric values. <code>org.apache.kafka.common.Metric#value()</code> has been deprecated and
+ will return <code>0.0</code> in such cases to minimise the probability of breaking users who read the value of every client
+ metric (via a <code>MetricsReporter</code> implementation or by calling the <code>metrics()</code> method).
+ <code>org.apache.kafka.common.Metric#metricValue()</code> can be used to retrieve numeric and non-numeric metric values.</li>
+ <li>Every Kafka rate metric now has a corresponding cumulative count metric with the suffix <code>-total</code>
+ to simplify downstream processing. For example, <code>records-consumed-rate</code> has a corresponding
+ metric named <code>records-consumed-total</code>.</li>
+ <li>Mx4j will only be enabled if the system property <code>kafka_mx4jenable</code> is set to <code>true</code>. Due to a logic
+ inversion bug, it was previously enabled by default and disabled if <code>kafka_mx4jenable</code> was set to <code>true</code>.</li>
+ <li>The package <code>org.apache.kafka.common.security.auth</code> in the clients jar has been made public and added to the javadocs.
+ Internal classes which had previously been located in this package have been moved elsewhere.</li>
+ <li>When using an Authorizer and a user doesn't have required permissions on a topic, the broker
+ will return TOPIC_AUTHORIZATION_FAILED errors to requests irrespective of topic existence on broker.
+ If the user have required permissions and the topic doesn't exists, then the UNKNOWN_TOPIC_OR_PARTITION
+ error code will be returned. </li>
+ <li>config/consumer.properties file updated to use new consumer config properties.</li>
+</ul>
+
+<h5><a id="upgrade_100_new_protocols" href="#upgrade_100_new_protocols">New Protocol Versions</a></h5>
+<ul>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-112%3A+Handle+disk+failure+for+JBOD">KIP-112</a>: LeaderAndIsrRequest v1 introduces a partition-level <code>is_new</code> field. </li>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-112%3A+Handle+disk+failure+for+JBOD">KIP-112</a>: UpdateMetadataRequest v4 introduces a partition-level <code>offline_replicas</code> field. </li>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-112%3A+Handle+disk+failure+for+JBOD">KIP-112</a>: MetadataResponse v5 introduces a partition-level <code>offline_replicas</code> field. </li>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-112%3A+Handle+disk+failure+for+JBOD">KIP-112</a>: ProduceResponse v4 introduces error code for KafkaStorageException. </li>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-112%3A+Handle+disk+failure+for+JBOD">KIP-112</a>: FetchResponse v6 introduces error code for KafkaStorageException. </li>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-152+-+Improve+diagnostics+for+SASL+authentication+failures">KIP-152</a>:
+ SaslAuthenticate request has been added to enable reporting of authentication failures. This request will
+ be used if the SaslHandshake request version is greater than 0. </li>
+</ul>
+
+<h5><a id="upgrade_100_streams" href="#upgrade_100_streams">Upgrading a 1.0.0 Kafka Streams Application</a></h5>
+<ul>
+ <li> Upgrading your Streams application from 0.11.0 to 1.0.0 does not require a broker upgrade.
+ A Kafka Streams 1.0.0 application can connect to 0.11.0, 0.10.2 and 0.10.1 brokers (it is not possible to connect to 0.10.0 brokers though). </li>
+ <li> If you are monitoring on streams metrics, you will need make some changes to the metrics names in your reporting and monitoring code, because the metrics sensor hierarchy was changed. </li>
+ <li> There are a few public APIs including <code>ProcessorContext#schedule()</code>, <code>Processor#punctuate()</code> and <code>KStreamBuilder</code>, <code>TopologyBuilder</code> are being deprecated by new APIs.
+ We recommend making corresponding code changes, which should be very minor since the new APIs look quite similar, when you upgrade.
+ <li> See <a href="/{{version}}/documentation/streams#streams_api_changes_100">Streams API changes in 1.0.0</a> for more details. </li>
+</ul>
+
+<h4><a id="upgrade_11_0_0" href="#upgrade_11_0_0">Upgrading from 0.8.x, 0.9.x, 0.10.0.x, 0.10.1.x or 0.10.2.x to 0.11.0.0</a></h4>
+<p>Kafka 0.11.0.0 introduces a new message format version as well as wire protocol changes. By following the recommended rolling upgrade plan below,
+ you guarantee no downtime during the upgrade. However, please review the <a href="#upgrade_1100_notable">notable changes in 0.11.0.0</a> before upgrading.
+</p>
+
+<p>Starting with version 0.10.2, Java clients (producer and consumer) have acquired the ability to communicate with older brokers. Version 0.11.0
+ clients can talk to version 0.10.0 or newer brokers. However, if your brokers are older than 0.10.0, you must upgrade all the brokers in the
+ Kafka cluster before upgrading your clients. Version 0.11.0 brokers support 0.8.x and newer clients.
+</p>
+
+<p><b>For a rolling upgrade:</b></p>
+
+<ol>
+ <li> Update server.properties on all brokers and add the following properties. CURRENT_KAFKA_VERSION refers to the version you
+ are upgrading from. CURRENT_MESSAGE_FORMAT_VERSION refers to the current message format version currently in use. If you have
+ not overridden the message format previously, then CURRENT_MESSAGE_FORMAT_VERSION should be set to match CURRENT_KAFKA_VERSION.
+ <ul>
+ <li>inter.broker.protocol.version=CURRENT_KAFKA_VERSION (e.g. 0.8.2, 0.9.0, 0.10.0, 0.10.1 or 0.10.2).</li>
+ <li>log.message.format.version=CURRENT_MESSAGE_FORMAT_VERSION (See <a href="#upgrade_10_performance_impact">potential performance impact
+ following the upgrade</a> for the details on what this configuration does.)</li>
+ </ul>
+ </li>
+ <li> Upgrade the brokers one at a time: shut down the broker, update the code, and restart it. </li>
+ <li> Once the entire cluster is upgraded, bump the protocol version by editing <code>inter.broker.protocol.version</code> and setting it to 0.11.0, but
+ do not change <code>log.message.format.version</code> yet. </li>
+ <li> Restart the brokers one by one for the new protocol version to take effect. </li>
+ <li> Once all (or most) consumers have been upgraded to 0.11.0 or later, then change log.message.format.version to 0.11.0 on each
+ broker and restart them one by one. Note that the older Scala consumer does not support the new message format, so to avoid
+ the performance cost of down-conversion (or to take advantage of <a href="#upgrade_11_exactly_once_semantics">exactly once semantics</a>),
+ the new Java consumer must be used.</li>
+</ol>
+
+<p><b>Additional Upgrade Notes:</b></p>
+
+<ol>
+ <li>If you are willing to accept downtime, you can simply take all the brokers down, update the code and start them back up. They will start
+ with the new protocol by default.</li>
+ <li>Bumping the protocol version and restarting can be done any time after the brokers are upgraded. It does not have to be immediately after.
+ Similarly for the message format version.</li>
+ <li>It is also possible to enable the 0.11.0 message format on individual topics using the topic admin tool (<code>bin/kafka-topics.sh</code>)
+ prior to updating the global setting <code>log.message.format.version</code>.</li>
+ <li>If you are upgrading from a version prior to 0.10.0, it is NOT necessary to first update the message format to 0.10.0
+ before you switch to 0.11.0.</li>
+</ol>
+
+<h5><a id="upgrade_1100_notable" href="#upgrade_1100_notable">Notable changes in 0.11.0.0</a></h5>
+<ul>
+ <li>Unclean leader election is now disabled by default. The new default favors durability over availability. Users who wish to
+ to retain the previous behavior should set the broker config <code>unclean.leader.election.enable</code> to <code>true</code>.</li>
+ <li>Producer configs <code>block.on.buffer.full</code>, <code>metadata.fetch.timeout.ms</code> and <code>timeout.ms</code> have been
+ removed. They were initially deprecated in Kafka 0.9.0.0.</li>
+ <li>The <code>offsets.topic.replication.factor</code> broker config is now enforced upon auto topic creation. Internal
+ auto topic creation will fail with a GROUP_COORDINATOR_NOT_AVAILABLE error until the cluster size meets this
+ replication factor requirement.</li>
+ <li> When compressing data with snappy, the producer and broker will use the compression scheme's default block size (2 x 32 KB)
+ instead of 1 KB in order to improve the compression ratio. There have been reports of data compressed with the smaller
+ block size being 50% larger than when compressed with the larger block size. For the snappy case, a producer with 5000
+ partitions will require an additional 315 MB of JVM heap.</li>
+ <li> Similarly, when compressing data with gzip, the producer and broker will use 8 KB instead of 1 KB as the buffer size. The default
+ for gzip is excessively low (512 bytes). </li>
+ <li>The broker configuration <code>max.message.bytes</code> now applies to the total size of a batch of messages.
+ Previously the setting applied to batches of compressed messages, or to non-compressed messages individually.
+ A message batch may consist of only a single message, so in most cases, the limitation on the size of
+ individual messages is only reduced by the overhead of the batch format. However, there are some subtle implications
+ for message format conversion (see <a href="#upgrade_11_message_format">below</a> for more detail). Note also
+ that while previously the broker would ensure that at least one message is returned in each fetch request (regardless of the
+ total and partition-level fetch sizes), the same behavior now applies to one message batch.</li>
+ <li>GC log rotation is enabled by default, see KAFKA-3754 for details.</li>
+ <li>Deprecated constructors of RecordMetadata, MetricName and Cluster classes have been removed.</li>
+ <li>Added user headers support through a new Headers interface providing user headers read and write access.</li>
+ <li>ProducerRecord and ConsumerRecord expose the new Headers API via <code>Headers headers()</code> method call.</li>
+ <li>ExtendedSerializer and ExtendedDeserializer interfaces are introduced to support serialization and deserialization for headers. Headers will be ignored if the configured serializer and deserializer are not the above classes.</li>
+ <li>A new config, <code>group.initial.rebalance.delay.ms</code>, was introduced.
+ This config specifies the time, in milliseconds, that the <code>GroupCoordinator</code> will delay the initial consumer rebalance.
+ The rebalance will be further delayed by the value of <code>group.initial.rebalance.delay.ms</code> as new members join the group, up to a maximum of <code>max.poll.interval.ms</code>.
+ The default value for this is 3 seconds.
+ During development and testing it might be desirable to set this to 0 in order to not delay test execution time.
+ </li>
+ <li><code>org.apache.kafka.common.Cluster#partitionsForTopic</code>, <code>partitionsForNode</code> and <code>availablePartitionsForTopic</code> methods
+ will return an empty list instead of <code>null</code> (which is considered a bad practice) in case the metadata for the required topic does not exist.
+ </li>
+ <li>Streams API configuration parameters <code>timestamp.extractor</code>, <code>key.serde</code>, and <code>value.serde</code> were deprecated and
+ replaced by <code>default.timestamp.extractor</code>, <code>default.key.serde</code>, and <code>default.value.serde</code>, respectively.
+ </li>
+ <li>For offset commit failures in the Java consumer's <code>commitAsync</code> APIs, we no longer expose the underlying
+ cause when instances of <code>RetriableCommitFailedException</code> are passed to the commit callback. See
+ <a href="https://issues.apache.org/jira/browse/KAFKA-5052">KAFKA-5052</a> for more detail.
+ </li>
+</ul>
+
+<h5><a id="upgrade_1100_new_protocols" href="#upgrade_1100_new_protocols">New Protocol Versions</a></h5>
+<ul>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-107%3A+Add+purgeDataBefore()+API+in+AdminClient">KIP-107</a>: FetchRequest v5 introduces a partition-level <code>log_start_offset</code> field. </li>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-107%3A+Add+purgeDataBefore()+API+in+AdminClient">KIP-107</a>: FetchResponse v5 introduces a partition-level <code>log_start_offset</code> field. </li>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-82+-+Add+Record+Headers">KIP-82</a>: ProduceRequest v3 introduces an array of <code>header</code> in the message protocol, containing <code>key</code> field and <code>value</code> field.</li>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-82+-+Add+Record+Headers">KIP-82</a>: FetchResponse v5 introduces an array of <code>header</code> in the message protocol, containing <code>key</code> field and <code>value</code> field.</li>
+</ul>
+
+<h5><a id="upgrade_11_exactly_once_semantics" href="#upgrade_11_exactly_once_semantics">Notes on Exactly Once Semantics</a></h5>
+<p>Kafka 0.11.0 includes support for idempotent and transactional capabilities in the producer. Idempotent delivery
+ ensures that messages are delivered exactly once to a particular topic partition during the lifetime of a single producer.
+ Transactional delivery allows producers to send data to multiple partitions such that either all messages are successfully
+ delivered, or none of them are. Together, these capabilities enable "exactly once semantics" in Kafka. More details on these
+ features are available in the user guide, but below we add a few specific notes on enabling them in an upgraded cluster.
+ Note that enabling EoS is not required and there is no impact on the broker's behavior if unused.</p>
+
+<ol>
+ <li>Only the new Java producer and consumer support exactly once semantics.</li>
+ <li>These features depend crucially on the <a href="#upgrade_11_message_format">0.11.0 message format</a>. Attempting to use them
+ on an older format will result in unsupported version errors.</li>
+ <li>Transaction state is stored in a new internal topic <code>__transaction_state</code>. This topic is not created until the
+ the first attempt to use a transactional request API. Similar to the consumer offsets topic, there are several settings
+ to control the topic's configuration. For example, <code>transaction.state.log.min.isr</code> controls the minimum ISR for
+ this topic. See the configuration section in the user guide for a full list of options.</li>
+ <li>For secure clusters, the transactional APIs require new ACLs which can be turned on with the <code>bin/kafka-acls.sh</code>.
+ tool.</li>
+ <li>EoS in Kafka introduces new request APIs and modifies several existing ones. See
+ <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-98+-+Exactly+Once+Delivery+and+Transactional+Messaging#KIP-98-ExactlyOnceDeliveryandTransactionalMessaging-RPCProtocolSummary">KIP-98</a>
+ for the full details</li>
+</ol>
+
+<h5><a id="upgrade_11_message_format" href="#upgrade_11_message_format">Notes on the new message format in 0.11.0</a></h5>
+<p>The 0.11.0 message format includes several major enhancements in order to support better delivery semantics for the producer
+ (see <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-98+-+Exactly+Once+Delivery+and+Transactional+Messaging">KIP-98</a>)
+ and improved replication fault tolerance
+ (see <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-101+-+Alter+Replication+Protocol+to+use+Leader+Epoch+rather+than+High+Watermark+for+Truncation">KIP-101</a>).
+ Although the new format contains more information to make these improvements possible, we have made the batch format much
+ more efficient. As long as the number of messages per batch is more than 2, you can expect lower overall overhead. For smaller
+ batches, however, there may be a small performance impact. See <a href="bit.ly/kafka-eos-perf">here</a> for the results of our
+ initial performance analysis of the new message format. You can also find more detail on the message format in the
+ <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-98+-+Exactly+Once+Delivery+and+Transactional+Messaging#KIP-98-ExactlyOnceDeliveryandTransactionalMessaging-MessageFormat">KIP-98</a> proposal.
+</p>
+<p>One of the notable differences in the new message format is that even uncompressed messages are stored together as a single batch.
+ This has a few implications for the broker configuration <code>max.message.bytes</code>, which limits the size of a single batch. First,
+ if an older client produces messages to a topic partition using the old format, and the messages are individually smaller than
+ <code>max.message.bytes</code>, the broker may still reject them after they are merged into a single batch during the up-conversion process.
+ Generally this can happen when the aggregate size of the individual messages is larger than <code>max.message.bytes</code>. There is a similar
+ effect for older consumers reading messages down-converted from the new format: if the fetch size is not set at least as large as
+ <code>max.message.bytes</code>, the consumer may not be able to make progress even if the individual uncompressed messages are smaller
+ than the configured fetch size. This behavior does not impact the Java client for 0.10.1.0 and later since it uses an updated fetch protocol
+ which ensures that at least one message can be returned even if it exceeds the fetch size. To get around these problems, you should ensure
+ 1) that the producer's batch size is not set larger than <code>max.message.bytes</code>, and 2) that the consumer's fetch size is set at
+ least as large as <code>max.message.bytes</code>.
+</p>
+<p>Most of the discussion on the performance impact of <a href="#upgrade_10_performance_impact">upgrading to the 0.10.0 message format</a>
+ remains pertinent to the 0.11.0 upgrade. This mainly affects clusters that are not secured with TLS since "zero-copy" transfer
+ is already not possible in that case. In order to avoid the cost of down-conversion, you should ensure that consumer applications
+ are upgraded to the latest 0.11.0 client. Significantly, since the old consumer has been deprecated in 0.11.0.0, it does not support
+ the new message format. You must upgrade to use the new consumer to use the new message format without the cost of down-conversion.
+ Note that 0.11.0 consumers support backwards compatibility with brokers 0.10.0 brokers and upward, so it is possible to upgrade the
+ clients first before the brokers.
+</p>
+
+<h4><a id="upgrade_10_2_0" href="#upgrade_10_2_0">Upgrading from 0.8.x, 0.9.x, 0.10.0.x or 0.10.1.x to 0.10.2.0</a></h4>
+<p>0.10.2.0 has wire protocol changes. By following the recommended rolling upgrade plan below, you guarantee no downtime during the upgrade.
+However, please review the <a href="#upgrade_1020_notable">notable changes in 0.10.2.0</a> before upgrading.
+</p>
+
+<p>Starting with version 0.10.2, Java clients (producer and consumer) have acquired the ability to communicate with older brokers. Version 0.10.2
+clients can talk to version 0.10.0 or newer brokers. However, if your brokers are older than 0.10.0, you must upgrade all the brokers in the
+Kafka cluster before upgrading your clients. Version 0.10.2 brokers support 0.8.x and newer clients.
+</p>
+
+<p><b>For a rolling upgrade:</b></p>
+
+<ol>
+ <li> Update server.properties file on all brokers and add the following properties:
+ <ul>
+ <li>inter.broker.protocol.version=CURRENT_KAFKA_VERSION (e.g. 0.8.2, 0.9.0, 0.10.0 or 0.10.1).</li>
+ <li>log.message.format.version=CURRENT_KAFKA_VERSION (See <a href="#upgrade_10_performance_impact">potential performance impact following the upgrade</a> for the details on what this configuration does.)
+ </ul>
+ </li>
+ <li> Upgrade the brokers one at a time: shut down the broker, update the code, and restart it. </li>
+ <li> Once the entire cluster is upgraded, bump the protocol version by editing inter.broker.protocol.version and setting it to 0.10.2. </li>
+ <li> If your previous message format is 0.10.0, change log.message.format.version to 0.10.2 (this is a no-op as the message format is the same for 0.10.0, 0.10.1 and 0.10.2).
+ If your previous message format version is lower than 0.10.0, do not change log.message.format.version yet - this parameter should only change once all consumers have been upgraded to 0.10.0.0 or later.</li>
+ <li> Restart the brokers one by one for the new protocol version to take effect. </li>
+ <li> If log.message.format.version is still lower than 0.10.0 at this point, wait until all consumers have been upgraded to 0.10.0 or later,
+ then change log.message.format.version to 0.10.2 on each broker and restart them one by one. </li>
+</ol>
+
+<p><b>Note:</b> If you are willing to accept downtime, you can simply take all the brokers down, update the code and start all of them. They will start with the new protocol by default.
+
+<p><b>Note:</b> Bumping the protocol version and restarting can be done any time after the brokers were upgraded. It does not have to be immediately after.
+
+<h5><a id="upgrade_1020_streams" href="#upgrade_1020_streams">Upgrading a 0.10.1 Kafka Streams Application</a></h5>
+<ul>
+ <li> Upgrading your Streams application from 0.10.1 to 0.10.2 does not require a broker upgrade.
+ A Kafka Streams 0.10.2 application can connect to 0.10.2 and 0.10.1 brokers (it is not possible to connect to 0.10.0 brokers though). </li>
+ <li> You need to recompile your code. Just swapping the Kafka Streams library jar file will not work and will break your application. </li>
+ <li> If you use a custom (i.e., user implemented) timestamp extractor, you will need to update this code, because the <code>TimestampExtractor</code> interface was changed. </li>
+ <li> If you register custom metrics, you will need to update this code, because the <code>StreamsMetric</code> interface was changed. </li>
+ <li> See <a href="/{{version}}/documentation/streams#streams_api_changes_0102">Streams API changes in 0.10.2</a> for more details. </li>
+</ul>
+
+<h5><a id="upgrade_10201_notable" href="#upgrade_10201_notable">Notable changes in 0.10.2.1</a></h5>
+<ul>
+ <li> The default values for two configurations of the StreamsConfig class were changed to improve the resiliency of Kafka Streams applications. The internal Kafka Streams producer <code>retries</code> default value was changed from 0 to 10. The internal Kafka Streams consumer <code>max.poll.interval.ms</code> default value was changed from 300000 to <code>Integer.MAX_VALUE</code>.
+ </li>
+</ul>
+
+<h5><a id="upgrade_1020_notable" href="#upgrade_1020_notable">Notable changes in 0.10.2.0</a></h5>
+<ul>
+ <li>The Java clients (producer and consumer) have acquired the ability to communicate with older brokers. Version 0.10.2 clients
+ can talk to version 0.10.0 or newer brokers. Note that some features are not available or are limited when older brokers
+ are used. </li>
+ <li>Several methods on the Java consumer may now throw <code>InterruptException</code> if the calling thread is interrupted.
+ Please refer to the <code>KafkaConsumer</code> Javadoc for a more in-depth explanation of this change.</li>
+ <li>Java consumer now shuts down gracefully. By default, the consumer waits up to 30 seconds to complete pending requests.
+ A new close API with timeout has been added to <code>KafkaConsumer</code> to control the maximum wait time.</li>
+ <li>Multiple regular expressions separated by commas can be passed to MirrorMaker with the new Java consumer via the --whitelist option. This
+ makes the behaviour consistent with MirrorMaker when used the old Scala consumer.</li>
+ <li>Upgrading your Streams application from 0.10.1 to 0.10.2 does not require a broker upgrade.
+ A Kafka Streams 0.10.2 application can connect to 0.10.2 and 0.10.1 brokers (it is not possible to connect to 0.10.0 brokers though).</li>
+ <li>The Zookeeper dependency was removed from the Streams API. The Streams API now uses the Kafka protocol to manage internal topics instead of
+ modifying Zookeeper directly. This eliminates the need for privileges to access Zookeeper directly and "StreamsConfig.ZOOKEEPER_CONFIG"
+ should not be set in the Streams app any more. If the Kafka cluster is secured, Streams apps must have the required security privileges to create new topics.</li>
+ <li>Several new fields including "security.protocol", "connections.max.idle.ms", "retry.backoff.ms", "reconnect.backoff.ms" and "request.timeout.ms" were added to
+ StreamsConfig class. User should pay attention to the default values and set these if needed. For more details please refer to <a href="/{{version}}/documentation/#streamsconfigs">3.5 Kafka Streams Configs</a>.</li>
+</ul>
+
+<h5><a id="upgrade_1020_new_protocols" href="#upgrade_1020_new_protocols">New Protocol Versions</a></h5>
+<ul>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-88%3A+OffsetFetch+Protocol+Update">KIP-88</a>: OffsetFetchRequest v2 supports retrieval of offsets for all topics if the <code>topics</code> array is set to <code>null</code>. </li>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-88%3A+OffsetFetch+Protocol+Update">KIP-88</a>: OffsetFetchResponse v2 introduces a top-level <code>error_code</code> field. </li>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-103%3A+Separation+of+Internal+and+External+traffic">KIP-103</a>: UpdateMetadataRequest v3 introduces a <code>listener_name</code> field to the elements of the <code>end_points</code> array. </li>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-108%3A+Create+Topic+Policy">KIP-108</a>: CreateTopicsRequest v1 introduces a <code>validate_only</code> field. </li>
+ <li> <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-108%3A+Create+Topic+Policy">KIP-108</a>: CreateTopicsResponse v1 introduces an <code>error_message</code> field to the elements of the <code>topic_errors</code> array. </li>
+</ul>
+
+<h4><a id="upgrade_10_1" href="#upgrade_10_1">Upgrading from 0.8.x, 0.9.x or 0.10.0.X to 0.10.1.0</a></h4>
+0.10.1.0 has wire protocol changes. By following the recommended rolling upgrade plan below, you guarantee no downtime during the upgrade.
+However, please notice the <a href="#upgrade_10_1_breaking">Potential breaking changes in 0.10.1.0</a> before upgrade.
+<br>
+Note: Because new protocols are introduced, it is important to upgrade your Kafka clusters before upgrading your clients (i.e. 0.10.1.x clients
+only support 0.10.1.x or later brokers while 0.10.1.x brokers also support older clients).
+
+<p><b>For a rolling upgrade:</b></p>
+
+<ol>
+ <li> Update server.properties file on all brokers and add the following properties:
+ <ul>
+ <li>inter.broker.protocol.version=CURRENT_KAFKA_VERSION (e.g. 0.8.2.0, 0.9.0.0 or 0.10.0.0).</li>
+ <li>log.message.format.version=CURRENT_KAFKA_VERSION (See <a href="#upgrade_10_performance_impact">potential performance impact following the upgrade</a> for the details on what this configuration does.)
+ </ul>
+ </li>
+ <li> Upgrade the brokers one at a time: shut down the broker, update the code, and restart it. </li>
+ <li> Once the entire cluster is upgraded, bump the protocol version by editing inter.broker.protocol.version and setting it to 0.10.1.0. </li>
+ <li> If your previous message format is 0.10.0, change log.message.format.version to 0.10.1 (this is a no-op as the message format is the same for both 0.10.0 and 0.10.1).
+ If your previous message format version is lower than 0.10.0, do not change log.message.format.version yet - this parameter should only change once all consumers have been upgraded to 0.10.0.0 or later.</li>
+ <li> Restart the brokers one by one for the new protocol version to take effect. </li>
+ <li> If log.message.format.version is still lower than 0.10.0 at this point, wait until all consumers have been upgraded to 0.10.0 or later,
+ then change log.message.format.version to 0.10.1 on each broker and restart them one by one. </li>
+</ol>
+
+<p><b>Note:</b> If you are willing to accept downtime, you can simply take all the brokers down, update the code and start all of them. They will start with the new protocol by default.
+
+<p><b>Note:</b> Bumping the protocol version and restarting can be done any time after the brokers were upgraded. It does not have to be immediately after.
+
+<h5><a id="upgrade_10_1_breaking" href="#upgrade_10_1_breaking">Potential breaking changes in 0.10.1.0</a></h5>
+<ul>
+ <li> The log retention time is no longer based on last modified time of the log segments. Instead it will be based on the largest timestamp of the messages in a log segment.</li>
+ <li> The log rolling time is no longer depending on log segment create time. Instead it is now based on the timestamp in the messages. More specifically. if the timestamp of the first message in the segment is T, the log will be rolled out when a new message has a timestamp greater than or equal to T + log.roll.ms </li>
+ <li> The open file handlers of 0.10.0 will increase by ~33% because of the addition of time index files for each segment.</li>
+ <li> The time index and offset index share the same index size configuration. Since each time index entry is 1.5x the size of offset index entry. User may need to increase log.index.size.max.bytes to avoid potential frequent log rolling. </li>
+ <li> Due to the increased number of index files, on some brokers with large amount the log segments (e.g. >15K), the log loading process during the broker startup could be longer. Based on our experiment, setting the num.recovery.threads.per.data.dir to one may reduce the log loading time. </li>
+</ul>
+
+<h5><a id="upgrade_1010_streams" href="#upgrade_1010_streams">Upgrading a 0.10.0 Kafka Streams Application</a></h5>
+<ul>
+ <li> Upgrading your Streams application from 0.10.0 to 0.10.1 does require a <a href="#upgrade_10_1">broker upgrade</a> because a Kafka Streams 0.10.1 application can only connect to 0.10.1 brokers. </li>
+ <li> There are couple of API changes, that are not backward compatible (cf. <a href="/{{version}}/documentation/streams#streams_api_changes_0101">Streams API changes in 0.10.1</a> for more details).
+ Thus, you need to update and recompile your code. Just swapping the Kafka Streams library jar file will not work and will break your application. </li>
+</ul>
+
+<h5><a id="upgrade_1010_notable" href="#upgrade_1010_notable">Notable changes in 0.10.1.0</a></h5>
+<ul>
+ <li> The new Java consumer is no longer in beta and we recommend it for all new development. The old Scala consumers are still supported, but they will be deprecated in the next release
+ and will be removed in a future major release. </li>
+ <li> The <code>--new-consumer</code>/<code>--new.consumer</code> switch is no longer required to use tools like MirrorMaker and the Console Consumer with the new consumer; one simply
+ needs to pass a Kafka broker to connect to instead of the ZooKeeper ensemble. In addition, usage of the Console Consumer with the old consumer has been deprecated and it will be
+ removed in a future major release. </li>
+ <li> Kafka clusters can now be uniquely identified by a cluster id. It will be automatically generated when a broker is upgraded to 0.10.1.0. The cluster id is available via the kafka.server:type=KafkaServer,name=ClusterId metric and it is part of the Metadata response. Serializers, client interceptors and metric reporters can receive the cluster id by implementing the ClusterResourceListener interface. </li>
+ <li> The BrokerState "RunningAsController" (value 4) has been removed. Due to a bug, a broker would only be in this state briefly before transitioning out of it and hence the impact of the removal should be minimal. The recommended way to detect if a given broker is the controller is via the kafka.controller:type=KafkaController,name=ActiveControllerCount metric. </li>
+ <li> The new Java Consumer now allows users to search offsets by timestamp on partitions. </li>
+ <li> The new Java Consumer now supports heartbeating from a background thread. There is a new configuration
+ <code>max.poll.interval.ms</code> which controls the maximum time between poll invocations before the consumer
+ will proactively leave the group (5 minutes by default). The value of the configuration
+ <code>request.timeout.ms</code> must always be larger than <code>max.poll.interval.ms</code> because this is the maximum
+ time that a JoinGroup request can block on the server while the consumer is rebalancing, so we have changed its default
+ value to just above 5 minutes. Finally, the default value of <code>session.timeout.ms</code> has been adjusted down to
+ 10 seconds, and the default value of <code>max.poll.records</code> has been changed to 500.</li>
+ <li> When using an Authorizer and a user doesn't have <b>Describe</b> authorization on a topic, the broker will no
+ longer return TOPIC_AUTHORIZATION_FAILED errors to requests since this leaks topic names. Instead, the UNKNOWN_TOPIC_OR_PARTITION
+ error code will be returned. This may cause unexpected timeouts or delays when using the producer and consumer since
+ Kafka clients will typically retry automatically on unknown topic errors. You should consult the client logs if you
+ suspect this could be happening.</li>
+ <li> Fetch responses have a size limit by default (50 MB for consumers and 10 MB for replication). The existing per partition limits also apply (1 MB for consumers
+ and replication). Note that neither of these limits is an absolute maximum as explained in the next point. </li>
+ <li> Consumers and replicas can make progress if a message larger than the response/partition size limit is found. More concretely, if the first message in the
+ first non-empty partition of the fetch is larger than either or both limits, the message will still be returned. </li>
+ <li> Overloaded constructors were added to <code>kafka.api.FetchRequest</code> and <code>kafka.javaapi.FetchRequest</code> to allow the caller to specify the
+ order of the partitions (since order is significant in v3). The previously existing constructors were deprecated and the partitions are shuffled before
+ the request is sent to avoid starvation issues. </li>
+</ul>
+
+<h5><a id="upgrade_1010_new_protocols" href="#upgrade_1010_new_protocols">New Protocol Versions</a></h5>
+<ul>
+ <li> ListOffsetRequest v1 supports accurate offset search based on timestamps. </li>
+ <li> MetadataResponse v2 introduces a new field: "cluster_id". </li>
+ <li> FetchRequest v3 supports limiting the response size (in addition to the existing per partition limit), it returns messages
+ bigger than the limits if required to make progress and the order of partitions in the request is now significant. </li>
+ <li> JoinGroup v1 introduces a new field: "rebalance_timeout". </li>
+</ul>
+
+<h4><a id="upgrade_10" href="#upgrade_10">Upgrading from 0.8.x or 0.9.x to 0.10.0.0</a></h4>
+0.10.0.0 has <a href="#upgrade_10_breaking">potential breaking changes</a> (please review before upgrading) and possible <a href="#upgrade_10_performance_impact"> performance impact following the upgrade</a>. By following the recommended rolling upgrade plan below, you guarantee no downtime and no performance impact during and following the upgrade.
+<br>
+Note: Because new protocols are introduced, it is important to upgrade your Kafka clusters before upgrading your clients.
+<p/>
+<b>Notes to clients with version 0.9.0.0: </b>Due to a bug introduced in 0.9.0.0,
+clients that depend on ZooKeeper (old Scala high-level Consumer and MirrorMaker if used with the old consumer) will not
+work with 0.10.0.x brokers. Therefore, 0.9.0.0 clients should be upgraded to 0.9.0.1 <b>before</b> brokers are upgraded to
+0.10.0.x. This step is not necessary for 0.8.X or 0.9.0.1 clients.
+
+<p><b>For a rolling upgrade:</b></p>
+
+<ol>
+ <li> Update server.properties file on all brokers and add the following properties:
+ <ul>
+ <li>inter.broker.protocol.version=CURRENT_KAFKA_VERSION (e.g. 0.8.2 or 0.9.0.0).</li>
+ <li>log.message.format.version=CURRENT_KAFKA_VERSION (See <a href="#upgrade_10_performance_impact">potential performance impact following the upgrade</a> for the details on what this configuration does.)
+ </ul>
+ </li>
+ <li> Upgrade the brokers. This can be done a broker at a time by simply bringing it down, updating the code, and restarting it. </li>
+ <li> Once the entire cluster is upgraded, bump the protocol version by editing inter.broker.protocol.version and setting it to 0.10.0.0. NOTE: You shouldn't touch log.message.format.version yet - this parameter should only change once all consumers have been upgraded to 0.10.0.0 </li>
+ <li> Restart the brokers one by one for the new protocol version to take effect. </li>
+ <li> Once all consumers have been upgraded to 0.10.0, change log.message.format.version to 0.10.0 on each broker and restart them one by one.
+ </li>
+</ol>
+
+<p><b>Note:</b> If you are willing to accept downtime, you can simply take all the brokers down, update the code and start all of them. They will start with the new protocol by default.
+
+<p><b>Note:</b> Bumping the protocol version and restarting can be done any time after the brokers were upgraded. It does not have to be immediately after.
+
+<h5><a id="upgrade_10_performance_impact" href="#upgrade_10_performance_impact">Potential performance impact following upgrade to 0.10.0.0</a></h5>
+<p>
+ The message format in 0.10.0 includes a new timestamp field and uses relative offsets for compressed messages.
+ The on disk message format can be configured through log.message.format.version in the server.properties file.
+ The default on-disk message format is 0.10.0. If a consumer client is on a version before 0.10.0.0, it only understands
+ message formats before 0.10.0. In this case, the broker is able to convert messages from the 0.10.0 format to an earlier format
+ before sending the response to the consumer on an older version. However, the broker can't use zero-copy transfer in this case.
+
+ Reports from the Kafka community on the performance impact have shown CPU utilization going from 20% before to 100% after an upgrade, which forced an immediate upgrade of all clients to bring performance back to normal.
+
+ To avoid such message conversion before consumers are upgraded to 0.10.0.0, one can set log.message.format.version to 0.8.2 or 0.9.0 when upgrading the broker to 0.10.0.0. This way, the broker can still use zero-copy transfer to send the data to the old consumers. Once consumers are upgraded, one can change the message format to 0.10.0 on the broker and enjoy the new message format that includes new timestamp and improved compression.
+
+ The conversion is supported to ensure compatibility and can be useful to support a few apps that have not updated to newer clients yet, but is impractical to support all consumer traffic on even an overprovisioned cluster. Therefore, it is critical to avoid the message conversion as much as possible when brokers have been upgraded but the majority of clients have not.
+</p>
+<p>
+ For clients that are upgraded to 0.10.0.0, there is no performance impact.
+</p>
+<p>
+ <b>Note:</b> By setting the message format version, one certifies that all existing messages are on or below that
+ message format version. Otherwise consumers before 0.10.0.0 might break. In particular, after the message format
+ is set to 0.10.0, one should not change it back to an earlier format as it may break consumers on versions before 0.10.0.0.
+</p>
+<p>
+ <b>Note:</b> Due to the additional timestamp introduced in each message, producers sending small messages may see a
+ message throughput degradation because of the increased overhead.
+ Likewise, replication now transmits an additional 8 bytes per message.
+ If you're running close to the network capacity of your cluster, it's possible that you'll overwhelm the network cards
+ and see failures and performance issues due to the overload.
+</p>
+ <b>Note:</b> If you have enabled compression on producers, you may notice reduced producer throughput and/or
+ lower compression rate on the broker in some cases. When receiving compressed messages, 0.10.0
+ brokers avoid recompressing the messages, which in general reduces the latency and improves the throughput. In
+ certain cases, however, this may reduce the batching size on the producer, which could lead to worse throughput. If this
+ happens, users can tune linger.ms and batch.size of the producer for better throughput. In addition, the producer buffer
+ used for compressing messages with snappy is smaller than the one used by the broker, which may have a negative
+ impact on the compression ratio for the messages on disk. We intend to make this configurable in a future Kafka
+ release.
+<p>
+
+</p>
+
+<h5><a id="upgrade_10_breaking" href="#upgrade_10_breaking">Potential breaking changes in 0.10.0.0</a></h5>
+<ul>
+ <li> Starting from Kafka 0.10.0.0, the message format version in Kafka is represented as the Kafka version. For example, message format 0.9.0 refers to the highest message version supported by Kafka 0.9.0. </li>
+ <li> Message format 0.10.0 has been introduced and it is used by default. It includes a timestamp field in the messages and relative offsets are used for compressed messages. </li>
+ <li> ProduceRequest/Response v2 has been introduced and it is used by default to support message format 0.10.0 </li>
+ <li> FetchRequest/Response v2 has been introduced and it is used by default to support message format 0.10.0 </li>
+ <li> MessageFormatter interface was changed from <code>def writeTo(key: Array[Byte], value: Array[Byte], output: PrintStream)</code> to
+ <code>def writeTo(consumerRecord: ConsumerRecord[Array[Byte], Array[Byte]], output: PrintStream)</code> </li>
+ <li> MessageReader interface was changed from <code>def readMessage(): KeyedMessage[Array[Byte], Array[Byte]]</code> to
+ <code>def readMessage(): ProducerRecord[Array[Byte], Array[Byte]]</code> </li>
+ <li> MessageFormatter's package was changed from <code>kafka.tools</code> to <code>kafka.common</code> </li>
+ <li> MessageReader's package was changed from <code>kafka.tools</code> to <code>kafka.common</code> </li>
+ <li> MirrorMakerMessageHandler no longer exposes the <code>handle(record: MessageAndMetadata[Array[Byte], Array[Byte]])</code> method as it was never called. </li>
+ <li> The 0.7 KafkaMigrationTool is no longer packaged with Kafka. If you need to migrate from 0.7 to 0.10.0, please migrate to 0.8 first and then follow the documented upgrade process to upgrade from 0.8 to 0.10.0. </li>
+ <li> The new consumer has standardized its APIs to accept <code>java.util.Collection</code> as the sequence type for method parameters. Existing code may have to be updated to work with the 0.10.0 client library. </li>
+ <li> LZ4-compressed message handling was changed to use an interoperable framing specification (LZ4f v1.5.1).
+ To maintain compatibility with old clients, this change only applies to Message format 0.10.0 and later.
+ Clients that Produce/Fetch LZ4-compressed messages using v0/v1 (Message format 0.9.0) should continue
+ to use the 0.9.0 framing implementation. Clients that use Produce/Fetch protocols v2 or later
+ should use interoperable LZ4f framing. A list of interoperable LZ4 libraries is available at http://www.lz4.org/
+</ul>
+
+<h5><a id="upgrade_10_notable" href="#upgrade_10_notable">Notable changes in 0.10.0.0</a></h5>
+
+<ul>
+ <li> Starting from Kafka 0.10.0.0, a new client library named <b>Kafka Streams</b> is available for stream processing on data stored in Kafka topics. This new client library only works with 0.10.x and upward versioned brokers due to message format changes mentioned above. For more information please read <a href="/{{version}}/documentation/streams">Streams documentation</a>.</li>
+ <li> The default value of the configuration parameter <code>receive.buffer.bytes</code> is now 64K for the new consumer.</li>
+ <li> The new consumer now exposes the configuration parameter <code>exclude.internal.topics</code> to restrict internal topics (such as the consumer offsets topic) from accidentally being included in regular expression subscriptions. By default, it is enabled.</li>
+ <li> The old Scala producer has been deprecated. Users should migrate their code to the Java producer included in the kafka-clients JAR as soon as possible. </li>
+ <li> The new consumer API has been marked stable. </li>
+</ul>
+
+<h4><a id="upgrade_9" href="#upgrade_9">Upgrading from 0.8.0, 0.8.1.X or 0.8.2.X to 0.9.0.0</a></h4>
+
+0.9.0.0 has <a href="#upgrade_9_breaking">potential breaking changes</a> (please review before upgrading) and an inter-broker protocol change from previous versions. This means that upgraded brokers and clients may not be compatible with older versions. It is important that you upgrade your Kafka cluster before upgrading your clients. If you are using MirrorMaker downstream clusters should be upgraded first as well.
+
+<p><b>For a rolling upgrade:</b></p>
+
+<ol>
+ <li> Update server.properties file on all brokers and add the following property: inter.broker.protocol.version=0.8.2.X </li>
+ <li> Upgrade the brokers. This can be done a broker at a time by simply bringing it down, updating the code, and restarting it. </li>
+ <li> Once the entire cluster is upgraded, bump the protocol version by editing inter.broker.protocol.version and setting it to 0.9.0.0.</li>
+ <li> Restart the brokers one by one for the new protocol version to take effect </li>
+</ol>
+
+<p><b>Note:</b> If you are willing to accept downtime, you can simply take all the brokers down, update the code and start all of them. They will start with the new protocol by default.
+
+<p><b>Note:</b> Bumping the protocol version and restarting can be done any time after the brokers were upgraded. It does not have to be immediately after.
+
+<h5><a id="upgrade_9_breaking" href="#upgrade_9_breaking">Potential breaking changes in 0.9.0.0</a></h5>
+
+<ul>
+ <li> Java 1.6 is no longer supported. </li>
+ <li> Scala 2.9 is no longer supported. </li>
+ <li> Broker IDs above 1000 are now reserved by default to automatically assigned broker IDs. If your cluster has existing broker IDs above that threshold make sure to increase the reserved.broker.max.id broker configuration property accordingly. </li>
+ <li> Configuration parameter replica.lag.max.messages was removed. Partition leaders will no longer consider the number of lagging messages when deciding which replicas are in sync. </li>
+ <li> Configuration parameter replica.lag.time.max.ms now refers not just to the time passed since last fetch request from replica, but also to time since the replica last caught up. Replicas that are still fetching messages from leaders but did not catch up to the latest messages in replica.lag.time.max.ms will be considered out of sync. </li>
+ <li> Compacted topics no longer accept messages without key and an exception is thrown by the producer if this is attempted. In 0.8.x, a message without key would cause the log compaction thread to subsequently complain and quit (and stop compacting all compacted topics). </li>
+ <li> MirrorMaker no longer supports multiple target clusters. As a result it will only accept a single --consumer.config parameter. To mirror multiple source clusters, you will need at least one MirrorMaker instance per source cluster, each with its own consumer configuration. </li>
+ <li> Tools packaged under <em>org.apache.kafka.clients.tools.*</em> have been moved to <em>org.apache.kafka.tools.*</em>. All included scripts will still function as usual, only custom code directly importing these classes will be affected. </li>
+ <li> The default Kafka JVM performance options (KAFKA_JVM_PERFORMANCE_OPTS) have been changed in kafka-run-class.sh. </li>
+ <li> The kafka-topics.sh script (kafka.admin.TopicCommand) now exits with non-zero exit code on failure. </li>
+ <li> The kafka-topics.sh script (kafka.admin.TopicCommand) will now print a warning when topic names risk metric collisions due to the use of a '.' or '_' in the topic name, and error in the case of an actual collision. </li>
+ <li> The kafka-console-producer.sh script (kafka.tools.ConsoleProducer) will use the Java producer instead of the old Scala producer be default, and users have to specify 'old-producer' to use the old producer. </li>
+ <li> By default, all command line tools will print all logging messages to stderr instead of stdout. </li>
+</ul>
+
+<h5><a id="upgrade_901_notable" href="#upgrade_901_notable">Notable changes in 0.9.0.1</a></h5>
+
+<ul>
+ <li> The new broker id generation feature can be disabled by setting broker.id.generation.enable to false. </li>
+ <li> Configuration parameter log.cleaner.enable is now true by default. This means topics with a cleanup.policy=compact will now be compacted by default, and 128 MB of heap will be allocated to the cleaner process via log.cleaner.dedupe.buffer.size. You may want to review log.cleaner.dedupe.buffer.size and the other log.cleaner configuration values based on your usage of compacted topics. </li>
+ <li> Default value of configuration parameter fetch.min.bytes for the new consumer is now 1 by default. </li>
+</ul>
+
+<h5>Deprecations in 0.9.0.0</h5>
+
+<ul>
+ <li> Altering topic configuration from the kafka-topics.sh script (kafka.admin.TopicCommand) has been deprecated. Going forward, please use the kafka-configs.sh script (kafka.admin.ConfigCommand) for this functionality. </li>
+ <li> The kafka-consumer-offset-checker.sh (kafka.tools.ConsumerOffsetChecker) has been deprecated. Going forward, please use kafka-consumer-groups.sh (kafka.admin.ConsumerGroupCommand) for this functionality. </li>
+ <li> The kafka.tools.ProducerPerformance class has been deprecated. Going forward, please use org.apache.kafka.tools.ProducerPerformance for this functionality (kafka-producer-perf-test.sh will also be changed to use the new class). </li>
+ <li> The producer config block.on.buffer.full has been deprecated and will be removed in future release. Currently its default value has been changed to false. The KafkaProducer will no longer throw BufferExhaustedException but instead will use max.block.ms value to block, after which it will throw a TimeoutException. If block.on.buffer.full property is set to true explicitly, it will set the max.block.ms to Long.MAX_VALUE and metadata.fetch.timeout.ms will not be honoured</li>
+</ul>
+
+<h4><a id="upgrade_82" href="#upgrade_82">Upgrading from 0.8.1 to 0.8.2</a></h4>
+
+0.8.2 is fully compatible with 0.8.1. The upgrade can be done one broker at a time by simply bringing it down, updating the code, and restarting it.
+
+<h4><a id="upgrade_81" href="#upgrade_81">Upgrading from 0.8.0 to 0.8.1</a></h4>
+
+0.8.1 is fully compatible with 0.8. The upgrade can be done one broker at a time by simply bringing it down, updating the code, and restarting it.
+
+<h4><a id="upgrade_7" href="#upgrade_7">Upgrading from 0.7</a></h4>
+
+Release 0.7 is incompatible with newer releases. Major changes were made to the API, ZooKeeper data structures, and protocol, and configuration in order to add replication (Which was missing in 0.7). The upgrade from 0.7 to later versions requires a <a href="https://cwiki.apache.org/confluence/display/KAFKA/Migrating+from+0.7+to+0.8">special tool</a> for migration. This migration can be done without downtime.
+
+</script>
+
+<div class="p-upgrade"></div>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/uses.html
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diff --git a/100/uses.html b/100/uses.html
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--- /dev/null
+++ b/100/uses.html
@@ -0,0 +1,81 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<p> Here is a description of a few of the popular use cases for Apache Kafka®.
+For an overview of a number of these areas in action, see <a href="https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying/">this blog post</a>. </p>
+
+<h4><a id="uses_messaging" href="#uses_messaging">Messaging</a></h4>
+
+Kafka works well as a replacement for a more traditional message broker.
+Message brokers are used for a variety of reasons (to decouple processing from data producers, to buffer unprocessed messages, etc).
+In comparison to most messaging systems Kafka has better throughput, built-in partitioning, replication, and fault-tolerance which makes it a good
+solution for large scale message processing applications.
+<p>
+In our experience messaging uses are often comparatively low-throughput, but may require low end-to-end latency and often depend on the strong
+durability guarantees Kafka provides.
+<p>
+In this domain Kafka is comparable to traditional messaging systems such as <a href="http://activemq.apache.org">ActiveMQ</a> or
+<a href="https://www.rabbitmq.com">RabbitMQ</a>.
+
+<h4><a id="uses_website" href="#uses_website">Website Activity Tracking</a></h4>
+
+The original use case for Kafka was to be able to rebuild a user activity tracking pipeline as a set of real-time publish-subscribe feeds.
+This means site activity (page views, searches, or other actions users may take) is published to central topics with one topic per activity type.
+These feeds are available for subscription for a range of use cases including real-time processing, real-time monitoring, and loading into Hadoop or
+offline data warehousing systems for offline processing and reporting.
+<p>
+Activity tracking is often very high volume as many activity messages are generated for each user page view.
+
+<h4><a id="uses_metrics" href="#uses_metrics">Metrics</a></h4>
+
+Kafka is often used for operational monitoring data.
+This involves aggregating statistics from distributed applications to produce centralized feeds of operational data.
+
+<h4><a id="uses_logs" href="#uses_logs">Log Aggregation</a></h4>
+
+Many people use Kafka as a replacement for a log aggregation solution.
+Log aggregation typically collects physical log files off servers and puts them in a central place (a file server or HDFS perhaps) for processing.
+Kafka abstracts away the details of files and gives a cleaner abstraction of log or event data as a stream of messages.
+This allows for lower-latency processing and easier support for multiple data sources and distributed data consumption.
+
+In comparison to log-centric systems like Scribe or Flume, Kafka offers equally good performance, stronger durability guarantees due to replication,
+and much lower end-to-end latency.
+
+<h4><a id="uses_streamprocessing" href="#uses_streamprocessing">Stream Processing</a></h4>
+
+Many users of Kafka process data in processing pipelines consisting of multiple stages, where raw input data is consumed from Kafka topics and then
+aggregated, enriched, or otherwise transformed into new topics for further consumption or follow-up processing.
+For example, a processing pipeline for recommending news articles might crawl article content from RSS feeds and publish it to an "articles" topic;
+further processing might normalize or deduplicate this content and published the cleansed article content to a new topic;
+a final processing stage might attempt to recommend this content to users.
+Such processing pipelines create graphs of real-time data flows based on the individual topics.
+Starting in 0.10.0.0, a light-weight but powerful stream processing library called <a href="/documentation/streams">Kafka Streams</a>
+is available in Apache Kafka to perform such data processing as described above.
+Apart from Kafka Streams, alternative open source stream processing tools include <a href="https://storm.apache.org/">Apache Storm</a> and
+<a href="http://samza.apache.org/">Apache Samza</a>.
+
+<h4><a id="uses_eventsourcing" href="#uses_eventsourcing">Event Sourcing</a></h4>
+
+<a href="http://martinfowler.com/eaaDev/EventSourcing.html">Event sourcing</a> is a style of application design where state changes are logged as a
+time-ordered sequence of records. Kafka's support for very large stored log data makes it an excellent backend for an application built in this style.
+
+<h4><a id="uses_commitlog" href="#uses_commitlog">Commit Log</a></h4>
+
+Kafka can serve as a kind of external commit-log for a distributed system. The log helps replicate data between nodes and acts as a re-syncing
+mechanism for failed nodes to restore their data.
+The <a href="/documentation.html#compaction">log compaction</a> feature in Kafka helps support this usage.
+In this usage Kafka is similar to <a href="http://zookeeper.apache.org/bookkeeper/">Apache BookKeeper</a> project.
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/code.html
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diff --git a/code.html b/code.html
index 400eab0..bfb32ee 100644
--- a/code.html
+++ b/code.html
@@ -20,7 +20,7 @@
The source code for the web site and documentation can be checked out from Apache git repo:
<pre>
git clone -b asf-site https://git-wip-us.apache.org/repos/asf/kafka-site.git
- </pre>
+ </pre>
A <a href="https://github.com/apache/kafka-site">github mirror</a> is used for submitting patches for website and documentation. We think documentation is a core part of the project and welcome any improvements, suggestions, or clarifications. The procedure of contributing to the documentation can also be found <a href="contributing.html">here</a>.
</p>
<p>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/documentation.html
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index 8f9c444..6e76806 100644
--- a/documentation.html
+++ b/documentation.html
@@ -1,2 +1,2 @@
<!-- should always link the the latest release's documentation -->
-<!--#include virtual="0110/documentation.html" -->
+<!--#include virtual="100/documentation.html" -->
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diff --git a/documentation/index.html b/documentation/index.html
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--- a/documentation/index.html
+++ b/documentation/index.html
@@ -1,2 +1,2 @@
<!-- should always link the the latest release's documentation -->
-<!--#include virtual="../0110/documentation.html" -->
+<!--#include virtual="../100/documentation.html" -->
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diff --git a/documentation/streams/architecture.html b/documentation/streams/architecture.html
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--- a/documentation/streams/architecture.html
+++ b/documentation/streams/architecture.html
@@ -1,2 +1,2 @@
<!-- should always link the the latest release's documentation -->
-<!--#include virtual="../../0110/streams/architecture.html" -->
\ No newline at end of file
+<!--#include virtual="../../100/streams/architecture.html" -->
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+++ b/documentation/streams/core-concepts.html
@@ -1,2 +1,2 @@
<!-- should always link the the latest release's documentation -->
-<!--#include virtual="../../0110/streams/core-concepts.html" -->
\ No newline at end of file
+<!--#include virtual="../../100/streams/core-concepts.html" -->
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/documentation/streams/developer-guide.html
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diff --git a/documentation/streams/developer-guide.html b/documentation/streams/developer-guide.html
index e97bb1d..721f593 100644
--- a/documentation/streams/developer-guide.html
+++ b/documentation/streams/developer-guide.html
@@ -1,2 +1,2 @@
<!-- should always link the the latest release's documentation -->
-<!--#include virtual="../../0110/streams/developer-guide.html" -->
\ No newline at end of file
+<!--#include virtual="../../100/streams/developer-guide.html" -->
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+++ b/documentation/streams/index.html
@@ -1,2 +1,2 @@
<!-- should always link the the latest release's documentation -->
-<!--#include virtual="../../0110/streams/index.html" -->
+<!--#include virtual="../../100/streams/index.html" -->
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diff --git a/documentation/streams/quickstart.html b/documentation/streams/quickstart.html
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+++ b/documentation/streams/quickstart.html
@@ -1,2 +1,2 @@
<!-- should always link the the latest release's documentation -->
-<!--#include virtual="../../0110/streams/quickstart.html" -->
\ No newline at end of file
+<!--#include virtual="../../100/streams/quickstart.html" -->
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diff --git a/documentation/streams/upgrade-guide.html b/documentation/streams/upgrade-guide.html
index ae699e5..1fa5c25 100644
--- a/documentation/streams/upgrade-guide.html
+++ b/documentation/streams/upgrade-guide.html
@@ -1,2 +1,2 @@
<!-- should always link the the latest release's documentation -->
-<!--#include virtual="../../0110/streams/upgrade-guide.html" -->
\ No newline at end of file
+<!--#include virtual="../../100/streams/upgrade-guide.html" -->
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/downloads.html
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diff --git a/downloads.html b/downloads.html
index 3a42a86..f3e09ae 100644
--- a/downloads.html
+++ b/downloads.html
@@ -4,11 +4,33 @@
<!--#include virtual="includes/_nav.htm" -->
<div class="right">
<h1>Download</h1>
- <p>0.11.0.1 is the latest release. The current stable version is 0.11.0.1.</p>
+ <p>1.0.0 is the latest release. The current stable version is 1.0.0.</p>
<p>
You can verify your download by following these <a href="http://www.apache.org/info/verification.html">procedures</a> and using these <a href="http://kafka.apache.org/KEYS">KEYS</a>.
</p>
+
+ <h3>1.0.0</h3>
+ <ul>
+ <li>
+ Released November 1, 2017
+ </li>
+ <li>
+ <a href="https://archive.apache.org/dist/kafka/1.0.0/RELEASE_NOTES.html">Release Notes</a>
+ </li>
+ <li>
+ Source download: <a href="https://www.apache.org/dyn/closer.cgi?path=/kafka/1.0.0/kafka-1.0.0-src.tgz">kafka-1.0.0-src.tgz</a> (<a href="https://dist.apache.org/repos/dist/release/kafka/1.0.0/kafka-1.0.0-src.tgz.asc">asc</a>, <a href="https://dist.apache.org/repos/dist/release/kafka/1.0.0.0/kafka-1.0.0-src.tgz.sha512">sha512</a>)
+ </li>
+ <li>
+ Binary downloads:
+ <ul>
+ <li>Scala 2.11 - <a href="https://www.apache.org/dyn/closer.cgi?path=/kafka/1.0.0/kafka_2.11-1.0.0.tgz">kafka_2.11-1.0.0.tgz</a> (<a href="https://dist.apache.org/repos/dist/release/kafka/1.0.0/kafka_2.11-1.0.0.tgz.asc">asc</a>, <a href="https://dist.apache.org/repos/dist/release/kafka/1.0.0/kafka_2.11-1.0.0.tgz.sha512">sha512</a>)</li>
+ <li>Scala 2.12 - <a href="https://www.apache.org/dyn/closer.cgi?path=/kafka/1.0.0/kafka_2.12-1.0.0.tgz">kafka_2.12-1.0.0.tgz</a> (<a href="https://dist.apache.org/repos/dist/release/kafka/1.0.0/kafka_2.12-1.0.0.tgz.asc">asc</a>, <a href="https://dist.apache.org/repos/dist/release/kafka/1.0.0/kafka_2.12-1.0.0.tgz.sha512">sha512</a>)</li>
+ </ul>
+ We build for multiple versions of Scala. This only matters if you are using Scala and you want a version built for the same Scala version you use. Otherwise any version should work (2.11 is recommended).
+ </li>
+ </ul>
+
<h3>0.11.0.1</h3>
<ul>
<li>
@@ -18,7 +40,7 @@
<a href="https://archive.apache.org/dist/kafka/0.11.0.1/RELEASE_NOTES.html">Release Notes</a>
</li>
<li>
- Source download: <a href="https://www.apache.org/dyn/closer.cgi?path=/kafka/0.11.0.1/kafka-0.11.0.1-src.tgz">kafka-0.11.0.1-src.tgz</a> (<a href="https://dist.apache.org/repos/dist/release/kafka/0.11.0.1/kafka-0.11.0.1-src.tgz.asc">asc</a>, <a href="https://dist.apache.org/repos/dist/release/kafka/0.11.0.0/kafka-0.11.0.0-src.tgz.md5">md5</a>)
+ Source download: <a href="https://www.apache.org/dyn/closer.cgi?path=/kafka/0.11.0.1/kafka-0.11.0.1-src.tgz">kafka-0.11.0.1-src.tgz</a> (<a href="https://dist.apache.org/repos/dist/release/kafka/0.11.0.1/kafka-0.11.0.1-src.tgz.asc">asc</a>, <a href="https://dist.apache.org/repos/dist/release/kafka/0.11.0.1/kafka-0.11.0.1-src.tgz.md5">md5</a>)
</li>
<li>
Binary downloads:
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/intro.html
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diff --git a/intro.html b/intro.html
index 8d6e025..6ff545b 100644
--- a/intro.html
+++ b/intro.html
@@ -4,7 +4,7 @@
<!--#include virtual="includes/_nav.htm" -->
<div class="right">
<h1>Introduction</h1>
- <!--#include virtual="0110/introduction.html" -->
+ <!--#include virtual="100/introduction.html" -->
<!--#include virtual="includes/_footer.htm" -->
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/protocol.html
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--- a/protocol.html
+++ b/protocol.html
@@ -1,2 +1,2 @@
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-<!--#include virtual="0110/protocol.html" -->
+<!--#include virtual="100/protocol.html" -->
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--- a/quickstart.html
+++ b/quickstart.html
@@ -4,7 +4,7 @@
<!--#include virtual="includes/_nav.htm" -->
<div class="right">
<h1>Quickstart</h1>
- <!--#include virtual="0110/quickstart.html" -->
+ <!--#include virtual="100/quickstart.html" -->
<!--#include virtual="includes/_footer.htm" -->
<script>
// Show selected style on nav item
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/uses.html
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index eb7d3d0..a09764a 100644
--- a/uses.html
+++ b/uses.html
@@ -5,7 +5,7 @@
<div class="right">
<h1>Use cases</h1>
-<!--#include virtual="0110/uses.html" -->
+<!--#include virtual="100/uses.html" -->
<!--#include virtual="includes/_footer.htm" -->
[05/14] kafka-site git commit: Update site for 1.0.0 release
Posted by ij...@apache.org.
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/streams/core-concepts.html
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diff --git a/100/streams/core-concepts.html b/100/streams/core-concepts.html
new file mode 100644
index 0000000..baf8d9b
--- /dev/null
+++ b/100/streams/core-concepts.html
@@ -0,0 +1,187 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<script><!--#include virtual="../js/templateData.js" --></script>
+
+<script id="content-template" type="text/x-handlebars-template">
+ <h1>Core Concepts</h1>
+
+ <p>
+ Kafka Streams is a client library for processing and analyzing data stored in Kafka.
+ It builds upon important stream processing concepts such as properly distinguishing between event time and processing time, windowing support, and simple yet efficient management and real-time querying of application state.
+ </p>
+ <p>
+ Kafka Streams has a <b>low barrier to entry</b>: You can quickly write and run a small-scale proof-of-concept on a single machine; and you only need to run additional instances of your application on multiple machines to scale up to high-volume production workloads.
+ Kafka Streams transparently handles the load balancing of multiple instances of the same application by leveraging Kafka's parallelism model.
+ </p>
+ <p>
+ Some highlights of Kafka Streams:
+ </p>
+
+ <ul>
+ <li>Designed as a <b>simple and lightweight client library</b>, which can be easily embedded in any Java application and integrated with any existing packaging, deployment and operational tools that users have for their streaming applications.</li>
+ <li>Has <b>no external dependencies on systems other than Apache Kafka itself</b> as the internal messaging layer; notably, it uses Kafka's partitioning model to horizontally scale processing while maintaining strong ordering guarantees.</li>
+ <li>Supports <b>fault-tolerant local state</b>, which enables very fast and efficient stateful operations like windowed joins and aggregations.</li>
+ <li>Supports <b>exactly-once</b> processing semantics to guarantee that each record will be processed once and only once even when there is a failure on either Streams clients or Kafka brokers in the middle of processing.</li>
+ <li>Employs <b>one-record-at-a-time processing</b> to achieve millisecond processing latency, and supports <b>event-time based windowing operations</b> with late arrival of records.</li>
+ <li>Offers necessary stream processing primitives, along with a <b>high-level Streams DSL</b> and a <b>low-level Processor API</b>.</li>
+
+ </ul>
+
+ <p>
+ We first summarize the key concepts of Kafka Streams.
+ </p>
+
+ <h3><a id="streams_topology" href="#streams_topology">Stream Processing Topology</a></h3>
+
+ <ul>
+ <li>A <b>stream</b> is the most important abstraction provided by Kafka Streams: it represents an unbounded, continuously updating data set. A stream is an ordered, replayable, and fault-tolerant sequence of immutable data records, where a <b>data record</b> is defined as a key-value pair.</li>
+ <li>A <b>stream processing application</b> is any program that makes use of the Kafka Streams library. It defines its computational logic through one or more <b>processor topologies</b>, where a processor topology is a graph of stream processors (nodes) that are connected by streams (edges).</li>
+ <li>A <b>stream processor</b> is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors. </li>
+ </ul>
+
+ There are two special processors in the topology:
+
+ <ul>
+ <li><b>Source Processor</b>: A source processor is a special type of stream processor that does not have any upstream processors. It produces an input stream to its topology from one or multiple Kafka topics by consuming records from these topics and forwarding them to its down-stream processors.</li>
+ <li><b>Sink Processor</b>: A sink processor is a special type of stream processor that does not have down-stream processors. It sends any received records from its up-stream processors to a specified Kafka topic.</li>
+ </ul>
+
+ Note that in normal processor nodes other remote systems can also be accessed while processing the current record. Therefore the processed results can either be streamed back into Kafka or written to an external system.
+
+ <img class="centered" src="/{{version}}/images/streams-architecture-topology.jpg" style="width:400px">
+
+ <p>
+ Kafka Streams offers two ways to define the stream processing topology: the <a href="/{{version}}/documentation/streams/developer-guide#streams_dsl"><b>Kafka Streams DSL</b></a> provides
+ the most common data transformation operations such as <code>map</code>, <code>filter</code>, <code>join</code> and <code>aggregations</code> out of the box; the lower-level <a href="/{{version}}/documentation/streams/developer-guide#streams_processor"><b>Processor API</b></a> allows
+ developers define and connect custom processors as well as to interact with <a href="#streams_state">state stores</a>.
+ </p>
+
+ <p>
+ A processor topology is merely a logical abstraction for your stream processing code.
+ At runtime, the logical topology is instantiated and replicated inside the application for parallel processing (see <a href="#streams_architecture_tasks"><b>Stream Partitions and Tasks</b></a> for details).
+ </p>
+
+ <h3><a id="streams_time" href="#streams_time">Time</a></h3>
+
+ <p>
+ A critical aspect in stream processing is the notion of <b>time</b>, and how it is modeled and integrated.
+ For example, some operations such as <b>windowing</b> are defined based on time boundaries.
+ </p>
+ <p>
+ Common notions of time in streams are:
+ </p>
+
+ <ul>
+ <li><b>Event time</b> - The point in time when an event or data record occurred, i.e. was originally created "at the source". <b>Example:</b> If the event is a geo-location change reported by a GPS sensor in a car, then the associated event-time would be the time when the GPS sensor captured the location change.</li>
+ <li><b>Processing time</b> - The point in time when the event or data record happens to be processed by the stream processing application, i.e. when the record is being consumed. The processing time may be milliseconds, hours, or days etc. later than the original event time. <b>Example:</b> Imagine an analytics application that reads and processes the geo-location data reported from car sensors to present it to a fleet management dashboard. Here, processing-time in the analytics application might be milliseconds or seconds (e.g. for real-time pipelines based on Apache Kafka and Kafka Streams) or hours (e.g. for batch pipelines based on Apache Hadoop or Apache Spark) after event-time.</li>
+ <li><b>Ingestion time</b> - The point in time when an event or data record is stored in a topic partition by a Kafka broker. The difference to event time is that this ingestion timestamp is generated when the record is appended to the target topic by the Kafka broker, not when the record is created "at the source". The difference to processing time is that processing time is when the stream processing application processes the record. <b>For example,</b> if a record is never processed, there is no notion of processing time for it, but it still has an ingestion time.</li>
+ </ul>
+ <p>
+ The choice between event-time and ingestion-time is actually done through the configuration of Kafka (not Kafka Streams): From Kafka 0.10.x onwards, timestamps are automatically embedded into Kafka messages. Depending on Kafka's configuration these timestamps represent event-time or ingestion-time. The respective Kafka configuration setting can be specified on the broker level or per topic. The default timestamp extractor in Kafka Streams will retrieve these embedded timestamps as-is. Hence, the effective time semantics of your application depend on the effective Kafka configuration for these embedded timestamps.
+ </p>
+ <p>
+ Kafka Streams assigns a <b>timestamp</b> to every data record via the <code>TimestampExtractor</code> interface.
+ These per-record timestamps describe the progress of a stream with regards to time and are leveraged by time-dependent operations such as window operations.
+ As a result, this time will only advance when a new record arrives at the processor.
+ We call this data-driven time the <b>stream time</b> of the application to differentiate with the <b>wall-clock time</b> when this application is actually executing.
+ Concrete implementations of the <code>TimestampExtractor</code> interface will then provide different semantics to the stream time definition.
+ For example retrieving or computing timestamps based on the actual contents of data records such as an embedded timestamp field to provide event time semantics,
+ and returning the current wall-clock time thereby yield processing time semantics to stream time.
+ Developers can thus enforce different notions of time depending on their business needs.
+ </p>
+
+ <p>
+ Finally, whenever a Kafka Streams application writes records to Kafka, then it will also assign timestamps to these new records. The way the timestamps are assigned depends on the context:
+ </p>
+
+ <ul>
+ <li> When new output records are generated via processing some input record, for example, <code>context.forward()</code> triggered in the <code>process()</code> function call, output record timestamps are inherited from input record timestamps directly.</li>
+ <li> When new output records are generated via periodic functions such as <code>Punctuator#punctuate()</code>, the output record timestamp is defined as the current internal time (obtained through <code>context.timestamp()</code>) of the stream task.</li>
+ <li> For aggregations, the timestamp of a resulting aggregate update record will be that of the latest arrived input record that triggered the update.</li>
+ </ul>
+
+ <h3><a id="streams_state" href="#streams_state">States</a></h3>
+
+ <p>
+ Some stream processing applications don't require state, which means the processing of a message is independent from
+ the processing of all other messages.
+ However, being able to maintain state opens up many possibilities for sophisticated stream processing applications: you
+ can join input streams, or group and aggregate data records. Many such stateful operators are provided by the <a href="/{{version}}/documentation/streams/developer-guide#streams_dsl"><b>Kafka Streams DSL</b></a>.
+ </p>
+ <p>
+ Kafka Streams provides so-called <b>state stores</b>, which can be used by stream processing applications to store and query data.
+ This is an important capability when implementing stateful operations.
+ Every task in Kafka Streams embeds one or more state stores that can be accessed via APIs to store and query data required for processing.
+ These state stores can either be a persistent key-value store, an in-memory hashmap, or another convenient data structure.
+ Kafka Streams offers fault-tolerance and automatic recovery for local state stores.
+ </p>
+ <p>
+ Kafka Streams allows direct read-only queries of the state stores by methods, threads, processes or applications external to the stream processing application that created the state stores. This is provided through a feature called <b>Interactive Queries</b>. All stores are named and Interactive Queries exposes only the read operations of the underlying implementation.
+ </p>
+ <br>
+
+ <h2><a id="streams_processing_guarantee" href="#streams_processing_guarantee">Processing Guarantees</a></h2>
+
+ <p>
+ In stream processing, one of the most frequently asked question is "does my stream processing system guarantee that each record is processed once and only once, even if some failures are encountered in the middle of processing?"
+ Failing to guarantee exactly-once stream processing is a deal-breaker for many applications that cannot tolerate any data-loss or data duplicates, and in that case a batch-oriented framework is usually used in addition
+ to the stream processing pipeline, known as the <a href="http://lambda-architecture.net/">Lambda Architecture</a>.
+ Prior to 0.11.0.0, Kafka only provides at-least-once delivery guarantees and hence any stream processing systems that leverage it as the backend storage could not guarantee end-to-end exactly-once semantics.
+ In fact, even for those stream processing systems that claim to support exactly-once processing, as long as they are reading from / writing to Kafka as the source / sink, their applications cannot actually guarantee that
+ no duplicates will be generated throughout the pipeline.
+
+ Since the 0.11.0.0 release, Kafka has added support to allow its producers to send messages to different topic partitions in a <a href="https://kafka.apache.org/documentation/#semantics">transactional and idempotent manner</a>,
+ and Kafka Streams has hence added the end-to-end exactly-once processing semantics by leveraging these features.
+ More specifically, it guarantees that for any record read from the source Kafka topics, its processing results will be reflected exactly once in the output Kafka topic as well as in the state stores for stateful operations.
+ Note the key difference between Kafka Streams end-to-end exactly-once guarantee with other stream processing frameworks' claimed guarantees is that Kafka Streams tightly integrates with the underlying Kafka storage system and ensure that
+ commits on the input topic offsets, updates on the state stores, and writes to the output topics will be completed atomically instead of treating Kafka as an external system that may have side-effects.
+ To read more details on how this is done inside Kafka Streams, readers are recommended to read <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-129%3A+Streams+Exactly-Once+Semantics">KIP-129</a>.
+
+ In order to achieve exactly-once semantics when running Kafka Streams applications, users can simply set the <code>processing.guarantee</code> config value to <b>exactly_once</b> (default value is <b>at_least_once</b>).
+ More details can be found in the <a href="/{{version}}/documentation#streamsconfigs"><b>Kafka Streams Configs</b></a> section.
+ </p>
+
+ <div class="pagination">
+ <a href="/{{version}}/documentation/streams/developer-guide" class="pagination__btn pagination__btn__prev">Previous</a>
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[11/14] kafka-site git commit: Update site for 1.0.0 release
Posted by ij...@apache.org.
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/generated/kafka_config.html
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diff --git a/100/generated/kafka_config.html b/100/generated/kafka_config.html
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+++ b/100/generated/kafka_config.html
@@ -0,0 +1,332 @@
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>zookeeper.connect</td><td>Zookeeper host string</td><td>string</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>advertised.host.name</td><td>DEPRECATED: only used when `advertised.listeners` or `listeners` are not set. Use `advertised.listeners` instead.
+Hostname to publish to ZooKeeper for clients to use. In IaaS environments, this may need to be different from the interface to which the broker binds. If this is not set, it will use the value for `host.name` if configured. Otherwise it will use the value returned from java.net.InetAddress.getCanonicalHostName().</td><td>string</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>advertised.listeners</td><td>Listeners to publish to ZooKeeper for clients to use, if different than the `listeners` config property. In IaaS environments, this may need to be different from the interface to which the broker binds. If this is not set, the value for `listeners` will be used. Unlike `listeners` it is not valid to advertise the 0.0.0.0 meta-address.</td><td>string</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>advertised.port</td><td>DEPRECATED: only used when `advertised.listeners` or `listeners` are not set. Use `advertised.listeners` instead.
+The port to publish to ZooKeeper for clients to use. In IaaS environments, this may need to be different from the port to which the broker binds. If this is not set, it will publish the same port that the broker binds to.</td><td>int</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>auto.create.topics.enable</td><td>Enable auto creation of topic on the server</td><td>boolean</td><td>true</td><td></td><td>high</td></tr>
+<tr>
+<td>auto.leader.rebalance.enable</td><td>Enables auto leader balancing. A background thread checks and triggers leader balance if required at regular intervals</td><td>boolean</td><td>true</td><td></td><td>high</td></tr>
+<tr>
+<td>background.threads</td><td>The number of threads to use for various background processing tasks</td><td>int</td><td>10</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>broker.id</td><td>The broker id for this server. If unset, a unique broker id will be generated.To avoid conflicts between zookeeper generated broker id's and user configured broker id's, generated broker ids start from reserved.broker.max.id + 1.</td><td>int</td><td>-1</td><td></td><td>high</td></tr>
+<tr>
+<td>compression.type</td><td>Specify the final compression type for a given topic. This configuration accepts the standard compression codecs ('gzip', 'snappy', 'lz4'). It additionally accepts 'uncompressed' which is equivalent to no compression; and 'producer' which means retain the original compression codec set by the producer.</td><td>string</td><td>producer</td><td></td><td>high</td></tr>
+<tr>
+<td>delete.topic.enable</td><td>Enables delete topic. Delete topic through the admin tool will have no effect if this config is turned off</td><td>boolean</td><td>true</td><td></td><td>high</td></tr>
+<tr>
+<td>host.name</td><td>DEPRECATED: only used when `listeners` is not set. Use `listeners` instead.
+hostname of broker. If this is set, it will only bind to this address. If this is not set, it will bind to all interfaces</td><td>string</td><td>""</td><td></td><td>high</td></tr>
+<tr>
+<td>leader.imbalance.check.interval.seconds</td><td>The frequency with which the partition rebalance check is triggered by the controller</td><td>long</td><td>300</td><td></td><td>high</td></tr>
+<tr>
+<td>leader.imbalance.per.broker.percentage</td><td>The ratio of leader imbalance allowed per broker. The controller would trigger a leader balance if it goes above this value per broker. The value is specified in percentage.</td><td>int</td><td>10</td><td></td><td>high</td></tr>
+<tr>
+<td>listeners</td><td>Listener List - Comma-separated list of URIs we will listen on and the listener names. If the listener name is not a security protocol, listener.security.protocol.map must also be set.
+ Specify hostname as 0.0.0.0 to bind to all interfaces.
+ Leave hostname empty to bind to default interface.
+ Examples of legal listener lists:
+ PLAINTEXT://myhost:9092,SSL://:9091
+ CLIENT://0.0.0.0:9092,REPLICATION://localhost:9093
+</td><td>string</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>log.dir</td><td>The directory in which the log data is kept (supplemental for log.dirs property)</td><td>string</td><td>/tmp/kafka-logs</td><td></td><td>high</td></tr>
+<tr>
+<td>log.dirs</td><td>The directories in which the log data is kept. If not set, the value in log.dir is used</td><td>string</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>log.flush.interval.messages</td><td>The number of messages accumulated on a log partition before messages are flushed to disk </td><td>long</td><td>9223372036854775807</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>log.flush.interval.ms</td><td>The maximum time in ms that a message in any topic is kept in memory before flushed to disk. If not set, the value in log.flush.scheduler.interval.ms is used</td><td>long</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>log.flush.offset.checkpoint.interval.ms</td><td>The frequency with which we update the persistent record of the last flush which acts as the log recovery point</td><td>int</td><td>60000</td><td>[0,...]</td><td>high</td></tr>
+<tr>
+<td>log.flush.scheduler.interval.ms</td><td>The frequency in ms that the log flusher checks whether any log needs to be flushed to disk</td><td>long</td><td>9223372036854775807</td><td></td><td>high</td></tr>
+<tr>
+<td>log.flush.start.offset.checkpoint.interval.ms</td><td>The frequency with which we update the persistent record of log start offset</td><td>int</td><td>60000</td><td>[0,...]</td><td>high</td></tr>
+<tr>
+<td>log.retention.bytes</td><td>The maximum size of the log before deleting it</td><td>long</td><td>-1</td><td></td><td>high</td></tr>
+<tr>
+<td>log.retention.hours</td><td>The number of hours to keep a log file before deleting it (in hours), tertiary to log.retention.ms property</td><td>int</td><td>168</td><td></td><td>high</td></tr>
+<tr>
+<td>log.retention.minutes</td><td>The number of minutes to keep a log file before deleting it (in minutes), secondary to log.retention.ms property. If not set, the value in log.retention.hours is used</td><td>int</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>log.retention.ms</td><td>The number of milliseconds to keep a log file before deleting it (in milliseconds), If not set, the value in log.retention.minutes is used</td><td>long</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>log.roll.hours</td><td>The maximum time before a new log segment is rolled out (in hours), secondary to log.roll.ms property</td><td>int</td><td>168</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>log.roll.jitter.hours</td><td>The maximum jitter to subtract from logRollTimeMillis (in hours), secondary to log.roll.jitter.ms property</td><td>int</td><td>0</td><td>[0,...]</td><td>high</td></tr>
+<tr>
+<td>log.roll.jitter.ms</td><td>The maximum jitter to subtract from logRollTimeMillis (in milliseconds). If not set, the value in log.roll.jitter.hours is used</td><td>long</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>log.roll.ms</td><td>The maximum time before a new log segment is rolled out (in milliseconds). If not set, the value in log.roll.hours is used</td><td>long</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>log.segment.bytes</td><td>The maximum size of a single log file</td><td>int</td><td>1073741824</td><td>[14,...]</td><td>high</td></tr>
+<tr>
+<td>log.segment.delete.delay.ms</td><td>The amount of time to wait before deleting a file from the filesystem</td><td>long</td><td>60000</td><td>[0,...]</td><td>high</td></tr>
+<tr>
+<td>message.max.bytes</td><td><p>The largest record batch size allowed by Kafka. If this is increased and there are consumers older than 0.10.2, the consumers' fetch size must also be increased so that the they can fetch record batches this large.</p><p>In the latest message format version, records are always grouped into batches for efficiency. In previous message format versions, uncompressed records are not grouped into batches and this limit only applies to a single record in that case.</p><p>This can be set per topic with the topic level <code>max.message.bytes</code> config.</p></td><td>int</td><td>1000012</td><td>[0,...]</td><td>high</td></tr>
+<tr>
+<td>min.insync.replicas</td><td>When a producer sets acks to "all" (or "-1"), min.insync.replicas specifies the minimum number of replicas that must acknowledge a write for the write to be considered successful. If this minimum cannot be met, then the producer will raise an exception (either NotEnoughReplicas or NotEnoughReplicasAfterAppend).<br>When used together, min.insync.replicas and acks allow you to enforce greater durability guarantees. A typical scenario would be to create a topic with a replication factor of 3, set min.insync.replicas to 2, and produce with acks of "all". This will ensure that the producer raises an exception if a majority of replicas do not receive a write.</td><td>int</td><td>1</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>num.io.threads</td><td>The number of threads that the server uses for processing requests, which may include disk I/O</td><td>int</td><td>8</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>num.network.threads</td><td>The number of threads that the server uses for receiving requests from the network and sending responses to the network</td><td>int</td><td>3</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>num.recovery.threads.per.data.dir</td><td>The number of threads per data directory to be used for log recovery at startup and flushing at shutdown</td><td>int</td><td>1</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>num.replica.fetchers</td><td>Number of fetcher threads used to replicate messages from a source broker. Increasing this value can increase the degree of I/O parallelism in the follower broker.</td><td>int</td><td>1</td><td></td><td>high</td></tr>
+<tr>
+<td>offset.metadata.max.bytes</td><td>The maximum size for a metadata entry associated with an offset commit</td><td>int</td><td>4096</td><td></td><td>high</td></tr>
+<tr>
+<td>offsets.commit.required.acks</td><td>The required acks before the commit can be accepted. In general, the default (-1) should not be overridden</td><td>short</td><td>-1</td><td></td><td>high</td></tr>
+<tr>
+<td>offsets.commit.timeout.ms</td><td>Offset commit will be delayed until all replicas for the offsets topic receive the commit or this timeout is reached. This is similar to the producer request timeout.</td><td>int</td><td>5000</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>offsets.load.buffer.size</td><td>Batch size for reading from the offsets segments when loading offsets into the cache.</td><td>int</td><td>5242880</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>offsets.retention.check.interval.ms</td><td>Frequency at which to check for stale offsets</td><td>long</td><td>600000</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>offsets.retention.minutes</td><td>Offsets older than this retention period will be discarded</td><td>int</td><td>1440</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>offsets.topic.compression.codec</td><td>Compression codec for the offsets topic - compression may be used to achieve "atomic" commits</td><td>int</td><td>0</td><td></td><td>high</td></tr>
+<tr>
+<td>offsets.topic.num.partitions</td><td>The number of partitions for the offset commit topic (should not change after deployment)</td><td>int</td><td>50</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>offsets.topic.replication.factor</td><td>The replication factor for the offsets topic (set higher to ensure availability). Internal topic creation will fail until the cluster size meets this replication factor requirement.</td><td>short</td><td>3</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>offsets.topic.segment.bytes</td><td>The offsets topic segment bytes should be kept relatively small in order to facilitate faster log compaction and cache loads</td><td>int</td><td>104857600</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>port</td><td>DEPRECATED: only used when `listeners` is not set. Use `listeners` instead.
+the port to listen and accept connections on</td><td>int</td><td>9092</td><td></td><td>high</td></tr>
+<tr>
+<td>queued.max.requests</td><td>The number of queued requests allowed before blocking the network threads</td><td>int</td><td>500</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>quota.consumer.default</td><td>DEPRECATED: Used only when dynamic default quotas are not configured for <user, <client-id> or <user, client-id> in Zookeeper. Any consumer distinguished by clientId/consumer group will get throttled if it fetches more bytes than this value per-second</td><td>long</td><td>9223372036854775807</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>quota.producer.default</td><td>DEPRECATED: Used only when dynamic default quotas are not configured for <user>, <client-id> or <user, client-id> in Zookeeper. Any producer distinguished by clientId will get throttled if it produces more bytes than this value per-second</td><td>long</td><td>9223372036854775807</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>replica.fetch.min.bytes</td><td>Minimum bytes expected for each fetch response. If not enough bytes, wait up to replicaMaxWaitTimeMs</td><td>int</td><td>1</td><td></td><td>high</td></tr>
+<tr>
+<td>replica.fetch.wait.max.ms</td><td>max wait time for each fetcher request issued by follower replicas. This value should always be less than the replica.lag.time.max.ms at all times to prevent frequent shrinking of ISR for low throughput topics</td><td>int</td><td>500</td><td></td><td>high</td></tr>
+<tr>
+<td>replica.high.watermark.checkpoint.interval.ms</td><td>The frequency with which the high watermark is saved out to disk</td><td>long</td><td>5000</td><td></td><td>high</td></tr>
+<tr>
+<td>replica.lag.time.max.ms</td><td>If a follower hasn't sent any fetch requests or hasn't consumed up to the leaders log end offset for at least this time, the leader will remove the follower from isr</td><td>long</td><td>10000</td><td></td><td>high</td></tr>
+<tr>
+<td>replica.socket.receive.buffer.bytes</td><td>The socket receive buffer for network requests</td><td>int</td><td>65536</td><td></td><td>high</td></tr>
+<tr>
+<td>replica.socket.timeout.ms</td><td>The socket timeout for network requests. Its value should be at least replica.fetch.wait.max.ms</td><td>int</td><td>30000</td><td></td><td>high</td></tr>
+<tr>
+<td>request.timeout.ms</td><td>The configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted.</td><td>int</td><td>30000</td><td></td><td>high</td></tr>
+<tr>
+<td>socket.receive.buffer.bytes</td><td>The SO_RCVBUF buffer of the socket sever sockets. If the value is -1, the OS default will be used.</td><td>int</td><td>102400</td><td></td><td>high</td></tr>
+<tr>
+<td>socket.request.max.bytes</td><td>The maximum number of bytes in a socket request</td><td>int</td><td>104857600</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>socket.send.buffer.bytes</td><td>The SO_SNDBUF buffer of the socket sever sockets. If the value is -1, the OS default will be used.</td><td>int</td><td>102400</td><td></td><td>high</td></tr>
+<tr>
+<td>transaction.max.timeout.ms</td><td>The maximum allowed timeout for transactions. If a client’s requested transaction time exceed this, then the broker will return an error in InitProducerIdRequest. This prevents a client from too large of a timeout, which can stall consumers reading from topics included in the transaction.</td><td>int</td><td>900000</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>transaction.state.log.load.buffer.size</td><td>Batch size for reading from the transaction log segments when loading producer ids and transactions into the cache.</td><td>int</td><td>5242880</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>transaction.state.log.min.isr</td><td>Overridden min.insync.replicas config for the transaction topic.</td><td>int</td><td>2</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>transaction.state.log.num.partitions</td><td>The number of partitions for the transaction topic (should not change after deployment).</td><td>int</td><td>50</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>transaction.state.log.replication.factor</td><td>The replication factor for the transaction topic (set higher to ensure availability). Internal topic creation will fail until the cluster size meets this replication factor requirement.</td><td>short</td><td>3</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>transaction.state.log.segment.bytes</td><td>The transaction topic segment bytes should be kept relatively small in order to facilitate faster log compaction and cache loads</td><td>int</td><td>104857600</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>transactional.id.expiration.ms</td><td>The maximum amount of time in ms that the transaction coordinator will wait before proactively expire a producer's transactional id without receiving any transaction status updates from it.</td><td>int</td><td>604800000</td><td>[1,...]</td><td>high</td></tr>
+<tr>
+<td>unclean.leader.election.enable</td><td>Indicates whether to enable replicas not in the ISR set to be elected as leader as a last resort, even though doing so may result in data loss</td><td>boolean</td><td>false</td><td></td><td>high</td></tr>
+<tr>
+<td>zookeeper.connection.timeout.ms</td><td>The max time that the client waits to establish a connection to zookeeper. If not set, the value in zookeeper.session.timeout.ms is used</td><td>int</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>zookeeper.session.timeout.ms</td><td>Zookeeper session timeout</td><td>int</td><td>6000</td><td></td><td>high</td></tr>
+<tr>
+<td>zookeeper.set.acl</td><td>Set client to use secure ACLs</td><td>boolean</td><td>false</td><td></td><td>high</td></tr>
+<tr>
+<td>broker.id.generation.enable</td><td>Enable automatic broker id generation on the server. When enabled the value configured for reserved.broker.max.id should be reviewed.</td><td>boolean</td><td>true</td><td></td><td>medium</td></tr>
+<tr>
+<td>broker.rack</td><td>Rack of the broker. This will be used in rack aware replication assignment for fault tolerance. Examples: `RACK1`, `us-east-1d`</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>connections.max.idle.ms</td><td>Idle connections timeout: the server socket processor threads close the connections that idle more than this</td><td>long</td><td>600000</td><td></td><td>medium</td></tr>
+<tr>
+<td>controlled.shutdown.enable</td><td>Enable controlled shutdown of the server</td><td>boolean</td><td>true</td><td></td><td>medium</td></tr>
+<tr>
+<td>controlled.shutdown.max.retries</td><td>Controlled shutdown can fail for multiple reasons. This determines the number of retries when such failure happens</td><td>int</td><td>3</td><td></td><td>medium</td></tr>
+<tr>
+<td>controlled.shutdown.retry.backoff.ms</td><td>Before each retry, the system needs time to recover from the state that caused the previous failure (Controller fail over, replica lag etc). This config determines the amount of time to wait before retrying.</td><td>long</td><td>5000</td><td></td><td>medium</td></tr>
+<tr>
+<td>controller.socket.timeout.ms</td><td>The socket timeout for controller-to-broker channels</td><td>int</td><td>30000</td><td></td><td>medium</td></tr>
+<tr>
+<td>default.replication.factor</td><td>default replication factors for automatically created topics</td><td>int</td><td>1</td><td></td><td>medium</td></tr>
+<tr>
+<td>delete.records.purgatory.purge.interval.requests</td><td>The purge interval (in number of requests) of the delete records request purgatory</td><td>int</td><td>1</td><td></td><td>medium</td></tr>
+<tr>
+<td>fetch.purgatory.purge.interval.requests</td><td>The purge interval (in number of requests) of the fetch request purgatory</td><td>int</td><td>1000</td><td></td><td>medium</td></tr>
+<tr>
+<td>group.initial.rebalance.delay.ms</td><td>The amount of time the group coordinator will wait for more consumers to join a new group before performing the first rebalance. A longer delay means potentially fewer rebalances, but increases the time until processing begins.</td><td>int</td><td>3000</td><td></td><td>medium</td></tr>
+<tr>
+<td>group.max.session.timeout.ms</td><td>The maximum allowed session timeout for registered consumers. Longer timeouts give consumers more time to process messages in between heartbeats at the cost of a longer time to detect failures.</td><td>int</td><td>300000</td><td></td><td>medium</td></tr>
+<tr>
+<td>group.min.session.timeout.ms</td><td>The minimum allowed session timeout for registered consumers. Shorter timeouts result in quicker failure detection at the cost of more frequent consumer heartbeating, which can overwhelm broker resources.</td><td>int</td><td>6000</td><td></td><td>medium</td></tr>
+<tr>
+<td>inter.broker.listener.name</td><td>Name of listener used for communication between brokers. If this is unset, the listener name is defined by security.inter.broker.protocol. It is an error to set this and security.inter.broker.protocol properties at the same time.</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>inter.broker.protocol.version</td><td>Specify which version of the inter-broker protocol will be used.
+ This is typically bumped after all brokers were upgraded to a new version.
+ Example of some valid values are: 0.8.0, 0.8.1, 0.8.1.1, 0.8.2, 0.8.2.0, 0.8.2.1, 0.9.0.0, 0.9.0.1 Check ApiVersion for the full list.</td><td>string</td><td>1.0-IV0</td><td></td><td>medium</td></tr>
+<tr>
+<td>log.cleaner.backoff.ms</td><td>The amount of time to sleep when there are no logs to clean</td><td>long</td><td>15000</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>log.cleaner.dedupe.buffer.size</td><td>The total memory used for log deduplication across all cleaner threads</td><td>long</td><td>134217728</td><td></td><td>medium</td></tr>
+<tr>
+<td>log.cleaner.delete.retention.ms</td><td>How long are delete records retained?</td><td>long</td><td>86400000</td><td></td><td>medium</td></tr>
+<tr>
+<td>log.cleaner.enable</td><td>Enable the log cleaner process to run on the server. Should be enabled if using any topics with a cleanup.policy=compact including the internal offsets topic. If disabled those topics will not be compacted and continually grow in size.</td><td>boolean</td><td>true</td><td></td><td>medium</td></tr>
+<tr>
+<td>log.cleaner.io.buffer.load.factor</td><td>Log cleaner dedupe buffer load factor. The percentage full the dedupe buffer can become. A higher value will allow more log to be cleaned at once but will lead to more hash collisions</td><td>double</td><td>0.9</td><td></td><td>medium</td></tr>
+<tr>
+<td>log.cleaner.io.buffer.size</td><td>The total memory used for log cleaner I/O buffers across all cleaner threads</td><td>int</td><td>524288</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>log.cleaner.io.max.bytes.per.second</td><td>The log cleaner will be throttled so that the sum of its read and write i/o will be less than this value on average</td><td>double</td><td>1.7976931348623157E308</td><td></td><td>medium</td></tr>
+<tr>
+<td>log.cleaner.min.cleanable.ratio</td><td>The minimum ratio of dirty log to total log for a log to eligible for cleaning</td><td>double</td><td>0.5</td><td></td><td>medium</td></tr>
+<tr>
+<td>log.cleaner.min.compaction.lag.ms</td><td>The minimum time a message will remain uncompacted in the log. Only applicable for logs that are being compacted.</td><td>long</td><td>0</td><td></td><td>medium</td></tr>
+<tr>
+<td>log.cleaner.threads</td><td>The number of background threads to use for log cleaning</td><td>int</td><td>1</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>log.cleanup.policy</td><td>The default cleanup policy for segments beyond the retention window. A comma separated list of valid policies. Valid policies are: "delete" and "compact"</td><td>list</td><td>delete</td><td>[compact, delete]</td><td>medium</td></tr>
+<tr>
+<td>log.index.interval.bytes</td><td>The interval with which we add an entry to the offset index</td><td>int</td><td>4096</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>log.index.size.max.bytes</td><td>The maximum size in bytes of the offset index</td><td>int</td><td>10485760</td><td>[4,...]</td><td>medium</td></tr>
+<tr>
+<td>log.message.format.version</td><td>Specify the message format version the broker will use to append messages to the logs. The value should be a valid ApiVersion. Some examples are: 0.8.2, 0.9.0.0, 0.10.0, check ApiVersion for more details. By setting a particular message format version, the user is certifying that all the existing messages on disk are smaller or equal than the specified version. Setting this value incorrectly will cause consumers with older versions to break as they will receive messages with a format that they don't understand.</td><td>string</td><td>1.0-IV0</td><td></td><td>medium</td></tr>
+<tr>
+<td>log.message.timestamp.difference.max.ms</td><td>The maximum difference allowed between the timestamp when a broker receives a message and the timestamp specified in the message. If log.message.timestamp.type=CreateTime, a message will be rejected if the difference in timestamp exceeds this threshold. This configuration is ignored if log.message.timestamp.type=LogAppendTime.The maximum timestamp difference allowed should be no greater than log.retention.ms to avoid unnecessarily frequent log rolling.</td><td>long</td><td>9223372036854775807</td><td></td><td>medium</td></tr>
+<tr>
+<td>log.message.timestamp.type</td><td>Define whether the timestamp in the message is message create time or log append time. The value should be either `CreateTime` or `LogAppendTime`</td><td>string</td><td>CreateTime</td><td>[CreateTime, LogAppendTime]</td><td>medium</td></tr>
+<tr>
+<td>log.preallocate</td><td>Should pre allocate file when create new segment? If you are using Kafka on Windows, you probably need to set it to true.</td><td>boolean</td><td>false</td><td></td><td>medium</td></tr>
+<tr>
+<td>log.retention.check.interval.ms</td><td>The frequency in milliseconds that the log cleaner checks whether any log is eligible for deletion</td><td>long</td><td>300000</td><td>[1,...]</td><td>medium</td></tr>
+<tr>
+<td>max.connections.per.ip</td><td>The maximum number of connections we allow from each ip address</td><td>int</td><td>2147483647</td><td>[1,...]</td><td>medium</td></tr>
+<tr>
+<td>max.connections.per.ip.overrides</td><td>Per-ip or hostname overrides to the default maximum number of connections</td><td>string</td><td>""</td><td></td><td>medium</td></tr>
+<tr>
+<td>num.partitions</td><td>The default number of log partitions per topic</td><td>int</td><td>1</td><td>[1,...]</td><td>medium</td></tr>
+<tr>
+<td>principal.builder.class</td><td>The fully qualified name of a class that implements the KafkaPrincipalBuilder interface, which is used to build the KafkaPrincipal object used during authorization. This config also supports the deprecated PrincipalBuilder interface which was previously used for client authentication over SSL. If no principal builder is defined, the default behavior depends on the security protocol in use. For SSL authentication, the principal name will be the distinguished name from the client certificate if one is provided; otherwise, if client authentication is not required, the principal name will be ANONYMOUS. For SASL authentication, the principal will be derived using the rules defined by <code>sasl.kerberos.principal.to.local.rules</code> if GSSAPI is in use, and the SASL authentication ID for other mechanisms. For PLAINTEXT, the principal will be ANONYMOUS.</td><td>class</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>producer.purgatory.purge.interval.requests</td><td>The purge interval (in number of requests) of the producer request purgatory</td><td>int</td><td>1000</td><td></td><td>medium</td></tr>
+<tr>
+<td>queued.max.request.bytes</td><td>The number of queued bytes allowed before no more requests are read</td><td>long</td><td>-1</td><td></td><td>medium</td></tr>
+<tr>
+<td>replica.fetch.backoff.ms</td><td>The amount of time to sleep when fetch partition error occurs.</td><td>int</td><td>1000</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>replica.fetch.max.bytes</td><td>The number of bytes of messages to attempt to fetch for each partition. This is not an absolute maximum, if the first record batch in the first non-empty partition of the fetch is larger than this value, the record batch will still be returned to ensure that progress can be made. The maximum record batch size accepted by the broker is defined via <code>message.max.bytes</code> (broker config) or <code>max.message.bytes</code> (topic config).</td><td>int</td><td>1048576</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>replica.fetch.response.max.bytes</td><td>Maximum bytes expected for the entire fetch response. Records are fetched in batches, and if the first record batch in the first non-empty partition of the fetch is larger than this value, the record batch will still be returned to ensure that progress can be made. As such, this is not an absolute maximum. The maximum record batch size accepted by the broker is defined via <code>message.max.bytes</code> (broker config) or <code>max.message.bytes</code> (topic config).</td><td>int</td><td>10485760</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>reserved.broker.max.id</td><td>Max number that can be used for a broker.id</td><td>int</td><td>1000</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>sasl.enabled.mechanisms</td><td>The list of SASL mechanisms enabled in the Kafka server. The list may contain any mechanism for which a security provider is available. Only GSSAPI is enabled by default.</td><td>list</td><td>GSSAPI</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.kerberos.kinit.cmd</td><td>Kerberos kinit command path.</td><td>string</td><td>/usr/bin/kinit</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.kerberos.min.time.before.relogin</td><td>Login thread sleep time between refresh attempts.</td><td>long</td><td>60000</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.kerberos.principal.to.local.rules</td><td>A list of rules for mapping from principal names to short names (typically operating system usernames). The rules are evaluated in order and the first rule that matches a principal name is used to map it to a short name. Any later rules in the list are ignored. By default, principal names of the form {username}/{hostname}@{REALM} are mapped to {username}. For more details on the format please see <a href="#security_authz"> security authorization and acls</a>. Note that this configuration is ignored if an extension of KafkaPrincipalBuilder is provided by the <code>principal.builder.class</code> configuration.</td><td>list</td><td>DEFAULT</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.kerberos.service.name</td><td>The Kerberos principal name that Kafka runs as. This can be defined either in Kafka's JAAS config or in Kafka's config.</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.kerberos.ticket.renew.jitter</td><td>Percentage of random jitter added to the renewal time.</td><td>double</td><td>0.05</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.kerberos.ticket.renew.window.factor</td><td>Login thread will sleep until the specified window factor of time from last refresh to ticket's expiry has been reached, at which time it will try to renew the ticket.</td><td>double</td><td>0.8</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.mechanism.inter.broker.protocol</td><td>SASL mechanism used for inter-broker communication. Default is GSSAPI.</td><td>string</td><td>GSSAPI</td><td></td><td>medium</td></tr>
+<tr>
+<td>security.inter.broker.protocol</td><td>Security protocol used to communicate between brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL. It is an error to set this and inter.broker.listener.name properties at the same time.</td><td>string</td><td>PLAINTEXT</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.cipher.suites</td><td>A list of cipher suites. This is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. By default all the available cipher suites are supported.</td><td>list</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.client.auth</td><td>Configures kafka broker to request client authentication. The following settings are common: <ul> <li><code>ssl.client.auth=required</code> If set to required client authentication is required. <li><code>ssl.client.auth=requested</code> This means client authentication is optional. unlike requested , if this option is set client can choose not to provide authentication information about itself <li><code>ssl.client.auth=none</code> This means client authentication is not needed.</td><td>string</td><td>none</td><td>[required, requested, none]</td><td>medium</td></tr>
+<tr>
+<td>ssl.enabled.protocols</td><td>The list of protocols enabled for SSL connections.</td><td>list</td><td>TLSv1.2,TLSv1.1,TLSv1</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.key.password</td><td>The password of the private key in the key store file. This is optional for client.</td><td>password</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.keymanager.algorithm</td><td>The algorithm used by key manager factory for SSL connections. Default value is the key manager factory algorithm configured for the Java Virtual Machine.</td><td>string</td><td>SunX509</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.keystore.location</td><td>The location of the key store file. This is optional for client and can be used for two-way authentication for client.</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.keystore.password</td><td>The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured. </td><td>password</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.keystore.type</td><td>The file format of the key store file. This is optional for client.</td><td>string</td><td>JKS</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.protocol</td><td>The SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.</td><td>string</td><td>TLS</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.provider</td><td>The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.trustmanager.algorithm</td><td>The algorithm used by trust manager factory for SSL connections. Default value is the trust manager factory algorithm configured for the Java Virtual Machine.</td><td>string</td><td>PKIX</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.truststore.location</td><td>The location of the trust store file. </td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.truststore.password</td><td>The password for the trust store file. If a password is not set access to the truststore is still available, but integrity checking is disabled.</td><td>password</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.truststore.type</td><td>The file format of the trust store file.</td><td>string</td><td>JKS</td><td></td><td>medium</td></tr>
+<tr>
+<td>alter.config.policy.class.name</td><td>The alter configs policy class that should be used for validation. The class should implement the <code>org.apache.kafka.server.policy.AlterConfigPolicy</code> interface.</td><td>class</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>authorizer.class.name</td><td>The authorizer class that should be used for authorization</td><td>string</td><td>""</td><td></td><td>low</td></tr>
+<tr>
+<td>create.topic.policy.class.name</td><td>The create topic policy class that should be used for validation. The class should implement the <code>org.apache.kafka.server.policy.CreateTopicPolicy</code> interface.</td><td>class</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>listener.security.protocol.map</td><td>Map between listener names and security protocols. This must be defined for the same security protocol to be usable in more than one port or IP. For example, internal and external traffic can be separated even if SSL is required for both. Concretely, the user could define listeners with names INTERNAL and EXTERNAL and this property as: `INTERNAL:SSL,EXTERNAL:SSL`. As shown, key and value are separated by a colon and map entries are separated by commas. Each listener name should only appear once in the map. Different security (SSL and SASL) settings can be configured for each listener by adding a normalised prefix (the listener name is lowercased) to the config name. For example, to set a different keystore for the INTERNAL listener, a config with name `listener.name.internal.ssl.keystore.location` would be set. If the config for the listener name is not set, the config will fallback to the generic config (i.e. `ssl.keystore.location`). </td
><td>string</td><td>PLAINTEXT:PLAINTEXT,SSL:SSL,SASL_PLAINTEXT:SASL_PLAINTEXT,SASL_SSL:SASL_SSL</td><td></td><td>low</td></tr>
+<tr>
+<td>metric.reporters</td><td>A list of classes to use as metrics reporters. Implementing the <code>org.apache.kafka.common.metrics.MetricsReporter</code> interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.</td><td>list</td><td>""</td><td></td><td>low</td></tr>
+<tr>
+<td>metrics.num.samples</td><td>The number of samples maintained to compute metrics.</td><td>int</td><td>2</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>metrics.recording.level</td><td>The highest recording level for metrics.</td><td>string</td><td>INFO</td><td></td><td>low</td></tr>
+<tr>
+<td>metrics.sample.window.ms</td><td>The window of time a metrics sample is computed over.</td><td>long</td><td>30000</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>quota.window.num</td><td>The number of samples to retain in memory for client quotas</td><td>int</td><td>11</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>quota.window.size.seconds</td><td>The time span of each sample for client quotas</td><td>int</td><td>1</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>replication.quota.window.num</td><td>The number of samples to retain in memory for replication quotas</td><td>int</td><td>11</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>replication.quota.window.size.seconds</td><td>The time span of each sample for replication quotas</td><td>int</td><td>1</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>ssl.endpoint.identification.algorithm</td><td>The endpoint identification algorithm to validate server hostname using server certificate. </td><td>string</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.secure.random.implementation</td><td>The SecureRandom PRNG implementation to use for SSL cryptography operations. </td><td>string</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>transaction.abort.timed.out.transaction.cleanup.interval.ms</td><td>The interval at which to rollback transactions that have timed out</td><td>int</td><td>60000</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>transaction.remove.expired.transaction.cleanup.interval.ms</td><td>The interval at which to remove transactions that have expired due to <code>transactional.id.expiration.ms<code> passing</td><td>int</td><td>3600000</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>zookeeper.sync.time.ms</td><td>How far a ZK follower can be behind a ZK leader</td><td>int</td><td>2000</td><td></td><td>low</td></tr>
+</tbody></table>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/generated/producer_config.html
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+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>bootstrap.servers</td><td>A list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping—this list only impacts the initial hosts used to discover the full set of servers. This list should be in the form <code>host1:port1,host2:port2,...</code>. Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down).</td><td>list</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>key.serializer</td><td>Serializer class for key that implements the <code>org.apache.kafka.common.serialization.Serializer</code> interface.</td><td>class</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>value.serializer</td><td>Serializer class for value that implements the <code>org.apache.kafka.common.serialization.Serializer</code> interface.</td><td>class</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>acks</td><td>The number of acknowledgments the producer requires the leader to have received before considering a request complete. This controls the durability of records that are sent. The following settings are allowed: <ul> <li><code>acks=0</code> If set to zero then the producer will not wait for any acknowledgment from the server at all. The record will be immediately added to the socket buffer and considered sent. No guarantee can be made that the server has received the record in this case, and the <code>retries</code> configuration will not take effect (as the client won't generally know of any failures). The offset given back for each record will always be set to -1. <li><code>acks=1</code> This will mean the leader will write the record to its local log but will respond without awaiting full acknowledgement from all followers. In this case should the leader fail immediately after acknowledging the record but before the followers have replicated it then the record wi
ll be lost. <li><code>acks=all</code> This means the leader will wait for the full set of in-sync replicas to acknowledge the record. This guarantees that the record will not be lost as long as at least one in-sync replica remains alive. This is the strongest available guarantee. This is equivalent to the acks=-1 setting.</td><td>string</td><td>1</td><td>[all, -1, 0, 1]</td><td>high</td></tr>
+<tr>
+<td>buffer.memory</td><td>The total bytes of memory the producer can use to buffer records waiting to be sent to the server. If records are sent faster than they can be delivered to the server the producer will block for <code>max.block.ms</code> after which it will throw an exception.<p>This setting should correspond roughly to the total memory the producer will use, but is not a hard bound since not all memory the producer uses is used for buffering. Some additional memory will be used for compression (if compression is enabled) as well as for maintaining in-flight requests.</td><td>long</td><td>33554432</td><td>[0,...]</td><td>high</td></tr>
+<tr>
+<td>compression.type</td><td>The compression type for all data generated by the producer. The default is none (i.e. no compression). Valid values are <code>none</code>, <code>gzip</code>, <code>snappy</code>, or <code>lz4</code>. Compression is of full batches of data, so the efficacy of batching will also impact the compression ratio (more batching means better compression).</td><td>string</td><td>none</td><td></td><td>high</td></tr>
+<tr>
+<td>retries</td><td>Setting a value greater than zero will cause the client to resend any record whose send fails with a potentially transient error. Note that this retry is no different than if the client resent the record upon receiving the error. Allowing retries without setting <code>max.in.flight.requests.per.connection</code> to 1 will potentially change the ordering of records because if two batches are sent to a single partition, and the first fails and is retried but the second succeeds, then the records in the second batch may appear first.</td><td>int</td><td>0</td><td>[0,...,2147483647]</td><td>high</td></tr>
+<tr>
+<td>ssl.key.password</td><td>The password of the private key in the key store file. This is optional for client.</td><td>password</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.keystore.location</td><td>The location of the key store file. This is optional for client and can be used for two-way authentication for client.</td><td>string</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.keystore.password</td><td>The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured. </td><td>password</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.truststore.location</td><td>The location of the trust store file. </td><td>string</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.truststore.password</td><td>The password for the trust store file. If a password is not set access to the truststore is still available, but integrity checking is disabled.</td><td>password</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>batch.size</td><td>The producer will attempt to batch records together into fewer requests whenever multiple records are being sent to the same partition. This helps performance on both the client and the server. This configuration controls the default batch size in bytes. <p>No attempt will be made to batch records larger than this size. <p>Requests sent to brokers will contain multiple batches, one for each partition with data available to be sent. <p>A small batch size will make batching less common and may reduce throughput (a batch size of zero will disable batching entirely). A very large batch size may use memory a bit more wastefully as we will always allocate a buffer of the specified batch size in anticipation of additional records.</td><td>int</td><td>16384</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>client.id</td><td>An id string to pass to the server when making requests. The purpose of this is to be able to track the source of requests beyond just ip/port by allowing a logical application name to be included in server-side request logging.</td><td>string</td><td>""</td><td></td><td>medium</td></tr>
+<tr>
+<td>connections.max.idle.ms</td><td>Close idle connections after the number of milliseconds specified by this config.</td><td>long</td><td>540000</td><td></td><td>medium</td></tr>
+<tr>
+<td>linger.ms</td><td>The producer groups together any records that arrive in between request transmissions into a single batched request. Normally this occurs only under load when records arrive faster than they can be sent out. However in some circumstances the client may want to reduce the number of requests even under moderate load. This setting accomplishes this by adding a small amount of artificial delay—that is, rather than immediately sending out a record the producer will wait for up to the given delay to allow other records to be sent so that the sends can be batched together. This can be thought of as analogous to Nagle's algorithm in TCP. This setting gives the upper bound on the delay for batching: once we get <code>batch.size</code> worth of records for a partition it will be sent immediately regardless of this setting, however if we have fewer than this many bytes accumulated for this partition we will 'linger' for the specified time waiting for more records to
show up. This setting defaults to 0 (i.e. no delay). Setting <code>linger.ms=5</code>, for example, would have the effect of reducing the number of requests sent but would add up to 5ms of latency to records sent in the absense of load.</td><td>long</td><td>0</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>max.block.ms</td><td>The configuration controls how long <code>KafkaProducer.send()</code> and <code>KafkaProducer.partitionsFor()</code> will block.These methods can be blocked either because the buffer is full or metadata unavailable.Blocking in the user-supplied serializers or partitioner will not be counted against this timeout.</td><td>long</td><td>60000</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>max.request.size</td><td>The maximum size of a request in bytes. This setting will limit the number of record batches the producer will send in a single request to avoid sending huge requests. This is also effectively a cap on the maximum record batch size. Note that the server has its own cap on record batch size which may be different from this.</td><td>int</td><td>1048576</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>partitioner.class</td><td>Partitioner class that implements the <code>org.apache.kafka.clients.producer.Partitioner</code> interface.</td><td>class</td><td>org.apache.kafka.clients.producer.internals.DefaultPartitioner</td><td></td><td>medium</td></tr>
+<tr>
+<td>receive.buffer.bytes</td><td>The size of the TCP receive buffer (SO_RCVBUF) to use when reading data. If the value is -1, the OS default will be used.</td><td>int</td><td>32768</td><td>[-1,...]</td><td>medium</td></tr>
+<tr>
+<td>request.timeout.ms</td><td>The configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted. This should be larger than replica.lag.time.max.ms (a broker configuration) to reduce the possibility of message duplication due to unnecessary producer retries.</td><td>int</td><td>30000</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>sasl.jaas.config</td><td>JAAS login context parameters for SASL connections in the format used by JAAS configuration files. JAAS configuration file format is described <a href="http://docs.oracle.com/javase/8/docs/technotes/guides/security/jgss/tutorials/LoginConfigFile.html">here</a>. The format for the value is: '<loginModuleClass> <controlFlag> (<optionName>=<optionValue>)*;'</td><td>password</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.kerberos.service.name</td><td>The Kerberos principal name that Kafka runs as. This can be defined either in Kafka's JAAS config or in Kafka's config.</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.mechanism</td><td>SASL mechanism used for client connections. This may be any mechanism for which a security provider is available. GSSAPI is the default mechanism.</td><td>string</td><td>GSSAPI</td><td></td><td>medium</td></tr>
+<tr>
+<td>security.protocol</td><td>Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.</td><td>string</td><td>PLAINTEXT</td><td></td><td>medium</td></tr>
+<tr>
+<td>send.buffer.bytes</td><td>The size of the TCP send buffer (SO_SNDBUF) to use when sending data. If the value is -1, the OS default will be used.</td><td>int</td><td>131072</td><td>[-1,...]</td><td>medium</td></tr>
+<tr>
+<td>ssl.enabled.protocols</td><td>The list of protocols enabled for SSL connections.</td><td>list</td><td>TLSv1.2,TLSv1.1,TLSv1</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.keystore.type</td><td>The file format of the key store file. This is optional for client.</td><td>string</td><td>JKS</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.protocol</td><td>The SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.</td><td>string</td><td>TLS</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.provider</td><td>The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.truststore.type</td><td>The file format of the trust store file.</td><td>string</td><td>JKS</td><td></td><td>medium</td></tr>
+<tr>
+<td>enable.idempotence</td><td>When set to 'true', the producer will ensure that exactly one copy of each message is written in the stream. If 'false', producer retries due to broker failures, etc., may write duplicates of the retried message in the stream. Note that enabling idempotence requires <code>max.in.flight.requests.per.connection</code> to be less than or equal to 5, <code>retries</code> to be greater than 0 and acks must be 'all'. If these values are not explicitly set by the user, suitable values will be chosen. If incompatible values are set, a ConfigException will be thrown.</td><td>boolean</td><td>false</td><td></td><td>low</td></tr>
+<tr>
+<td>interceptor.classes</td><td>A list of classes to use as interceptors. Implementing the <code>org.apache.kafka.clients.producer.ProducerInterceptor</code> interface allows you to intercept (and possibly mutate) the records received by the producer before they are published to the Kafka cluster. By default, there are no interceptors.</td><td>list</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>max.in.flight.requests.per.connection</td><td>The maximum number of unacknowledged requests the client will send on a single connection before blocking. Note that if this setting is set to be greater than 1 and there are failed sends, there is a risk of message re-ordering due to retries (i.e., if retries are enabled).</td><td>int</td><td>5</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>metadata.max.age.ms</td><td>The period of time in milliseconds after which we force a refresh of metadata even if we haven't seen any partition leadership changes to proactively discover any new brokers or partitions.</td><td>long</td><td>300000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>metric.reporters</td><td>A list of classes to use as metrics reporters. Implementing the <code>org.apache.kafka.common.metrics.MetricsReporter</code> interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.</td><td>list</td><td>""</td><td></td><td>low</td></tr>
+<tr>
+<td>metrics.num.samples</td><td>The number of samples maintained to compute metrics.</td><td>int</td><td>2</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>metrics.recording.level</td><td>The highest recording level for metrics.</td><td>string</td><td>INFO</td><td>[INFO, DEBUG]</td><td>low</td></tr>
+<tr>
+<td>metrics.sample.window.ms</td><td>The window of time a metrics sample is computed over.</td><td>long</td><td>30000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>reconnect.backoff.max.ms</td><td>The maximum amount of time in milliseconds to wait when reconnecting to a broker that has repeatedly failed to connect. If provided, the backoff per host will increase exponentially for each consecutive connection failure, up to this maximum. After calculating the backoff increase, 20% random jitter is added to avoid connection storms.</td><td>long</td><td>1000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>reconnect.backoff.ms</td><td>The base amount of time to wait before attempting to reconnect to a given host. This avoids repeatedly connecting to a host in a tight loop. This backoff applies to all connection attempts by the client to a broker.</td><td>long</td><td>50</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>retry.backoff.ms</td><td>The amount of time to wait before attempting to retry a failed request to a given topic partition. This avoids repeatedly sending requests in a tight loop under some failure scenarios.</td><td>long</td><td>100</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.kinit.cmd</td><td>Kerberos kinit command path.</td><td>string</td><td>/usr/bin/kinit</td><td></td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.min.time.before.relogin</td><td>Login thread sleep time between refresh attempts.</td><td>long</td><td>60000</td><td></td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.ticket.renew.jitter</td><td>Percentage of random jitter added to the renewal time.</td><td>double</td><td>0.05</td><td></td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.ticket.renew.window.factor</td><td>Login thread will sleep until the specified window factor of time from last refresh to ticket's expiry has been reached, at which time it will try to renew the ticket.</td><td>double</td><td>0.8</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.cipher.suites</td><td>A list of cipher suites. This is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. By default all the available cipher suites are supported.</td><td>list</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.endpoint.identification.algorithm</td><td>The endpoint identification algorithm to validate server hostname using server certificate. </td><td>string</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.keymanager.algorithm</td><td>The algorithm used by key manager factory for SSL connections. Default value is the key manager factory algorithm configured for the Java Virtual Machine.</td><td>string</td><td>SunX509</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.secure.random.implementation</td><td>The SecureRandom PRNG implementation to use for SSL cryptography operations. </td><td>string</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.trustmanager.algorithm</td><td>The algorithm used by trust manager factory for SSL connections. Default value is the trust manager factory algorithm configured for the Java Virtual Machine.</td><td>string</td><td>PKIX</td><td></td><td>low</td></tr>
+<tr>
+<td>transaction.timeout.ms</td><td>The maximum amount of time in ms that the transaction coordinator will wait for a transaction status update from the producer before proactively aborting the ongoing transaction.If this value is larger than the max.transaction.timeout.ms setting in the broker, the request will fail with a `InvalidTransactionTimeout` error.</td><td>int</td><td>60000</td><td></td><td>low</td></tr>
+<tr>
+<td>transactional.id</td><td>The TransactionalId to use for transactional delivery. This enables reliability semantics which span multiple producer sessions since it allows the client to guarantee that transactions using the same TransactionalId have been completed prior to starting any new transactions. If no TransactionalId is provided, then the producer is limited to idempotent delivery. Note that enable.idempotence must be enabled if a TransactionalId is configured. The default is empty, which means transactions cannot be used.</td><td>string</td><td>null</td><td>non-empty string</td><td>low</td></tr>
+</tbody></table>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/generated/producer_metrics.html
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+<table class="data-table"><tbody>
+<tr>
+<td colspan=3 class="mbeanName" style="background-color:#ccc; font-weight: bold;">kafka.producer:type=producer-metrics,client-id="{client-id}"</td></tr>
+<tr>
+<th style="width: 90px"></th>
+<th>Attribute name</th>
+<th>Description</th>
+</tr>
+<tr>
+<td></td><td>batch-size-avg</td><td>The average number of bytes sent per partition per-request.</td></tr>
+<tr>
+<td></td><td>batch-size-max</td><td>The max number of bytes sent per partition per-request.</td></tr>
+<tr>
+<td></td><td>batch-split-rate</td><td>The average number of batch splits per second</td></tr>
+<tr>
+<td></td><td>batch-split-total</td><td>The total number of batch splits</td></tr>
+<tr>
+<td></td><td>compression-rate-avg</td><td>The average compression rate of record batches.</td></tr>
+<tr>
+<td></td><td>metadata-age</td><td>The age in seconds of the current producer metadata being used.</td></tr>
+<tr>
+<td></td><td>produce-throttle-time-avg</td><td>The average time in ms a request was throttled by a broker</td></tr>
+<tr>
+<td></td><td>produce-throttle-time-max</td><td>The maximum time in ms a request was throttled by a broker</td></tr>
+<tr>
+<td></td><td>record-error-rate</td><td>The average per-second number of record sends that resulted in errors</td></tr>
+<tr>
+<td></td><td>record-error-total</td><td>The total number of record sends that resulted in errors</td></tr>
+<tr>
+<td></td><td>record-queue-time-avg</td><td>The average time in ms record batches spent in the send buffer.</td></tr>
+<tr>
+<td></td><td>record-queue-time-max</td><td>The maximum time in ms record batches spent in the send buffer.</td></tr>
+<tr>
+<td></td><td>record-retry-rate</td><td>The average per-second number of retried record sends</td></tr>
+<tr>
+<td></td><td>record-retry-total</td><td>The total number of retried record sends</td></tr>
+<tr>
+<td></td><td>record-send-rate</td><td>The average number of records sent per second.</td></tr>
+<tr>
+<td></td><td>record-send-total</td><td>The total number of records sent.</td></tr>
+<tr>
+<td></td><td>record-size-avg</td><td>The average record size</td></tr>
+<tr>
+<td></td><td>record-size-max</td><td>The maximum record size</td></tr>
+<tr>
+<td></td><td>records-per-request-avg</td><td>The average number of records per request.</td></tr>
+<tr>
+<td></td><td>request-latency-avg</td><td>The average request latency in ms</td></tr>
+<tr>
+<td></td><td>request-latency-max</td><td>The maximum request latency in ms</td></tr>
+<tr>
+<td></td><td>requests-in-flight</td><td>The current number of in-flight requests awaiting a response.</td></tr>
+<tr>
+<td colspan=3 class="mbeanName" style="background-color:#ccc; font-weight: bold;">kafka.producer:type=producer-topic-metrics,client-id="{client-id}",topic="{topic}"</td></tr>
+<tr>
+<th style="width: 90px"></th>
+<th>Attribute name</th>
+<th>Description</th>
+</tr>
+<tr>
+<td></td><td>byte-rate</td><td>The average number of bytes sent per second for a topic.</td></tr>
+<tr>
+<td></td><td>byte-total</td><td>The total number of bytes sent for a topic.</td></tr>
+<tr>
+<td></td><td>compression-rate</td><td>The average compression rate of record batches for a topic.</td></tr>
+<tr>
+<td></td><td>record-error-rate</td><td>The average per-second number of record sends that resulted in errors for a topic</td></tr>
+<tr>
+<td></td><td>record-error-total</td><td>The total number of record sends that resulted in errors for a topic</td></tr>
+<tr>
+<td></td><td>record-retry-rate</td><td>The average per-second number of retried record sends for a topic</td></tr>
+<tr>
+<td></td><td>record-retry-total</td><td>The total number of retried record sends for a topic</td></tr>
+<tr>
+<td></td><td>record-send-rate</td><td>The average number of records sent per second for a topic.</td></tr>
+<tr>
+<td></td><td>record-send-total</td><td>The total number of records sent for a topic.</td></tr>
+</tbody></table>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/generated/protocol_api_keys.html
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+<table class="data-table"><tbody>
+<tr><th>Name</th>
+<th>Key</th>
+</tr><tr>
+<td><a href="#The_Messages_Produce">Produce</a></td><td>0</td></tr>
+<tr>
+<td><a href="#The_Messages_Fetch">Fetch</a></td><td>1</td></tr>
+<tr>
+<td><a href="#The_Messages_ListOffsets">ListOffsets</a></td><td>2</td></tr>
+<tr>
+<td><a href="#The_Messages_Metadata">Metadata</a></td><td>3</td></tr>
+<tr>
+<td><a href="#The_Messages_LeaderAndIsr">LeaderAndIsr</a></td><td>4</td></tr>
+<tr>
+<td><a href="#The_Messages_StopReplica">StopReplica</a></td><td>5</td></tr>
+<tr>
+<td><a href="#The_Messages_UpdateMetadata">UpdateMetadata</a></td><td>6</td></tr>
+<tr>
+<td><a href="#The_Messages_ControlledShutdown">ControlledShutdown</a></td><td>7</td></tr>
+<tr>
+<td><a href="#The_Messages_OffsetCommit">OffsetCommit</a></td><td>8</td></tr>
+<tr>
+<td><a href="#The_Messages_OffsetFetch">OffsetFetch</a></td><td>9</td></tr>
+<tr>
+<td><a href="#The_Messages_FindCoordinator">FindCoordinator</a></td><td>10</td></tr>
+<tr>
+<td><a href="#The_Messages_JoinGroup">JoinGroup</a></td><td>11</td></tr>
+<tr>
+<td><a href="#The_Messages_Heartbeat">Heartbeat</a></td><td>12</td></tr>
+<tr>
+<td><a href="#The_Messages_LeaveGroup">LeaveGroup</a></td><td>13</td></tr>
+<tr>
+<td><a href="#The_Messages_SyncGroup">SyncGroup</a></td><td>14</td></tr>
+<tr>
+<td><a href="#The_Messages_DescribeGroups">DescribeGroups</a></td><td>15</td></tr>
+<tr>
+<td><a href="#The_Messages_ListGroups">ListGroups</a></td><td>16</td></tr>
+<tr>
+<td><a href="#The_Messages_SaslHandshake">SaslHandshake</a></td><td>17</td></tr>
+<tr>
+<td><a href="#The_Messages_ApiVersions">ApiVersions</a></td><td>18</td></tr>
+<tr>
+<td><a href="#The_Messages_CreateTopics">CreateTopics</a></td><td>19</td></tr>
+<tr>
+<td><a href="#The_Messages_DeleteTopics">DeleteTopics</a></td><td>20</td></tr>
+<tr>
+<td><a href="#The_Messages_DeleteRecords">DeleteRecords</a></td><td>21</td></tr>
+<tr>
+<td><a href="#The_Messages_InitProducerId">InitProducerId</a></td><td>22</td></tr>
+<tr>
+<td><a href="#The_Messages_OffsetForLeaderEpoch">OffsetForLeaderEpoch</a></td><td>23</td></tr>
+<tr>
+<td><a href="#The_Messages_AddPartitionsToTxn">AddPartitionsToTxn</a></td><td>24</td></tr>
+<tr>
+<td><a href="#The_Messages_AddOffsetsToTxn">AddOffsetsToTxn</a></td><td>25</td></tr>
+<tr>
+<td><a href="#The_Messages_EndTxn">EndTxn</a></td><td>26</td></tr>
+<tr>
+<td><a href="#The_Messages_WriteTxnMarkers">WriteTxnMarkers</a></td><td>27</td></tr>
+<tr>
+<td><a href="#The_Messages_TxnOffsetCommit">TxnOffsetCommit</a></td><td>28</td></tr>
+<tr>
+<td><a href="#The_Messages_DescribeAcls">DescribeAcls</a></td><td>29</td></tr>
+<tr>
+<td><a href="#The_Messages_CreateAcls">CreateAcls</a></td><td>30</td></tr>
+<tr>
+<td><a href="#The_Messages_DeleteAcls">DeleteAcls</a></td><td>31</td></tr>
+<tr>
+<td><a href="#The_Messages_DescribeConfigs">DescribeConfigs</a></td><td>32</td></tr>
+<tr>
+<td><a href="#The_Messages_AlterConfigs">AlterConfigs</a></td><td>33</td></tr>
+<tr>
+<td><a href="#The_Messages_AlterReplicaLogDirs">AlterReplicaLogDirs</a></td><td>34</td></tr>
+<tr>
+<td><a href="#The_Messages_DescribeLogDirs">DescribeLogDirs</a></td><td>35</td></tr>
+<tr>
+<td><a href="#The_Messages_SaslAuthenticate">SaslAuthenticate</a></td><td>36</td></tr>
+<tr>
+<td><a href="#The_Messages_CreatePartitions">CreatePartitions</a></td><td>37</td></tr>
+</table>
+
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/generated/protocol_errors.html
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diff --git a/100/generated/protocol_errors.html b/100/generated/protocol_errors.html
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+<table class="data-table"><tbody>
+<tr><th>Error</th>
+<th>Code</th>
+<th>Retriable</th>
+<th>Description</th>
+</tr>
+<tr><td>UNKNOWN_SERVER_ERROR</td><td>-1</td><td>False</td><td>The server experienced an unexpected error when processing the request</td></tr>
+<tr><td>NONE</td><td>0</td><td>False</td><td></td></tr>
+<tr><td>OFFSET_OUT_OF_RANGE</td><td>1</td><td>False</td><td>The requested offset is not within the range of offsets maintained by the server.</td></tr>
+<tr><td>CORRUPT_MESSAGE</td><td>2</td><td>True</td><td>This message has failed its CRC checksum, exceeds the valid size, or is otherwise corrupt.</td></tr>
+<tr><td>UNKNOWN_TOPIC_OR_PARTITION</td><td>3</td><td>True</td><td>This server does not host this topic-partition.</td></tr>
+<tr><td>INVALID_FETCH_SIZE</td><td>4</td><td>False</td><td>The requested fetch size is invalid.</td></tr>
+<tr><td>LEADER_NOT_AVAILABLE</td><td>5</td><td>True</td><td>There is no leader for this topic-partition as we are in the middle of a leadership election.</td></tr>
+<tr><td>NOT_LEADER_FOR_PARTITION</td><td>6</td><td>True</td><td>This server is not the leader for that topic-partition.</td></tr>
+<tr><td>REQUEST_TIMED_OUT</td><td>7</td><td>True</td><td>The request timed out.</td></tr>
+<tr><td>BROKER_NOT_AVAILABLE</td><td>8</td><td>False</td><td>The broker is not available.</td></tr>
+<tr><td>REPLICA_NOT_AVAILABLE</td><td>9</td><td>False</td><td>The replica is not available for the requested topic-partition</td></tr>
+<tr><td>MESSAGE_TOO_LARGE</td><td>10</td><td>False</td><td>The request included a message larger than the max message size the server will accept.</td></tr>
+<tr><td>STALE_CONTROLLER_EPOCH</td><td>11</td><td>False</td><td>The controller moved to another broker.</td></tr>
+<tr><td>OFFSET_METADATA_TOO_LARGE</td><td>12</td><td>False</td><td>The metadata field of the offset request was too large.</td></tr>
+<tr><td>NETWORK_EXCEPTION</td><td>13</td><td>True</td><td>The server disconnected before a response was received.</td></tr>
+<tr><td>COORDINATOR_LOAD_IN_PROGRESS</td><td>14</td><td>True</td><td>The coordinator is loading and hence can't process requests.</td></tr>
+<tr><td>COORDINATOR_NOT_AVAILABLE</td><td>15</td><td>True</td><td>The coordinator is not available.</td></tr>
+<tr><td>NOT_COORDINATOR</td><td>16</td><td>True</td><td>This is not the correct coordinator.</td></tr>
+<tr><td>INVALID_TOPIC_EXCEPTION</td><td>17</td><td>False</td><td>The request attempted to perform an operation on an invalid topic.</td></tr>
+<tr><td>RECORD_LIST_TOO_LARGE</td><td>18</td><td>False</td><td>The request included message batch larger than the configured segment size on the server.</td></tr>
+<tr><td>NOT_ENOUGH_REPLICAS</td><td>19</td><td>True</td><td>Messages are rejected since there are fewer in-sync replicas than required.</td></tr>
+<tr><td>NOT_ENOUGH_REPLICAS_AFTER_APPEND</td><td>20</td><td>True</td><td>Messages are written to the log, but to fewer in-sync replicas than required.</td></tr>
+<tr><td>INVALID_REQUIRED_ACKS</td><td>21</td><td>False</td><td>Produce request specified an invalid value for required acks.</td></tr>
+<tr><td>ILLEGAL_GENERATION</td><td>22</td><td>False</td><td>Specified group generation id is not valid.</td></tr>
+<tr><td>INCONSISTENT_GROUP_PROTOCOL</td><td>23</td><td>False</td><td>The group member's supported protocols are incompatible with those of existing members or first group member tried to join with empty protocol type or empty protocol list.</td></tr>
+<tr><td>INVALID_GROUP_ID</td><td>24</td><td>False</td><td>The configured groupId is invalid</td></tr>
+<tr><td>UNKNOWN_MEMBER_ID</td><td>25</td><td>False</td><td>The coordinator is not aware of this member.</td></tr>
+<tr><td>INVALID_SESSION_TIMEOUT</td><td>26</td><td>False</td><td>The session timeout is not within the range allowed by the broker (as configured by group.min.session.timeout.ms and group.max.session.timeout.ms).</td></tr>
+<tr><td>REBALANCE_IN_PROGRESS</td><td>27</td><td>False</td><td>The group is rebalancing, so a rejoin is needed.</td></tr>
+<tr><td>INVALID_COMMIT_OFFSET_SIZE</td><td>28</td><td>False</td><td>The committing offset data size is not valid</td></tr>
+<tr><td>TOPIC_AUTHORIZATION_FAILED</td><td>29</td><td>False</td><td>Not authorized to access topics: [Topic authorization failed.]</td></tr>
+<tr><td>GROUP_AUTHORIZATION_FAILED</td><td>30</td><td>False</td><td>Not authorized to access group: Group authorization failed.</td></tr>
+<tr><td>CLUSTER_AUTHORIZATION_FAILED</td><td>31</td><td>False</td><td>Cluster authorization failed.</td></tr>
+<tr><td>INVALID_TIMESTAMP</td><td>32</td><td>False</td><td>The timestamp of the message is out of acceptable range.</td></tr>
+<tr><td>UNSUPPORTED_SASL_MECHANISM</td><td>33</td><td>False</td><td>The broker does not support the requested SASL mechanism.</td></tr>
+<tr><td>ILLEGAL_SASL_STATE</td><td>34</td><td>False</td><td>Request is not valid given the current SASL state.</td></tr>
+<tr><td>UNSUPPORTED_VERSION</td><td>35</td><td>False</td><td>The version of API is not supported.</td></tr>
+<tr><td>TOPIC_ALREADY_EXISTS</td><td>36</td><td>False</td><td>Topic with this name already exists.</td></tr>
+<tr><td>INVALID_PARTITIONS</td><td>37</td><td>False</td><td>Number of partitions is invalid.</td></tr>
+<tr><td>INVALID_REPLICATION_FACTOR</td><td>38</td><td>False</td><td>Replication-factor is invalid.</td></tr>
+<tr><td>INVALID_REPLICA_ASSIGNMENT</td><td>39</td><td>False</td><td>Replica assignment is invalid.</td></tr>
+<tr><td>INVALID_CONFIG</td><td>40</td><td>False</td><td>Configuration is invalid.</td></tr>
+<tr><td>NOT_CONTROLLER</td><td>41</td><td>True</td><td>This is not the correct controller for this cluster.</td></tr>
+<tr><td>INVALID_REQUEST</td><td>42</td><td>False</td><td>This most likely occurs because of a request being malformed by the client library or the message was sent to an incompatible broker. See the broker logs for more details.</td></tr>
+<tr><td>UNSUPPORTED_FOR_MESSAGE_FORMAT</td><td>43</td><td>False</td><td>The message format version on the broker does not support the request.</td></tr>
+<tr><td>POLICY_VIOLATION</td><td>44</td><td>False</td><td>Request parameters do not satisfy the configured policy.</td></tr>
+<tr><td>OUT_OF_ORDER_SEQUENCE_NUMBER</td><td>45</td><td>False</td><td>The broker received an out of order sequence number</td></tr>
+<tr><td>DUPLICATE_SEQUENCE_NUMBER</td><td>46</td><td>False</td><td>The broker received a duplicate sequence number</td></tr>
+<tr><td>INVALID_PRODUCER_EPOCH</td><td>47</td><td>False</td><td>Producer attempted an operation with an old epoch. Either there is a newer producer with the same transactionalId, or the producer's transaction has been expired by the broker.</td></tr>
+<tr><td>INVALID_TXN_STATE</td><td>48</td><td>False</td><td>The producer attempted a transactional operation in an invalid state</td></tr>
+<tr><td>INVALID_PRODUCER_ID_MAPPING</td><td>49</td><td>False</td><td>The producer attempted to use a producer id which is not currently assigned to its transactional id</td></tr>
+<tr><td>INVALID_TRANSACTION_TIMEOUT</td><td>50</td><td>False</td><td>The transaction timeout is larger than the maximum value allowed by the broker (as configured by max.transaction.timeout.ms).</td></tr>
+<tr><td>CONCURRENT_TRANSACTIONS</td><td>51</td><td>False</td><td>The producer attempted to update a transaction while another concurrent operation on the same transaction was ongoing</td></tr>
+<tr><td>TRANSACTION_COORDINATOR_FENCED</td><td>52</td><td>False</td><td>Indicates that the transaction coordinator sending a WriteTxnMarker is no longer the current coordinator for a given producer</td></tr>
+<tr><td>TRANSACTIONAL_ID_AUTHORIZATION_FAILED</td><td>53</td><td>False</td><td>Transactional Id authorization failed</td></tr>
+<tr><td>SECURITY_DISABLED</td><td>54</td><td>False</td><td>Security features are disabled.</td></tr>
+<tr><td>OPERATION_NOT_ATTEMPTED</td><td>55</td><td>False</td><td>The broker did not attempt to execute this operation. This may happen for batched RPCs where some operations in the batch failed, causing the broker to respond without trying the rest.</td></tr>
+<tr><td>KAFKA_STORAGE_ERROR</td><td>56</td><td>True</td><td>Disk error when trying to access log file on the disk.</td></tr>
+<tr><td>LOG_DIR_NOT_FOUND</td><td>57</td><td>False</td><td>The user-specified log directory is not found in the broker config.</td></tr>
+<tr><td>SASL_AUTHENTICATION_FAILED</td><td>58</td><td>False</td><td>SASL Authentication failed.</td></tr>
+<tr><td>UNKNOWN_PRODUCER_ID</td><td>59</td><td>False</td><td>This exception is raised by the broker if it could not locate the producer metadata associated with the producerId in question. This could happen if, for instance, the producer's records were deleted because their retention time had elapsed. Once the last records of the producerId are removed, the producer's metadata is removed from the broker, and future appends by the producer will return this exception.</td></tr>
+<tr><td>REASSIGNMENT_IN_PROGRESS</td><td>60</td><td>False</td><td>A partition reassignment is in progress</td></tr>
+</table>
+
[06/14] kafka-site git commit: Update site for 1.0.0 release
Posted by ij...@apache.org.
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/security.html
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<script id="security-template" type="text/x-handlebars-template">
+ <h3><a id="security_overview" href="#security_overview">7.1 Security Overview</a></h3>
+ In release 0.9.0.0, the Kafka community added a number of features that, used either separately or together, increases security in a Kafka cluster. The following security measures are currently supported:
+ <ol>
+ <li>Authentication of connections to brokers from clients (producers and consumers), other brokers and tools, using either SSL or SASL. Kafka supports the following SASL mechanisms:
+ <ul>
+ <li>SASL/GSSAPI (Kerberos) - starting at version 0.9.0.0</li>
+ <li>SASL/PLAIN - starting at version 0.10.0.0</li>
+ <li>SASL/SCRAM-SHA-256 and SASL/SCRAM-SHA-512 - starting at version 0.10.2.0</li>
+ </ul></li>
+ <li>Authentication of connections from brokers to ZooKeeper</li>
+ <li>Encryption of data transferred between brokers and clients, between brokers, or between brokers and tools using SSL (Note that there is a performance degradation when SSL is enabled, the magnitude of which depends on the CPU type and the JVM implementation.)</li>
+ <li>Authorization of read / write operations by clients</li>
+ <li>Authorization is pluggable and integration with external authorization services is supported</li>
+ </ol>
+
+ It's worth noting that security is optional - non-secured clusters are supported, as well as a mix of authenticated, unauthenticated, encrypted and non-encrypted clients.
+
+ The guides below explain how to configure and use the security features in both clients and brokers.
+
+ <h3><a id="security_ssl" href="#security_ssl">7.2 Encryption and Authentication using SSL</a></h3>
+ Apache Kafka allows clients to connect over SSL. By default, SSL is disabled but can be turned on as needed.
+
+ <ol>
+ <li><h4><a id="security_ssl_key" href="#security_ssl_key">Generate SSL key and certificate for each Kafka broker</a></h4>
+ The first step of deploying one or more brokers with the SSL support is to generate the key and the certificate for each machine in the cluster. You can use Java's keytool utility to accomplish this task.
+ We will generate the key into a temporary keystore initially so that we can export and sign it later with CA.
+ <pre class="brush: bash;">
+ keytool -keystore server.keystore.jks -alias localhost -validity {validity} -genkey -keyalg RSA</pre>
+
+ You need to specify two parameters in the above command:
+ <ol>
+ <li>keystore: the keystore file that stores the certificate. The keystore file contains the private key of the certificate; therefore, it needs to be kept safely.</li>
+ <li>validity: the valid time of the certificate in days.</li>
+ </ol>
+ <br>
+ Note: By default the property <code>ssl.endpoint.identification.algorithm</code> is not defined, so hostname verification is not performed. In order to enable hostname verification, set the following property:
+
+ <pre class="brush: text;"> ssl.endpoint.identification.algorithm=HTTPS </pre>
+
+ Once enabled, clients will verify the server's fully qualified domain name (FQDN) against one of the following two fields:
+ <ol>
+ <li>Common Name (CN)
+ <li>Subject Alternative Name (SAN)
+ </ol>
+ <br>
+ Both fields are valid, RFC-2818 recommends the use of SAN however. SAN is also more flexible, allowing for multiple DNS entries to be declared. Another advantage is that the CN can be set to a more meaningful value for authorization purposes. To add a SAN field append the following argument <code> -ext SAN=DNS:{FQDN} </code> to the keytool command:
+ <pre class="brush: bash;">
+ keytool -keystore server.keystore.jks -alias localhost -validity {validity} -genkey -keyalg RSA -ext SAN=DNS:{FQDN}
+ </pre>
+ The following command can be run afterwards to verify the contents of the generated certificate:
+ <pre class="brush: bash;">
+ keytool -list -v -keystore server.keystore.jks
+ </pre>
+ </li>
+ <li><h4><a id="security_ssl_ca" href="#security_ssl_ca">Creating your own CA</a></h4>
+ After the first step, each machine in the cluster has a public-private key pair, and a certificate to identify the machine. The certificate, however, is unsigned, which means that an attacker can create such a certificate to pretend to be any machine.<p>
+ Therefore, it is important to prevent forged certificates by signing them for each machine in the cluster. A certificate authority (CA) is responsible for signing certificates. CA works likes a government that issues passports—the government stamps (signs) each passport so that the passport becomes difficult to forge. Other governments verify the stamps to ensure the passport is authentic. Similarly, the CA signs the certificates, and the cryptography guarantees that a signed certificate is computationally difficult to forge. Thus, as long as the CA is a genuine and trusted authority, the clients have high assurance that they are connecting to the authentic machines.
+ <pre class="brush: bash;">
+ openssl req -new -x509 -keyout ca-key -out ca-cert -days 365</pre>
+
+ The generated CA is simply a public-private key pair and certificate, and it is intended to sign other certificates.<br>
+
+ The next step is to add the generated CA to the **clients' truststore** so that the clients can trust this CA:
+ <pre class="brush: bash;">
+ keytool -keystore client.truststore.jks -alias CARoot -import -file ca-cert</pre>
+
+ <b>Note:</b> If you configure the Kafka brokers to require client authentication by setting ssl.client.auth to be "requested" or "required" on the <a href="#config_broker">Kafka brokers config</a> then you must provide a truststore for the Kafka brokers as well and it should have all the CA certificates that clients' keys were signed by.
+ <pre class="brush: bash;">
+ keytool -keystore server.truststore.jks -alias CARoot -import -file ca-cert</pre>
+
+ In contrast to the keystore in step 1 that stores each machine's own identity, the truststore of a client stores all the certificates that the client should trust. Importing a certificate into one's truststore also means trusting all certificates that are signed by that certificate. As the analogy above, trusting the government (CA) also means trusting all passports (certificates) that it has issued. This attribute is called the chain of trust, and it is particularly useful when deploying SSL on a large Kafka cluster. You can sign all certificates in the cluster with a single CA, and have all machines share the same truststore that trusts the CA. That way all machines can authenticate all other machines.</li>
+
+ <li><h4><a id="security_ssl_signing" href="#security_ssl_signing">Signing the certificate</a></h4>
+ The next step is to sign all certificates generated by step 1 with the CA generated in step 2. First, you need to export the certificate from the keystore:
+ <pre class="brush: bash;">
+ keytool -keystore server.keystore.jks -alias localhost -certreq -file cert-file</pre>
+
+ Then sign it with the CA:
+ <pre class="brush: bash;">
+ openssl x509 -req -CA ca-cert -CAkey ca-key -in cert-file -out cert-signed -days {validity} -CAcreateserial -passin pass:{ca-password}</pre>
+
+ Finally, you need to import both the certificate of the CA and the signed certificate into the keystore:
+ <pre class="brush: bash;">
+ keytool -keystore server.keystore.jks -alias CARoot -import -file ca-cert
+ keytool -keystore server.keystore.jks -alias localhost -import -file cert-signed</pre>
+
+ The definitions of the parameters are the following:
+ <ol>
+ <li>keystore: the location of the keystore</li>
+ <li>ca-cert: the certificate of the CA</li>
+ <li>ca-key: the private key of the CA</li>
+ <li>ca-password: the passphrase of the CA</li>
+ <li>cert-file: the exported, unsigned certificate of the server</li>
+ <li>cert-signed: the signed certificate of the server</li>
+ </ol>
+
+ Here is an example of a bash script with all above steps. Note that one of the commands assumes a password of `test1234`, so either use that password or edit the command before running it.
+ <pre>
+ #!/bin/bash
+ #Step 1
+ keytool -keystore server.keystore.jks -alias localhost -validity 365 -keyalg RSA -genkey
+ #Step 2
+ openssl req -new -x509 -keyout ca-key -out ca-cert -days 365
+ keytool -keystore server.truststore.jks -alias CARoot -import -file ca-cert
+ keytool -keystore client.truststore.jks -alias CARoot -import -file ca-cert
+ #Step 3
+ keytool -keystore server.keystore.jks -alias localhost -certreq -file cert-file
+ openssl x509 -req -CA ca-cert -CAkey ca-key -in cert-file -out cert-signed -days 365 -CAcreateserial -passin pass:test1234
+ keytool -keystore server.keystore.jks -alias CARoot -import -file ca-cert
+ keytool -keystore server.keystore.jks -alias localhost -import -file cert-signed</pre></li>
+ <li><h4><a id="security_configbroker" href="#security_configbroker">Configuring Kafka Brokers</a></h4>
+ Kafka Brokers support listening for connections on multiple ports.
+ We need to configure the following property in server.properties, which must have one or more comma-separated values:
+ <pre>listeners</pre>
+
+ If SSL is not enabled for inter-broker communication (see below for how to enable it), both PLAINTEXT and SSL ports will be necessary.
+ <pre class="brush: text;">
+ listeners=PLAINTEXT://host.name:port,SSL://host.name:port</pre>
+
+ Following SSL configs are needed on the broker side
+ <pre class="brush: text;">
+ ssl.keystore.location=/var/private/ssl/server.keystore.jks
+ ssl.keystore.password=test1234
+ ssl.key.password=test1234
+ ssl.truststore.location=/var/private/ssl/server.truststore.jks
+ ssl.truststore.password=test1234</pre>
+
+ Note: ssl.truststore.password is technically optional but highly recommended. If a password is not set access to the truststore is still available, but integrity checking is disabled.
+
+ Optional settings that are worth considering:
+ <ol>
+ <li>ssl.client.auth=none ("required" => client authentication is required, "requested" => client authentication is requested and client without certs can still connect. The usage of "requested" is discouraged as it provides a false sense of security and misconfigured clients will still connect successfully.)</li>
+ <li>ssl.cipher.suites (Optional). A cipher suite is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. (Default is an empty list)</li>
+ <li>ssl.enabled.protocols=TLSv1.2,TLSv1.1,TLSv1 (list out the SSL protocols that you are going to accept from clients. Do note that SSL is deprecated in favor of TLS and using SSL in production is not recommended)</li>
+ <li>ssl.keystore.type=JKS</li>
+ <li>ssl.truststore.type=JKS</li>
+ <li>ssl.secure.random.implementation=SHA1PRNG</li>
+ </ol>
+ If you want to enable SSL for inter-broker communication, add the following to the server.properties file (it defaults to PLAINTEXT)
+ <pre>
+ security.inter.broker.protocol=SSL</pre>
+
+ <p>
+ Due to import regulations in some countries, the Oracle implementation limits the strength of cryptographic algorithms available by default. If stronger algorithms are needed (for example, AES with 256-bit keys), the <a href="http://www.oracle.com/technetwork/java/javase/downloads/index.html">JCE Unlimited Strength Jurisdiction Policy Files</a> must be obtained and installed in the JDK/JRE. See the
+ <a href="https://docs.oracle.com/javase/8/docs/technotes/guides/security/SunProviders.html">JCA Providers Documentation</a> for more information.
+ </p>
+
+ <p>
+ The JRE/JDK will have a default pseudo-random number generator (PRNG) that is used for cryptography operations, so it is not required to configure the
+ implementation used with the <pre>ssl.secure.random.implementation</pre>. However, there are performance issues with some implementations (notably, the
+ default chosen on Linux systems, <pre>NativePRNG</pre>, utilizes a global lock). In cases where performance of SSL connections becomes an issue,
+ consider explicitly setting the implementation to be used. The <pre>SHA1PRNG</pre> implementation is non-blocking, and has shown very good performance
+ characteristics under heavy load (50 MB/sec of produced messages, plus replication traffic, per-broker).
+ </p>
+
+ Once you start the broker you should be able to see in the server.log
+ <pre>
+ with addresses: PLAINTEXT -> EndPoint(192.168.64.1,9092,PLAINTEXT),SSL -> EndPoint(192.168.64.1,9093,SSL)</pre>
+
+ To check quickly if the server keystore and truststore are setup properly you can run the following command
+ <pre>openssl s_client -debug -connect localhost:9093 -tls1</pre> (Note: TLSv1 should be listed under ssl.enabled.protocols)<br>
+ In the output of this command you should see server's certificate:
+ <pre>
+ -----BEGIN CERTIFICATE-----
+ {variable sized random bytes}
+ -----END CERTIFICATE-----
+ subject=/C=US/ST=CA/L=Santa Clara/O=org/OU=org/CN=Sriharsha Chintalapani
+ issuer=/C=US/ST=CA/L=Santa Clara/O=org/OU=org/CN=kafka/emailAddress=test@test.com</pre>
+ If the certificate does not show up or if there are any other error messages then your keystore is not setup properly.</li>
+
+ <li><h4><a id="security_configclients" href="#security_configclients">Configuring Kafka Clients</a></h4>
+ SSL is supported only for the new Kafka Producer and Consumer, the older API is not supported. The configs for SSL will be the same for both producer and consumer.<br>
+ If client authentication is not required in the broker, then the following is a minimal configuration example:
+ <pre class="brush: text;">
+ security.protocol=SSL
+ ssl.truststore.location=/var/private/ssl/client.truststore.jks
+ ssl.truststore.password=test1234</pre>
+
+ Note: ssl.truststore.password is technically optional but highly recommended. If a password is not set access to the truststore is still available, but integrity checking is disabled.
+
+ If client authentication is required, then a keystore must be created like in step 1 and the following must also be configured:
+ <pre class="brush: text;">
+ ssl.keystore.location=/var/private/ssl/client.keystore.jks
+ ssl.keystore.password=test1234
+ ssl.key.password=test1234</pre>
+
+ Other configuration settings that may also be needed depending on our requirements and the broker configuration:
+ <ol>
+ <li>ssl.provider (Optional). The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.</li>
+ <li>ssl.cipher.suites (Optional). A cipher suite is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol.</li>
+ <li>ssl.enabled.protocols=TLSv1.2,TLSv1.1,TLSv1. It should list at least one of the protocols configured on the broker side</li>
+ <li>ssl.truststore.type=JKS</li>
+ <li>ssl.keystore.type=JKS</li>
+ </ol>
+ <br>
+ Examples using console-producer and console-consumer:
+ <pre class="brush: bash;">
+ kafka-console-producer.sh --broker-list localhost:9093 --topic test --producer.config client-ssl.properties
+ kafka-console-consumer.sh --bootstrap-server localhost:9093 --topic test --consumer.config client-ssl.properties</pre>
+ </li>
+ </ol>
+ <h3><a id="security_sasl" href="#security_sasl">7.3 Authentication using SASL</a></h3>
+
+ <ol>
+ <li><h4><a id="security_sasl_jaasconfig" href="#security_sasl_jaasconfig">JAAS configuration</a></h4>
+ <p>Kafka uses the Java Authentication and Authorization Service
+ (<a href="https://docs.oracle.com/javase/8/docs/technotes/guides/security/jaas/JAASRefGuide.html">JAAS</a>)
+ for SASL configuration.</p>
+ <ol>
+ <li><h5><a id="security_jaas_broker"
+ href="#security_jaas_broker">JAAS configuration for Kafka brokers</a></h5>
+
+ <p><tt>KafkaServer</tt> is the section name in the JAAS file used by each
+ KafkaServer/Broker. This section provides SASL configuration options
+ for the broker including any SASL client connections made by the broker
+ for inter-broker communication.</p>
+
+ <p><tt>Client</tt> section is used to authenticate a SASL connection with
+ zookeeper. It also allows the brokers to set SASL ACL on zookeeper
+ nodes which locks these nodes down so that only the brokers can
+ modify it. It is necessary to have the same principal name across all
+ brokers. If you want to use a section name other than Client, set the
+ system property <tt>zookeeper.sasl.clientconfig</tt> to the appropriate
+ name (<i>e.g.</i>, <tt>-Dzookeeper.sasl.clientconfig=ZkClient</tt>).</p>
+
+ <p>ZooKeeper uses "zookeeper" as the service name by default. If you
+ want to change this, set the system property
+ <tt>zookeeper.sasl.client.username</tt> to the appropriate name
+ (<i>e.g.</i>, <tt>-Dzookeeper.sasl.client.username=zk</tt>).</p></li>
+
+ <li><h5><a id="security_jaas_client"
+ href="#security_jaas_client">JAAS configuration for Kafka clients</a></h5>
+
+ <p>Clients may configure JAAS using the client configuration property
+ <a href="#security_client_dynamicjaas">sasl.jaas.config</a>
+ or using the <a href="#security_client_staticjaas">static JAAS config file</a>
+ similar to brokers.</p>
+
+ <ol>
+ <li><h6><a id="security_client_dynamicjaas"
+ href="#security_client_dynamicjaas">JAAS configuration using client configuration property</a></h6>
+ <p>Clients may specify JAAS configuration as a producer or consumer property without
+ creating a physical configuration file. This mode also enables different producers
+ and consumers within the same JVM to use different credentials by specifying
+ different properties for each client. If both static JAAS configuration system property
+ <code>java.security.auth.login.config</code> and client property <code>sasl.jaas.config</code>
+ are specified, the client property will be used.</p>
+
+ <p>See <a href="#security_sasl_kerberos_clientconfig">GSSAPI (Kerberos)</a>,
+ <a href="#security_sasl_plain_clientconfig">PLAIN</a> or
+ <a href="#security_sasl_scram_clientconfig">SCRAM</a> for example configurations.</p></li>
+
+ <li><h6><a id="security_client_staticjaas" href="#security_client_staticjaas">JAAS configuration using static config file</a></h6>
+ To configure SASL authentication on the clients using static JAAS config file:
+ <ol>
+ <li>Add a JAAS config file with a client login section named <tt>KafkaClient</tt>. Configure
+ a login module in <tt>KafkaClient</tt> for the selected mechanism as described in the examples
+ for setting up <a href="#security_sasl_kerberos_clientconfig">GSSAPI (Kerberos)</a>,
+ <a href="#security_sasl_plain_clientconfig">PLAIN</a> or
+ <a href="#security_sasl_scram_clientconfig">SCRAM</a>.
+ For example, <a href="#security_sasl_gssapi_clientconfig">GSSAPI</a>
+ credentials may be configured as:
+ <pre class="brush: text;">
+ KafkaClient {
+ com.sun.security.auth.module.Krb5LoginModule required
+ useKeyTab=true
+ storeKey=true
+ keyTab="/etc/security/keytabs/kafka_client.keytab"
+ principal="kafka-client-1@EXAMPLE.COM";
+ };</pre>
+ </li>
+ <li>Pass the JAAS config file location as JVM parameter to each client JVM. For example:
+ <pre class="brush: bash;"> -Djava.security.auth.login.config=/etc/kafka/kafka_client_jaas.conf</pre></li>
+ </ol>
+ </li>
+ </ol>
+ </li>
+ </ol>
+ </li>
+ <li><h4><a id="security_sasl_config"
+ href="#security_sasl_config">SASL configuration</a></h4>
+
+ <p>SASL may be used with PLAINTEXT or SSL as the transport layer using the
+ security protocol SASL_PLAINTEXT or SASL_SSL respectively. If SASL_SSL is
+ used, then <a href="#security_ssl">SSL must also be configured</a>.</p>
+
+ <ol>
+ <li><h5><a id="security_sasl_mechanism"
+ href="#security_sasl_mechanism">SASL mechanisms</a></h5>
+ Kafka supports the following SASL mechanisms:
+ <ul>
+ <li><a href="#security_sasl_kerberos">GSSAPI</a> (Kerberos)</li>
+ <li><a href="#security_sasl_plain">PLAIN</a></li>
+ <li><a href="#security_sasl_scram">SCRAM-SHA-256</a></li>
+ <li><a href="#security_sasl_scram">SCRAM-SHA-512</a></li>
+ </ul>
+ </li>
+ <li><h5><a id="security_sasl_brokerconfig"
+ href="#security_sasl_brokerconfig">SASL configuration for Kafka brokers</a></h5>
+ <ol>
+ <li>Configure a SASL port in server.properties, by adding at least one of
+ SASL_PLAINTEXT or SASL_SSL to the <i>listeners</i> parameter, which
+ contains one or more comma-separated values:
+ <pre> listeners=SASL_PLAINTEXT://host.name:port</pre>
+ If you are only configuring a SASL port (or if you want
+ the Kafka brokers to authenticate each other using SASL) then make sure
+ you set the same SASL protocol for inter-broker communication:
+ <pre> security.inter.broker.protocol=SASL_PLAINTEXT (or SASL_SSL)</pre></li>
+ <li>Select one or more <a href="#security_sasl_mechanism">supported mechanisms</a>
+ to enable in the broker and follow the steps to configure SASL for the mechanism.
+ To enable multiple mechanisms in the broker, follow the steps
+ <a href="#security_sasl_multimechanism">here</a>.</li>
+ </ol>
+ </li>
+ <li><h5><a id="security_sasl_clientconfig"
+ href="#security_sasl_clientconfig">SASL configuration for Kafka clients</a></h5>
+ <p>SASL authentication is only supported for the new Java Kafka producer and
+ consumer, the older API is not supported.</p>
+
+ <p>To configure SASL authentication on the clients, select a SASL
+ <a href="#security_sasl_mechanism">mechanism</a> that is enabled in
+ the broker for client authentication and follow the steps to configure SASL
+ for the selected mechanism.</p></li>
+ </ol>
+ </li>
+ <li><h4><a id="security_sasl_kerberos" href="#security_sasl_kerberos">Authentication using SASL/Kerberos</a></h4>
+ <ol>
+ <li><h5><a id="security_sasl_kerberos_prereq" href="#security_sasl_kerberos_prereq">Prerequisites</a></h5>
+ <ol>
+ <li><b>Kerberos</b><br>
+ If your organization is already using a Kerberos server (for example, by using Active Directory), there is no need to install a new server just for Kafka. Otherwise you will need to install one, your Linux vendor likely has packages for Kerberos and a short guide on how to install and configure it (<a href="https://help.ubuntu.com/community/Kerberos">Ubuntu</a>, <a href="https://access.redhat.com/documentation/en-US/Red_Hat_Enterprise_Linux/6/html/Managing_Smart_Cards/installing-kerberos.html">Redhat</a>). Note that if you are using Oracle Java, you will need to download JCE policy files for your Java version and copy them to $JAVA_HOME/jre/lib/security.</li>
+ <li><b>Create Kerberos Principals</b><br>
+ If you are using the organization's Kerberos or Active Directory server, ask your Kerberos administrator for a principal for each Kafka broker in your cluster and for every operating system user that will access Kafka with Kerberos authentication (via clients and tools).</br>
+ If you have installed your own Kerberos, you will need to create these principals yourself using the following commands:
+ <pre class="brush: bash;">
+ sudo /usr/sbin/kadmin.local -q 'addprinc -randkey kafka/{hostname}@{REALM}'
+ sudo /usr/sbin/kadmin.local -q "ktadd -k /etc/security/keytabs/{keytabname}.keytab kafka/{hostname}@{REALM}"</pre></li>
+ <li><b>Make sure all hosts can be reachable using hostnames</b> - it is a Kerberos requirement that all your hosts can be resolved with their FQDNs.</li>
+ </ol>
+ <li><h5><a id="security_sasl_kerberos_brokerconfig" href="#security_sasl_kerberos_brokerconfig">Configuring Kafka Brokers</a></h5>
+ <ol>
+ <li>Add a suitably modified JAAS file similar to the one below to each Kafka broker's config directory, let's call it kafka_server_jaas.conf for this example (note that each broker should have its own keytab):
+ <pre class="brush: text;">
+ KafkaServer {
+ com.sun.security.auth.module.Krb5LoginModule required
+ useKeyTab=true
+ storeKey=true
+ keyTab="/etc/security/keytabs/kafka_server.keytab"
+ principal="kafka/kafka1.hostname.com@EXAMPLE.COM";
+ };
+
+ // Zookeeper client authentication
+ Client {
+ com.sun.security.auth.module.Krb5LoginModule required
+ useKeyTab=true
+ storeKey=true
+ keyTab="/etc/security/keytabs/kafka_server.keytab"
+ principal="kafka/kafka1.hostname.com@EXAMPLE.COM";
+ };</pre>
+
+ </li>
+ <tt>KafkaServer</tt> section in the JAAS file tells the broker which principal to use and the location of the keytab where this principal is stored. It
+ allows the broker to login using the keytab specified in this section. See <a href="#security_sasl_brokernotes">notes</a> for more details on Zookeeper SASL configuration.
+ <li>Pass the JAAS and optionally the krb5 file locations as JVM parameters to each Kafka broker (see <a href="https://docs.oracle.com/javase/8/docs/technotes/guides/security/jgss/tutorials/KerberosReq.html">here</a> for more details):
+ <pre> -Djava.security.krb5.conf=/etc/kafka/krb5.conf
+ -Djava.security.auth.login.config=/etc/kafka/kafka_server_jaas.conf</pre>
+ </li>
+ <li>Make sure the keytabs configured in the JAAS file are readable by the operating system user who is starting kafka broker.</li>
+ <li>Configure SASL port and SASL mechanisms in server.properties as described <a href="#security_sasl_brokerconfig">here</a>. For example:
+ <pre> listeners=SASL_PLAINTEXT://host.name:port
+ security.inter.broker.protocol=SASL_PLAINTEXT
+ sasl.mechanism.inter.broker.protocol=GSSAPI
+ sasl.enabled.mechanisms=GSSAPI
+ </pre>
+ </li>We must also configure the service name in server.properties, which should match the principal name of the kafka brokers. In the above example, principal is "kafka/kafka1.hostname.com@EXAMPLE.com", so:
+ <pre> sasl.kerberos.service.name=kafka</pre>
+
+ </ol></li>
+ <li><h5><a id="security_sasl_kerberos_clientconfig" href="#security_kerberos_sasl_clientconfig">Configuring Kafka Clients</a></h5>
+ To configure SASL authentication on the clients:
+ <ol>
+ <li>
+ Clients (producers, consumers, connect workers, etc) will authenticate to the cluster with their
+ own principal (usually with the same name as the user running the client), so obtain or create
+ these principals as needed. Then configure the JAAS configuration property for each client.
+ Different clients within a JVM may run as different users by specifiying different principals.
+ The property <code>sasl.jaas.config</code> in producer.properties or consumer.properties describes
+ how clients like producer and consumer can connect to the Kafka Broker. The following is an example
+ configuration for a client using a keytab (recommended for long-running processes):
+ <pre>
+ sasl.jaas.config=com.sun.security.auth.module.Krb5LoginModule required \
+ useKeyTab=true \
+ storeKey=true \
+ keyTab="/etc/security/keytabs/kafka_client.keytab" \
+ principal="kafka-client-1@EXAMPLE.COM";</pre>
+
+ For command-line utilities like kafka-console-consumer or kafka-console-producer, kinit can be used
+ along with "useTicketCache=true" as in:
+ <pre>
+ sasl.jaas.config=com.sun.security.auth.module.Krb5LoginModule required \
+ useTicketCache=true;</pre>
+
+ JAAS configuration for clients may alternatively be specified as a JVM parameter similar to brokers
+ as described <a href="#security_client_staticjaas">here</a>. Clients use the login section named
+ <tt>KafkaClient</tt>. This option allows only one user for all client connections from a JVM.</li>
+ <li>Make sure the keytabs configured in the JAAS configuration are readable by the operating system user who is starting kafka client.</li>
+ <li>Optionally pass the krb5 file locations as JVM parameters to each client JVM (see <a href="https://docs.oracle.com/javase/8/docs/technotes/guides/security/jgss/tutorials/KerberosReq.html">here</a> for more details):
+ <pre> -Djava.security.krb5.conf=/etc/kafka/krb5.conf</pre></li>
+ <li>Configure the following properties in producer.properties or consumer.properties:
+ <pre>
+ security.protocol=SASL_PLAINTEXT (or SASL_SSL)
+ sasl.mechanism=GSSAPI
+ sasl.kerberos.service.name=kafka</pre></li>
+ </ol>
+ </li>
+ </ol>
+ </li>
+
+ <li><h4><a id="security_sasl_plain" href="#security_sasl_plain">Authentication using SASL/PLAIN</a></h4>
+ <p>SASL/PLAIN is a simple username/password authentication mechanism that is typically used with TLS for encryption to implement secure authentication.
+ Kafka supports a default implementation for SASL/PLAIN which can be extended for production use as described <a href="#security_sasl_plain_production">here</a>.</p>
+ The username is used as the authenticated <code>Principal</code> for configuration of ACLs etc.
+ <ol>
+ <li><h5><a id="security_sasl_plain_brokerconfig" href="#security_sasl_plain_brokerconfig">Configuring Kafka Brokers</a></h5>
+ <ol>
+ <li>Add a suitably modified JAAS file similar to the one below to each Kafka broker's config directory, let's call it kafka_server_jaas.conf for this example:
+ <pre class="brush: text;">
+ KafkaServer {
+ org.apache.kafka.common.security.plain.PlainLoginModule required
+ username="admin"
+ password="admin-secret"
+ user_admin="admin-secret"
+ user_alice="alice-secret";
+ };</pre>
+ This configuration defines two users (<i>admin</i> and <i>alice</i>). The properties <tt>username</tt> and <tt>password</tt>
+ in the <tt>KafkaServer</tt> section are used by the broker to initiate connections to other brokers. In this example,
+ <i>admin</i> is the user for inter-broker communication. The set of properties <tt>user_<i>userName</i></tt> defines
+ the passwords for all users that connect to the broker and the broker validates all client connections including
+ those from other brokers using these properties.</li>
+ <li>Pass the JAAS config file location as JVM parameter to each Kafka broker:
+ <pre> -Djava.security.auth.login.config=/etc/kafka/kafka_server_jaas.conf</pre></li>
+ <li>Configure SASL port and SASL mechanisms in server.properties as described <a href="#security_sasl_brokerconfig">here</a>. For example:
+ <pre> listeners=SASL_SSL://host.name:port
+ security.inter.broker.protocol=SASL_SSL
+ sasl.mechanism.inter.broker.protocol=PLAIN
+ sasl.enabled.mechanisms=PLAIN</pre></li>
+ </ol>
+ </li>
+
+ <li><h5><a id="security_sasl_plain_clientconfig" href="#security_sasl_plain_clientconfig">Configuring Kafka Clients</a></h5>
+ To configure SASL authentication on the clients:
+ <ol>
+ <li>Configure the JAAS configuration property for each client in producer.properties or consumer.properties.
+ The login module describes how the clients like producer and consumer can connect to the Kafka Broker.
+ The following is an example configuration for a client for the PLAIN mechanism:
+ <pre class="brush: text;">
+ sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required \
+ username="alice" \
+ password="alice-secret";</pre>
+ <p>The options <tt>username</tt> and <tt>password</tt> are used by clients to configure
+ the user for client connections. In this example, clients connect to the broker as user <i>alice</i>.
+ Different clients within a JVM may connect as different users by specifying different user names
+ and passwords in <code>sasl.jaas.config</code>.</p>
+
+ <p>JAAS configuration for clients may alternatively be specified as a JVM parameter similar to brokers
+ as described <a href="#security_client_staticjaas">here</a>. Clients use the login section named
+ <tt>KafkaClient</tt>. This option allows only one user for all client connections from a JVM.</p></li>
+ <li>Configure the following properties in producer.properties or consumer.properties:
+ <pre>
+ security.protocol=SASL_SSL
+ sasl.mechanism=PLAIN</pre></li>
+ </ol>
+ </li>
+ <li><h5><a id="security_sasl_plain_production" href="#security_sasl_plain_production">Use of SASL/PLAIN in production</a></h5>
+ <ul>
+ <li>SASL/PLAIN should be used only with SSL as transport layer to ensure that clear passwords are not transmitted on the wire without encryption.</li>
+ <li>The default implementation of SASL/PLAIN in Kafka specifies usernames and passwords in the JAAS configuration file as shown
+ <a href="#security_sasl_plain_brokerconfig">here</a>. To avoid storing passwords on disk, you can plug in your own implementation of
+ <code>javax.security.auth.spi.LoginModule</code> that provides usernames and passwords from an external source. The login module implementation should
+ provide username as the public credential and password as the private credential of the <code>Subject</code>. The default implementation
+ <code>org.apache.kafka.common.security.plain.PlainLoginModule</code> can be used as an example.</li>
+ <li>In production systems, external authentication servers may implement password authentication. Kafka brokers can be integrated with these servers by adding
+ your own implementation of <code>javax.security.sasl.SaslServer</code>. The default implementation included in Kafka in the package
+ <code>org.apache.kafka.common.security.plain</code> can be used as an example to get started.
+ <ul>
+ <li>New providers must be installed and registered in the JVM. Providers can be installed by adding provider classes to
+ the normal <tt>CLASSPATH</tt> or bundled as a jar file and added to <tt><i>JAVA_HOME</i>/lib/ext</tt>.</li>
+ <li>Providers can be registered statically by adding a provider to the security properties file
+ <tt><i>JAVA_HOME</i>/lib/security/java.security</tt>.
+ <pre> security.provider.n=providerClassName</pre>
+ where <i>providerClassName</i> is the fully qualified name of the new provider and <i>n</i> is the preference order with
+ lower numbers indicating higher preference.</li>
+ <li>Alternatively, you can register providers dynamically at runtime by invoking <code>Security.addProvider</code> at the beginning of the client
+ application or in a static initializer in the login module. For example:
+ <pre> Security.addProvider(new PlainSaslServerProvider());</pre></li>
+ <li>For more details, see <a href="http://docs.oracle.com/javase/8/docs/technotes/guides/security/crypto/CryptoSpec.html">JCA Reference</a>.</li>
+ </ul>
+ </li>
+ </ul>
+ </li>
+ </ol>
+ </li>
+
+ <li><h4><a id="security_sasl_scram" href="#security_sasl_scram">Authentication using SASL/SCRAM</a></h4>
+ <p>Salted Challenge Response Authentication Mechanism (SCRAM) is a family of SASL mechanisms that
+ addresses the security concerns with traditional mechanisms that perform username/password authentication
+ like PLAIN and DIGEST-MD5. The mechanism is defined in <a href="https://tools.ietf.org/html/rfc5802">RFC 5802</a>.
+ Kafka supports <a href="https://tools.ietf.org/html/rfc7677">SCRAM-SHA-256</a> and SCRAM-SHA-512 which
+ can be used with TLS to perform secure authentication. The username is used as the authenticated
+ <code>Principal</code> for configuration of ACLs etc. The default SCRAM implementation in Kafka
+ stores SCRAM credentials in Zookeeper and is suitable for use in Kafka installations where Zookeeper
+ is on a private network. Refer to <a href="#security_sasl_scram_security">Security Considerations</a>
+ for more details.</p>
+ <ol>
+ <li><h5><a id="security_sasl_scram_credentials" href="#security_sasl_scram_credentials">Creating SCRAM Credentials</a></h5>
+ <p>The SCRAM implementation in Kafka uses Zookeeper as credential store. Credentials can be created in
+ Zookeeper using <tt>kafka-configs.sh</tt>. For each SCRAM mechanism enabled, credentials must be created
+ by adding a config with the mechanism name. Credentials for inter-broker communication must be created
+ before Kafka brokers are started. Client credentials may be created and updated dynamically and updated
+ credentials will be used to authenticate new connections.</p>
+ <p>Create SCRAM credentials for user <i>alice</i> with password <i>alice-secret</i>:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --alter --add-config 'SCRAM-SHA-256=[iterations=8192,password=alice-secret],SCRAM-SHA-512=[password=alice-secret]' --entity-type users --entity-name alice
+ </pre>
+ <p>The default iteration count of 4096 is used if iterations are not specified. A random salt is created
+ and the SCRAM identity consisting of salt, iterations, StoredKey and ServerKey are stored in Zookeeper.
+ See <a href="https://tools.ietf.org/html/rfc5802">RFC 5802</a> for details on SCRAM identity and the individual fields.
+ <p>The following examples also require a user <i>admin</i> for inter-broker communication which can be created using:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --alter --add-config 'SCRAM-SHA-256=[password=admin-secret],SCRAM-SHA-512=[password=admin-secret]' --entity-type users --entity-name admin
+ </pre>
+ <p>Existing credentials may be listed using the <i>--describe</i> option:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --describe --entity-type users --entity-name alice
+ </pre>
+ <p>Credentials may be deleted for one or more SCRAM mechanisms using the <i>--delete</i> option:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --alter --delete-config 'SCRAM-SHA-512' --entity-type users --entity-name alice
+ </pre>
+ </li>
+ <li><h5><a id="security_sasl_scram_brokerconfig" href="#security_sasl_scram_brokerconfig">Configuring Kafka Brokers</a></h5>
+ <ol>
+ <li>Add a suitably modified JAAS file similar to the one below to each Kafka broker's config directory, let's call it kafka_server_jaas.conf for this example:
+ <pre>
+ KafkaServer {
+ org.apache.kafka.common.security.scram.ScramLoginModule required
+ username="admin"
+ password="admin-secret";
+ };</pre>
+ The properties <tt>username</tt> and <tt>password</tt> in the <tt>KafkaServer</tt> section are used by
+ the broker to initiate connections to other brokers. In this example, <i>admin</i> is the user for
+ inter-broker communication.</li>
+ <li>Pass the JAAS config file location as JVM parameter to each Kafka broker:
+ <pre> -Djava.security.auth.login.config=/etc/kafka/kafka_server_jaas.conf</pre></li>
+ <li>Configure SASL port and SASL mechanisms in server.properties as described <a href="#security_sasl_brokerconfig">here</a>.</pre> For example:
+ <pre>
+ listeners=SASL_SSL://host.name:port
+ security.inter.broker.protocol=SASL_SSL
+ sasl.mechanism.inter.broker.protocol=SCRAM-SHA-256 (or SCRAM-SHA-512)
+ sasl.enabled.mechanisms=SCRAM-SHA-256 (or SCRAM-SHA-512)</pre></li>
+ </ol>
+ </li>
+
+ <li><h5><a id="security_sasl_scram_clientconfig" href="#security_sasl_scram_clientconfig">Configuring Kafka Clients</a></h5>
+ To configure SASL authentication on the clients:
+ <ol>
+ <li>Configure the JAAS configuration property for each client in producer.properties or consumer.properties.
+ The login module describes how the clients like producer and consumer can connect to the Kafka Broker.
+ The following is an example configuration for a client for the SCRAM mechanisms:
+ <pre class="brush: text;">
+ sasl.jaas.config=org.apache.kafka.common.security.scram.ScramLoginModule required \
+ username="alice" \
+ password="alice-secret";</pre>
+
+ <p>The options <tt>username</tt> and <tt>password</tt> are used by clients to configure
+ the user for client connections. In this example, clients connect to the broker as user <i>alice</i>.
+ Different clients within a JVM may connect as different users by specifying different user names
+ and passwords in <code>sasl.jaas.config</code>.</p>
+
+ <p>JAAS configuration for clients may alternatively be specified as a JVM parameter similar to brokers
+ as described <a href="#security_client_staticjaas">here</a>. Clients use the login section named
+ <tt>KafkaClient</tt>. This option allows only one user for all client connections from a JVM.</p></li>
+ <li>Configure the following properties in producer.properties or consumer.properties:
+ <pre>
+ security.protocol=SASL_SSL
+ sasl.mechanism=SCRAM-SHA-256 (or SCRAM-SHA-512)</pre></li>
+ </ol>
+ </li>
+ <li><h5><a id="security_sasl_scram_security" href="#security_sasl_scram_security">Security Considerations for SASL/SCRAM</a></h5>
+ <ul>
+ <li>The default implementation of SASL/SCRAM in Kafka stores SCRAM credentials in Zookeeper. This
+ is suitable for production use in installations where Zookeeper is secure and on a private network.</li>
+ <li>Kafka supports only the strong hash functions SHA-256 and SHA-512 with a minimum iteration count
+ of 4096. Strong hash functions combined with strong passwords and high iteration counts protect
+ against brute force attacks if Zookeeper security is compromised.</li>
+ <li>SCRAM should be used only with TLS-encryption to prevent interception of SCRAM exchanges. This
+ protects against dictionary or brute force attacks and against impersonation if Zookeeper is compromised.</li>
+ <li>The default SASL/SCRAM implementation may be overridden using custom login modules in installations
+ where Zookeeper is not secure. See <a href="#security_sasl_plain_production">here</a> for details.</li>
+ <li>For more details on security considerations, refer to
+ <a href="https://tools.ietf.org/html/rfc5802#section-9">RFC 5802</a>.
+ </ul>
+ </li>
+ </ol>
+ </li>
+
+ <li><h4><a id="security_sasl_multimechanism" href="#security_sasl_multimechanism">Enabling multiple SASL mechanisms in a broker</a></h4>
+ <ol>
+ <li>Specify configuration for the login modules of all enabled mechanisms in the <tt>KafkaServer</tt> section of the JAAS config file. For example:
+ <pre>
+ KafkaServer {
+ com.sun.security.auth.module.Krb5LoginModule required
+ useKeyTab=true
+ storeKey=true
+ keyTab="/etc/security/keytabs/kafka_server.keytab"
+ principal="kafka/kafka1.hostname.com@EXAMPLE.COM";
+
+ org.apache.kafka.common.security.plain.PlainLoginModule required
+ username="admin"
+ password="admin-secret"
+ user_admin="admin-secret"
+ user_alice="alice-secret";
+ };</pre></li>
+ <li>Enable the SASL mechanisms in server.properties: <pre> sasl.enabled.mechanisms=GSSAPI,PLAIN,SCRAM-SHA-256,SCRAM-SHA-512</pre></li>
+ <li>Specify the SASL security protocol and mechanism for inter-broker communication in server.properties if required:
+ <pre>
+ security.inter.broker.protocol=SASL_PLAINTEXT (or SASL_SSL)
+ sasl.mechanism.inter.broker.protocol=GSSAPI (or one of the other enabled mechanisms)</pre></li>
+ <li>Follow the mechanism-specific steps in <a href="#security_sasl_kerberos_brokerconfig">GSSAPI (Kerberos)</a>,
+ <a href="#security_sasl_plain_brokerconfig">PLAIN</a> and <a href="#security_sasl_scram_brokerconfig">SCRAM</a>
+ to configure SASL for the enabled mechanisms.</li>
+ </ol>
+ </li>
+ <li><h4><a id="saslmechanism_rolling_upgrade" href="#saslmechanism_rolling_upgrade">Modifying SASL mechanism in a Running Cluster</a></h4>
+ <p>SASL mechanism can be modified in a running cluster using the following sequence:</p>
+ <ol>
+ <li>Enable new SASL mechanism by adding the mechanism to <tt>sasl.enabled.mechanisms</tt> in server.properties for each broker. Update JAAS config file to include both
+ mechanisms as described <a href="#security_sasl_multimechanism">here</a>. Incrementally bounce the cluster nodes.</li>
+ <li>Restart clients using the new mechanism.</li>
+ <li>To change the mechanism of inter-broker communication (if this is required), set <tt>sasl.mechanism.inter.broker.protocol</tt> in server.properties to the new mechanism and
+ incrementally bounce the cluster again.</li>
+ <li>To remove old mechanism (if this is required), remove the old mechanism from <tt>sasl.enabled.mechanisms</tt> in server.properties and remove the entries for the
+ old mechanism from JAAS config file. Incrementally bounce the cluster again.</li>
+ </ol>
+ </li>
+ </ol>
+
+ <h3><a id="security_authz" href="#security_authz">7.4 Authorization and ACLs</a></h3>
+ Kafka ships with a pluggable Authorizer and an out-of-box authorizer implementation that uses zookeeper to store all the acls. Kafka acls are defined in the general format of "Principal P is [Allowed/Denied] Operation O From Host H On Resource R". You can read more about the acl structure on KIP-11. In order to add, remove or list acls you can use the Kafka authorizer CLI. By default, if a Resource R has no associated acls, no one other than super users is allowed to access R. If you want to change that behavior, you can include the following in server.properties.
+ <pre>allow.everyone.if.no.acl.found=true</pre>
+ One can also add super users in server.properties like the following (note that the delimiter is semicolon since SSL user names may contain comma).
+ <pre>super.users=User:Bob;User:Alice</pre>
+ By default, the SSL user name will be of the form "CN=writeuser,OU=Unknown,O=Unknown,L=Unknown,ST=Unknown,C=Unknown". One can change that by setting a customized PrincipalBuilder in server.properties like the following.
+ <pre>principal.builder.class=CustomizedPrincipalBuilderClass</pre>
+ By default, the SASL user name will be the primary part of the Kerberos principal. One can change that by setting <code>sasl.kerberos.principal.to.local.rules</code> to a customized rule in server.properties.
+ The format of <code>sasl.kerberos.principal.to.local.rules</code> is a list where each rule works in the same way as the auth_to_local in <a href="http://web.mit.edu/Kerberos/krb5-latest/doc/admin/conf_files/krb5_conf.html">Kerberos configuration file (krb5.conf)</a>. Each rules starts with RULE: and contains an expression in the format [n:string](regexp)s/pattern/replacement/g. See the kerberos documentation for more details. An example of adding a rule to properly translate user@MYDOMAIN.COM to user while also keeping the default rule in place is:
+ <pre>sasl.kerberos.principal.to.local.rules=RULE:[1:$1@$0](.*@MYDOMAIN.COM)s/@.*//,DEFAULT</pre>
+
+ <h4><a id="security_authz_cli" href="#security_authz_cli">Command Line Interface</a></h4>
+ Kafka Authorization management CLI can be found under bin directory with all the other CLIs. The CLI script is called <b>kafka-acls.sh</b>. Following lists all the options that the script supports:
+ <p></p>
+ <table class="data-table">
+ <tr>
+ <th>Option</th>
+ <th>Description</th>
+ <th>Default</th>
+ <th>Option type</th>
+ </tr>
+ <tr>
+ <td>--add</td>
+ <td>Indicates to the script that user is trying to add an acl.</td>
+ <td></td>
+ <td>Action</td>
+ </tr>
+ <tr>
+ <td>--remove</td>
+ <td>Indicates to the script that user is trying to remove an acl.</td>
+ <td></td>
+ <td>Action</td>
+ </tr>
+ <tr>
+ <td>--list</td>
+ <td>Indicates to the script that user is trying to list acls.</td>
+ <td></td>
+ <td>Action</td>
+ </tr>
+ <tr>
+ <td>--authorizer</td>
+ <td>Fully qualified class name of the authorizer.</td>
+ <td>kafka.security.auth.SimpleAclAuthorizer</td>
+ <td>Configuration</td>
+ </tr>
+ <tr>
+ <td>--authorizer-properties</td>
+ <td>key=val pairs that will be passed to authorizer for initialization. For the default authorizer the example values are: zookeeper.connect=localhost:2181</td>
+ <td></td>
+ <td>Configuration</td>
+ </tr>
+ <tr>
+ <td>--cluster</td>
+ <td>Specifies cluster as resource.</td>
+ <td></td>
+ <td>Resource</td>
+ </tr>
+ <tr>
+ <td>--topic [topic-name]</td>
+ <td>Specifies the topic as resource.</td>
+ <td></td>
+ <td>Resource</td>
+ </tr>
+ <tr>
+ <td>--group [group-name]</td>
+ <td>Specifies the consumer-group as resource.</td>
+ <td></td>
+ <td>Resource</td>
+ </tr>
+ <tr>
+ <td>--allow-principal</td>
+ <td>Principal is in PrincipalType:name format that will be added to ACL with Allow permission. <br>You can specify multiple --allow-principal in a single command.</td>
+ <td></td>
+ <td>Principal</td>
+ </tr>
+ <tr>
+ <td>--deny-principal</td>
+ <td>Principal is in PrincipalType:name format that will be added to ACL with Deny permission. <br>You can specify multiple --deny-principal in a single command.</td>
+ <td></td>
+ <td>Principal</td>
+ </tr>
+ <tr>
+ <td>--allow-host</td>
+ <td>IP address from which principals listed in --allow-principal will have access.</td>
+ <td> if --allow-principal is specified defaults to * which translates to "all hosts"</td>
+ <td>Host</td>
+ </tr>
+ <tr>
+ <td>--deny-host</td>
+ <td>IP address from which principals listed in --deny-principal will be denied access.</td>
+ <td>if --deny-principal is specified defaults to * which translates to "all hosts"</td>
+ <td>Host</td>
+ </tr>
+ <tr>
+ <td>--operation</td>
+ <td>Operation that will be allowed or denied.<br>
+ Valid values are : Read, Write, Create, Delete, Alter, Describe, ClusterAction, All</td>
+ <td>All</td>
+ <td>Operation</td>
+ </tr>
+ <tr>
+ <td>--producer</td>
+ <td> Convenience option to add/remove acls for producer role. This will generate acls that allows WRITE,
+ DESCRIBE on topic and CREATE on cluster.</td>
+ <td></td>
+ <td>Convenience</td>
+ </tr>
+ <tr>
+ <td>--consumer</td>
+ <td> Convenience option to add/remove acls for consumer role. This will generate acls that allows READ,
+ DESCRIBE on topic and READ on consumer-group.</td>
+ <td></td>
+ <td>Convenience</td>
+ </tr>
+ <tr>
+ <td>--force</td>
+ <td> Convenience option to assume yes to all queries and do not prompt.</td>
+ <td></td>
+ <td>Convenience</td>
+ </tr>
+ </tbody></table>
+
+ <h4><a id="security_authz_examples" href="#security_authz_examples">Examples</a></h4>
+ <ul>
+ <li><b>Adding Acls</b><br>
+ Suppose you want to add an acl "Principals User:Bob and User:Alice are allowed to perform Operation Read and Write on Topic Test-Topic from IP 198.51.100.0 and IP 198.51.100.1". You can do that by executing the CLI with following options:
+ <pre class="brush: bash;">bin/kafka-acls.sh --authorizer-properties zookeeper.connect=localhost:2181 --add --allow-principal User:Bob --allow-principal User:Alice --allow-host 198.51.100.0 --allow-host 198.51.100.1 --operation Read --operation Write --topic Test-topic</pre>
+ By default, all principals that don't have an explicit acl that allows access for an operation to a resource are denied. In rare cases where an allow acl is defined that allows access to all but some principal we will have to use the --deny-principal and --deny-host option. For example, if we want to allow all users to Read from Test-topic but only deny User:BadBob from IP 198.51.100.3 we can do so using following commands:
+ <pre class="brush: bash;">bin/kafka-acls.sh --authorizer-properties zookeeper.connect=localhost:2181 --add --allow-principal User:* --allow-host * --deny-principal User:BadBob --deny-host 198.51.100.3 --operation Read --topic Test-topic</pre>
+ Note that ``--allow-host`` and ``deny-host`` only support IP addresses (hostnames are not supported).
+ Above examples add acls to a topic by specifying --topic [topic-name] as the resource option. Similarly user can add acls to cluster by specifying --cluster and to a consumer group by specifying --group [group-name].</li>
+
+ <li><b>Removing Acls</b><br>
+ Removing acls is pretty much the same. The only difference is instead of --add option users will have to specify --remove option. To remove the acls added by the first example above we can execute the CLI with following options:
+ <pre class="brush: bash;"> bin/kafka-acls.sh --authorizer-properties zookeeper.connect=localhost:2181 --remove --allow-principal User:Bob --allow-principal User:Alice --allow-host 198.51.100.0 --allow-host 198.51.100.1 --operation Read --operation Write --topic Test-topic </pre></li>
+
+ <li><b>List Acls</b><br>
+ We can list acls for any resource by specifying the --list option with the resource. To list all acls for Test-topic we can execute the CLI with following options:
+ <pre class="brush: bash;">bin/kafka-acls.sh --authorizer-properties zookeeper.connect=localhost:2181 --list --topic Test-topic</pre></li>
+
+ <li><b>Adding or removing a principal as producer or consumer</b><br>
+ The most common use case for acl management are adding/removing a principal as producer or consumer so we added convenience options to handle these cases. In order to add User:Bob as a producer of Test-topic we can execute the following command:
+ <pre class="brush: bash;"> bin/kafka-acls.sh --authorizer-properties zookeeper.connect=localhost:2181 --add --allow-principal User:Bob --producer --topic Test-topic</pre>
+ Similarly to add Alice as a consumer of Test-topic with consumer group Group-1 we just have to pass --consumer option:
+ <pre class="brush: bash;"> bin/kafka-acls.sh --authorizer-properties zookeeper.connect=localhost:2181 --add --allow-principal User:Bob --consumer --topic Test-topic --group Group-1 </pre>
+ Note that for consumer option we must also specify the consumer group.
+ In order to remove a principal from producer or consumer role we just need to pass --remove option. </li>
+ </ul>
+
+ <h3><a id="security_rolling_upgrade" href="#security_rolling_upgrade">7.5 Incorporating Security Features in a Running Cluster</a></h3>
+ You can secure a running cluster via one or more of the supported protocols discussed previously. This is done in phases:
+ <p></p>
+ <ul>
+ <li>Incrementally bounce the cluster nodes to open additional secured port(s).</li>
+ <li>Restart clients using the secured rather than PLAINTEXT port (assuming you are securing the client-broker connection).</li>
+ <li>Incrementally bounce the cluster again to enable broker-to-broker security (if this is required)</li>
+ <li>A final incremental bounce to close the PLAINTEXT port.</li>
+ </ul>
+ <p></p>
+ The specific steps for configuring SSL and SASL are described in sections <a href="#security_ssl">7.2</a> and <a href="#security_sasl">7.3</a>.
+ Follow these steps to enable security for your desired protocol(s).
+ <p></p>
+ The security implementation lets you configure different protocols for both broker-client and broker-broker communication.
+ These must be enabled in separate bounces. A PLAINTEXT port must be left open throughout so brokers and/or clients can continue to communicate.
+ <p></p>
+
+ When performing an incremental bounce stop the brokers cleanly via a SIGTERM. It's also good practice to wait for restarted replicas to return to the ISR list before moving onto the next node.
+ <p></p>
+ As an example, say we wish to encrypt both broker-client and broker-broker communication with SSL. In the first incremental bounce, a SSL port is opened on each node:
+ <pre>
+ listeners=PLAINTEXT://broker1:9091,SSL://broker1:9092
+ </pre>
+
+ We then restart the clients, changing their config to point at the newly opened, secured port:
+
+ <pre>
+ bootstrap.servers = [broker1:9092,...]
+ security.protocol = SSL
+ ...etc
+ </pre>
+
+ In the second incremental server bounce we instruct Kafka to use SSL as the broker-broker protocol (which will use the same SSL port):
+
+ <pre>
+ listeners=PLAINTEXT://broker1:9091,SSL://broker1:9092
+ security.inter.broker.protocol=SSL
+ </pre>
+
+ In the final bounce we secure the cluster by closing the PLAINTEXT port:
+
+ <pre>
+ listeners=SSL://broker1:9092
+ security.inter.broker.protocol=SSL
+ </pre>
+
+ Alternatively we might choose to open multiple ports so that different protocols can be used for broker-broker and broker-client communication. Say we wished to use SSL encryption throughout (i.e. for broker-broker and broker-client communication) but we'd like to add SASL authentication to the broker-client connection also. We would achieve this by opening two additional ports during the first bounce:
+
+ <pre>
+ listeners=PLAINTEXT://broker1:9091,SSL://broker1:9092,SASL_SSL://broker1:9093
+ </pre>
+
+ We would then restart the clients, changing their config to point at the newly opened, SASL & SSL secured port:
+
+ <pre>
+ bootstrap.servers = [broker1:9093,...]
+ security.protocol = SASL_SSL
+ ...etc
+ </pre>
+
+ The second server bounce would switch the cluster to use encrypted broker-broker communication via the SSL port we previously opened on port 9092:
+
+ <pre>
+ listeners=PLAINTEXT://broker1:9091,SSL://broker1:9092,SASL_SSL://broker1:9093
+ security.inter.broker.protocol=SSL
+ </pre>
+
+ The final bounce secures the cluster by closing the PLAINTEXT port.
+
+ <pre>
+ listeners=SSL://broker1:9092,SASL_SSL://broker1:9093
+ security.inter.broker.protocol=SSL
+ </pre>
+
+ ZooKeeper can be secured independently of the Kafka cluster. The steps for doing this are covered in section <a href="#zk_authz_migration">7.6.2</a>.
+
+
+ <h3><a id="zk_authz" href="#zk_authz">7.6 ZooKeeper Authentication</a></h3>
+ <h4><a id="zk_authz_new" href="#zk_authz_new">7.6.1 New clusters</a></h4>
+ To enable ZooKeeper authentication on brokers, there are two necessary steps:
+ <ol>
+ <li> Create a JAAS login file and set the appropriate system property to point to it as described above</li>
+ <li> Set the configuration property <tt>zookeeper.set.acl</tt> in each broker to true</li>
+ </ol>
+
+ The metadata stored in ZooKeeper for the Kafka cluster is world-readable, but can only be modified by the brokers. The rationale behind this decision is that the data stored in ZooKeeper is not sensitive, but inappropriate manipulation of that data can cause cluster disruption. We also recommend limiting the access to ZooKeeper via network segmentation (only brokers and some admin tools need access to ZooKeeper if the new Java consumer and producer clients are used).
+
+ <h4><a id="zk_authz_migration" href="#zk_authz_migration">7.6.2 Migrating clusters</a></h4>
+ If you are running a version of Kafka that does not support security or simply with security disabled, and you want to make the cluster secure, then you need to execute the following steps to enable ZooKeeper authentication with minimal disruption to your operations:
+ <ol>
+ <li>Perform a rolling restart setting the JAAS login file, which enables brokers to authenticate. At the end of the rolling restart, brokers are able to manipulate znodes with strict ACLs, but they will not create znodes with those ACLs</li>
+ <li>Perform a second rolling restart of brokers, this time setting the configuration parameter <tt>zookeeper.set.acl</tt> to true, which enables the use of secure ACLs when creating znodes</li>
+ <li>Execute the ZkSecurityMigrator tool. To execute the tool, there is this script: <tt>./bin/zookeeper-security-migration.sh</tt> with <tt>zookeeper.acl</tt> set to secure. This tool traverses the corresponding sub-trees changing the ACLs of the znodes</li>
+ </ol>
+ <p>It is also possible to turn off authentication in a secure cluster. To do it, follow these steps:</p>
+ <ol>
+ <li>Perform a rolling restart of brokers setting the JAAS login file, which enables brokers to authenticate, but setting <tt>zookeeper.set.acl</tt> to false. At the end of the rolling restart, brokers stop creating znodes with secure ACLs, but are still able to authenticate and manipulate all znodes</li>
+ <li>Execute the ZkSecurityMigrator tool. To execute the tool, run this script <tt>./bin/zookeeper-security-migration.sh</tt> with <tt>zookeeper.acl</tt> set to unsecure. This tool traverses the corresponding sub-trees changing the ACLs of the znodes</li>
+ <li>Perform a second rolling restart of brokers, this time omitting the system property that sets the JAAS login file</li>
+ </ol>
+ Here is an example of how to run the migration tool:
+ <pre class="brush: bash;">
+ ./bin/zookeeper-security-migration.sh --zookeeper.acl=secure --zookeeper.connect=localhost:2181
+ </pre>
+ <p>Run this to see the full list of parameters:</p>
+ <pre class="brush: bash;">
+ ./bin/zookeeper-security-migration.sh --help
+ </pre>
+ <h4><a id="zk_authz_ensemble" href="#zk_authz_ensemble">7.6.3 Migrating the ZooKeeper ensemble</a></h4>
+ It is also necessary to enable authentication on the ZooKeeper ensemble. To do it, we need to perform a rolling restart of the server and set a few properties. Please refer to the ZooKeeper documentation for more detail:
+ <ol>
+ <li><a href="http://zookeeper.apache.org/doc/r3.4.9/zookeeperProgrammers.html#sc_ZooKeeperAccessControl">Apache ZooKeeper documentation</a></li>
+ <li><a href="https://cwiki.apache.org/confluence/display/ZOOKEEPER/Zookeeper+and+SASL">Apache ZooKeeper wiki</a></li>
+ </ol>
+</script>
+
+<div class="p-security"></div>
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+<script id="content-template" type="text/x-handlebars-template">
+ <h1>Architecture</h1>
+
+ Kafka Streams simplifies application development by building on the Kafka producer and consumer libraries and leveraging the native capabilities of
+ Kafka to offer data parallelism, distributed coordination, fault tolerance, and operational simplicity. In this section, we describe how Kafka Streams works underneath the covers.
+
+ <p>
+ The picture below shows the anatomy of an application that uses the Kafka Streams library. Let's walk through some details.
+ </p>
+ <img class="centered" src="/{{version}}/images/streams-architecture-overview.jpg" style="width:750px">
+
+ <h3><a id="streams_architecture_tasks" href="#streams_architecture_tasks">Stream Partitions and Tasks</a></h3>
+
+ <p>
+ The messaging layer of Kafka partitions data for storing and transporting it. Kafka Streams partitions data for processing it.
+ In both cases, this partitioning is what enables data locality, elasticity, scalability, high performance, and fault tolerance.
+ Kafka Streams uses the concepts of <b>partitions</b> and <b>tasks</b> as logical units of its parallelism model based on Kafka topic partitions.
+ There are close links between Kafka Streams and Kafka in the context of parallelism:
+ </p>
+
+ <ul>
+ <li>Each <b>stream partition</b> is a totally ordered sequence of data records and maps to a Kafka <b>topic partition</b>.</li>
+ <li>A <b>data record</b> in the stream maps to a Kafka <b>message</b> from that topic.</li>
+ <li>The <b>keys</b> of data records determine the partitioning of data in both Kafka and Kafka Streams, i.e., how data is routed to specific partitions within topics.</li>
+ </ul>
+
+ <p>
+ An application's processor topology is scaled by breaking it into multiple tasks.
+ More specifically, Kafka Streams creates a fixed number of tasks based on the input stream partitions for the application,
+ with each task assigned a list of partitions from the input streams (i.e., Kafka topics). The assignment of partitions to tasks
+ never changes so that each task is a fixed unit of parallelism of the application. Tasks can then instantiate their own processor topology
+ based on the assigned partitions; they also maintain a buffer for each of its assigned partitions and process messages one-at-a-time from
+ these record buffers. As a result stream tasks can be processed independently and in parallel without manual intervention.
+ </p>
+
+ <p>
+ It is important to understand that Kafka Streams is not a resource manager, but a library that "runs" anywhere its stream processing application runs.
+ Multiple instances of the application are executed either on the same machine, or spread across multiple machines and tasks can be distributed automatically
+ by the library to those running application instances. The assignment of partitions to tasks never changes; if an application instance fails, all its assigned
+ tasks will be automatically restarted on other instances and continue to consume from the same stream partitions.
+ </p>
+
+ <p>
+ The following diagram shows two tasks each assigned with one partition of the input streams.
+ </p>
+ <img class="centered" src="/{{version}}/images/streams-architecture-tasks.jpg" style="width:400px">
+ <br>
+
+ <h3><a id="streams_architecture_threads" href="#streams_architecture_threads">Threading Model</a></h3>
+
+ <p>
+ Kafka Streams allows the user to configure the number of <b>threads</b> that the library can use to parallelize processing within an application instance.
+ Each thread can execute one or more tasks with their processor topologies independently. For example, the following diagram shows one stream thread running two stream tasks.
+ </p>
+ <img class="centered" src="/{{version}}/images/streams-architecture-threads.jpg" style="width:400px">
+
+ <p>
+ Starting more stream threads or more instances of the application merely amounts to replicating the topology and having it process a different subset of Kafka partitions, effectively parallelizing processing.
+ It is worth noting that there is no shared state amongst the threads, so no inter-thread coordination is necessary. This makes it very simple to run topologies in parallel across the application instances and threads.
+ The assignment of Kafka topic partitions amongst the various stream threads is transparently handled by Kafka Streams leveraging <a href="https://cwiki.apache.org/confluence/display/KAFKA/Kafka+Client-side+Assignment+Proposal">Kafka's coordination</a> functionality.
+ </p>
+
+ <p>
+ As we described above, scaling your stream processing application with Kafka Streams is easy: you merely need to start additional instances of your application,
+ and Kafka Streams takes care of distributing partitions amongst tasks that run in the application instances. You can start as many threads of the application
+ as there are input Kafka topic partitions so that, across all running instances of an application, every thread (or rather, the tasks it runs) has at least one input partition to process.
+ </p>
+ <br>
+
+ <h3><a id="streams_architecture_state" href="#streams_architecture_state">Local State Stores</a></h3>
+
+ <p>
+ Kafka Streams provides so-called <b>state stores</b>, which can be used by stream processing applications to store and query data,
+ which is an important capability when implementing stateful operations. The <a href="/{{version}}/documentation/streams/developer-guide#streams_dsl">Kafka Streams DSL</a>, for example, automatically creates
+ and manages such state stores when you are calling stateful operators such as <code>join()</code> or <code>aggregate()</code>, or when you are windowing a stream.
+ </p>
+
+ <p>
+ Every stream task in a Kafka Streams application may embed one or more local state stores that can be accessed via APIs to store and query data required for processing.
+ Kafka Streams offers fault-tolerance and automatic recovery for such local state stores.
+ </p>
+
+ <p>
+ The following diagram shows two stream tasks with their dedicated local state stores.
+ </p>
+ <img class="centered" src="/{{version}}/images/streams-architecture-states.jpg" style="width:400px">
+ <br>
+
+ <h3><a id="streams_architecture_recovery" href="#streams_architecture_recovery">Fault Tolerance</a></h3>
+
+ <p>
+ Kafka Streams builds on fault-tolerance capabilities integrated natively within Kafka. Kafka partitions are highly available and replicated; so when stream data is persisted to Kafka it is available
+ even if the application fails and needs to re-process it. Tasks in Kafka Streams leverage the fault-tolerance capability
+ offered by the <a href="https://www.confluent.io/blog/tutorial-getting-started-with-the-new-apache-kafka-0.9-consumer-client/">Kafka consumer client</a> to handle failures.
+ If a task runs on a machine that fails, Kafka Streams automatically restarts the task in one of the remaining running instances of the application.
+ </p>
+
+ <p>
+ In addition, Kafka Streams makes sure that the local state stores are robust to failures, too. For each state store, it maintains a replicated changelog Kafka topic in which it tracks any state updates.
+ These changelog topics are partitioned as well so that each local state store instance, and hence the task accessing the store, has its own dedicated changelog topic partition.
+ <a href="/{{version}}/documentation/#compaction">Log compaction</a> is enabled on the changelog topics so that old data can be purged safely to prevent the topics from growing indefinitely.
+ If tasks run on a machine that fails and are restarted on another machine, Kafka Streams guarantees to restore their associated state stores to the content before the failure by
+ replaying the corresponding changelog topics prior to resuming the processing on the newly started tasks. As a result, failure handling is completely transparent to the end user.
+ </p>
+
+ <p>
+ Note that the cost of task (re)initialization typically depends primarily on the time for restoring the state by replaying the state stores' associated changelog topics.
+ To minimize this restoration time, users can configure their applications to have <b>standby replicas</b> of local states (i.e. fully replicated copies of the state).
+ When a task migration happens, Kafka Streams then attempts to assign a task to an application instance where such a standby replica already exists in order to minimize
+ the task (re)initialization cost. See <code>num.standby.replicas</code> in the <a href="/{{version}}/documentation/#streamsconfigs"><b>Kafka Streams Configs</b></a> section.
+ </p>
+
+ <div class="pagination">
+ <a href="/{{version}}/documentation/streams/core-concepts" class="pagination__btn pagination__btn__prev">Previous</a>
+ <a href="/{{version}}/documentation/streams/upgrade-guide" class="pagination__btn pagination__btn__next">Next</a>
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[08/14] kafka-site git commit: Update site for 1.0.0 release
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+<script id="ops-template" type="text/x-handlebars-template">
+
+ Here is some information on actually running Kafka as a production system based on usage and experience at LinkedIn. Please send us any additional tips you know of.
+
+ <h3><a id="basic_ops" href="#basic_ops">6.1 Basic Kafka Operations</a></h3>
+
+ This section will review the most common operations you will perform on your Kafka cluster. All of the tools reviewed in this section are available under the <code>bin/</code> directory of the Kafka distribution and each tool will print details on all possible commandline options if it is run with no arguments.
+
+ <h4><a id="basic_ops_add_topic" href="#basic_ops_add_topic">Adding and removing topics</a></h4>
+
+ You have the option of either adding topics manually or having them be created automatically when data is first published to a non-existent topic. If topics are auto-created then you may want to tune the default <a href="#topicconfigs">topic configurations</a> used for auto-created topics.
+ <p>
+ Topics are added and modified using the topic tool:
+ <pre class="brush: bash;">
+ > bin/kafka-topics.sh --zookeeper zk_host:port/chroot --create --topic my_topic_name
+ --partitions 20 --replication-factor 3 --config x=y
+ </pre>
+ The replication factor controls how many servers will replicate each message that is written. If you have a replication factor of 3 then up to 2 servers can fail before you will lose access to your data. We recommend you use a replication factor of 2 or 3 so that you can transparently bounce machines without interrupting data consumption.
+ <p>
+ The partition count controls how many logs the topic will be sharded into. There are several impacts of the partition count. First each partition must fit entirely on a single server. So if you have 20 partitions the full data set (and read and write load) will be handled by no more than 20 servers (not counting replicas). Finally the partition count impacts the maximum parallelism of your consumers. This is discussed in greater detail in the <a href="#intro_consumers">concepts section</a>.
+ <p>
+ Each sharded partition log is placed into its own folder under the Kafka log directory. The name of such folders consists of the topic name, appended by a dash (-) and the partition id. Since a typical folder name can not be over 255 characters long, there will be a limitation on the length of topic names. We assume the number of partitions will not ever be above 100,000. Therefore, topic names cannot be longer than 249 characters. This leaves just enough room in the folder name for a dash and a potentially 5 digit long partition id.
+ <p>
+ The configurations added on the command line override the default settings the server has for things like the length of time data should be retained. The complete set of per-topic configurations is documented <a href="#topicconfigs">here</a>.
+
+ <h4><a id="basic_ops_modify_topic" href="#basic_ops_modify_topic">Modifying topics</a></h4>
+
+ You can change the configuration or partitioning of a topic using the same topic tool.
+ <p>
+ To add partitions you can do
+ <pre class="brush: bash;">
+ > bin/kafka-topics.sh --zookeeper zk_host:port/chroot --alter --topic my_topic_name
+ --partitions 40
+ </pre>
+ Be aware that one use case for partitions is to semantically partition data, and adding partitions doesn't change the partitioning of existing data so this may disturb consumers if they rely on that partition. That is if data is partitioned by <code>hash(key) % number_of_partitions</code> then this partitioning will potentially be shuffled by adding partitions but Kafka will not attempt to automatically redistribute data in any way.
+ <p>
+ To add configs:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper zk_host:port/chroot --entity-type topics --entity-name my_topic_name --alter --add-config x=y
+ </pre>
+ To remove a config:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper zk_host:port/chroot --entity-type topics --entity-name my_topic_name --alter --delete-config x
+ </pre>
+ And finally deleting a topic:
+ <pre class="brush: bash;">
+ > bin/kafka-topics.sh --zookeeper zk_host:port/chroot --delete --topic my_topic_name
+ </pre>
+ <p>
+ Kafka does not currently support reducing the number of partitions for a topic.
+ <p>
+ Instructions for changing the replication factor of a topic can be found <a href="#basic_ops_increase_replication_factor">here</a>.
+
+ <h4><a id="basic_ops_restarting" href="#basic_ops_restarting">Graceful shutdown</a></h4>
+
+ The Kafka cluster will automatically detect any broker shutdown or failure and elect new leaders for the partitions on that machine. This will occur whether a server fails or it is brought down intentionally for maintenance or configuration changes. For the latter cases Kafka supports a more graceful mechanism for stopping a server than just killing it.
+
+ When a server is stopped gracefully it has two optimizations it will take advantage of:
+ <ol>
+ <li>It will sync all its logs to disk to avoid needing to do any log recovery when it restarts (i.e. validating the checksum for all messages in the tail of the log). Log recovery takes time so this speeds up intentional restarts.
+ <li>It will migrate any partitions the server is the leader for to other replicas prior to shutting down. This will make the leadership transfer faster and minimize the time each partition is unavailable to a few milliseconds.
+ </ol>
+
+ Syncing the logs will happen automatically whenever the server is stopped other than by a hard kill, but the controlled leadership migration requires using a special setting:
+ <pre class="brush: text;">
+ controlled.shutdown.enable=true
+ </pre>
+ Note that controlled shutdown will only succeed if <i>all</i> the partitions hosted on the broker have replicas (i.e. the replication factor is greater than 1 <i>and</i> at least one of these replicas is alive). This is generally what you want since shutting down the last replica would make that topic partition unavailable.
+
+ <h4><a id="basic_ops_leader_balancing" href="#basic_ops_leader_balancing">Balancing leadership</a></h4>
+
+ Whenever a broker stops or crashes leadership for that broker's partitions transfers to other replicas. This means that by default when the broker is restarted it will only be a follower for all its partitions, meaning it will not be used for client reads and writes.
+ <p>
+ To avoid this imbalance, Kafka has a notion of preferred replicas. If the list of replicas for a partition is 1,5,9 then node 1 is preferred as the leader to either node 5 or 9 because it is earlier in the replica list. You can have the Kafka cluster try to restore leadership to the restored replicas by running the command:
+ <pre class="brush: bash;">
+ > bin/kafka-preferred-replica-election.sh --zookeeper zk_host:port/chroot
+ </pre>
+
+ Since running this command can be tedious you can also configure Kafka to do this automatically by setting the following configuration:
+ <pre class="brush: text;">
+ auto.leader.rebalance.enable=true
+ </pre>
+
+ <h4><a id="basic_ops_racks" href="#basic_ops_racks">Balancing Replicas Across Racks</a></h4>
+ The rack awareness feature spreads replicas of the same partition across different racks. This extends the guarantees Kafka provides for broker-failure to cover rack-failure, limiting the risk of data loss should all the brokers on a rack fail at once. The feature can also be applied to other broker groupings such as availability zones in EC2.
+ <p></p>
+ You can specify that a broker belongs to a particular rack by adding a property to the broker config:
+ <pre class="brush: text;"> broker.rack=my-rack-id</pre>
+ When a topic is <a href="#basic_ops_add_topic">created</a>, <a href="#basic_ops_modify_topic">modified</a> or replicas are <a href="#basic_ops_cluster_expansion">redistributed</a>, the rack constraint will be honoured, ensuring replicas span as many racks as they can (a partition will span min(#racks, replication-factor) different racks).
+ <p></p>
+ The algorithm used to assign replicas to brokers ensures that the number of leaders per broker will be constant, regardless of how brokers are distributed across racks. This ensures balanced throughput.
+ <p></p>
+ However if racks are assigned different numbers of brokers, the assignment of replicas will not be even. Racks with fewer brokers will get more replicas, meaning they will use more storage and put more resources into replication. Hence it is sensible to configure an equal number of brokers per rack.
+
+ <h4><a id="basic_ops_mirror_maker" href="#basic_ops_mirror_maker">Mirroring data between clusters</a></h4>
+
+ We refer to the process of replicating data <i>between</i> Kafka clusters "mirroring" to avoid confusion with the replication that happens amongst the nodes in a single cluster. Kafka comes with a tool for mirroring data between Kafka clusters. The tool consumes from a source cluster and produces to a destination cluster.
+
+ A common use case for this kind of mirroring is to provide a replica in another datacenter. This scenario will be discussed in more detail in the next section.
+ <p>
+ You can run many such mirroring processes to increase throughput and for fault-tolerance (if one process dies, the others will take overs the additional load).
+ <p>
+ Data will be read from topics in the source cluster and written to a topic with the same name in the destination cluster. In fact the mirror maker is little more than a Kafka consumer and producer hooked together.
+ <p>
+ The source and destination clusters are completely independent entities: they can have different numbers of partitions and the offsets will not be the same. For this reason the mirror cluster is not really intended as a fault-tolerance mechanism (as the consumer position will be different); for that we recommend using normal in-cluster replication. The mirror maker process will, however, retain and use the message key for partitioning so order is preserved on a per-key basis.
+ <p>
+ Here is an example showing how to mirror a single topic (named <i>my-topic</i>) from an input cluster:
+ <pre class="brush: bash;">
+ > bin/kafka-mirror-maker.sh
+ --consumer.config consumer.properties
+ --producer.config producer.properties --whitelist my-topic
+ </pre>
+ Note that we specify the list of topics with the <code>--whitelist</code> option. This option allows any regular expression using <a href="http://docs.oracle.com/javase/7/docs/api/java/util/regex/Pattern.html">Java-style regular expressions</a>. So you could mirror two topics named <i>A</i> and <i>B</i> using <code>--whitelist 'A|B'</code>. Or you could mirror <i>all</i> topics using <code>--whitelist '*'</code>. Make sure to quote any regular expression to ensure the shell doesn't try to expand it as a file path. For convenience we allow the use of ',' instead of '|' to specify a list of topics.
+ <p>
+ Sometimes it is easier to say what it is that you <i>don't</i> want. Instead of using <code>--whitelist</code> to say what you want
+ to mirror you can use <code>--blacklist</code> to say what to exclude. This also takes a regular expression argument.
+ However, <code>--blacklist</code> is not supported when the new consumer has been enabled (i.e. when <code>bootstrap.servers</code>
+ has been defined in the consumer configuration).
+ <p>
+ Combining mirroring with the configuration <code>auto.create.topics.enable=true</code> makes it possible to have a replica cluster that will automatically create and replicate all data in a source cluster even as new topics are added.
+
+ <h4><a id="basic_ops_consumer_lag" href="#basic_ops_consumer_lag">Checking consumer position</a></h4>
+ Sometimes it's useful to see the position of your consumers. We have a tool that will show the position of all consumers in a consumer group as well as how far behind the end of the log they are. To run this tool on a consumer group named <i>my-group</i> consuming a topic named <i>my-topic</i> would look like this:
+ <pre class="brush: bash;">
+ > bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --describe --group my-group
+
+ Note: This will only show information about consumers that use the Java consumer API (non-ZooKeeper-based consumers).
+
+ TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG CONSUMER-ID HOST CLIENT-ID
+ my-topic 0 2 4 2 consumer-1-029af89c-873c-4751-a720-cefd41a669d6 /127.0.0.1 consumer-1
+ my-topic 1 2 3 1 consumer-1-029af89c-873c-4751-a720-cefd41a669d6 /127.0.0.1 consumer-1
+ my-topic 2 2 3 1 consumer-2-42c1abd4-e3b2-425d-a8bb-e1ea49b29bb2 /127.0.0.1 consumer-2
+ </pre>
+
+ This tool also works with ZooKeeper-based consumers:
+ <pre class="brush: bash;">
+ > bin/kafka-consumer-groups.sh --zookeeper localhost:2181 --describe --group my-group
+
+ Note: This will only show information about consumers that use ZooKeeper (not those using the Java consumer API).
+
+ TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG CONSUMER-ID
+ my-topic 0 2 4 2 my-group_consumer-1
+ my-topic 1 2 3 1 my-group_consumer-1
+ my-topic 2 2 3 1 my-group_consumer-2
+ </pre>
+
+ <h4><a id="basic_ops_consumer_group" href="#basic_ops_consumer_group">Managing Consumer Groups</a></h4>
+
+ With the ConsumerGroupCommand tool, we can list, describe, or delete consumer groups. Note that deletion is only available when the group metadata is stored in
+ ZooKeeper. When using the <a href="http://kafka.apache.org/documentation.html#newconsumerapi">new consumer API</a> (where
+ the broker handles coordination of partition handling and rebalance), the group is deleted when the last committed offset for that group expires.
+
+ For example, to list all consumer groups across all topics:
+
+ <pre class="brush: bash;">
+ > bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --list
+
+ test-consumer-group
+ </pre>
+
+ To view offsets, as mentioned earlier, we "describe" the consumer group like this:
+
+ <pre class="brush: bash;">
+ > bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --describe --group test-consumer-group
+
+ TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG CONSUMER-ID HOST CLIENT-ID
+ test-foo 0 1 3 2 consumer-1-a5d61779-4d04-4c50-a6d6-fb35d942642d /127.0.0.1 consumer-1
+ </pre>
+
+ If you are using the old high-level consumer and storing the group metadata in ZooKeeper (i.e. <code>offsets.storage=zookeeper</code>), pass
+ <code>--zookeeper</code> instead of <code>bootstrap-server</code>:
+
+ <pre class="brush: bash;">
+ > bin/kafka-consumer-groups.sh --zookeeper localhost:2181 --list
+ </pre>
+
+ <h4><a id="basic_ops_cluster_expansion" href="#basic_ops_cluster_expansion">Expanding your cluster</a></h4>
+
+ Adding servers to a Kafka cluster is easy, just assign them a unique broker id and start up Kafka on your new servers. However these new servers will not automatically be assigned any data partitions, so unless partitions are moved to them they won't be doing any work until new topics are created. So usually when you add machines to your cluster you will want to migrate some existing data to these machines.
+ <p>
+ The process of migrating data is manually initiated but fully automated. Under the covers what happens is that Kafka will add the new server as a follower of the partition it is migrating and allow it to fully replicate the existing data in that partition. When the new server has fully replicated the contents of this partition and joined the in-sync replica one of the existing replicas will delete their partition's data.
+ <p>
+ The partition reassignment tool can be used to move partitions across brokers. An ideal partition distribution would ensure even data load and partition sizes across all brokers. The partition reassignment tool does not have the capability to automatically study the data distribution in a Kafka cluster and move partitions around to attain an even load distribution. As such, the admin has to figure out which topics or partitions should be moved around.
+ <p>
+ The partition reassignment tool can run in 3 mutually exclusive modes:
+ <ul>
+ <li>--generate: In this mode, given a list of topics and a list of brokers, the tool generates a candidate reassignment to move all partitions of the specified topics to the new brokers. This option merely provides a convenient way to generate a partition reassignment plan given a list of topics and target brokers.</li>
+ <li>--execute: In this mode, the tool kicks off the reassignment of partitions based on the user provided reassignment plan. (using the --reassignment-json-file option). This can either be a custom reassignment plan hand crafted by the admin or provided by using the --generate option</li>
+ <li>--verify: In this mode, the tool verifies the status of the reassignment for all partitions listed during the last --execute. The status can be either of successfully completed, failed or in progress</li>
+ </ul>
+ <h5><a id="basic_ops_automigrate" href="#basic_ops_automigrate">Automatically migrating data to new machines</a></h5>
+ The partition reassignment tool can be used to move some topics off of the current set of brokers to the newly added brokers. This is typically useful while expanding an existing cluster since it is easier to move entire topics to the new set of brokers, than moving one partition at a time. When used to do this, the user should provide a list of topics that should be moved to the new set of brokers and a target list of new brokers. The tool then evenly distributes all partitions for the given list of topics across the new set of brokers. During this move, the replication factor of the topic is kept constant. Effectively the replicas for all partitions for the input list of topics are moved from the old set of brokers to the newly added brokers.
+ <p>
+ For instance, the following example will move all partitions for topics foo1,foo2 to the new set of brokers 5,6. At the end of this move, all partitions for topics foo1 and foo2 will <i>only</i> exist on brokers 5,6.
+ <p>
+ Since the tool accepts the input list of topics as a json file, you first need to identify the topics you want to move and create the json file as follows:
+ <pre class="brush: bash;">
+ > cat topics-to-move.json
+ {"topics": [{"topic": "foo1"},
+ {"topic": "foo2"}],
+ "version":1
+ }
+ </pre>
+ Once the json file is ready, use the partition reassignment tool to generate a candidate assignment:
+ <pre class="brush: bash;">
+ > bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --topics-to-move-json-file topics-to-move.json --broker-list "5,6" --generate
+ Current partition replica assignment
+
+ {"version":1,
+ "partitions":[{"topic":"foo1","partition":2,"replicas":[1,2]},
+ {"topic":"foo1","partition":0,"replicas":[3,4]},
+ {"topic":"foo2","partition":2,"replicas":[1,2]},
+ {"topic":"foo2","partition":0,"replicas":[3,4]},
+ {"topic":"foo1","partition":1,"replicas":[2,3]},
+ {"topic":"foo2","partition":1,"replicas":[2,3]}]
+ }
+
+ Proposed partition reassignment configuration
+
+ {"version":1,
+ "partitions":[{"topic":"foo1","partition":2,"replicas":[5,6]},
+ {"topic":"foo1","partition":0,"replicas":[5,6]},
+ {"topic":"foo2","partition":2,"replicas":[5,6]},
+ {"topic":"foo2","partition":0,"replicas":[5,6]},
+ {"topic":"foo1","partition":1,"replicas":[5,6]},
+ {"topic":"foo2","partition":1,"replicas":[5,6]}]
+ }
+ </pre>
+ <p>
+ The tool generates a candidate assignment that will move all partitions from topics foo1,foo2 to brokers 5,6. Note, however, that at this point, the partition movement has not started, it merely tells you the current assignment and the proposed new assignment. The current assignment should be saved in case you want to rollback to it. The new assignment should be saved in a json file (e.g. expand-cluster-reassignment.json) to be input to the tool with the --execute option as follows:
+ <pre class="brush: bash;">
+ > bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --reassignment-json-file expand-cluster-reassignment.json --execute
+ Current partition replica assignment
+
+ {"version":1,
+ "partitions":[{"topic":"foo1","partition":2,"replicas":[1,2]},
+ {"topic":"foo1","partition":0,"replicas":[3,4]},
+ {"topic":"foo2","partition":2,"replicas":[1,2]},
+ {"topic":"foo2","partition":0,"replicas":[3,4]},
+ {"topic":"foo1","partition":1,"replicas":[2,3]},
+ {"topic":"foo2","partition":1,"replicas":[2,3]}]
+ }
+
+ Save this to use as the --reassignment-json-file option during rollback
+ Successfully started reassignment of partitions
+ {"version":1,
+ "partitions":[{"topic":"foo1","partition":2,"replicas":[5,6]},
+ {"topic":"foo1","partition":0,"replicas":[5,6]},
+ {"topic":"foo2","partition":2,"replicas":[5,6]},
+ {"topic":"foo2","partition":0,"replicas":[5,6]},
+ {"topic":"foo1","partition":1,"replicas":[5,6]},
+ {"topic":"foo2","partition":1,"replicas":[5,6]}]
+ }
+ </pre>
+ <p>
+ Finally, the --verify option can be used with the tool to check the status of the partition reassignment. Note that the same expand-cluster-reassignment.json (used with the --execute option) should be used with the --verify option:
+ <pre class="brush: bash;">
+ > bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --reassignment-json-file expand-cluster-reassignment.json --verify
+ Status of partition reassignment:
+ Reassignment of partition [foo1,0] completed successfully
+ Reassignment of partition [foo1,1] is in progress
+ Reassignment of partition [foo1,2] is in progress
+ Reassignment of partition [foo2,0] completed successfully
+ Reassignment of partition [foo2,1] completed successfully
+ Reassignment of partition [foo2,2] completed successfully
+ </pre>
+
+ <h5><a id="basic_ops_partitionassignment" href="#basic_ops_partitionassignment">Custom partition assignment and migration</a></h5>
+ The partition reassignment tool can also be used to selectively move replicas of a partition to a specific set of brokers. When used in this manner, it is assumed that the user knows the reassignment plan and does not require the tool to generate a candidate reassignment, effectively skipping the --generate step and moving straight to the --execute step
+ <p>
+ For instance, the following example moves partition 0 of topic foo1 to brokers 5,6 and partition 1 of topic foo2 to brokers 2,3:
+ <p>
+ The first step is to hand craft the custom reassignment plan in a json file:
+ <pre class="brush: bash;">
+ > cat custom-reassignment.json
+ {"version":1,"partitions":[{"topic":"foo1","partition":0,"replicas":[5,6]},{"topic":"foo2","partition":1,"replicas":[2,3]}]}
+ </pre>
+ Then, use the json file with the --execute option to start the reassignment process:
+ <pre class="brush: bash;">
+ > bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --reassignment-json-file custom-reassignment.json --execute
+ Current partition replica assignment
+
+ {"version":1,
+ "partitions":[{"topic":"foo1","partition":0,"replicas":[1,2]},
+ {"topic":"foo2","partition":1,"replicas":[3,4]}]
+ }
+
+ Save this to use as the --reassignment-json-file option during rollback
+ Successfully started reassignment of partitions
+ {"version":1,
+ "partitions":[{"topic":"foo1","partition":0,"replicas":[5,6]},
+ {"topic":"foo2","partition":1,"replicas":[2,3]}]
+ }
+ </pre>
+ <p>
+ The --verify option can be used with the tool to check the status of the partition reassignment. Note that the same expand-cluster-reassignment.json (used with the --execute option) should be used with the --verify option:
+ <pre class="brush: bash;">
+ > bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --reassignment-json-file custom-reassignment.json --verify
+ Status of partition reassignment:
+ Reassignment of partition [foo1,0] completed successfully
+ Reassignment of partition [foo2,1] completed successfully
+ </pre>
+
+ <h4><a id="basic_ops_decommissioning_brokers" href="#basic_ops_decommissioning_brokers">Decommissioning brokers</a></h4>
+ The partition reassignment tool does not have the ability to automatically generate a reassignment plan for decommissioning brokers yet. As such, the admin has to come up with a reassignment plan to move the replica for all partitions hosted on the broker to be decommissioned, to the rest of the brokers. This can be relatively tedious as the reassignment needs to ensure that all the replicas are not moved from the decommissioned broker to only one other broker. To make this process effortless, we plan to add tooling support for decommissioning brokers in the future.
+
+ <h4><a id="basic_ops_increase_replication_factor" href="#basic_ops_increase_replication_factor">Increasing replication factor</a></h4>
+ Increasing the replication factor of an existing partition is easy. Just specify the extra replicas in the custom reassignment json file and use it with the --execute option to increase the replication factor of the specified partitions.
+ <p>
+ For instance, the following example increases the replication factor of partition 0 of topic foo from 1 to 3. Before increasing the replication factor, the partition's only replica existed on broker 5. As part of increasing the replication factor, we will add more replicas on brokers 6 and 7.
+ <p>
+ The first step is to hand craft the custom reassignment plan in a json file:
+ <pre class="brush: bash;">
+ > cat increase-replication-factor.json
+ {"version":1,
+ "partitions":[{"topic":"foo","partition":0,"replicas":[5,6,7]}]}
+ </pre>
+ Then, use the json file with the --execute option to start the reassignment process:
+ <pre class="brush: bash;">
+ > bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --reassignment-json-file increase-replication-factor.json --execute
+ Current partition replica assignment
+
+ {"version":1,
+ "partitions":[{"topic":"foo","partition":0,"replicas":[5]}]}
+
+ Save this to use as the --reassignment-json-file option during rollback
+ Successfully started reassignment of partitions
+ {"version":1,
+ "partitions":[{"topic":"foo","partition":0,"replicas":[5,6,7]}]}
+ </pre>
+ <p>
+ The --verify option can be used with the tool to check the status of the partition reassignment. Note that the same increase-replication-factor.json (used with the --execute option) should be used with the --verify option:
+ <pre class="brush: bash;">
+ > bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --reassignment-json-file increase-replication-factor.json --verify
+ Status of partition reassignment:
+ Reassignment of partition [foo,0] completed successfully
+ </pre>
+ You can also verify the increase in replication factor with the kafka-topics tool:
+ <pre class="brush: bash;">
+ > bin/kafka-topics.sh --zookeeper localhost:2181 --topic foo --describe
+ Topic:foo PartitionCount:1 ReplicationFactor:3 Configs:
+ Topic: foo Partition: 0 Leader: 5 Replicas: 5,6,7 Isr: 5,6,7
+ </pre>
+
+ <h4><a id="rep-throttle" href="#rep-throttle">Limiting Bandwidth Usage during Data Migration</a></h4>
+ Kafka lets you apply a throttle to replication traffic, setting an upper bound on the bandwidth used to move replicas from machine to machine. This is useful when rebalancing a cluster, bootstrapping a new broker or adding or removing brokers, as it limits the impact these data-intensive operations will have on users.
+ <p></p>
+ There are two interfaces that can be used to engage a throttle. The simplest, and safest, is to apply a throttle when invoking the kafka-reassign-partitions.sh, but kafka-configs.sh can also be used to view and alter the throttle values directly.
+ <p></p>
+ So for example, if you were to execute a rebalance, with the below command, it would move partitions at no more than 50MB/s.
+ <pre class="brush: bash;">$ bin/kafka-reassign-partitions.sh --zookeeper myhost:2181--execute --reassignment-json-file bigger-cluster.json —throttle 50000000</pre>
+ When you execute this script you will see the throttle engage:
+ <pre class="brush: bash;">
+ The throttle limit was set to 50000000 B/s
+ Successfully started reassignment of partitions.</pre>
+ <p>Should you wish to alter the throttle, during a rebalance, say to increase the throughput so it completes quicker, you can do this by re-running the execute command passing the same reassignment-json-file:</p>
+ <pre class="brush: bash;">$ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --execute --reassignment-json-file bigger-cluster.json --throttle 700000000
+ There is an existing assignment running.
+ The throttle limit was set to 700000000 B/s</pre>
+
+ <p>Once the rebalance completes the administrator can check the status of the rebalance using the --verify option.
+ If the rebalance has completed, the throttle will be removed via the --verify command. It is important that
+ administrators remove the throttle in a timely manner once rebalancing completes by running the command with
+ the --verify option. Failure to do so could cause regular replication traffic to be throttled. </p>
+ <p>When the --verify option is executed, and the reassignment has completed, the script will confirm that the throttle was removed:</p>
+
+ <pre class="brush: bash;">
+ > bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --verify --reassignment-json-file bigger-cluster.json
+ Status of partition reassignment:
+ Reassignment of partition [my-topic,1] completed successfully
+ Reassignment of partition [mytopic,0] completed successfully
+ Throttle was removed.</pre>
+
+ <p>The administrator can also validate the assigned configs using the kafka-configs.sh. There are two pairs of throttle
+ configuration used to manage the throttling process. The throttle value itself. This is configured, at a broker
+ level, using the dynamic properties: </p>
+
+ <pre class="brush: text;">leader.replication.throttled.rate
+ follower.replication.throttled.rate</pre>
+
+ <p>There is also an enumerated set of throttled replicas: </p>
+
+ <pre class="brush: text;">leader.replication.throttled.replicas
+ follower.replication.throttled.replicas</pre>
+
+ <p>Which are configured per topic. All four config values are automatically assigned by kafka-reassign-partitions.sh
+ (discussed below). </p>
+ <p>To view the throttle limit configuration:</p>
+
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --describe --zookeeper localhost:2181 --entity-type brokers
+ Configs for brokers '2' are leader.replication.throttled.rate=700000000,follower.replication.throttled.rate=700000000
+ Configs for brokers '1' are leader.replication.throttled.rate=700000000,follower.replication.throttled.rate=700000000</pre>
+
+ <p>This shows the throttle applied to both leader and follower side of the replication protocol. By default both sides
+ are assigned the same throttled throughput value. </p>
+
+ <p>To view the list of throttled replicas:</p>
+
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --describe --zookeeper localhost:2181 --entity-type topics
+ Configs for topic 'my-topic' are leader.replication.throttled.replicas=1:102,0:101,
+ follower.replication.throttled.replicas=1:101,0:102</pre>
+
+ <p>Here we see the leader throttle is applied to partition 1 on broker 102 and partition 0 on broker 101. Likewise the
+ follower throttle is applied to partition 1 on
+ broker 101 and partition 0 on broker 102. </p>
+
+ <p>By default kafka-reassign-partitions.sh will apply the leader throttle to all replicas that exist before the
+ rebalance, any one of which might be leader.
+ It will apply the follower throttle to all move destinations. So if there is a partition with replicas on brokers
+ 101,102, being reassigned to 102,103, a leader throttle,
+ for that partition, would be applied to 101,102 and a follower throttle would be applied to 103 only. </p>
+
+
+ <p>If required, you can also use the --alter switch on kafka-configs.sh to alter the throttle configurations manually.
+ </p>
+
+ <h5>Safe usage of throttled replication</h5>
+
+ <p>Some care should be taken when using throttled replication. In particular:</p>
+
+ <p><i>(1) Throttle Removal:</i></p>
+ The throttle should be removed in a timely manner once reassignment completes (by running kafka-reassign-partitions
+ —verify).
+
+ <p><i>(2) Ensuring Progress:</i></p>
+ <p>If the throttle is set too low, in comparison to the incoming write rate, it is possible for replication to not
+ make progress. This occurs when:</p>
+ <pre>max(BytesInPerSec) > throttle</pre>
+ <p>
+ Where BytesInPerSec is the metric that monitors the write throughput of producers into each broker. </p>
+ <p>The administrator can monitor whether replication is making progress, during the rebalance, using the metric:</p>
+
+ <pre>kafka.server:type=FetcherLagMetrics,name=ConsumerLag,clientId=([-.\w]+),topic=([-.\w]+),partition=([0-9]+)</pre>
+
+ <p>The lag should constantly decrease during replication. If the metric does not decrease the administrator should
+ increase the
+ throttle throughput as described above. </p>
+
+
+ <h4><a id="quotas" href="#quotas">Setting quotas</a></h4>
+ Quotas overrides and defaults may be configured at (user, client-id), user or client-id levels as described <a href="#design_quotas">here</a>.
+ By default, clients receive an unlimited quota.
+
+ It is possible to set custom quotas for each (user, client-id), user or client-id group.
+ <p>
+ Configure custom quota for (user=user1, client-id=clientA):
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200' --entity-type users --entity-name user1 --entity-type clients --entity-name clientA
+ Updated config for entity: user-principal 'user1', client-id 'clientA'.
+ </pre>
+
+ Configure custom quota for user=user1:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200' --entity-type users --entity-name user1
+ Updated config for entity: user-principal 'user1'.
+ </pre>
+
+ Configure custom quota for client-id=clientA:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200' --entity-type clients --entity-name clientA
+ Updated config for entity: client-id 'clientA'.
+ </pre>
+
+ It is possible to set default quotas for each (user, client-id), user or client-id group by specifying <i>--entity-default</i> option instead of <i>--entity-name</i>.
+ <p>
+ Configure default client-id quota for user=userA:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200' --entity-type users --entity-name user1 --entity-type clients --entity-default
+ Updated config for entity: user-principal 'user1', default client-id.
+ </pre>
+
+ Configure default quota for user:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200' --entity-type users --entity-default
+ Updated config for entity: default user-principal.
+ </pre>
+
+ Configure default quota for client-id:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200' --entity-type clients --entity-default
+ Updated config for entity: default client-id.
+ </pre>
+
+ Here's how to describe the quota for a given (user, client-id):
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --describe --entity-type users --entity-name user1 --entity-type clients --entity-name clientA
+ Configs for user-principal 'user1', client-id 'clientA' are producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200
+ </pre>
+ Describe quota for a given user:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --describe --entity-type users --entity-name user1
+ Configs for user-principal 'user1' are producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200
+ </pre>
+ Describe quota for a given client-id:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --describe --entity-type clients --entity-name clientA
+ Configs for client-id 'clientA' are producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200
+ </pre>
+ If entity name is not specified, all entities of the specified type are described. For example, describe all users:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --describe --entity-type users
+ Configs for user-principal 'user1' are producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200
+ Configs for default user-principal are producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200
+ </pre>
+ Similarly for (user, client):
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --describe --entity-type users --entity-type clients
+ Configs for user-principal 'user1', default client-id are producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200
+ Configs for user-principal 'user1', client-id 'clientA' are producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200
+ </pre>
+ <p>
+ It is possible to set default quotas that apply to all client-ids by setting these configs on the brokers. These properties are applied only if quota overrides or defaults are not configured in Zookeeper. By default, each client-id receives an unlimited quota. The following sets the default quota per producer and consumer client-id to 10MB/sec.
+ <pre class="brush: text;">
+ quota.producer.default=10485760
+ quota.consumer.default=10485760
+ </pre>
+ Note that these properties are being deprecated and may be removed in a future release. Defaults configured using kafka-configs.sh take precedence over these properties.
+
+ <h3><a id="datacenters" href="#datacenters">6.2 Datacenters</a></h3>
+
+ Some deployments will need to manage a data pipeline that spans multiple datacenters. Our recommended approach to this is to deploy a local Kafka cluster in each datacenter with application instances in each datacenter interacting only with their local cluster and mirroring between clusters (see the documentation on the <a href="#basic_ops_mirror_maker">mirror maker tool</a> for how to do this).
+ <p>
+ This deployment pattern allows datacenters to act as independent entities and allows us to manage and tune inter-datacenter replication centrally. This allows each facility to stand alone and operate even if the inter-datacenter links are unavailable: when this occurs the mirroring falls behind until the link is restored at which time it catches up.
+ <p>
+ For applications that need a global view of all data you can use mirroring to provide clusters which have aggregate data mirrored from the local clusters in <i>all</i> datacenters. These aggregate clusters are used for reads by applications that require the full data set.
+ <p>
+ This is not the only possible deployment pattern. It is possible to read from or write to a remote Kafka cluster over the WAN, though obviously this will add whatever latency is required to get the cluster.
+ <p>
+ Kafka naturally batches data in both the producer and consumer so it can achieve high-throughput even over a high-latency connection. To allow this though it may be necessary to increase the TCP socket buffer sizes for the producer, consumer, and broker using the <code>socket.send.buffer.bytes</code> and <code>socket.receive.buffer.bytes</code> configurations. The appropriate way to set this is documented <a href="http://en.wikipedia.org/wiki/Bandwidth-delay_product">here</a>.
+ <p>
+ It is generally <i>not</i> advisable to run a <i>single</i> Kafka cluster that spans multiple datacenters over a high-latency link. This will incur very high replication latency both for Kafka writes and ZooKeeper writes, and neither Kafka nor ZooKeeper will remain available in all locations if the network between locations is unavailable.
+
+ <h3><a id="config" href="#config">6.3 Kafka Configuration</a></h3>
+
+ <h4><a id="clientconfig" href="#clientconfig">Important Client Configurations</a></h4>
+ The most important old Scala producer configurations control
+ <ul>
+ <li>acks</li>
+ <li>compression</li>
+ <li>sync vs async production</li>
+ <li>batch size (for async producers)</li>
+ </ul>
+ The most important new Java producer configurations control
+ <ul>
+ <li>acks</li>
+ <li>compression</li>
+ <li>batch size</li>
+ </ul>
+ The most important consumer configuration is the fetch size.
+ <p>
+ All configurations are documented in the <a href="#configuration">configuration</a> section.
+ <p>
+ <h4><a id="prodconfig" href="#prodconfig">A Production Server Config</a></h4>
+ Here is an example production server configuration:
+ <pre class="brush: text;">
+ # ZooKeeper
+ zookeeper.connect=[list of ZooKeeper servers]
+
+ # Log configuration
+ num.partitions=8
+ default.replication.factor=3
+ log.dir=[List of directories. Kafka should have its own dedicated disk(s) or SSD(s).]
+
+ # Other configurations
+ broker.id=[An integer. Start with 0 and increment by 1 for each new broker.]
+ listeners=[list of listeners]
+ auto.create.topics.enable=false
+ min.insync.replicas=2
+ queued.max.requests=[number of concurrent requests]
+ </pre>
+
+ Our client configuration varies a fair amount between different use cases.
+
+ <h3><a id="java" href="#java">6.4 Java Version</a></h3>
+
+ From a security perspective, we recommend you use the latest released version of JDK 1.8 as older freely available versions have disclosed security vulnerabilities.
+
+ LinkedIn is currently running JDK 1.8 u5 (looking to upgrade to a newer version) with the G1 collector. If you decide to use the G1 collector (the current default) and you are still on JDK 1.7, make sure you are on u51 or newer. LinkedIn tried out u21 in testing, but they had a number of problems with the GC implementation in that version.
+
+ LinkedIn's tuning looks like this:
+ <pre class="brush: text;">
+ -Xmx6g -Xms6g -XX:MetaspaceSize=96m -XX:+UseG1GC
+ -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:G1HeapRegionSize=16M
+ -XX:MinMetaspaceFreeRatio=50 -XX:MaxMetaspaceFreeRatio=80
+ </pre>
+
+ For reference, here are the stats on one of LinkedIn's busiest clusters (at peak):
+ <ul>
+ <li>60 brokers</li>
+ <li>50k partitions (replication factor 2)</li>
+ <li>800k messages/sec in</li>
+ <li>300 MB/sec inbound, 1 GB/sec+ outbound</li>
+ </ul>
+
+ The tuning looks fairly aggressive, but all of the brokers in that cluster have a 90% GC pause time of about 21ms, and they're doing less than 1 young GC per second.
+
+ <h3><a id="hwandos" href="#hwandos">6.5 Hardware and OS</a></h3>
+ We are using dual quad-core Intel Xeon machines with 24GB of memory.
+ <p>
+ You need sufficient memory to buffer active readers and writers. You can do a back-of-the-envelope estimate of memory needs by assuming you want to be able to buffer for 30 seconds and compute your memory need as write_throughput*30.
+ <p>
+ The disk throughput is important. We have 8x7200 rpm SATA drives. In general disk throughput is the performance bottleneck, and more disks is better. Depending on how you configure flush behavior you may or may not benefit from more expensive disks (if you force flush often then higher RPM SAS drives may be better).
+
+ <h4><a id="os" href="#os">OS</a></h4>
+ Kafka should run well on any unix system and has been tested on Linux and Solaris.
+ <p>
+ We have seen a few issues running on Windows and Windows is not currently a well supported platform though we would be happy to change that.
+ <p>
+ It is unlikely to require much OS-level tuning, but there are two potentially important OS-level configurations:
+ <ul>
+ <li>File descriptor limits: Kafka uses file descriptors for log segments and open connections. If a broker hosts many partitions, consider that the broker needs at least (number_of_partitions)*(partition_size/segment_size) to track all log segments in addition to the number of connections the broker makes. We recommend at least 100000 allowed file descriptors for the broker processes as a starting point.
+ <li>Max socket buffer size: can be increased to enable high-performance data transfer between data centers as <a href="http://www.psc.edu/index.php/networking/641-tcp-tune">described here</a>.
+ </ul>
+ <p>
+
+ <h4><a id="diskandfs" href="#diskandfs">Disks and Filesystem</a></h4>
+ We recommend using multiple drives to get good throughput and not sharing the same drives used for Kafka data with application logs or other OS filesystem activity to ensure good latency. You can either RAID these drives together into a single volume or format and mount each drive as its own directory. Since Kafka has replication the redundancy provided by RAID can also be provided at the application level. This choice has several tradeoffs.
+ <p>
+ If you configure multiple data directories partitions will be assigned round-robin to data directories. Each partition will be entirely in one of the data directories. If data is not well balanced among partitions this can lead to load imbalance between disks.
+ <p>
+ RAID can potentially do better at balancing load between disks (although it doesn't always seem to) because it balances load at a lower level. The primary downside of RAID is that it is usually a big performance hit for write throughput and reduces the available disk space.
+ <p>
+ Another potential benefit of RAID is the ability to tolerate disk failures. However our experience has been that rebuilding the RAID array is so I/O intensive that it effectively disables the server, so this does not provide much real availability improvement.
+
+ <h4><a id="appvsosflush" href="#appvsosflush">Application vs. OS Flush Management</a></h4>
+ Kafka always immediately writes all data to the filesystem and supports the ability to configure the flush policy that controls when data is forced out of the OS cache and onto disk using the flush. This flush policy can be controlled to force data to disk after a period of time or after a certain number of messages has been written. There are several choices in this configuration.
+ <p>
+ Kafka must eventually call fsync to know that data was flushed. When recovering from a crash for any log segment not known to be fsync'd Kafka will check the integrity of each message by checking its CRC and also rebuild the accompanying offset index file as part of the recovery process executed on startup.
+ <p>
+ Note that durability in Kafka does not require syncing data to disk, as a failed node will always recover from its replicas.
+ <p>
+ We recommend using the default flush settings which disable application fsync entirely. This means relying on the background flush done by the OS and Kafka's own background flush. This provides the best of all worlds for most uses: no knobs to tune, great throughput and latency, and full recovery guarantees. We generally feel that the guarantees provided by replication are stronger than sync to local disk, however the paranoid still may prefer having both and application level fsync policies are still supported.
+ <p>
+ The drawback of using application level flush settings is that it is less efficient in its disk usage pattern (it gives the OS less leeway to re-order writes) and it can introduce latency as fsync in most Linux filesystems blocks writes to the file whereas the background flushing does much more granular page-level locking.
+ <p>
+ In general you don't need to do any low-level tuning of the filesystem, but in the next few sections we will go over some of this in case it is useful.
+
+ <h4><a id="linuxflush" href="#linuxflush">Understanding Linux OS Flush Behavior</a></h4>
+
+ In Linux, data written to the filesystem is maintained in <a href="http://en.wikipedia.org/wiki/Page_cache">pagecache</a> until it must be written out to disk (due to an application-level fsync or the OS's own flush policy). The flushing of data is done by a set of background threads called pdflush (or in post 2.6.32 kernels "flusher threads").
+ <p>
+ Pdflush has a configurable policy that controls how much dirty data can be maintained in cache and for how long before it must be written back to disk.
+ This policy is described <a href="http://web.archive.org/web/20160518040713/http://www.westnet.com/~gsmith/content/linux-pdflush.htm">here</a>.
+ When Pdflush cannot keep up with the rate of data being written it will eventually cause the writing process to block incurring latency in the writes to slow down the accumulation of data.
+ <p>
+ You can see the current state of OS memory usage by doing
+ <pre class="brush: bash;"> > cat /proc/meminfo </pre>
+ The meaning of these values are described in the link above.
+ <p>
+ Using pagecache has several advantages over an in-process cache for storing data that will be written out to disk:
+ <ul>
+ <li>The I/O scheduler will batch together consecutive small writes into bigger physical writes which improves throughput.
+ <li>The I/O scheduler will attempt to re-sequence writes to minimize movement of the disk head which improves throughput.
+ <li>It automatically uses all the free memory on the machine
+ </ul>
+
+ <h4><a id="filesystems" href="#filesystems">Filesystem Selection</a></h4>
+ <p>Kafka uses regular files on disk, and as such it has no hard dependency on a specific filesystem. The two filesystems which have the most usage, however, are EXT4 and XFS. Historically, EXT4 has had more usage, but recent improvements to the XFS filesystem have shown it to have better performance characteristics for Kafka's workload with no compromise in stability.</p>
+ <p>Comparison testing was performed on a cluster with significant message loads, using a variety of filesystem creation and mount options. The primary metric in Kafka that was monitored was the "Request Local Time", indicating the amount of time append operations were taking. XFS resulted in much better local times (160ms vs. 250ms+ for the best EXT4 configuration), as well as lower average wait times. The XFS performance also showed less variability in disk performance.</p>
+ <h5><a id="generalfs" href="#generalfs">General Filesystem Notes</a></h5>
+ For any filesystem used for data directories, on Linux systems, the following options are recommended to be used at mount time:
+ <ul>
+ <li>noatime: This option disables updating of a file's atime (last access time) attribute when the file is read. This can eliminate a significant number of filesystem writes, especially in the case of bootstrapping consumers. Kafka does not rely on the atime attributes at all, so it is safe to disable this.</li>
+ </ul>
+ <h5><a id="xfs" href="#xfs">XFS Notes</a></h5>
+ The XFS filesystem has a significant amount of auto-tuning in place, so it does not require any change in the default settings, either at filesystem creation time or at mount. The only tuning parameters worth considering are:
+ <ul>
+ <li>largeio: This affects the preferred I/O size reported by the stat call. While this can allow for higher performance on larger disk writes, in practice it had minimal or no effect on performance.</li>
+ <li>nobarrier: For underlying devices that have battery-backed cache, this option can provide a little more performance by disabling periodic write flushes. However, if the underlying device is well-behaved, it will report to the filesystem that it does not require flushes, and this option will have no effect.</li>
+ </ul>
+ <h5><a id="ext4" href="#ext4">EXT4 Notes</a></h5>
+ EXT4 is a serviceable choice of filesystem for the Kafka data directories, however getting the most performance out of it will require adjusting several mount options. In addition, these options are generally unsafe in a failure scenario, and will result in much more data loss and corruption. For a single broker failure, this is not much of a concern as the disk can be wiped and the replicas rebuilt from the cluster. In a multiple-failure scenario, such as a power outage, this can mean underlying filesystem (and therefore data) corruption that is not easily recoverable. The following options can be adjusted:
+ <ul>
+ <li>data=writeback: Ext4 defaults to data=ordered which puts a strong order on some writes. Kafka does not require this ordering as it does very paranoid data recovery on all unflushed log. This setting removes the ordering constraint and seems to significantly reduce latency.
+ <li>Disabling journaling: Journaling is a tradeoff: it makes reboots faster after server crashes but it introduces a great deal of additional locking which adds variance to write performance. Those who don't care about reboot time and want to reduce a major source of write latency spikes can turn off journaling entirely.
+ <li>commit=num_secs: This tunes the frequency with which ext4 commits to its metadata journal. Setting this to a lower value reduces the loss of unflushed data during a crash. Setting this to a higher value will improve throughput.
+ <li>nobh: This setting controls additional ordering guarantees when using data=writeback mode. This should be safe with Kafka as we do not depend on write ordering and improves throughput and latency.
+ <li>delalloc: Delayed allocation means that the filesystem avoid allocating any blocks until the physical write occurs. This allows ext4 to allocate a large extent instead of smaller pages and helps ensure the data is written sequentially. This feature is great for throughput. It does seem to involve some locking in the filesystem which adds a bit of latency variance.
+ </ul>
+
+ <h3><a id="monitoring" href="#monitoring">6.6 Monitoring</a></h3>
+
+ Kafka uses Yammer Metrics for metrics reporting in the server and Scala clients. The Java clients use Kafka Metrics, a built-in metrics registry that minimizes transitive dependencies pulled into client applications. Both expose metrics via JMX and can be configured to report stats using pluggable stats reporters to hook up to your monitoring system.
+ <p>
+ The easiest way to see the available metrics is to fire up jconsole and point it at a running kafka client or server; this will allow browsing all metrics with JMX.
+ <p>
+ We do graphing and alerting on the following metrics:
+ <table class="data-table">
+ <tbody><tr>
+ <th>Description</th>
+ <th>Mbean name</th>
+ <th>Normal value</th>
+ </tr>
+ <tr>
+ <td>Message in rate</td>
+ <td>kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec</td>
+ <td></td>
+ </tr>
+ <tr>
+ <td>Byte in rate</td>
+ <td>kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec</td>
+ <td></td>
+ </tr>
+ <tr>
+ <td>Request rate</td>
+ <td>kafka.network:type=RequestMetrics,name=RequestsPerSec,request={Produce|FetchConsumer|FetchFollower}</td>
+ <td></td>
+ </tr>
+ <tr>
+ <td>Byte out rate</td>
+ <td>kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec</td>
+ <td></td>
+ </tr>
+ <tr>
+ <td>Log flush rate and time</td>
+ <td>kafka.log:type=LogFlushStats,name=LogFlushRateAndTimeMs</td>
+ <td></td>
+ </tr>
+ <tr>
+ <td># of under replicated partitions (|ISR| < |all replicas|)</td>
+ <td>kafka.server:type=ReplicaManager,name=UnderReplicatedPartitions</td>
+ <td>0</td>
+ </tr>
+ <tr>
+ <td># of under minIsr partitions (|ISR| < min.insync.replicas)</td>
+ <td>kafka.server:type=ReplicaManager,name=UnderMinIsrPartitionCount</td>
+ <td>0</td>
+ </tr>
+ <tr>
+ <td># of offline log directories</td>
+ <td>kafka.log:type=LogManager,name=OfflineLogDirectoryCount</td>
+ <td>0</td>
+ </tr>
+ <tr>
+ <td>Is controller active on broker</td>
+ <td>kafka.controller:type=KafkaController,name=ActiveControllerCount</td>
+ <td>only one broker in the cluster should have 1</td>
+ </tr>
+ <tr>
+ <td>Leader election rate</td>
+ <td>kafka.controller:type=ControllerStats,name=LeaderElectionRateAndTimeMs</td>
+ <td>non-zero when there are broker failures</td>
+ </tr>
+ <tr>
+ <td>Unclean leader election rate</td>
+ <td>kafka.controller:type=ControllerStats,name=UncleanLeaderElectionsPerSec</td>
+ <td>0</td>
+ </tr>
+ <tr>
+ <td>Partition counts</td>
+ <td>kafka.server:type=ReplicaManager,name=PartitionCount</td>
+ <td>mostly even across brokers</td>
+ </tr>
+ <tr>
+ <td>Leader replica counts</td>
+ <td>kafka.server:type=ReplicaManager,name=LeaderCount</td>
+ <td>mostly even across brokers</td>
+ </tr>
+ <tr>
+ <td>ISR shrink rate</td>
+ <td>kafka.server:type=ReplicaManager,name=IsrShrinksPerSec</td>
+ <td>If a broker goes down, ISR for some of the partitions will
+ shrink. When that broker is up again, ISR will be expanded
+ once the replicas are fully caught up. Other than that, the
+ expected value for both ISR shrink rate and expansion rate is 0. </td>
+ </tr>
+ <tr>
+ <td>ISR expansion rate</td>
+ <td>kafka.server:type=ReplicaManager,name=IsrExpandsPerSec</td>
+ <td>See above</td>
+ </tr>
+ <tr>
+ <td>Max lag in messages btw follower and leader replicas</td>
+ <td>kafka.server:type=ReplicaFetcherManager,name=MaxLag,clientId=Replica</td>
+ <td>lag should be proportional to the maximum batch size of a produce request.</td>
+ </tr>
+ <tr>
+ <td>Lag in messages per follower replica</td>
+ <td>kafka.server:type=FetcherLagMetrics,name=ConsumerLag,clientId=([-.\w]+),topic=([-.\w]+),partition=([0-9]+)</td>
+ <td>lag should be proportional to the maximum batch size of a produce request.</td>
+ </tr>
+ <tr>
+ <td>Requests waiting in the producer purgatory</td>
+ <td>kafka.server:type=DelayedOperationPurgatory,name=PurgatorySize,delayedOperation=Produce</td>
+ <td>non-zero if ack=-1 is used</td>
+ </tr>
+ <tr>
+ <td>Requests waiting in the fetch purgatory</td>
+ <td>kafka.server:type=DelayedOperationPurgatory,name=PurgatorySize,delayedOperation=Fetch</td>
+ <td>size depends on fetch.wait.max.ms in the consumer</td>
+ </tr>
+ <tr>
+ <td>Request total time</td>
+ <td>kafka.network:type=RequestMetrics,name=TotalTimeMs,request={Produce|FetchConsumer|FetchFollower}</td>
+ <td>broken into queue, local, remote and response send time</td>
+ </tr>
+ <tr>
+ <td>Time the request waits in the request queue</td>
+ <td>kafka.network:type=RequestMetrics,name=RequestQueueTimeMs,request={Produce|FetchConsumer|FetchFollower}</td>
+ <td></td>
+ </tr>
+ <tr>
+ <td>Time the request is processed at the leader</td>
+ <td>kafka.network:type=RequestMetrics,name=LocalTimeMs,request={Produce|FetchConsumer|FetchFollower}</td>
+ <td></td>
+ </tr>
+ <tr>
+ <td>Time the request waits for the follower</td>
+ <td>kafka.network:type=RequestMetrics,name=RemoteTimeMs,request={Produce|FetchConsumer|FetchFollower}</td>
+ <td>non-zero for produce requests when ack=-1</td>
+ </tr>
+ <tr>
+ <td>Time the request waits in the response queue</td>
+ <td>kafka.network:type=RequestMetrics,name=ResponseQueueTimeMs,request={Produce|FetchConsumer|FetchFollower}</td>
+ <td></td>
+ </tr>
+ <tr>
+ <td>Time to send the response</td>
+ <td>kafka.network:type=RequestMetrics,name=ResponseSendTimeMs,request={Produce|FetchConsumer|FetchFollower}</td>
+ <td></td>
+ </tr>
+ <tr>
+ <td>Number of messages the consumer lags behind the producer by. Published by the consumer, not broker.</td>
+ <td>
+ <p><em>Old consumer:</em> kafka.consumer:type=ConsumerFetcherManager,name=MaxLag,clientId=([-.\w]+)</p>
+ <p><em>New consumer:</em> kafka.consumer:type=consumer-fetch-manager-metrics,client-id={client-id} Attribute: records-lag-max</p>
+ </td>
+ <td></td>
+ </tr>
+ <tr>
+ <td>The average fraction of time the network processors are idle</td>
+ <td>kafka.network:type=SocketServer,name=NetworkProcessorAvgIdlePercent</td>
+ <td>between 0 and 1, ideally > 0.3</td>
+ </tr>
+ <tr>
+ <td>The average fraction of time the request handler threads are idle</td>
+ <td>kafka.server:type=KafkaRequestHandlerPool,name=RequestHandlerAvgIdlePercent</td>
+ <td>between 0 and 1, ideally > 0.3</td>
+ </tr>
+ <tr>
+ <td>Bandwidth quota metrics per (user, client-id), user or client-id</td>
+ <td>kafka.server:type={Produce|Fetch},user=([-.\w]+),client-id=([-.\w]+)</td>
+ <td>Two attributes. throttle-time indicates the amount of time in ms the client was throttled. Ideally = 0.
+ byte-rate indicates the data produce/consume rate of the client in bytes/sec.
+ For (user, client-id) quotas, both user and client-id are specified. If per-client-id quota is applied to the client, user is not specified. If per-user quota is applied, client-id is not specified.</td>
+ </tr>
+ <tr>
+ <td>Request quota metrics per (user, client-id), user or client-id</td>
+ <td>kafka.server:type=Request,user=([-.\w]+),client-id=([-.\w]+)</td>
+ <td>Two attributes. throttle-time indicates the amount of time in ms the client was throttled. Ideally = 0.
+ request-time indicates the percentage of time spent in broker network and I/O threads to process requests from client group.
+ For (user, client-id) quotas, both user and client-id are specified. If per-client-id quota is applied to the client, user is not specified. If per-user quota is applied, client-id is not specified.</td>
+ </tr>
+ <tr>
+ <td>Requests exempt from throttling</td>
+ <td>kafka.server:type=Request</td>
+ <td>exempt-throttle-time indicates the percentage of time spent in broker network and I/O threads to process requests
+ that are exempt from throttling.</td>
+ </tr>
+ </tbody></table>
+
+ <h4><a id="selector_monitoring" href="#selector_monitoring">Common monitoring metrics for producer/consumer/connect/streams</a></h4>
+
+ The following metrics are available on producer/consumer/connector/streams instances. For specific metrics, please see following sections.
+
+ <table class="data-table">
+ <tbody>
+ <tr>
+ <th>Metric/Attribute name</th>
+ <th>Description</th>
+ <th>Mbean name</th>
+ </tr>
+ <tr>
+ <td>connection-close-rate</td>
+ <td>Connections closed per second in the window.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>connection-creation-rate</td>
+ <td>New connections established per second in the window.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>network-io-rate</td>
+ <td>The average number of network operations (reads or writes) on all connections per second.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>outgoing-byte-rate</td>
+ <td>The average number of outgoing bytes sent per second to all servers.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>request-rate</td>
+ <td>The average number of requests sent per second.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>request-size-avg</td>
+ <td>The average size of all requests in the window.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>request-size-max</td>
+ <td>The maximum size of any request sent in the window.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>incoming-byte-rate</td>
+ <td>Bytes/second read off all sockets.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>response-rate</td>
+ <td>Responses received sent per second.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>select-rate</td>
+ <td>Number of times the I/O layer checked for new I/O to perform per second.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>io-wait-time-ns-avg</td>
+ <td>The average length of time the I/O thread spent waiting for a socket ready for reads or writes in nanoseconds.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>io-wait-ratio</td>
+ <td>The fraction of time the I/O thread spent waiting.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>io-time-ns-avg</td>
+ <td>The average length of time for I/O per select call in nanoseconds.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>io-ratio</td>
+ <td>The fraction of time the I/O thread spent doing I/O.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>connection-count</td>
+ <td>The current number of active connections.</td>
+ <td>kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ </tbody>
+ </table>
+
+ <h4><a id="common_node_monitoring" href="#common_node_monitoring">Common Per-broker metrics for producer/consumer/connect/streams</a></h4>
+
+ The following metrics are available on producer/consumer/connector/streams instances. For specific metrics, please see following sections.
+
+ <table class="data-table">
+ <tbody>
+ <tr>
+ <th>Metric/Attribute name</th>
+ <th>Description</th>
+ <th>Mbean name</th>
+ </tr>
+ <tr>
+ <td>outgoing-byte-rate</td>
+ <td>The average number of outgoing bytes sent per second for a node.</td>
+ <td>kafka.producer:type=[consumer|producer|connect]-node-metrics,client-id=([-.\w]+),node-id=([0-9]+)</td>
+ </tr>
+ <tr>
+ <td>request-rate</td>
+ <td>The average number of requests sent per second for a node.</td>
+ <td>kafka.producer:type=[consumer|producer|connect]-node-metrics,client-id=([-.\w]+),node-id=([0-9]+)</td>
+ </tr>
+ <tr>
+ <td>request-size-avg</td>
+ <td>The average size of all requests in the window for a node.</td>
+ <td>kafka.producer:type=[consumer|producer|connect]-node-metrics,client-id=([-.\w]+),node-id=([0-9]+)</td>
+ </tr>
+ <tr>
+ <td>request-size-max</td>
+ <td>The maximum size of any request sent in the window for a node.</td>
+ <td>kafka.producer:type=[consumer|producer|connect]-node-metrics,client-id=([-.\w]+),node-id=([0-9]+)</td>
+ </tr>
+ <tr>
+ <td>incoming-byte-rate</td>
+ <td>The average number of responses received per second for a node.</td>
+ <td>kafka.producer:type=[consumer|producer|connect]-node-metrics,client-id=([-.\w]+),node-id=([0-9]+)</td>
+ </tr>
+ <tr>
+ <td>request-latency-avg</td>
+ <td>The average request latency in ms for a node.</td>
+ <td>kafka.producer:type=[consumer|producer|connect]-node-metrics,client-id=([-.\w]+),node-id=([0-9]+)</td>
+ </tr>
+ <tr>
+ <td>request-latency-max</td>
+ <td>The maximum request latency in ms for a node.</td>
+ <td>kafka.producer:type=[consumer|producer|connect]-node-metrics,client-id=([-.\w]+),node-id=([0-9]+)</td>
+ </tr>
+ <tr>
+ <td>response-rate</td>
+ <td>Responses received sent per second for a node.</td>
+ <td>kafka.producer:type=[consumer|producer|connect]-node-metrics,client-id=([-.\w]+),node-id=([0-9]+)</td>
+ </tr>
+ </tbody>
+ </table>
+
+ <h4><a id="producer_monitoring" href="#producer_monitoring">Producer monitoring</a></h4>
+
+ The following metrics are available on producer instances.
+
+ <table class="data-table">
+ <tbody><tr>
+ <th>Metric/Attribute name</th>
+ <th>Description</th>
+ <th>Mbean name</th>
+ </tr>
+ <tr>
+ <td>waiting-threads</td>
+ <td>The number of user threads blocked waiting for buffer memory to enqueue their records.</td>
+ <td>kafka.producer:type=producer-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>buffer-total-bytes</td>
+ <td>The maximum amount of buffer memory the client can use (whether or not it is currently used).</td>
+ <td>kafka.producer:type=producer-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>buffer-available-bytes</td>
+ <td>The total amount of buffer memory that is not being used (either unallocated or in the free list).</td>
+ <td>kafka.producer:type=producer-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>bufferpool-wait-time</td>
+ <td>The fraction of time an appender waits for space allocation.</td>
+ <td>kafka.producer:type=producer-metrics,client-id=([-.\w]+)</td>
+ </tr>
+
+ </tbody></table>
+
+ <h5><a id="producer_sender_monitoring" href="#producer_sender_monitoring">Producer Sender Metrics</a></h5>
+
+ <!--#include virtual="generated/producer_metrics.html" -->
+
+
+ <h4><a id="new_consumer_monitoring" href="#new_consumer_monitoring">New consumer monitoring</a></h4>
+
+ The following metrics are available on new consumer instances.
+
+ <h5><a id="new_consumer_group_monitoring" href="#new_consumer_group_monitoring">Consumer Group Metrics</a></h5>
+ <table class="data-table">
+ <tbody>
+ <tr>
+ <th>Metric/Attribute name</th>
+ <th>Description</th>
+ <th>Mbean name</th>
+ </tr>
+ <tr>
+ <td>commit-latency-avg</td>
+ <td>The average time taken for a commit request</td>
+ <td>kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>commit-latency-max</td>
+ <td>The max time taken for a commit request</td>
+ <td>kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>commit-rate</td>
+ <td>The number of commit calls per second</td>
+ <td>kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>assigned-partitions</td>
+ <td>The number of partitions currently assigned to this consumer</td>
+ <td>kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>heartbeat-response-time-max</td>
+ <td>The max time taken to receive a response to a heartbeat request</td>
+ <td>kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>heartbeat-rate</td>
+ <td>The average number of heartbeats per second</td>
+ <td>kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>join-time-avg</td>
+ <td>The average time taken for a group rejoin</td>
+ <td>kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>join-time-max</td>
+ <td>The max time taken for a group rejoin</td>
+ <td>kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>join-rate</td>
+ <td>The number of group joins per second</td>
+ <td>kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>sync-time-avg</td>
+ <td>The average time taken for a group sync</td>
+ <td>kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>sync-time-max</td>
+ <td>The max time taken for a group sync</td>
+ <td>kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>sync-rate</td>
+ <td>The number of group syncs per second</td>
+ <td>kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>last-heartbeat-seconds-ago</td>
+ <td>The number of seconds since the last controller heartbeat</td>
+ <td>kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ </tbody>
+ </table>
+
+ <h5><a id="new_consumer_fetch_monitoring" href="#new_consumer_fetch_monitoring">Consumer Fetch Metrics</a></h5>
+
+ <!--#include virtual="generated/consumer_metrics.html" -->
+
+ <h4><a id="connect_monitoring" href="#connect_monitoring">Connect Monitoring</a></h4>
+
+ A Connect worker process contains all the producer and consumer metrics as well as metrics specific to Connect.
+ The worker process itself has a number of metrics, while each connector and task have additional metrics.
+
+ <!--#include virtual="generated/connect_metrics.html" -->
+
+ <h4><a id="kafka_streams_monitoring" href="#kafka_streams_monitoring">Streams Monitoring</a></h4>
+
+ A Kafka Streams instance contains all the producer and consumer metrics as well as additional metrics specific to streams.
+ By default Kafka Streams has metrics with two recording levels: debug and info. The debug level records all metrics, while
+ the info level records only the thread-level metrics.
+
+ <p>
+ Note that the metrics have a 3-layer hierarchy. At the top level there are per-thread metrics. Each thread has tasks, with their
+ own metrics. Each task has a number of processor nodes, with their own metrics. Each task also has a number of state stores
+ and record caches, all with their own metrics.
+ </p>
+
+ Use the following configuration option to specify which metrics
+ you want collected:
+
+<pre>metrics.recording.level="info"</pre>
+
+<h5><a id="kafka_streams_thread_monitoring" href="#kafka_streams_thread_monitoring">Thread Metrics</a></h5>
+All the following metrics have a recording level of ``info``:
+<table class="data-table">
+ <tbody>
+ <tr>
+ <th>Metric/Attribute name</th>
+ <th>Description</th>
+ <th>Mbean name</th>
+ </tr>
+ <tr>
+ <td>commit-latency-avg</td>
+ <td>The average execution time in ms for committing, across all running tasks of this thread.</td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>commit-latency-max</td>
+ <td>The maximum execution time in ms for committing across all running tasks of this thread.</td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>poll-latency-avg</td>
+ <td>The average execution time in ms for polling, across all running tasks of this thread.</td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>poll-latency-max</td>
+ <td>The maximum execution time in ms for polling across all running tasks of this thread.</td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>process-latency-avg</td>
+ <td>The average execution time in ms for processing, across all running tasks of this thread.</td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>process-latency-max</td>
+ <td>The maximum execution time in ms for processing across all running tasks of this thread.</td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>punctuate-latency-avg</td>
+ <td>The average execution time in ms for punctuating, across all running tasks of this thread.</td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>punctuate-latency-max</td>
+ <td>The maximum execution time in ms for punctuating across all running tasks of this thread.</td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>commit-rate</td>
+ <td>The average number of commits per second across all tasks.</td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>poll-rate</td>
+ <td>The average number of polls per second across all tasks.</td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>process-rate</td>
+ <td>The average number of process calls per second across all tasks.</td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>punctuate-rate</td>
+ <td>The average number of punctuates per second across all tasks.</td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>task-created-rate</td>
+ <td>The average number of newly created tasks per second.</td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>task-closed-rate</td>
+ <td>The average number of tasks closed per second.</td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>skipped-records-rate</td>
+ <td>The average number of skipped records per second. </td>
+ <td>kafka.streams:type=stream-metrics,client-id=([-.\w]+)</td>
+ </tr>
+ </tbody>
+</table>
+
+<h5><a id="kafka_streams_task_monitoring" href="#kafka_streams_task_monitoring">Task Metrics</a></h5>
+All the following metrics have a recording level of ``debug``:
+ <table class="data-table">
+ <tbody>
+ <tr>
+ <th>Metric/Attribute name</th>
+ <th>Description</th>
+ <th>Mbean name</th>
+ </tr>
+ <tr>
+ <td>commit-latency-avg</td>
+ <td>The average commit time in ns for this task. </td>
+ <td>kafka.streams:type=stream-task-metrics,client-id=([-.\w]+),task-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>commit-latency-max</td>
+ <td>The maximum commit time in ns for this task. </td>
+ <td>kafka.streams:type=stream-task-metrics,client-id=([-.\w]+),task-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>commit-rate</td>
+ <td>The average number of commit calls per second. </td>
+ <td>kafka.streams:type=stream-task-metrics,client-id=([-.\w]+),task-id=([-.\w]+)</td>
+ </tr>
+ </tbody>
+</table>
+
+ <h5><a id="kafka_streams_node_monitoring" href="#kafka_streams_node_monitoring">Processor Node Metrics</a></h5>
+All the following metrics have a recording level of ``debug``:
+ <table class="data-table">
+ <tbody>
+ <tr>
+ <th>Metric/Attribute name</th>
+ <th>Description</th>
+ <th>Mbean name</th>
+ </tr>
+ <tr>
+ <td>process-latency-avg</td>
+ <td>The average process execution time in ns. </td>
+ <td>kafka.streams:type=stream-processor-node-metrics,client-id=([-.\w]+),task-id=([-.\w]+),processor-node-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>process-latency-max</td>
+ <td>The maximum process execution time in ns. </td>
+ <td>kafka.streams:type=stream-processor-node-metrics,client-id=([-.\w]+),task-id=([-.\w]+),processor-node-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>punctuate-latency-avg</td>
+ <td>The average punctuate execution time in ns. </td>
+ <td>kafka.streams:type=stream-processor-node-metrics,client-id=([-.\w]+),task-id=([-.\w]+),processor-node-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>punctuate-latency-max</td>
+ <td>The maximum punctuate execution time in ns. </td>
+ <td>kafka.streams:type=stream-processor-node-metrics,client-id=([-.\w]+),task-id=([-.\w]+),processor-node-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>create-latency-avg</td>
+ <td>The average create execution time in ns. </td>
+ <td>kafka.streams:type=stream-processor-node-metrics,client-id=([-.\w]+),task-id=([-.\w]+),processor-node-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>create-latency-max</td>
+ <td>The maximum create execution time in ns. </td>
+ <td>kafka.streams:type=stream-processor-node-metrics,client-id=([-.\w]+),task-id=([-.\w]+),processor-node-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>destroy-latency-avg</td>
+ <td>The average destroy execution time in ns. </td>
+ <td>kafka.streams:type=stream-processor-node-metrics,client-id=([-.\w]+),task-id=([-.\w]+),processor-node-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>destroy-latency-max</td>
+ <td>The maximum destroy execution time in ns. </td>
+ <td>kafka.streams:type=stream-processor-node-metrics,client-id=([-.\w]+),task-id=([-.\w]+),processor-node-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>process-rate</td>
+ <td>The average number of process operations per second. </td>
+ <td>kafka.streams:type=stream-processor-node-metrics,client-id=([-.\w]+),task-id=([-.\w]+),processor-node-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>punctuate-rate</td>
+ <td>The average number of punctuate operations per second. </td>
+ <td>kafka.streams:type=stream-processor-node-metrics,client-id=([-.\w]+),task-id=([-.\w]+),processor-node-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>create-rate</td>
+ <td>The average number of create operations per second. </td>
+ <td>kafka.streams:type=stream-processor-node-metrics,client-id=([-.\w]+),task-id=([-.\w]+),processor-node-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>destroy-rate</td>
+ <td>The average number of destroy operations per second. </td>
+ <td>kafka.streams:type=stream-processor-node-metrics,client-id=([-.\w]+),task-id=([-.\w]+),processor-node-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>forward-rate</td>
+ <td>The average rate of records being forwarded downstream, from source nodes only, per second. </td>
+ <td>kafka.streams:type=stream-processor-node-metrics,client-id=([-.\w]+),task-id=([-.\w]+),processor-node-id=([-.\w]+)</td>
+ </tr>
+ </tbody>
+ </table>
+
+ <h5><a id="kafka_streams_store_monitoring" href="#kafka_streams_store_monitoring">State Store Metrics</a></h5>
+All the following metrics have a recording level of ``debug``:
+
+ <table class="data-table">
+ <tbody>
+ <tr>
+ <th>Metric/Attribute name</th>
+ <th>Description</th>
+ <th>Mbean name</th>
+ </tr>
+ <tr>
+ <td>put-latency-avg</td>
+ <td>The average put execution time in ns. </td>
+ <td>kafka.streams:type=stream-[store-type]-state-metrics,client-id=([-.\w]+),task-id=([-.\w]+),[store-type]-state-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>put-latency-max</td>
+ <td>The maximum put execution time in ns. </td>
+ <td>kafka.streams:type=stream-[store-type]-state-metrics,client-id=([-.\w]+),task-id=([-.\w]+),[store-type]-state-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>put-if-absent-latency-avg</td>
+ <td>The average put-if-absent execution time in ns. </td>
+ <td>kafka.streams:type=stream-[store-type]-state-metrics,client-id=([-.\w]+),task-id=([-.\w]+),[store-type]-state-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>put-if-absent-latency-max</td>
+ <td>The maximum put-if-absent execution time in ns. </td>
+ <td>kafka.streams:type=stream-[store-type]-state-metrics,client-id=([-.\w]+),task-id=([-.\w]+),[store-type]-state-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>get-latency-avg</td>
+ <td>The average get execution time in ns. </td>
+ <td>kafka.streams:type=stream-[store-type]-state-metrics,client-id=([-.\w]+),task-id=([-.\w]+),[store-type]-state-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>get-latency-max</td>
+ <td>The maximum get execution time in ns. </td>
+ <td>kafka.streams:type=stream-[store-type]-state-metrics,client-id=([-.\w]+),task-id=([-.\w]+),[store-type]-state-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>delete-latency-avg</td>
+ <td>The average delete execution time in ns. </td>
+ <td>kafka.streams:type=stream-[store-type]-state-metrics,client-id=([-.\w]+),task-id=([-.\w]+),[store-type]-state-id=([-.\w]+)</td>
+ </tr>
+ <tr>
+ <td>delete-latency-max</td>
+ <td>The maximum delete execution time in ns. </td>
+ <td>kafka.streams:type=stream-[store-type]-state-metrics,client-id=([-
<TRUNCATED>
[09/14] kafka-site git commit: Update site for 1.0.0 release
Posted by ij...@apache.org.
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/generated/streams_config.html
----------------------------------------------------------------------
diff --git a/100/generated/streams_config.html b/100/generated/streams_config.html
new file mode 100644
index 0000000..be6e3c1
--- /dev/null
+++ b/100/generated/streams_config.html
@@ -0,0 +1,86 @@
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>application.id</td><td>An identifier for the stream processing application. Must be unique within the Kafka cluster. It is used as 1) the default client-id prefix, 2) the group-id for membership management, 3) the changelog topic prefix.</td><td>string</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>bootstrap.servers</td><td>A list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping—this list only impacts the initial hosts used to discover the full set of servers. This list should be in the form <code>host1:port1,host2:port2,...</code>. Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down).</td><td>list</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>replication.factor</td><td>The replication factor for change log topics and repartition topics created by the stream processing application.</td><td>int</td><td>1</td><td></td><td>high</td></tr>
+<tr>
+<td>state.dir</td><td>Directory location for state store.</td><td>string</td><td>/tmp/kafka-streams</td><td></td><td>high</td></tr>
+<tr>
+<td>cache.max.bytes.buffering</td><td>Maximum number of memory bytes to be used for buffering across all threads</td><td>long</td><td>10485760</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>client.id</td><td>An ID prefix string used for the client IDs of internal consumer, producer and restore-consumer, with pattern '<client.id>-StreamThread-<threadSequenceNumber>-<consumer|producer|restore-consumer>'.</td><td>string</td><td>""</td><td></td><td>medium</td></tr>
+<tr>
+<td>default.deserialization.exception.handler</td><td>Exception handling class that implements the <code>org.apache.kafka.streams.errors.DeserializationExceptionHandler</code> interface.</td><td>class</td><td>org.apache.kafka.streams.errors.LogAndFailExceptionHandler</td><td></td><td>medium</td></tr>
+<tr>
+<td>default.key.serde</td><td> Default serializer / deserializer class for key that implements the <code>org.apache.kafka.common.serialization.Serde</code> interface.</td><td>class</td><td>org.apache.kafka.common.serialization.Serdes$ByteArraySerde</td><td></td><td>medium</td></tr>
+<tr>
+<td>default.timestamp.extractor</td><td>Default timestamp extractor class that implements the <code>org.apache.kafka.streams.processor.TimestampExtractor</code> interface.</td><td>class</td><td>org.apache.kafka.streams.processor.FailOnInvalidTimestamp</td><td></td><td>medium</td></tr>
+<tr>
+<td>default.value.serde</td><td>Default serializer / deserializer class for value that implements the <code>org.apache.kafka.common.serialization.Serde</code> interface.</td><td>class</td><td>org.apache.kafka.common.serialization.Serdes$ByteArraySerde</td><td></td><td>medium</td></tr>
+<tr>
+<td>num.standby.replicas</td><td>The number of standby replicas for each task.</td><td>int</td><td>0</td><td></td><td>medium</td></tr>
+<tr>
+<td>num.stream.threads</td><td>The number of threads to execute stream processing.</td><td>int</td><td>1</td><td></td><td>medium</td></tr>
+<tr>
+<td>processing.guarantee</td><td>The processing guarantee that should be used. Possible values are <code>at_least_once</code> (default) and <code>exactly_once</code>.</td><td>string</td><td>at_least_once</td><td>[at_least_once, exactly_once]</td><td>medium</td></tr>
+<tr>
+<td>security.protocol</td><td>Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.</td><td>string</td><td>PLAINTEXT</td><td></td><td>medium</td></tr>
+<tr>
+<td>application.server</td><td>A host:port pair pointing to an embedded user defined endpoint that can be used for discovering the locations of state stores within a single KafkaStreams application</td><td>string</td><td>""</td><td></td><td>low</td></tr>
+<tr>
+<td>buffered.records.per.partition</td><td>The maximum number of records to buffer per partition.</td><td>int</td><td>1000</td><td></td><td>low</td></tr>
+<tr>
+<td>commit.interval.ms</td><td>The frequency with which to save the position of the processor. (Note, if 'processing.guarantee' is set to 'exactly_once', the default value is 100, otherwise the default value is 30000.</td><td>long</td><td>30000</td><td></td><td>low</td></tr>
+<tr>
+<td>connections.max.idle.ms</td><td>Close idle connections after the number of milliseconds specified by this config.</td><td>long</td><td>540000</td><td></td><td>low</td></tr>
+<tr>
+<td>key.serde</td><td>Serializer / deserializer class for key that implements the <code>org.apache.kafka.common.serialization.Serde</code> interface. This config is deprecated, use <code>default.key.serde</code> instead</td><td>class</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>metadata.max.age.ms</td><td>The period of time in milliseconds after which we force a refresh of metadata even if we haven't seen any partition leadership changes to proactively discover any new brokers or partitions.</td><td>long</td><td>300000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>metric.reporters</td><td>A list of classes to use as metrics reporters. Implementing the <code>org.apache.kafka.common.metrics.MetricsReporter</code> interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.</td><td>list</td><td>""</td><td></td><td>low</td></tr>
+<tr>
+<td>metrics.num.samples</td><td>The number of samples maintained to compute metrics.</td><td>int</td><td>2</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>metrics.recording.level</td><td>The highest recording level for metrics.</td><td>string</td><td>INFO</td><td>[INFO, DEBUG]</td><td>low</td></tr>
+<tr>
+<td>metrics.sample.window.ms</td><td>The window of time a metrics sample is computed over.</td><td>long</td><td>30000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>partition.grouper</td><td>Partition grouper class that implements the <code>org.apache.kafka.streams.processor.PartitionGrouper</code> interface.</td><td>class</td><td>org.apache.kafka.streams.processor.DefaultPartitionGrouper</td><td></td><td>low</td></tr>
+<tr>
+<td>poll.ms</td><td>The amount of time in milliseconds to block waiting for input.</td><td>long</td><td>100</td><td></td><td>low</td></tr>
+<tr>
+<td>receive.buffer.bytes</td><td>The size of the TCP receive buffer (SO_RCVBUF) to use when reading data. If the value is -1, the OS default will be used.</td><td>int</td><td>32768</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>reconnect.backoff.max.ms</td><td>The maximum amount of time in milliseconds to wait when reconnecting to a broker that has repeatedly failed to connect. If provided, the backoff per host will increase exponentially for each consecutive connection failure, up to this maximum. After calculating the backoff increase, 20% random jitter is added to avoid connection storms.</td><td>long</td><td>1000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>reconnect.backoff.ms</td><td>The base amount of time to wait before attempting to reconnect to a given host. This avoids repeatedly connecting to a host in a tight loop. This backoff applies to all connection attempts by the client to a broker.</td><td>long</td><td>50</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>request.timeout.ms</td><td>The configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted.</td><td>int</td><td>40000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>retry.backoff.ms</td><td>The amount of time to wait before attempting to retry a failed request to a given topic partition. This avoids repeatedly sending requests in a tight loop under some failure scenarios.</td><td>long</td><td>100</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>rocksdb.config.setter</td><td>A Rocks DB config setter class or class name that implements the <code>org.apache.kafka.streams.state.RocksDBConfigSetter</code> interface</td><td>class</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>send.buffer.bytes</td><td>The size of the TCP send buffer (SO_SNDBUF) to use when sending data. If the value is -1, the OS default will be used.</td><td>int</td><td>131072</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>state.cleanup.delay.ms</td><td>The amount of time in milliseconds to wait before deleting state when a partition has migrated. Only state directories that have not been modified for at least state.cleanup.delay.ms will be removed</td><td>long</td><td>600000</td><td></td><td>low</td></tr>
+<tr>
+<td>timestamp.extractor</td><td>Timestamp extractor class that implements the <code>org.apache.kafka.streams.processor.TimestampExtractor</code> interface. This config is deprecated, use <code>default.timestamp.extractor</code> instead</td><td>class</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>value.serde</td><td>Serializer / deserializer class for value that implements the <code>org.apache.kafka.common.serialization.Serde</code> interface. This config is deprecated, use <code>default.value.serde</code> instead</td><td>class</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>windowstore.changelog.additional.retention.ms</td><td>Added to a windows maintainMs to ensure data is not deleted from the log prematurely. Allows for clock drift. Default is 1 day</td><td>long</td><td>86400000</td><td></td><td>low</td></tr>
+<tr>
+<td>zookeeper.connect</td><td>Zookeeper connect string for Kafka topics management. This config is deprecated and will be ignored as Streams API does not use Zookeeper anymore.</td><td>string</td><td>""</td><td></td><td>low</td></tr>
+</tbody></table>
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+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Server Default Property</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>cleanup.policy</td><td>A string that is either "delete" or "compact". This string designates the retention policy to use on old log segments. The default policy ("delete") will discard old segments when their retention time or size limit has been reached. The "compact" setting will enable <a href="#compaction">log compaction</a> on the topic.</td><td>list</td><td>delete</td><td>[compact, delete]</td><td>log.cleanup.policy</td><td>medium</td></tr>
+<tr>
+<td>compression.type</td><td>Specify the final compression type for a given topic. This configuration accepts the standard compression codecs ('gzip', 'snappy', lz4). It additionally accepts 'uncompressed' which is equivalent to no compression; and 'producer' which means retain the original compression codec set by the producer.</td><td>string</td><td>producer</td><td>[uncompressed, snappy, lz4, gzip, producer]</td><td>compression.type</td><td>medium</td></tr>
+<tr>
+<td>delete.retention.ms</td><td>The amount of time to retain delete tombstone markers for <a href="#compaction">log compacted</a> topics. This setting also gives a bound on the time in which a consumer must complete a read if they begin from offset 0 to ensure that they get a valid snapshot of the final stage (otherwise delete tombstones may be collected before they complete their scan).</td><td>long</td><td>86400000</td><td>[0,...]</td><td>log.cleaner.delete.retention.ms</td><td>medium</td></tr>
+<tr>
+<td>file.delete.delay.ms</td><td>The time to wait before deleting a file from the filesystem</td><td>long</td><td>60000</td><td>[0,...]</td><td>log.segment.delete.delay.ms</td><td>medium</td></tr>
+<tr>
+<td>flush.messages</td><td>This setting allows specifying an interval at which we will force an fsync of data written to the log. For example if this was set to 1 we would fsync after every message; if it were 5 we would fsync after every five messages. In general we recommend you not set this and use replication for durability and allow the operating system's background flush capabilities as it is more efficient. This setting can be overridden on a per-topic basis (see <a href="#topicconfigs">the per-topic configuration section</a>).</td><td>long</td><td>9223372036854775807</td><td>[0,...]</td><td>log.flush.interval.messages</td><td>medium</td></tr>
+<tr>
+<td>flush.ms</td><td>This setting allows specifying a time interval at which we will force an fsync of data written to the log. For example if this was set to 1000 we would fsync after 1000 ms had passed. In general we recommend you not set this and use replication for durability and allow the operating system's background flush capabilities as it is more efficient.</td><td>long</td><td>9223372036854775807</td><td>[0,...]</td><td>log.flush.interval.ms</td><td>medium</td></tr>
+<tr>
+<td>follower.replication.throttled.replicas</td><td>A list of replicas for which log replication should be throttled on the follower side. The list should describe a set of replicas in the form [PartitionId]:[BrokerId],[PartitionId]:[BrokerId]:... or alternatively the wildcard '*' can be used to throttle all replicas for this topic.</td><td>list</td><td>""</td><td>[partitionId],[brokerId]:[partitionId],[brokerId]:...</td><td>follower.replication.throttled.replicas</td><td>medium</td></tr>
+<tr>
+<td>index.interval.bytes</td><td>This setting controls how frequently Kafka adds an index entry to it's offset index. The default setting ensures that we index a message roughly every 4096 bytes. More indexing allows reads to jump closer to the exact position in the log but makes the index larger. You probably don't need to change this.</td><td>int</td><td>4096</td><td>[0,...]</td><td>log.index.interval.bytes</td><td>medium</td></tr>
+<tr>
+<td>leader.replication.throttled.replicas</td><td>A list of replicas for which log replication should be throttled on the leader side. The list should describe a set of replicas in the form [PartitionId]:[BrokerId],[PartitionId]:[BrokerId]:... or alternatively the wildcard '*' can be used to throttle all replicas for this topic.</td><td>list</td><td>""</td><td>[partitionId],[brokerId]:[partitionId],[brokerId]:...</td><td>leader.replication.throttled.replicas</td><td>medium</td></tr>
+<tr>
+<td>max.message.bytes</td><td><p>The largest record batch size allowed by Kafka. If this is increased and there are consumers older than 0.10.2, the consumers' fetch size must also be increased so that the they can fetch record batches this large.</p><p>In the latest message format version, records are always grouped into batches for efficiency. In previous message format versions, uncompressed records are not grouped into batches and this limit only applies to a single record in that case.</p></td><td>int</td><td>1000012</td><td>[0,...]</td><td>message.max.bytes</td><td>medium</td></tr>
+<tr>
+<td>message.format.version</td><td>Specify the message format version the broker will use to append messages to the logs. The value should be a valid ApiVersion. Some examples are: 0.8.2, 0.9.0.0, 0.10.0, check ApiVersion for more details. By setting a particular message format version, the user is certifying that all the existing messages on disk are smaller or equal than the specified version. Setting this value incorrectly will cause consumers with older versions to break as they will receive messages with a format that they don't understand.</td><td>string</td><td>1.0-IV0</td><td></td><td>log.message.format.version</td><td>medium</td></tr>
+<tr>
+<td>message.timestamp.difference.max.ms</td><td>The maximum difference allowed between the timestamp when a broker receives a message and the timestamp specified in the message. If message.timestamp.type=CreateTime, a message will be rejected if the difference in timestamp exceeds this threshold. This configuration is ignored if message.timestamp.type=LogAppendTime.</td><td>long</td><td>9223372036854775807</td><td>[0,...]</td><td>log.message.timestamp.difference.max.ms</td><td>medium</td></tr>
+<tr>
+<td>message.timestamp.type</td><td>Define whether the timestamp in the message is message create time or log append time. The value should be either `CreateTime` or `LogAppendTime`</td><td>string</td><td>CreateTime</td><td></td><td>log.message.timestamp.type</td><td>medium</td></tr>
+<tr>
+<td>min.cleanable.dirty.ratio</td><td>This configuration controls how frequently the log compactor will attempt to clean the log (assuming <a href="#compaction">log compaction</a> is enabled). By default we will avoid cleaning a log where more than 50% of the log has been compacted. This ratio bounds the maximum space wasted in the log by duplicates (at 50% at most 50% of the log could be duplicates). A higher ratio will mean fewer, more efficient cleanings but will mean more wasted space in the log.</td><td>double</td><td>0.5</td><td>[0,...,1]</td><td>log.cleaner.min.cleanable.ratio</td><td>medium</td></tr>
+<tr>
+<td>min.compaction.lag.ms</td><td>The minimum time a message will remain uncompacted in the log. Only applicable for logs that are being compacted.</td><td>long</td><td>0</td><td>[0,...]</td><td>log.cleaner.min.compaction.lag.ms</td><td>medium</td></tr>
+<tr>
+<td>min.insync.replicas</td><td>When a producer sets acks to "all" (or "-1"), this configuration specifies the minimum number of replicas that must acknowledge a write for the write to be considered successful. If this minimum cannot be met, then the producer will raise an exception (either NotEnoughReplicas or NotEnoughReplicasAfterAppend).<br>When used together, min.insync.replicas and acks allow you to enforce greater durability guarantees. A typical scenario would be to create a topic with a replication factor of 3, set min.insync.replicas to 2, and produce with acks of "all". This will ensure that the producer raises an exception if a majority of replicas do not receive a write.</td><td>int</td><td>1</td><td>[1,...]</td><td>min.insync.replicas</td><td>medium</td></tr>
+<tr>
+<td>preallocate</td><td>True if we should preallocate the file on disk when creating a new log segment.</td><td>boolean</td><td>false</td><td></td><td>log.preallocate</td><td>medium</td></tr>
+<tr>
+<td>retention.bytes</td><td>This configuration controls the maximum size a partition (which consists of log segments) can grow to before we will discard old log segments to free up space if we are using the "delete" retention policy. By default there is no size limit only a time limit. Since this limit is enforced at the partition level, multiply it by the number of partitions to compute the topic retention in bytes.</td><td>long</td><td>-1</td><td></td><td>log.retention.bytes</td><td>medium</td></tr>
+<tr>
+<td>retention.ms</td><td>This configuration controls the maximum time we will retain a log before we will discard old log segments to free up space if we are using the "delete" retention policy. This represents an SLA on how soon consumers must read their data.</td><td>long</td><td>604800000</td><td></td><td>log.retention.ms</td><td>medium</td></tr>
+<tr>
+<td>segment.bytes</td><td>This configuration controls the segment file size for the log. Retention and cleaning is always done a file at a time so a larger segment size means fewer files but less granular control over retention.</td><td>int</td><td>1073741824</td><td>[14,...]</td><td>log.segment.bytes</td><td>medium</td></tr>
+<tr>
+<td>segment.index.bytes</td><td>This configuration controls the size of the index that maps offsets to file positions. We preallocate this index file and shrink it only after log rolls. You generally should not need to change this setting.</td><td>int</td><td>10485760</td><td>[0,...]</td><td>log.index.size.max.bytes</td><td>medium</td></tr>
+<tr>
+<td>segment.jitter.ms</td><td>The maximum random jitter subtracted from the scheduled segment roll time to avoid thundering herds of segment rolling</td><td>long</td><td>0</td><td>[0,...]</td><td>log.roll.jitter.ms</td><td>medium</td></tr>
+<tr>
+<td>segment.ms</td><td>This configuration controls the period of time after which Kafka will force the log to roll even if the segment file isn't full to ensure that retention can delete or compact old data.</td><td>long</td><td>604800000</td><td>[0,...]</td><td>log.roll.ms</td><td>medium</td></tr>
+<tr>
+<td>unclean.leader.election.enable</td><td>Indicates whether to enable replicas not in the ISR set to be elected as leader as a last resort, even though doing so may result in data loss.</td><td>boolean</td><td>false</td><td></td><td>unclean.leader.election.enable</td><td>medium</td></tr>
+</tbody></table>
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<script id="implementation-template" type="text/x-handlebars-template">
+ <h3><a id="networklayer" href="#networklayer">5.1 Network Layer</a></h3>
+ <p>
+ The network layer is a fairly straight-forward NIO server, and will not be described in great detail. The sendfile implementation is done by giving the <code>MessageSet</code> interface a <code>writeTo</code> method. This allows the file-backed message set to use the more efficient <code>transferTo</code> implementation instead of an in-process buffered write. The threading model is a single acceptor thread and <i>N</i> processor threads which handle a fixed number of connections each. This design has been pretty thoroughly tested <a href="http://sna-projects.com/blog/2009/08/introducing-the-nio-socketserver-implementation">elsewhere</a> and found to be simple to implement and fast. The protocol is kept quite simple to allow for future implementation of clients in other languages.
+ </p>
+ <h3><a id="messages" href="#messages">5.2 Messages</a></h3>
+ <p>
+ Messages consist of a variable-length header, a variable length opaque key byte array and a variable length opaque value byte array. The format of the header is described in the following section.
+ Leaving the key and value opaque is the right decision: there is a great deal of progress being made on serialization libraries right now, and any particular choice is unlikely to be right for all uses. Needless to say a particular application using Kafka would likely mandate a particular serialization type as part of its usage. The <code>RecordBatch</code> interface is simply an iterator over messages with specialized methods for bulk reading and writing to an NIO <code>Channel</code>.</p>
+
+ <h3><a id="messageformat" href="#messageformat">5.3 Message Format</a></h3>
+ <p>
+ Messages (aka Records) are always written in batches. The technical term for a batch of messages is a record batch, and a record batch contains one or more records. In the degenerate case, we could have a record batch containing a single record.
+ Record batches and records have their own headers. The format of each is described below for Kafka version 0.11.0 and later (message format version v2, or magic=2). <a href="https://cwiki.apache.org/confluence/display/KAFKA/A+Guide+To+The+Kafka+Protocol#AGuideToTheKafkaProtocol-Messagesets">Click here</a> for details about message formats 0 and 1.</p>
+
+ <h4><a id="recordbatch" href="#recordbatch">5.3.1 Record Batch</a></h4>
+ <p> The following is the on-disk format of a RecordBatch. </p>
+ <p><pre class="brush: java;">
+ baseOffset: int64
+ batchLength: int32
+ partitionLeaderEpoch: int32
+ magic: int8 (current magic value is 2)
+ crc: int32
+ attributes: int16
+ bit 0~2:
+ 0: no compression
+ 1: gzip
+ 2: snappy
+ 3: lz4
+ bit 3: timestampType
+ bit 4: isTransactional (0 means not transactional)
+ bit 5: isControlBatch (0 means not a control batch)
+ bit 6~15: unused
+ lastOffsetDelta: int32
+ firstTimestamp: int64
+ maxTimestamp: int64
+ producerId: int64
+ producerEpoch: int16
+ baseSequence: int32
+ records: [Record]
+ </pre></p>
+ <p> Note that when compression is enabled, the compressed record data is serialized directly following the count of the number of records. </p>
+
+ <p>The CRC covers the data from the attributes to the end of the batch (i.e. all the bytes that follow the CRC). It is located after the magic byte, which
+ means that clients must parse the magic byte before deciding how to interpret the bytes between the batch length and the magic byte. The partition leader
+ epoch field is not included in the CRC computation to avoid the need to recompute the CRC when this field is assigned for every batch that is received by
+ the broker. The CRC-32C (Castagnoli) polynomial is used for the computation.</p>
+
+ <p>On compaction: unlike the older message formats, magic v2 and above preserves the first and last offset/sequence numbers from the original batch when the log is cleaned. This is required in order to be able to restore the
+ producer's state when the log is reloaded. If we did not retain the last sequence number, for example, then after a partition leader failure, the producer might see an OutOfSequence error. The base sequence number must
+ be preserved for duplicate checking (the broker checks incoming Produce requests for duplicates by verifying that the first and last sequence numbers of the incoming batch match the last from that producer). As a result,
+ it is possible to have empty batches in the log when all the records in the batch are cleaned but batch is still retained in order to preserve a producer's last sequence number. One oddity here is that the baseTimestamp
+ field is not preserved during compaction, so it will change if the first record in the batch is compacted away.</p>
+
+ <h5><a id="controlbatch" href="#controlbatch">5.3.1.1 Control Batches</a></h5>
+ <p>A control batch contains a single record called the control record. Control records should not be passed on to applications. Instead, they are used by consumers to filter out aborted transactional messages.</p>
+ <p> The key of a control record conforms to the following schema: </p>
+ <p><pre class="brush: java">
+ version: int16 (current version is 0)
+ type: int16 (0 indicates an abort marker, 1 indicates a commit)
+ </pre></p>
+ <p>The schema for the value of a control record is dependent on the type. The value is opaque to clients.</p>
+
+ <h4><a id="record" href="#record">5.3.2 Record</a></h4>
+ <p>Record level headers were introduced in Kafka 0.11.0. The on-disk format of a record with Headers is delineated below. </p>
+ <p><pre class="brush: java;">
+ length: varint
+ attributes: int8
+ bit 0~7: unused
+ timestampDelta: varint
+ offsetDelta: varint
+ keyLength: varint
+ key: byte[]
+ valueLen: varint
+ value: byte[]
+ Headers => [Header]
+ </pre></p>
+ <h5><a id="recordheader" href="#recordheader">5.4.2.1 Record Header</a></h5>
+ <p><pre class="brush: java;">
+ headerKeyLength: varint
+ headerKey: String
+ headerValueLength: varint
+ Value: byte[]
+ </pre></p>
+ <p>We use the the same varint encoding as Protobuf. More information on the latter can be found <a href="https://developers.google.com/protocol-buffers/docs/encoding#varints">here</a>. The count of headers in a record
+ is also encoded as a varint.</p>
+
+ <h3><a id="log" href="#log">5.4 Log</a></h3>
+ <p>
+ A log for a topic named "my_topic" with two partitions consists of two directories (namely <code>my_topic_0</code> and <code>my_topic_1</code>) populated with data files containing the messages for that topic. The format of the log files is a sequence of "log entries""; each log entry is a 4 byte integer <i>N</i> storing the message length which is followed by the <i>N</i> message bytes. Each message is uniquely identified by a 64-bit integer <i>offset</i> giving the byte position of the start of this message in the stream of all messages ever sent to that topic on that partition. The on-disk format of each message is given below. Each log file is named with the offset of the first message it contains. So the first file created will be 00000000000.kafka, and each additional file will have an integer name roughly <i>S</i> bytes from the previous file where <i>S</i> is the max log file size given in the configuration.
+ </p>
+ <p>
+ The exact binary format for records is versioned and maintained as a standard interface so record batches can be transferred between producer, broker, and client without recopying or conversion when desirable. The previous section included details about the on-disk format of records.</p>
+ </p>
+ <p>
+ The use of the message offset as the message id is unusual. Our original idea was to use a GUID generated by the producer, and maintain a mapping from GUID to offset on each broker. But since a consumer must maintain an ID for each server, the global uniqueness of the GUID provides no value. Furthermore, the complexity of maintaining the mapping from a random id to an offset requires a heavy weight index structure which must be synchronized with disk, essentially requiring a full persistent random-access data structure. Thus to simplify the lookup structure we decided to use a simple per-partition atomic counter which could be coupled with the partition id and node id to uniquely identify a message; this makes the lookup structure simpler, though multiple seeks per consumer request are still likely. However once we settled on a counter, the jump to directly using the offset seemed natural—both after all are monotonically increasing integers unique to a partition. Since the
offset is hidden from the consumer API this decision is ultimately an implementation detail and we went with the more efficient approach.
+ </p>
+ <img class="centered" src="/{{version}}/images/kafka_log.png">
+ <h4><a id="impl_writes" href="#impl_writes">Writes</a></h4>
+ <p>
+ The log allows serial appends which always go to the last file. This file is rolled over to a fresh file when it reaches a configurable size (say 1GB). The log takes two configuration parameters: <i>M</i>, which gives the number of messages to write before forcing the OS to flush the file to disk, and <i>S</i>, which gives a number of seconds after which a flush is forced. This gives a durability guarantee of losing at most <i>M</i> messages or <i>S</i> seconds of data in the event of a system crash.
+ </p>
+ <h4><a id="impl_reads" href="#impl_reads">Reads</a></h4>
+ <p>
+ Reads are done by giving the 64-bit logical offset of a message and an <i>S</i>-byte max chunk size. This will return an iterator over the messages contained in the <i>S</i>-byte buffer. <i>S</i> is intended to be larger than any single message, but in the event of an abnormally large message, the read can be retried multiple times, each time doubling the buffer size, until the message is read successfully. A maximum message and buffer size can be specified to make the server reject messages larger than some size, and to give a bound to the client on the maximum it needs to ever read to get a complete message. It is likely that the read buffer ends with a partial message, this is easily detected by the size delimiting.
+ </p>
+ <p>
+ The actual process of reading from an offset requires first locating the log segment file in which the data is stored, calculating the file-specific offset from the global offset value, and then reading from that file offset. The search is done as a simple binary search variation against an in-memory range maintained for each file.
+ </p>
+ <p>
+ The log provides the capability of getting the most recently written message to allow clients to start subscribing as of "right now". This is also useful in the case the consumer fails to consume its data within its SLA-specified number of days. In this case when the client attempts to consume a non-existent offset it is given an OutOfRangeException and can either reset itself or fail as appropriate to the use case.
+ </p>
+
+ <p> The following is the format of the results sent to the consumer.
+
+ <pre class="brush: text;">
+ MessageSetSend (fetch result)
+
+ total length : 4 bytes
+ error code : 2 bytes
+ message 1 : x bytes
+ ...
+ message n : x bytes
+ </pre>
+
+ <pre class="brush: text;">
+ MultiMessageSetSend (multiFetch result)
+
+ total length : 4 bytes
+ error code : 2 bytes
+ messageSetSend 1
+ ...
+ messageSetSend n
+ </pre>
+ <h4><a id="impl_deletes" href="#impl_deletes">Deletes</a></h4>
+ <p>
+ Data is deleted one log segment at a time. The log manager allows pluggable delete policies to choose which files are eligible for deletion. The current policy deletes any log with a modification time of more than <i>N</i> days ago, though a policy which retained the last <i>N</i> GB could also be useful. To avoid locking reads while still allowing deletes that modify the segment list we use a copy-on-write style segment list implementation that provides consistent views to allow a binary search to proceed on an immutable static snapshot view of the log segments while deletes are progressing.
+ </p>
+ <h4><a id="impl_guarantees" href="#impl_guarantees">Guarantees</a></h4>
+ <p>
+ The log provides a configuration parameter <i>M</i> which controls the maximum number of messages that are written before forcing a flush to disk. On startup a log recovery process is run that iterates over all messages in the newest log segment and verifies that each message entry is valid. A message entry is valid if the sum of its size and offset are less than the length of the file AND the CRC32 of the message payload matches the CRC stored with the message. In the event corruption is detected the log is truncated to the last valid offset.
+ </p>
+ <p>
+ Note that two kinds of corruption must be handled: truncation in which an unwritten block is lost due to a crash, and corruption in which a nonsense block is ADDED to the file. The reason for this is that in general the OS makes no guarantee of the write order between the file inode and the actual block data so in addition to losing written data the file can gain nonsense data if the inode is updated with a new size but a crash occurs before the block containing that data is written. The CRC detects this corner case, and prevents it from corrupting the log (though the unwritten messages are, of course, lost).
+ </p>
+
+ <h3><a id="distributionimpl" href="#distributionimpl">5.5 Distribution</a></h3>
+ <h4><a id="impl_offsettracking" href="#impl_offsettracking">Consumer Offset Tracking</a></h4>
+ <p>
+ The high-level consumer tracks the maximum offset it has consumed in each partition and periodically commits its offset vector so that it can resume from those offsets in the event of a restart. Kafka provides the option to store all the offsets for a given consumer group in a designated broker (for that group) called the <i>offset manager</i>. i.e., any consumer instance in that consumer group should send its offset commits and fetches to that offset manager (broker). The high-level consumer handles this automatically. If you use the simple consumer you will need to manage offsets manually. This is currently unsupported in the Java simple consumer which can only commit or fetch offsets in ZooKeeper. If you use the Scala simple consumer you can discover the offset manager and explicitly commit or fetch offsets to the offset manager. A consumer can look up its offset manager by issuing a GroupCoordinatorRequest to any Kafka broker and reading the GroupCoordinatorResponse which wi
ll contain the offset manager. The consumer can then proceed to commit or fetch offsets from the offsets manager broker. In case the offset manager moves, the consumer will need to rediscover the offset manager. If you wish to manage your offsets manually, you can take a look at these <a href="https://cwiki.apache.org/confluence/display/KAFKA/Committing+and+fetching+consumer+offsets+in+Kafka">code samples that explain how to issue OffsetCommitRequest and OffsetFetchRequest</a>.
+ </p>
+
+ <p>
+ When the offset manager receives an OffsetCommitRequest, it appends the request to a special <a href="#compaction">compacted</a> Kafka topic named <i>__consumer_offsets</i>. The offset manager sends a successful offset commit response to the consumer only after all the replicas of the offsets topic receive the offsets. In case the offsets fail to replicate within a configurable timeout, the offset commit will fail and the consumer may retry the commit after backing off. (This is done automatically by the high-level consumer.) The brokers periodically compact the offsets topic since it only needs to maintain the most recent offset commit per partition. The offset manager also caches the offsets in an in-memory table in order to serve offset fetches quickly.
+ </p>
+
+ <p>
+ When the offset manager receives an offset fetch request, it simply returns the last committed offset vector from the offsets cache. In case the offset manager was just started or if it just became the offset manager for a new set of consumer groups (by becoming a leader for a partition of the offsets topic), it may need to load the offsets topic partition into the cache. In this case, the offset fetch will fail with an OffsetsLoadInProgress exception and the consumer may retry the OffsetFetchRequest after backing off. (This is done automatically by the high-level consumer.)
+ </p>
+
+ <h5><a id="offsetmigration" href="#offsetmigration">Migrating offsets from ZooKeeper to Kafka</a></h5>
+ <p>
+ Kafka consumers in earlier releases store their offsets by default in ZooKeeper. It is possible to migrate these consumers to commit offsets into Kafka by following these steps:
+ <ol>
+ <li>Set <code>offsets.storage=kafka</code> and <code>dual.commit.enabled=true</code> in your consumer config.
+ </li>
+ <li>Do a rolling bounce of your consumers and then verify that your consumers are healthy.
+ </li>
+ <li>Set <code>dual.commit.enabled=false</code> in your consumer config.
+ </li>
+ <li>Do a rolling bounce of your consumers and then verify that your consumers are healthy.
+ </li>
+ </ol>
+ A roll-back (i.e., migrating from Kafka back to ZooKeeper) can also be performed using the above steps if you set <code>offsets.storage=zookeeper</code>.
+ </p>
+
+ <h4><a id="impl_zookeeper" href="#impl_zookeeper">ZooKeeper Directories</a></h4>
+ <p>
+ The following gives the ZooKeeper structures and algorithms used for co-ordination between consumers and brokers.
+ </p>
+
+ <h4><a id="impl_zknotation" href="#impl_zknotation">Notation</a></h4>
+ <p>
+ When an element in a path is denoted [xyz], that means that the value of xyz is not fixed and there is in fact a ZooKeeper znode for each possible value of xyz. For example /topics/[topic] would be a directory named /topics containing a sub-directory for each topic name. Numerical ranges are also given such as [0...5] to indicate the subdirectories 0, 1, 2, 3, 4. An arrow -> is used to indicate the contents of a znode. For example /hello -> world would indicate a znode /hello containing the value "world".
+ </p>
+
+ <h4><a id="impl_zkbroker" href="#impl_zkbroker">Broker Node Registry</a></h4>
+ <pre class="brush: json;">
+ /brokers/ids/[0...N] --> {"jmx_port":...,"timestamp":...,"endpoints":[...],"host":...,"version":...,"port":...} (ephemeral node)
+ </pre>
+ <p>
+ This is a list of all present broker nodes, each of which provides a unique logical broker id which identifies it to consumers (which must be given as part of its configuration). On startup, a broker node registers itself by creating a znode with the logical broker id under /brokers/ids. The purpose of the logical broker id is to allow a broker to be moved to a different physical machine without affecting consumers. An attempt to register a broker id that is already in use (say because two servers are configured with the same broker id) results in an error.
+ </p>
+ <p>
+ Since the broker registers itself in ZooKeeper using ephemeral znodes, this registration is dynamic and will disappear if the broker is shutdown or dies (thus notifying consumers it is no longer available).
+ </p>
+ <h4><a id="impl_zktopic" href="#impl_zktopic">Broker Topic Registry</a></h4>
+ <pre class="brush: json;">
+ /brokers/topics/[topic]/partitions/[0...N]/state --> {"controller_epoch":...,"leader":...,"version":...,"leader_epoch":...,"isr":[...]} (ephemeral node)
+ </pre>
+
+ <p>
+ Each broker registers itself under the topics it maintains and stores the number of partitions for that topic.
+ </p>
+
+ <h4><a id="impl_zkconsumers" href="#impl_zkconsumers">Consumers and Consumer Groups</a></h4>
+ <p>
+ Consumers of topics also register themselves in ZooKeeper, in order to coordinate with each other and balance the consumption of data. Consumers can also store their offsets in ZooKeeper by setting <code>offsets.storage=zookeeper</code>. However, this offset storage mechanism will be deprecated in a future release. Therefore, it is recommended to <a href="#offsetmigration">migrate offsets storage to Kafka</a>.
+ </p>
+
+ <p>
+ Multiple consumers can form a group and jointly consume a single topic. Each consumer in the same group is given a shared group_id.
+ For example if one consumer is your foobar process, which is run across three machines, then you might assign this group of consumers the id "foobar". This group id is provided in the configuration of the consumer, and is your way to tell the consumer which group it belongs to.
+ </p>
+
+ <p>
+ The consumers in a group divide up the partitions as fairly as possible, each partition is consumed by exactly one consumer in a consumer group.
+ </p>
+
+ <h4><a id="impl_zkconsumerid" href="#impl_zkconsumerid">Consumer Id Registry</a></h4>
+ <p>
+ In addition to the group_id which is shared by all consumers in a group, each consumer is given a transient, unique consumer_id (of the form hostname:uuid) for identification purposes. Consumer ids are registered in the following directory.
+ <pre class="brush: json;">
+ /consumers/[group_id]/ids/[consumer_id] --> {"version":...,"subscription":{...:...},"pattern":...,"timestamp":...} (ephemeral node)
+ </pre>
+ Each of the consumers in the group registers under its group and creates a znode with its consumer_id. The value of the znode contains a map of <topic, #streams>. This id is simply used to identify each of the consumers which is currently active within a group. This is an ephemeral node so it will disappear if the consumer process dies.
+ </p>
+
+ <h4><a id="impl_zkconsumeroffsets" href="#impl_zkconsumeroffsets">Consumer Offsets</a></h4>
+ <p>
+ Consumers track the maximum offset they have consumed in each partition. This value is stored in a ZooKeeper directory if <code>offsets.storage=zookeeper</code>.
+ </p>
+ <pre class="brush: json;">
+ /consumers/[group_id]/offsets/[topic]/[partition_id] --> offset_counter_value (persistent node)
+ </pre>
+
+ <h4><a id="impl_zkowner" href="#impl_zkowner">Partition Owner registry</a></h4>
+
+ <p>
+ Each broker partition is consumed by a single consumer within a given consumer group. The consumer must establish its ownership of a given partition before any consumption can begin. To establish its ownership, a consumer writes its own id in an ephemeral node under the particular broker partition it is claiming.
+ </p>
+
+ <pre class="brush: json;">
+ /consumers/[group_id]/owners/[topic]/[partition_id] --> consumer_node_id (ephemeral node)
+ </pre>
+
+ <h4><a id="impl_clusterid" href="#impl_clusterid">Cluster Id</a></h4>
+
+ <p>
+ The cluster id is a unique and immutable identifier assigned to a Kafka cluster. The cluster id can have a maximum of 22 characters and the allowed characters are defined by the regular expression [a-zA-Z0-9_\-]+, which corresponds to the characters used by the URL-safe Base64 variant with no padding. Conceptually, it is auto-generated when a cluster is started for the first time.
+ </p>
+ <p>
+ Implementation-wise, it is generated when a broker with version 0.10.1 or later is successfully started for the first time. The broker tries to get the cluster id from the <code>/cluster/id</code> znode during startup. If the znode does not exist, the broker generates a new cluster id and creates the znode with this cluster id.
+ </p>
+
+ <h4><a id="impl_brokerregistration" href="#impl_brokerregistration">Broker node registration</a></h4>
+
+ <p>
+ The broker nodes are basically independent, so they only publish information about what they have. When a broker joins, it registers itself under the broker node registry directory and writes information about its host name and port. The broker also register the list of existing topics and their logical partitions in the broker topic registry. New topics are registered dynamically when they are created on the broker.
+ </p>
+
+ <h4><a id="impl_consumerregistration" href="#impl_consumerregistration">Consumer registration algorithm</a></h4>
+
+ <p>
+ When a consumer starts, it does the following:
+ <ol>
+ <li> Register itself in the consumer id registry under its group.
+ </li>
+ <li> Register a watch on changes (new consumers joining or any existing consumers leaving) under the consumer id registry. (Each change triggers rebalancing among all consumers within the group to which the changed consumer belongs.)
+ </li>
+ <li> Register a watch on changes (new brokers joining or any existing brokers leaving) under the broker id registry. (Each change triggers rebalancing among all consumers in all consumer groups.) </li>
+ <li> If the consumer creates a message stream using a topic filter, it also registers a watch on changes (new topics being added) under the broker topic registry. (Each change will trigger re-evaluation of the available topics to determine which topics are allowed by the topic filter. A new allowed topic will trigger rebalancing among all consumers within the consumer group.)</li>
+ <li> Force itself to rebalance within in its consumer group.
+ </li>
+ </ol>
+ </p>
+
+ <h4><a id="impl_consumerrebalance" href="#impl_consumerrebalance">Consumer rebalancing algorithm</a></h4>
+ <p>
+ The consumer rebalancing algorithms allows all the consumers in a group to come into consensus on which consumer is consuming which partitions. Consumer rebalancing is triggered on each addition or removal of both broker nodes and other consumers within the same group. For a given topic and a given consumer group, broker partitions are divided evenly among consumers within the group. A partition is always consumed by a single consumer. This design simplifies the implementation. Had we allowed a partition to be concurrently consumed by multiple consumers, there would be contention on the partition and some kind of locking would be required. If there are more consumers than partitions, some consumers won't get any data at all. During rebalancing, we try to assign partitions to consumers in such a way that reduces the number of broker nodes each consumer has to connect to.
+ </p>
+ <p>
+ Each consumer does the following during rebalancing:
+ </p>
+ <pre class="brush: text;">
+ 1. For each topic T that C<sub>i</sub> subscribes to
+ 2. let P<sub>T</sub> be all partitions producing topic T
+ 3. let C<sub>G</sub> be all consumers in the same group as C<sub>i</sub> that consume topic T
+ 4. sort P<sub>T</sub> (so partitions on the same broker are clustered together)
+ 5. sort C<sub>G</sub>
+ 6. let i be the index position of C<sub>i</sub> in C<sub>G</sub> and let N = size(P<sub>T</sub>)/size(C<sub>G</sub>)
+ 7. assign partitions from i*N to (i+1)*N - 1 to consumer C<sub>i</sub>
+ 8. remove current entries owned by C<sub>i</sub> from the partition owner registry
+ 9. add newly assigned partitions to the partition owner registry
+ (we may need to re-try this until the original partition owner releases its ownership)
+ </pre>
+ <p>
+ When rebalancing is triggered at one consumer, rebalancing should be triggered in other consumers within the same group about the same time.
+ </p>
+</script>
+
+<div class="p-implementation"></div>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/introduction.html
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<script><!--#include virtual="js/templateData.js" --></script>
+
+<script id="introduction-template" type="text/x-handlebars-template">
+ <h3> Apache Kafka® is <i>a distributed streaming platform</i>. What exactly does that mean?</h3>
+ <p>We think of a streaming platform as having three key capabilities:</p>
+ <ol>
+ <li>It lets you publish and subscribe to streams of records. In this respect it is similar to a message queue or enterprise messaging system.
+ <li>It lets you store streams of records in a fault-tolerant way.
+ <li>It lets you process streams of records as they occur.
+ </ol>
+ <p>What is Kafka good for?</p>
+ <p>It gets used for two broad classes of application:</p>
+ <ol>
+ <li>Building real-time streaming data pipelines that reliably get data between systems or applications
+ <li>Building real-time streaming applications that transform or react to the streams of data
+ </ol>
+ <p>To understand how Kafka does these things, let's dive in and explore Kafka's capabilities from the bottom up.</p>
+ <p>First a few concepts:</p>
+ <ul>
+ <li>Kafka is run as a cluster on one or more servers.
+ <li>The Kafka cluster stores streams of <i>records</i> in categories called <i>topics</i>.
+ <li>Each record consists of a key, a value, and a timestamp.
+ </ul>
+ <p>Kafka has four core APIs:</p>
+ <div style="overflow: hidden;">
+ <ul style="float: left; width: 40%;">
+ <li>The <a href="/documentation.html#producerapi">Producer API</a> allows an application to publish a stream of records to one or more Kafka topics.
+ <li>The <a href="/documentation.html#consumerapi">Consumer API</a> allows an application to subscribe to one or more topics and process the stream of records produced to them.
+ <li>The <a href="/documentation/streams">Streams API</a> allows an application to act as a <i>stream processor</i>, consuming an input stream from one or more topics and producing an output stream to one or more output topics, effectively transforming the input streams to output streams.
+ <li>The <a href="/documentation.html#connect">Connector API</a> allows building and running reusable producers or consumers that connect Kafka topics to existing applications or data systems. For example, a connector to a relational database might capture every change to a table.
+ </ul>
+ <img src="/{{version}}/images/kafka-apis.png" style="float: right; width: 50%;">
+ </div>
+ <p>
+ In Kafka the communication between the clients and the servers is done with a simple, high-performance, language agnostic <a href="https://kafka.apache.org/protocol.html">TCP protocol</a>. This protocol is versioned and maintains backwards compatibility with older version. We provide a Java client for Kafka, but clients are available in <a href="https://cwiki.apache.org/confluence/display/KAFKA/Clients">many languages</a>.</p>
+
+ <h4><a id="intro_topics" href="#intro_topics">Topics and Logs</a></h4>
+ <p>Let's first dive into the core abstraction Kafka provides for a stream of records—the topic.</p>
+ <p>A topic is a category or feed name to which records are published. Topics in Kafka are always multi-subscriber; that is, a topic can have zero, one, or many consumers that subscribe to the data written to it.</p>
+ <p> For each topic, the Kafka cluster maintains a partitioned log that looks like this: </p>
+ <img class="centered" src="/{{version}}/images/log_anatomy.png">
+
+ <p> Each partition is an ordered, immutable sequence of records that is continually appended to—a structured commit log. The records in the partitions are each assigned a sequential id number called the <i>offset</i> that uniquely identifies each record within the partition.
+ </p>
+ <p>
+ The Kafka cluster retains all published records—whether or not they have been consumed—using a configurable retention period. For example, if the retention policy is set to two days, then for the two days after a record is published, it is available for consumption, after which it will be discarded to free up space. Kafka's performance is effectively constant with respect to data size so storing data for a long time is not a problem.
+ </p>
+ <img class="centered" src="/{{version}}/images/log_consumer.png" style="width:400px">
+ <p>
+ In fact, the only metadata retained on a per-consumer basis is the offset or position of that consumer in the log. This offset is controlled by the consumer: normally a consumer will advance its offset linearly as it reads records, but, in fact, since the position is controlled by the consumer it can consume records in any order it likes. For example a consumer can reset to an older offset to reprocess data from the past or skip ahead to the most recent record and start consuming from "now".
+ </p>
+ <p>
+ This combination of features means that Kafka consumers are very cheap—they can come and go without much impact on the cluster or on other consumers. For example, you can use our command line tools to "tail" the contents of any topic without changing what is consumed by any existing consumers.
+ </p>
+ <p>
+ The partitions in the log serve several purposes. First, they allow the log to scale beyond a size that will fit on a single server. Each individual partition must fit on the servers that host it, but a topic may have many partitions so it can handle an arbitrary amount of data. Second they act as the unit of parallelism—more on that in a bit.
+ </p>
+
+ <h4><a id="intro_distribution" href="#intro_distribution">Distribution</a></h4>
+
+ <p>
+ The partitions of the log are distributed over the servers in the Kafka cluster with each server handling data and requests for a share of the partitions. Each partition is replicated across a configurable number of servers for fault tolerance.
+ </p>
+ <p>
+ Each partition has one server which acts as the "leader" and zero or more servers which act as "followers". The leader handles all read and write requests for the partition while the followers passively replicate the leader. If the leader fails, one of the followers will automatically become the new leader. Each server acts as a leader for some of its partitions and a follower for others so load is well balanced within the cluster.
+ </p>
+
+ <h4><a id="intro_producers" href="#intro_producers">Producers</a></h4>
+ <p>
+ Producers publish data to the topics of their choice. The producer is responsible for choosing which record to assign to which partition within the topic. This can be done in a round-robin fashion simply to balance load or it can be done according to some semantic partition function (say based on some key in the record). More on the use of partitioning in a second!
+ </p>
+
+ <h4><a id="intro_consumers" href="#intro_consumers">Consumers</a></h4>
+
+ <p>
+ Consumers label themselves with a <i>consumer group</i> name, and each record published to a topic is delivered to one consumer instance within each subscribing consumer group. Consumer instances can be in separate processes or on separate machines.
+ </p>
+ <p>
+ If all the consumer instances have the same consumer group, then the records will effectively be load balanced over the consumer instances.</p>
+ <p>
+ If all the consumer instances have different consumer groups, then each record will be broadcast to all the consumer processes.
+ </p>
+ <img class="centered" src="/{{version}}/images/consumer-groups.png">
+ <p>
+ A two server Kafka cluster hosting four partitions (P0-P3) with two consumer groups. Consumer group A has two consumer instances and group B has four.
+ </p>
+
+ <p>
+ More commonly, however, we have found that topics have a small number of consumer groups, one for each "logical subscriber". Each group is composed of many consumer instances for scalability and fault tolerance. This is nothing more than publish-subscribe semantics where the subscriber is a cluster of consumers instead of a single process.
+ </p>
+ <p>
+ The way consumption is implemented in Kafka is by dividing up the partitions in the log over the consumer instances so that each instance is the exclusive consumer of a "fair share" of partitions at any point in time. This process of maintaining membership in the group is handled by the Kafka protocol dynamically. If new instances join the group they will take over some partitions from other members of the group; if an instance dies, its partitions will be distributed to the remaining instances.
+ </p>
+ <p>
+ Kafka only provides a total order over records <i>within</i> a partition, not between different partitions in a topic. Per-partition ordering combined with the ability to partition data by key is sufficient for most applications. However, if you require a total order over records this can be achieved with a topic that has only one partition, though this will mean only one consumer process per consumer group.
+ </p>
+ <h4><a id="intro_guarantees" href="#intro_guarantees">Guarantees</a></h4>
+ <p>
+ At a high-level Kafka gives the following guarantees:
+ </p>
+ <ul>
+ <li>Messages sent by a producer to a particular topic partition will be appended in the order they are sent. That is, if a record M1 is sent by the same producer as a record M2, and M1 is sent first, then M1 will have a lower offset than M2 and appear earlier in the log.
+ <li>A consumer instance sees records in the order they are stored in the log.
+ <li>For a topic with replication factor N, we will tolerate up to N-1 server failures without losing any records committed to the log.
+ </ul>
+ <p>
+ More details on these guarantees are given in the design section of the documentation.
+ </p>
+ <h4><a id="kafka_mq" href="#kafka_mq">Kafka as a Messaging System</a></h4>
+ <p>
+ How does Kafka's notion of streams compare to a traditional enterprise messaging system?
+ </p>
+ <p>
+ Messaging traditionally has two models: <a href="http://en.wikipedia.org/wiki/Message_queue">queuing</a> and <a href="http://en.wikipedia.org/wiki/Publish%E2%80%93subscribe_pattern">publish-subscribe</a>. In a queue, a pool of consumers may read from a server and each record goes to one of them; in publish-subscribe the record is broadcast to all consumers. Each of these two models has a strength and a weakness. The strength of queuing is that it allows you to divide up the processing of data over multiple consumer instances, which lets you scale your processing. Unfortunately, queues aren't multi-subscriber—once one process reads the data it's gone. Publish-subscribe allows you broadcast data to multiple processes, but has no way of scaling processing since every message goes to every subscriber.
+ </p>
+ <p>
+ The consumer group concept in Kafka generalizes these two concepts. As with a queue the consumer group allows you to divide up processing over a collection of processes (the members of the consumer group). As with publish-subscribe, Kafka allows you to broadcast messages to multiple consumer groups.
+ </p>
+ <p>
+ The advantage of Kafka's model is that every topic has both these properties—it can scale processing and is also multi-subscriber—there is no need to choose one or the other.
+ </p>
+ <p>
+ Kafka has stronger ordering guarantees than a traditional messaging system, too.
+ </p>
+ <p>
+ A traditional queue retains records in-order on the server, and if multiple consumers consume from the queue then the server hands out records in the order they are stored. However, although the server hands out records in order, the records are delivered asynchronously to consumers, so they may arrive out of order on different consumers. This effectively means the ordering of the records is lost in the presence of parallel consumption. Messaging systems often work around this by having a notion of "exclusive consumer" that allows only one process to consume from a queue, but of course this means that there is no parallelism in processing.
+ </p>
+ <p>
+ Kafka does it better. By having a notion of parallelism—the partition—within the topics, Kafka is able to provide both ordering guarantees and load balancing over a pool of consumer processes. This is achieved by assigning the partitions in the topic to the consumers in the consumer group so that each partition is consumed by exactly one consumer in the group. By doing this we ensure that the consumer is the only reader of that partition and consumes the data in order. Since there are many partitions this still balances the load over many consumer instances. Note however that there cannot be more consumer instances in a consumer group than partitions.
+ </p>
+
+ <h4 id="kafka_storage">Kafka as a Storage System</h4>
+
+ <p>
+ Any message queue that allows publishing messages decoupled from consuming them is effectively acting as a storage system for the in-flight messages. What is different about Kafka is that it is a very good storage system.
+ </p>
+ <p>
+ Data written to Kafka is written to disk and replicated for fault-tolerance. Kafka allows producers to wait on acknowledgement so that a write isn't considered complete until it is fully replicated and guaranteed to persist even if the server written to fails.
+ </p>
+ <p>
+ The disk structures Kafka uses scale well—Kafka will perform the same whether you have 50 KB or 50 TB of persistent data on the server.
+ </p>
+ <p>
+ As a result of taking storage seriously and allowing the clients to control their read position, you can think of Kafka as a kind of special purpose distributed filesystem dedicated to high-performance, low-latency commit log storage, replication, and propagation.
+ </p>
+ <p>
+ For details about the Kafka's commit log storage and replication design, please read <a href="https://kafka.apache.org/documentation/#design">this</a> page.
+ </p>
+ <h4>Kafka for Stream Processing</h4>
+ <p>
+ It isn't enough to just read, write, and store streams of data, the purpose is to enable real-time processing of streams.
+ </p>
+ <p>
+ In Kafka a stream processor is anything that takes continual streams of data from input topics, performs some processing on this input, and produces continual streams of data to output topics.
+ </p>
+ <p>
+ For example, a retail application might take in input streams of sales and shipments, and output a stream of reorders and price adjustments computed off this data.
+ </p>
+ <p>
+ It is possible to do simple processing directly using the producer and consumer APIs. However for more complex transformations Kafka provides a fully integrated <a href="/documentation/streams">Streams API</a>. This allows building applications that do non-trivial processing that compute aggregations off of streams or join streams together.
+ </p>
+ <p>
+ This facility helps solve the hard problems this type of application faces: handling out-of-order data, reprocessing input as code changes, performing stateful computations, etc.
+ </p>
+ <p>
+ The streams API builds on the core primitives Kafka provides: it uses the producer and consumer APIs for input, uses Kafka for stateful storage, and uses the same group mechanism for fault tolerance among the stream processor instances.
+ </p>
+ <h4>Putting the Pieces Together</h4>
+ <p>
+ This combination of messaging, storage, and stream processing may seem unusual but it is essential to Kafka's role as a streaming platform.
+ </p>
+ <p>
+ A distributed file system like HDFS allows storing static files for batch processing. Effectively a system like this allows storing and processing <i>historical</i> data from the past.
+ </p>
+ <p>
+ A traditional enterprise messaging system allows processing future messages that will arrive after you subscribe. Applications built in this way process future data as it arrives.
+ </p>
+ <p>
+ Kafka combines both of these capabilities, and the combination is critical both for Kafka usage as a platform for streaming applications as well as for streaming data pipelines.
+ </p>
+ <p>
+ By combining storage and low-latency subscriptions, streaming applications can treat both past and future data the same way. That is a single application can process historical, stored data but rather than ending when it reaches the last record it can keep processing as future data arrives. This is a generalized notion of stream processing that subsumes batch processing as well as message-driven applications.
+ </p>
+ <p>
+ Likewise for streaming data pipelines the combination of subscription to real-time events make it possible to use Kafka for very low-latency pipelines; but the ability to store data reliably make it possible to use it for critical data where the delivery of data must be guaranteed or for integration with offline systems that load data only periodically or may go down for extended periods of time for maintenance. The stream processing facilities make it possible to transform data as it arrives.
+ </p>
+ <p>
+ For more information on the guarantees, APIs, and capabilities Kafka provides see the rest of the <a href="/documentation.html">documentation</a>.
+ </p>
+</script>
+
+<div class="p-introduction"></div>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/js/templateData.js
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+/*
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to You under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+// Define variables for doc templates
+var context={
+ "version": "100",
+ "dotVersion": "1.0",
+ "fullDotVersion": "1.0.0",
+ "scalaVersion": "2.11"
+};
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/migration.html
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<!--#include virtual="../includes/_header.htm" -->
+<h2><a id="migration" href="#migration">Migrating from 0.7.x to 0.8</a></h2>
+
+0.8 is our first (and hopefully last) release with a non-backwards-compatible wire protocol, ZooKeeper layout, and on-disk data format. This was a chance for us to clean up a lot of cruft and start fresh. This means performing a no-downtime upgrade is more painful than normal—you cannot just swap in the new code in-place.
+
+<h3><a id="migration_steps" href="#migration_steps">Migration Steps</a></h3>
+
+<ol>
+ <li>Setup a new cluster running 0.8.
+ <li>Use the 0.7 to 0.8 <a href="tools.html">migration tool</a> to mirror data from the 0.7 cluster into the 0.8 cluster.
+ <li>When the 0.8 cluster is fully caught up, redeploy all data <i>consumers</i> running the 0.8 client and reading from the 0.8 cluster.
+ <li>Finally migrate all 0.7 producers to 0.8 client publishing data to the 0.8 cluster.
+ <li>Decommission the 0.7 cluster.
+ <li>Drink.
+</ol>
+
+<!--#include virtual="../includes/_footer.htm" -->
[12/14] kafka-site git commit: Update site for 1.0.0 release
Posted by ij...@apache.org.
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/generated/admin_client_config.html
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+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>bootstrap.servers</td><td>A list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping—this list only impacts the initial hosts used to discover the full set of servers. This list should be in the form <code>host1:port1,host2:port2,...</code>. Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down).</td><td>list</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.key.password</td><td>The password of the private key in the key store file. This is optional for client.</td><td>password</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.keystore.location</td><td>The location of the key store file. This is optional for client and can be used for two-way authentication for client.</td><td>string</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.keystore.password</td><td>The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured. </td><td>password</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.truststore.location</td><td>The location of the trust store file. </td><td>string</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.truststore.password</td><td>The password for the trust store file. If a password is not set access to the truststore is still available, but integrity checking is disabled.</td><td>password</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>client.id</td><td>An id string to pass to the server when making requests. The purpose of this is to be able to track the source of requests beyond just ip/port by allowing a logical application name to be included in server-side request logging.</td><td>string</td><td>""</td><td></td><td>medium</td></tr>
+<tr>
+<td>connections.max.idle.ms</td><td>Close idle connections after the number of milliseconds specified by this config.</td><td>long</td><td>300000</td><td></td><td>medium</td></tr>
+<tr>
+<td>receive.buffer.bytes</td><td>The size of the TCP receive buffer (SO_RCVBUF) to use when reading data. If the value is -1, the OS default will be used.</td><td>int</td><td>65536</td><td>[-1,...]</td><td>medium</td></tr>
+<tr>
+<td>request.timeout.ms</td><td>The configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted.</td><td>int</td><td>120000</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>sasl.jaas.config</td><td>JAAS login context parameters for SASL connections in the format used by JAAS configuration files. JAAS configuration file format is described <a href="http://docs.oracle.com/javase/8/docs/technotes/guides/security/jgss/tutorials/LoginConfigFile.html">here</a>. The format for the value is: '<loginModuleClass> <controlFlag> (<optionName>=<optionValue>)*;'</td><td>password</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.kerberos.service.name</td><td>The Kerberos principal name that Kafka runs as. This can be defined either in Kafka's JAAS config or in Kafka's config.</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.mechanism</td><td>SASL mechanism used for client connections. This may be any mechanism for which a security provider is available. GSSAPI is the default mechanism.</td><td>string</td><td>GSSAPI</td><td></td><td>medium</td></tr>
+<tr>
+<td>security.protocol</td><td>Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.</td><td>string</td><td>PLAINTEXT</td><td></td><td>medium</td></tr>
+<tr>
+<td>send.buffer.bytes</td><td>The size of the TCP send buffer (SO_SNDBUF) to use when sending data. If the value is -1, the OS default will be used.</td><td>int</td><td>131072</td><td>[-1,...]</td><td>medium</td></tr>
+<tr>
+<td>ssl.enabled.protocols</td><td>The list of protocols enabled for SSL connections.</td><td>list</td><td>TLSv1.2,TLSv1.1,TLSv1</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.keystore.type</td><td>The file format of the key store file. This is optional for client.</td><td>string</td><td>JKS</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.protocol</td><td>The SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.</td><td>string</td><td>TLS</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.provider</td><td>The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.truststore.type</td><td>The file format of the trust store file.</td><td>string</td><td>JKS</td><td></td><td>medium</td></tr>
+<tr>
+<td>metadata.max.age.ms</td><td>The period of time in milliseconds after which we force a refresh of metadata even if we haven't seen any partition leadership changes to proactively discover any new brokers or partitions.</td><td>long</td><td>300000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>metric.reporters</td><td>A list of classes to use as metrics reporters. Implementing the <code>org.apache.kafka.common.metrics.MetricsReporter</code> interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.</td><td>list</td><td>""</td><td></td><td>low</td></tr>
+<tr>
+<td>metrics.num.samples</td><td>The number of samples maintained to compute metrics.</td><td>int</td><td>2</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>metrics.recording.level</td><td>The highest recording level for metrics.</td><td>string</td><td>INFO</td><td>[INFO, DEBUG]</td><td>low</td></tr>
+<tr>
+<td>metrics.sample.window.ms</td><td>The window of time a metrics sample is computed over.</td><td>long</td><td>30000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>reconnect.backoff.max.ms</td><td>The maximum amount of time in milliseconds to wait when reconnecting to a broker that has repeatedly failed to connect. If provided, the backoff per host will increase exponentially for each consecutive connection failure, up to this maximum. After calculating the backoff increase, 20% random jitter is added to avoid connection storms.</td><td>long</td><td>1000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>reconnect.backoff.ms</td><td>The base amount of time to wait before attempting to reconnect to a given host. This avoids repeatedly connecting to a host in a tight loop. This backoff applies to all connection attempts by the client to a broker.</td><td>long</td><td>50</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>retries</td><td>The maximum number of times to retry a call before failing it.</td><td>int</td><td>5</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>retry.backoff.ms</td><td>The amount of time to wait before attempting to retry a failed request. This avoids repeatedly sending requests in a tight loop under some failure scenarios.</td><td>long</td><td>100</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.kinit.cmd</td><td>Kerberos kinit command path.</td><td>string</td><td>/usr/bin/kinit</td><td></td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.min.time.before.relogin</td><td>Login thread sleep time between refresh attempts.</td><td>long</td><td>60000</td><td></td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.ticket.renew.jitter</td><td>Percentage of random jitter added to the renewal time.</td><td>double</td><td>0.05</td><td></td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.ticket.renew.window.factor</td><td>Login thread will sleep until the specified window factor of time from last refresh to ticket's expiry has been reached, at which time it will try to renew the ticket.</td><td>double</td><td>0.8</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.cipher.suites</td><td>A list of cipher suites. This is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. By default all the available cipher suites are supported.</td><td>list</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.endpoint.identification.algorithm</td><td>The endpoint identification algorithm to validate server hostname using server certificate. </td><td>string</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.keymanager.algorithm</td><td>The algorithm used by key manager factory for SSL connections. Default value is the key manager factory algorithm configured for the Java Virtual Machine.</td><td>string</td><td>SunX509</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.secure.random.implementation</td><td>The SecureRandom PRNG implementation to use for SSL cryptography operations. </td><td>string</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.trustmanager.algorithm</td><td>The algorithm used by trust manager factory for SSL connections. Default value is the trust manager factory algorithm configured for the Java Virtual Machine.</td><td>string</td><td>PKIX</td><td></td><td>low</td></tr>
+</tbody></table>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/generated/connect_config.html
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diff --git a/100/generated/connect_config.html b/100/generated/connect_config.html
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--- /dev/null
+++ b/100/generated/connect_config.html
@@ -0,0 +1,145 @@
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>config.storage.topic</td><td>The name of the Kafka topic where connector configurations are stored</td><td>string</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>group.id</td><td>A unique string that identifies the Connect cluster group this worker belongs to.</td><td>string</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>key.converter</td><td>Converter class used to convert between Kafka Connect format and the serialized form that is written to Kafka. This controls the format of the keys in messages written to or read from Kafka, and since this is independent of connectors it allows any connector to work with any serialization format. Examples of common formats include JSON and Avro.</td><td>class</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>offset.storage.topic</td><td>The name of the Kafka topic where connector offsets are stored</td><td>string</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>status.storage.topic</td><td>The name of the Kafka topic where connector and task status are stored</td><td>string</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>value.converter</td><td>Converter class used to convert between Kafka Connect format and the serialized form that is written to Kafka. This controls the format of the values in messages written to or read from Kafka, and since this is independent of connectors it allows any connector to work with any serialization format. Examples of common formats include JSON and Avro.</td><td>class</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>internal.key.converter</td><td>Converter class used to convert between Kafka Connect format and the serialized form that is written to Kafka. This controls the format of the keys in messages written to or read from Kafka, and since this is independent of connectors it allows any connector to work with any serialization format. Examples of common formats include JSON and Avro. This setting controls the format used for internal bookkeeping data used by the framework, such as configs and offsets, so users can typically use any functioning Converter implementation.</td><td>class</td><td></td><td></td><td>low</td></tr>
+<tr>
+<td>internal.value.converter</td><td>Converter class used to convert between Kafka Connect format and the serialized form that is written to Kafka. This controls the format of the values in messages written to or read from Kafka, and since this is independent of connectors it allows any connector to work with any serialization format. Examples of common formats include JSON and Avro. This setting controls the format used for internal bookkeeping data used by the framework, such as configs and offsets, so users can typically use any functioning Converter implementation.</td><td>class</td><td></td><td></td><td>low</td></tr>
+<tr>
+<td>bootstrap.servers</td><td>A list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping—this list only impacts the initial hosts used to discover the full set of servers. This list should be in the form <code>host1:port1,host2:port2,...</code>. Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down).</td><td>list</td><td>localhost:9092</td><td></td><td>high</td></tr>
+<tr>
+<td>heartbeat.interval.ms</td><td>The expected time between heartbeats to the group coordinator when using Kafka's group management facilities. Heartbeats are used to ensure that the worker's session stays active and to facilitate rebalancing when new members join or leave the group. The value must be set lower than <code>session.timeout.ms</code>, but typically should be set no higher than 1/3 of that value. It can be adjusted even lower to control the expected time for normal rebalances.</td><td>int</td><td>3000</td><td></td><td>high</td></tr>
+<tr>
+<td>rebalance.timeout.ms</td><td>The maximum allowed time for each worker to join the group once a rebalance has begun. This is basically a limit on the amount of time needed for all tasks to flush any pending data and commit offsets. If the timeout is exceeded, then the worker will be removed from the group, which will cause offset commit failures.</td><td>int</td><td>60000</td><td></td><td>high</td></tr>
+<tr>
+<td>session.timeout.ms</td><td>The timeout used to detect worker failures. The worker sends periodic heartbeats to indicate its liveness to the broker. If no heartbeats are received by the broker before the expiration of this session timeout, then the broker will remove the worker from the group and initiate a rebalance. Note that the value must be in the allowable range as configured in the broker configuration by <code>group.min.session.timeout.ms</code> and <code>group.max.session.timeout.ms</code>.</td><td>int</td><td>10000</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.key.password</td><td>The password of the private key in the key store file. This is optional for client.</td><td>password</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.keystore.location</td><td>The location of the key store file. This is optional for client and can be used for two-way authentication for client.</td><td>string</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.keystore.password</td><td>The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured. </td><td>password</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.truststore.location</td><td>The location of the trust store file. </td><td>string</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.truststore.password</td><td>The password for the trust store file. If a password is not set access to the truststore is still available, but integrity checking is disabled.</td><td>password</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>connections.max.idle.ms</td><td>Close idle connections after the number of milliseconds specified by this config.</td><td>long</td><td>540000</td><td></td><td>medium</td></tr>
+<tr>
+<td>receive.buffer.bytes</td><td>The size of the TCP receive buffer (SO_RCVBUF) to use when reading data. If the value is -1, the OS default will be used.</td><td>int</td><td>32768</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>request.timeout.ms</td><td>The configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted.</td><td>int</td><td>40000</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>sasl.jaas.config</td><td>JAAS login context parameters for SASL connections in the format used by JAAS configuration files. JAAS configuration file format is described <a href="http://docs.oracle.com/javase/8/docs/technotes/guides/security/jgss/tutorials/LoginConfigFile.html">here</a>. The format for the value is: '<loginModuleClass> <controlFlag> (<optionName>=<optionValue>)*;'</td><td>password</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.kerberos.service.name</td><td>The Kerberos principal name that Kafka runs as. This can be defined either in Kafka's JAAS config or in Kafka's config.</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.mechanism</td><td>SASL mechanism used for client connections. This may be any mechanism for which a security provider is available. GSSAPI is the default mechanism.</td><td>string</td><td>GSSAPI</td><td></td><td>medium</td></tr>
+<tr>
+<td>security.protocol</td><td>Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.</td><td>string</td><td>PLAINTEXT</td><td></td><td>medium</td></tr>
+<tr>
+<td>send.buffer.bytes</td><td>The size of the TCP send buffer (SO_SNDBUF) to use when sending data. If the value is -1, the OS default will be used.</td><td>int</td><td>131072</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>ssl.enabled.protocols</td><td>The list of protocols enabled for SSL connections.</td><td>list</td><td>TLSv1.2,TLSv1.1,TLSv1</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.keystore.type</td><td>The file format of the key store file. This is optional for client.</td><td>string</td><td>JKS</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.protocol</td><td>The SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.</td><td>string</td><td>TLS</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.provider</td><td>The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.truststore.type</td><td>The file format of the trust store file.</td><td>string</td><td>JKS</td><td></td><td>medium</td></tr>
+<tr>
+<td>worker.sync.timeout.ms</td><td>When the worker is out of sync with other workers and needs to resynchronize configurations, wait up to this amount of time before giving up, leaving the group, and waiting a backoff period before rejoining.</td><td>int</td><td>3000</td><td></td><td>medium</td></tr>
+<tr>
+<td>worker.unsync.backoff.ms</td><td>When the worker is out of sync with other workers and fails to catch up within worker.sync.timeout.ms, leave the Connect cluster for this long before rejoining.</td><td>int</td><td>300000</td><td></td><td>medium</td></tr>
+<tr>
+<td>access.control.allow.methods</td><td>Sets the methods supported for cross origin requests by setting the Access-Control-Allow-Methods header. The default value of the Access-Control-Allow-Methods header allows cross origin requests for GET, POST and HEAD.</td><td>string</td><td>""</td><td></td><td>low</td></tr>
+<tr>
+<td>access.control.allow.origin</td><td>Value to set the Access-Control-Allow-Origin header to for REST API requests.To enable cross origin access, set this to the domain of the application that should be permitted to access the API, or '*' to allow access from any domain. The default value only allows access from the domain of the REST API.</td><td>string</td><td>""</td><td></td><td>low</td></tr>
+<tr>
+<td>client.id</td><td>An id string to pass to the server when making requests. The purpose of this is to be able to track the source of requests beyond just ip/port by allowing a logical application name to be included in server-side request logging.</td><td>string</td><td>""</td><td></td><td>low</td></tr>
+<tr>
+<td>config.storage.replication.factor</td><td>Replication factor used when creating the configuration storage topic</td><td>short</td><td>3</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>metadata.max.age.ms</td><td>The period of time in milliseconds after which we force a refresh of metadata even if we haven't seen any partition leadership changes to proactively discover any new brokers or partitions.</td><td>long</td><td>300000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>metric.reporters</td><td>A list of classes to use as metrics reporters. Implementing the <code>org.apache.kafka.common.metrics.MetricsReporter</code> interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.</td><td>list</td><td>""</td><td></td><td>low</td></tr>
+<tr>
+<td>metrics.num.samples</td><td>The number of samples maintained to compute metrics.</td><td>int</td><td>2</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>metrics.recording.level</td><td>The highest recording level for metrics.</td><td>string</td><td>INFO</td><td>[INFO, DEBUG]</td><td>low</td></tr>
+<tr>
+<td>metrics.sample.window.ms</td><td>The window of time a metrics sample is computed over.</td><td>long</td><td>30000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>offset.flush.interval.ms</td><td>Interval at which to try committing offsets for tasks.</td><td>long</td><td>60000</td><td></td><td>low</td></tr>
+<tr>
+<td>offset.flush.timeout.ms</td><td>Maximum number of milliseconds to wait for records to flush and partition offset data to be committed to offset storage before cancelling the process and restoring the offset data to be committed in a future attempt.</td><td>long</td><td>5000</td><td></td><td>low</td></tr>
+<tr>
+<td>offset.storage.partitions</td><td>The number of partitions used when creating the offset storage topic</td><td>int</td><td>25</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>offset.storage.replication.factor</td><td>Replication factor used when creating the offset storage topic</td><td>short</td><td>3</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>plugin.path</td><td>List of paths separated by commas (,) that contain plugins (connectors, converters, transformations). The list should consist of top level directories that include any combination of:
+a) directories immediately containing jars with plugins and their dependencies
+b) uber-jars with plugins and their dependencies
+c) directories immediately containing the package directory structure of classes of plugins and their dependencies
+Note: symlinks will be followed to discover dependencies or plugins.
+Examples: plugin.path=/usr/local/share/java,/usr/local/share/kafka/plugins,/opt/connectors</td><td>list</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>reconnect.backoff.max.ms</td><td>The maximum amount of time in milliseconds to wait when reconnecting to a broker that has repeatedly failed to connect. If provided, the backoff per host will increase exponentially for each consecutive connection failure, up to this maximum. After calculating the backoff increase, 20% random jitter is added to avoid connection storms.</td><td>long</td><td>1000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>reconnect.backoff.ms</td><td>The base amount of time to wait before attempting to reconnect to a given host. This avoids repeatedly connecting to a host in a tight loop. This backoff applies to all connection attempts by the client to a broker.</td><td>long</td><td>50</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>rest.advertised.host.name</td><td>If this is set, this is the hostname that will be given out to other workers to connect to.</td><td>string</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>rest.advertised.port</td><td>If this is set, this is the port that will be given out to other workers to connect to.</td><td>int</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>rest.host.name</td><td>Hostname for the REST API. If this is set, it will only bind to this interface.</td><td>string</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>rest.port</td><td>Port for the REST API to listen on.</td><td>int</td><td>8083</td><td></td><td>low</td></tr>
+<tr>
+<td>retry.backoff.ms</td><td>The amount of time to wait before attempting to retry a failed request to a given topic partition. This avoids repeatedly sending requests in a tight loop under some failure scenarios.</td><td>long</td><td>100</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.kinit.cmd</td><td>Kerberos kinit command path.</td><td>string</td><td>/usr/bin/kinit</td><td></td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.min.time.before.relogin</td><td>Login thread sleep time between refresh attempts.</td><td>long</td><td>60000</td><td></td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.ticket.renew.jitter</td><td>Percentage of random jitter added to the renewal time.</td><td>double</td><td>0.05</td><td></td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.ticket.renew.window.factor</td><td>Login thread will sleep until the specified window factor of time from last refresh to ticket's expiry has been reached, at which time it will try to renew the ticket.</td><td>double</td><td>0.8</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.cipher.suites</td><td>A list of cipher suites. This is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. By default all the available cipher suites are supported.</td><td>list</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.endpoint.identification.algorithm</td><td>The endpoint identification algorithm to validate server hostname using server certificate. </td><td>string</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.keymanager.algorithm</td><td>The algorithm used by key manager factory for SSL connections. Default value is the key manager factory algorithm configured for the Java Virtual Machine.</td><td>string</td><td>SunX509</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.secure.random.implementation</td><td>The SecureRandom PRNG implementation to use for SSL cryptography operations. </td><td>string</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.trustmanager.algorithm</td><td>The algorithm used by trust manager factory for SSL connections. Default value is the trust manager factory algorithm configured for the Java Virtual Machine.</td><td>string</td><td>PKIX</td><td></td><td>low</td></tr>
+<tr>
+<td>status.storage.partitions</td><td>The number of partitions used when creating the status storage topic</td><td>int</td><td>5</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>status.storage.replication.factor</td><td>Replication factor used when creating the status storage topic</td><td>short</td><td>3</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>task.shutdown.graceful.timeout.ms</td><td>Amount of time to wait for tasks to shutdown gracefully. This is the total amount of time, not per task. All task have shutdown triggered, then they are waited on sequentially.</td><td>long</td><td>5000</td><td></td><td>low</td></tr>
+</tbody></table>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/generated/connect_metrics.html
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diff --git a/100/generated/connect_metrics.html b/100/generated/connect_metrics.html
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@@ -0,0 +1,158 @@
+<table class="data-table"><tbody>
+<tr>
+<td colspan=3 class="mbeanName" style="background-color:#ccc; font-weight: bold;">kafka.connect:type=connect-worker-metrics</td></tr>
+<tr>
+<th style="width: 90px"></th>
+<th>Attribute name</th>
+<th>Description</th>
+</tr>
+<tr>
+<td></td><td>connector-count</td><td>The number of connectors run in this worker.</td></tr>
+<tr>
+<td></td><td>connector-startup-attempts-total</td><td>The total number of connector startups that this worker has attempted.</td></tr>
+<tr>
+<td></td><td>connector-startup-failure-percentage</td><td>The average percentage of this worker's connectors starts that failed.</td></tr>
+<tr>
+<td></td><td>connector-startup-failure-total</td><td>The total number of connector starts that failed.</td></tr>
+<tr>
+<td></td><td>connector-startup-success-percentage</td><td>The average percentage of this worker's connectors starts that succeeded.</td></tr>
+<tr>
+<td></td><td>connector-startup-success-total</td><td>The total number of connector starts that succeeded.</td></tr>
+<tr>
+<td></td><td>task-count</td><td>The number of tasks run in this worker.</td></tr>
+<tr>
+<td></td><td>task-startup-attempts-total</td><td>The total number of task startups that this worker has attempted.</td></tr>
+<tr>
+<td></td><td>task-startup-failure-percentage</td><td>The average percentage of this worker's tasks starts that failed.</td></tr>
+<tr>
+<td></td><td>task-startup-failure-total</td><td>The total number of task starts that failed.</td></tr>
+<tr>
+<td></td><td>task-startup-success-percentage</td><td>The average percentage of this worker's tasks starts that succeeded.</td></tr>
+<tr>
+<td></td><td>task-startup-success-total</td><td>The total number of task starts that succeeded.</td></tr>
+<tr>
+<td colspan=3 class="mbeanName" style="background-color:#ccc; font-weight: bold;">kafka.connect:type=connect-worker-rebalance-metrics</td></tr>
+<tr>
+<th style="width: 90px"></th>
+<th>Attribute name</th>
+<th>Description</th>
+</tr>
+<tr>
+<td></td><td>completed-rebalances-total</td><td>The total number of rebalances completed by this worker.</td></tr>
+<tr>
+<td></td><td>epoch</td><td>The epoch or generation number of this worker.</td></tr>
+<tr>
+<td></td><td>leader-name</td><td>The name of the group leader.</td></tr>
+<tr>
+<td></td><td>rebalance-avg-time-ms</td><td>The average time in milliseconds spent by this worker to rebalance.</td></tr>
+<tr>
+<td></td><td>rebalance-max-time-ms</td><td>The maximum time in milliseconds spent by this worker to rebalance.</td></tr>
+<tr>
+<td></td><td>rebalancing</td><td>Whether this worker is currently rebalancing.</td></tr>
+<tr>
+<td></td><td>time-since-last-rebalance-ms</td><td>The time in milliseconds since this worker completed the most recent rebalance.</td></tr>
+<tr>
+<td colspan=3 class="mbeanName" style="background-color:#ccc; font-weight: bold;">kafka.connect:type=connector-metrics,connector="{connector}"</td></tr>
+<tr>
+<th style="width: 90px"></th>
+<th>Attribute name</th>
+<th>Description</th>
+</tr>
+<tr>
+<td></td><td>connector-class</td><td>The name of the connector class.</td></tr>
+<tr>
+<td></td><td>connector-type</td><td>The type of the connector. One of 'source' or 'sink'.</td></tr>
+<tr>
+<td></td><td>connector-version</td><td>The version of the connector class, as reported by the connector.</td></tr>
+<tr>
+<td></td><td>status</td><td>The status of the connector. One of 'unassigned', 'running', 'paused', 'failed', or 'destroyed'.</td></tr>
+<tr>
+<td colspan=3 class="mbeanName" style="background-color:#ccc; font-weight: bold;">kafka.connect:type=connector-task-metrics,connector="{connector}",task="{task}"</td></tr>
+<tr>
+<th style="width: 90px"></th>
+<th>Attribute name</th>
+<th>Description</th>
+</tr>
+<tr>
+<td></td><td>batch-size-avg</td><td>The average size of the batches processed by the connector.</td></tr>
+<tr>
+<td></td><td>batch-size-max</td><td>The maximum size of the batches processed by the connector.</td></tr>
+<tr>
+<td></td><td>offset-commit-avg-time-ms</td><td>The average time in milliseconds taken by this task to commit offsets.</td></tr>
+<tr>
+<td></td><td>offset-commit-failure-percentage</td><td>The average percentage of this task's offset commit attempts that failed.</td></tr>
+<tr>
+<td></td><td>offset-commit-max-time-ms</td><td>The maximum time in milliseconds taken by this task to commit offsets.</td></tr>
+<tr>
+<td></td><td>offset-commit-success-percentage</td><td>The average percentage of this task's offset commit attempts that succeeded.</td></tr>
+<tr>
+<td></td><td>pause-ratio</td><td>The fraction of time this task has spent in the pause state.</td></tr>
+<tr>
+<td></td><td>running-ratio</td><td>The fraction of time this task has spent in the running state.</td></tr>
+<tr>
+<td></td><td>status</td><td>The status of the connector task. One of 'unassigned', 'running', 'paused', 'failed', or 'destroyed'.</td></tr>
+<tr>
+<td colspan=3 class="mbeanName" style="background-color:#ccc; font-weight: bold;">kafka.connect:type=sink-task-metrics,connector="{connector}",task="{task}"</td></tr>
+<tr>
+<th style="width: 90px"></th>
+<th>Attribute name</th>
+<th>Description</th>
+</tr>
+<tr>
+<td></td><td>offset-commit-completion-rate</td><td>The average per-second number of offset commit completions that were completed successfully.</td></tr>
+<tr>
+<td></td><td>offset-commit-completion-total</td><td>The total number of offset commit completions that were completed successfully.</td></tr>
+<tr>
+<td></td><td>offset-commit-seq-no</td><td>The current sequence number for offset commits.</td></tr>
+<tr>
+<td></td><td>offset-commit-skip-rate</td><td>The average per-second number of offset commit completions that were received too late and skipped/ignored.</td></tr>
+<tr>
+<td></td><td>offset-commit-skip-total</td><td>The total number of offset commit completions that were received too late and skipped/ignored.</td></tr>
+<tr>
+<td></td><td>partition-count</td><td>The number of topic partitions assigned to this task belonging to the named sink connector in this worker.</td></tr>
+<tr>
+<td></td><td>put-batch-avg-time-ms</td><td>The average time taken by this task to put a batch of sinks records.</td></tr>
+<tr>
+<td></td><td>put-batch-max-time-ms</td><td>The maximum time taken by this task to put a batch of sinks records.</td></tr>
+<tr>
+<td></td><td>sink-record-active-count</td><td>The number of records that have been read from Kafka but not yet completely committed/flushed/acknowledged by the sink task.</td></tr>
+<tr>
+<td></td><td>sink-record-active-count-avg</td><td>The average number of records that have been read from Kafka but not yet completely committed/flushed/acknowledged by the sink task.</td></tr>
+<tr>
+<td></td><td>sink-record-active-count-max</td><td>The maximum number of records that have been read from Kafka but not yet completely committed/flushed/acknowledged by the sink task.</td></tr>
+<tr>
+<td></td><td>sink-record-lag-max</td><td>The maximum lag in terms of number of records that the sink task is behind the consumer's position for any topic partitions.</td></tr>
+<tr>
+<td></td><td>sink-record-read-rate</td><td>The average per-second number of records read from Kafka for this task belonging to the named sink connector in this worker. This is before transformations are applied.</td></tr>
+<tr>
+<td></td><td>sink-record-read-total</td><td>The total number of records read from Kafka by this task belonging to the named sink connector in this worker, since the task was last restarted.</td></tr>
+<tr>
+<td></td><td>sink-record-send-rate</td><td>The average per-second number of records output from the transformations and sent/put to this task belonging to the named sink connector in this worker. This is after transformations are applied and excludes any records filtered out by the transformations.</td></tr>
+<tr>
+<td></td><td>sink-record-send-total</td><td>The total number of records output from the transformations and sent/put to this task belonging to the named sink connector in this worker, since the task was last restarted.</td></tr>
+<tr>
+<td colspan=3 class="mbeanName" style="background-color:#ccc; font-weight: bold;">kafka.connect:type=source-task-metrics,connector="{connector}",task="{task}"</td></tr>
+<tr>
+<th style="width: 90px"></th>
+<th>Attribute name</th>
+<th>Description</th>
+</tr>
+<tr>
+<td></td><td>poll-batch-avg-time-ms</td><td>The average time in milliseconds taken by this task to poll for a batch of source records.</td></tr>
+<tr>
+<td></td><td>poll-batch-max-time-ms</td><td>The maximum time in milliseconds taken by this task to poll for a batch of source records.</td></tr>
+<tr>
+<td></td><td>source-record-active-count</td><td>The number of records that have been produced by this task but not yet completely written to Kafka.</td></tr>
+<tr>
+<td></td><td>source-record-active-count-avg</td><td>The average number of records that have been produced by this task but not yet completely written to Kafka.</td></tr>
+<tr>
+<td></td><td>source-record-active-count-max</td><td>The maximum number of records that have been produced by this task but not yet completely written to Kafka.</td></tr>
+<tr>
+<td></td><td>source-record-poll-rate</td><td>The average per-second number of records produced/polled (before transformation) by this task belonging to the named source connector in this worker.</td></tr>
+<tr>
+<td></td><td>source-record-poll-total</td><td>The total number of records produced/polled (before transformation) by this task belonging to the named source connector in this worker.</td></tr>
+<tr>
+<td></td><td>source-record-write-rate</td><td>The average per-second number of records output from the transformations and written to Kafka for this task belonging to the named source connector in this worker. This is after transformations are applied and excludes any records filtered out by the transformations.</td></tr>
+<tr>
+<td></td><td>source-record-write-total</td><td>The number of records output from the transformations and written to Kafka for this task belonging to the named source connector in this worker, since the task was last restarted.</td></tr>
+</tbody></table>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/generated/connect_transforms.html
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diff --git a/100/generated/connect_transforms.html b/100/generated/connect_transforms.html
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index 0000000..b56232a
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+++ b/100/generated/connect_transforms.html
@@ -0,0 +1,228 @@
+<div id="org.apache.kafka.connect.transforms.InsertField">
+<h5>org.apache.kafka.connect.transforms.InsertField</h5>
+Insert field(s) using attributes from the record metadata or a configured static value.<p/>Use the concrete transformation type designed for the record key (<code>org.apache.kafka.connect.transforms.InsertField$Key</code>) or value (<code>org.apache.kafka.connect.transforms.InsertField$Value</code>).
+<p/>
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>offset.field</td><td>Field name for Kafka offset - only applicable to sink connectors.<br/>Suffix with <code>!</code> to make this a required field, or <code>?</code> to keep it optional (the default).</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>partition.field</td><td>Field name for Kafka partition. Suffix with <code>!</code> to make this a required field, or <code>?</code> to keep it optional (the default).</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>static.field</td><td>Field name for static data field. Suffix with <code>!</code> to make this a required field, or <code>?</code> to keep it optional (the default).</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>static.value</td><td>Static field value, if field name configured.</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>timestamp.field</td><td>Field name for record timestamp. Suffix with <code>!</code> to make this a required field, or <code>?</code> to keep it optional (the default).</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>topic.field</td><td>Field name for Kafka topic. Suffix with <code>!</code> to make this a required field, or <code>?</code> to keep it optional (the default).</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+</tbody></table>
+</div>
+<div id="org.apache.kafka.connect.transforms.ReplaceField">
+<h5>org.apache.kafka.connect.transforms.ReplaceField</h5>
+Filter or rename fields.<p/>Use the concrete transformation type designed for the record key (<code>org.apache.kafka.connect.transforms.ReplaceField$Key</code>) or value (<code>org.apache.kafka.connect.transforms.ReplaceField$Value</code>).
+<p/>
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>blacklist</td><td>Fields to exclude. This takes precedence over the whitelist.</td><td>list</td><td>""</td><td></td><td>medium</td></tr>
+<tr>
+<td>renames</td><td>Field rename mappings.</td><td>list</td><td>""</td><td>list of colon-delimited pairs, e.g. <code>foo:bar,abc:xyz</code></td><td>medium</td></tr>
+<tr>
+<td>whitelist</td><td>Fields to include. If specified, only these fields will be used.</td><td>list</td><td>""</td><td></td><td>medium</td></tr>
+</tbody></table>
+</div>
+<div id="org.apache.kafka.connect.transforms.MaskField">
+<h5>org.apache.kafka.connect.transforms.MaskField</h5>
+Mask specified fields with a valid null value for the field type (i.e. 0, false, empty string, and so on).<p/>Use the concrete transformation type designed for the record key (<code>org.apache.kafka.connect.transforms.MaskField$Key</code>) or value (<code>org.apache.kafka.connect.transforms.MaskField$Value</code>).
+<p/>
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>fields</td><td>Names of fields to mask.</td><td>list</td><td></td><td>non-empty list</td><td>high</td></tr>
+</tbody></table>
+</div>
+<div id="org.apache.kafka.connect.transforms.ValueToKey">
+<h5>org.apache.kafka.connect.transforms.ValueToKey</h5>
+Replace the record key with a new key formed from a subset of fields in the record value.
+<p/>
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>fields</td><td>Field names on the record value to extract as the record key.</td><td>list</td><td></td><td>non-empty list</td><td>high</td></tr>
+</tbody></table>
+</div>
+<div id="org.apache.kafka.connect.transforms.HoistField">
+<h5>org.apache.kafka.connect.transforms.HoistField</h5>
+Wrap data using the specified field name in a Struct when schema present, or a Map in the case of schemaless data.<p/>Use the concrete transformation type designed for the record key (<code>org.apache.kafka.connect.transforms.HoistField$Key</code>) or value (<code>org.apache.kafka.connect.transforms.HoistField$Value</code>).
+<p/>
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>field</td><td>Field name for the single field that will be created in the resulting Struct or Map.</td><td>string</td><td></td><td></td><td>medium</td></tr>
+</tbody></table>
+</div>
+<div id="org.apache.kafka.connect.transforms.ExtractField">
+<h5>org.apache.kafka.connect.transforms.ExtractField</h5>
+Extract the specified field from a Struct when schema present, or a Map in the case of schemaless data. Any null values are passed through unmodified.<p/>Use the concrete transformation type designed for the record key (<code>org.apache.kafka.connect.transforms.ExtractField$Key</code>) or value (<code>org.apache.kafka.connect.transforms.ExtractField$Value</code>).
+<p/>
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>field</td><td>Field name to extract.</td><td>string</td><td></td><td></td><td>medium</td></tr>
+</tbody></table>
+</div>
+<div id="org.apache.kafka.connect.transforms.SetSchemaMetadata">
+<h5>org.apache.kafka.connect.transforms.SetSchemaMetadata</h5>
+Set the schema name, version or both on the record's key (<code>org.apache.kafka.connect.transforms.SetSchemaMetadata$Key</code>) or value (<code>org.apache.kafka.connect.transforms.SetSchemaMetadata$Value</code>) schema.
+<p/>
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>schema.name</td><td>Schema name to set.</td><td>string</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>schema.version</td><td>Schema version to set.</td><td>int</td><td>null</td><td></td><td>high</td></tr>
+</tbody></table>
+</div>
+<div id="org.apache.kafka.connect.transforms.TimestampRouter">
+<h5>org.apache.kafka.connect.transforms.TimestampRouter</h5>
+Update the record's topic field as a function of the original topic value and the record timestamp.<p/>This is mainly useful for sink connectors, since the topic field is often used to determine the equivalent entity name in the destination system(e.g. database table or search index name).
+<p/>
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>timestamp.format</td><td>Format string for the timestamp that is compatible with <code>java.text.SimpleDateFormat</code>.</td><td>string</td><td>yyyyMMdd</td><td></td><td>high</td></tr>
+<tr>
+<td>topic.format</td><td>Format string which can contain <code>${topic}</code> and <code>${timestamp}</code> as placeholders for the topic and timestamp, respectively.</td><td>string</td><td>${topic}-${timestamp}</td><td></td><td>high</td></tr>
+</tbody></table>
+</div>
+<div id="org.apache.kafka.connect.transforms.RegexRouter">
+<h5>org.apache.kafka.connect.transforms.RegexRouter</h5>
+Update the record topic using the configured regular expression and replacement string.<p/>Under the hood, the regex is compiled to a <code>java.util.regex.Pattern</code>. If the pattern matches the input topic, <code>java.util.regex.Matcher#replaceFirst()</code> is used with the replacement string to obtain the new topic.
+<p/>
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>regex</td><td>Regular expression to use for matching.</td><td>string</td><td></td><td>valid regex</td><td>high</td></tr>
+<tr>
+<td>replacement</td><td>Replacement string.</td><td>string</td><td></td><td></td><td>high</td></tr>
+</tbody></table>
+</div>
+<div id="org.apache.kafka.connect.transforms.Flatten">
+<h5>org.apache.kafka.connect.transforms.Flatten</h5>
+Flatten a nested data structure, generating names for each field by concatenating the field names at each level with a configurable delimiter character. Applies to Struct when schema present, or a Map in the case of schemaless data. The default delimiter is '.'.<p/>Use the concrete transformation type designed for the record key (<code>org.apache.kafka.connect.transforms.Flatten$Key</code>) or value (<code>org.apache.kafka.connect.transforms.Flatten$Value</code>).
+<p/>
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>delimiter</td><td>Delimiter to insert between field names from the input record when generating field names for the output record</td><td>string</td><td>.</td><td></td><td>medium</td></tr>
+</tbody></table>
+</div>
+<div id="org.apache.kafka.connect.transforms.Cast">
+<h5>org.apache.kafka.connect.transforms.Cast</h5>
+Cast fields or the entire key or value to a specific type, e.g. to force an integer field to a smaller width. Only simple primitive types are supported -- integers, floats, boolean, and string. <p/>Use the concrete transformation type designed for the record key (<code>org.apache.kafka.connect.transforms.Cast$Key</code>) or value (<code>org.apache.kafka.connect.transforms.Cast$Value</code>).
+<p/>
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>spec</td><td>List of fields and the type to cast them to of the form field1:type,field2:type to cast fields of Maps or Structs. A single type to cast the entire value. Valid types are int8, int16, int32, int64, float32, float64, boolean, and string.</td><td>list</td><td></td><td>list of colon-delimited pairs, e.g. <code>foo:bar,abc:xyz</code></td><td>high</td></tr>
+</tbody></table>
+</div>
+<div id="org.apache.kafka.connect.transforms.TimestampConverter">
+<h5>org.apache.kafka.connect.transforms.TimestampConverter</h5>
+Convert timestamps between different formats such as Unix epoch, strings, and Connect Date/Timestamp types.Applies to individual fields or to the entire value.<p/>Use the concrete transformation type designed for the record key (<code>org.apache.kafka.connect.transforms.TimestampConverter$Key</code>) or value (<code>org.apache.kafka.connect.transforms.TimestampConverter$Value</code>).
+<p/>
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>target.type</td><td>The desired timestamp representation: string, unix, Date, Time, or Timestamp</td><td>string</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>field</td><td>The field containing the timestamp, or empty if the entire value is a timestamp</td><td>string</td><td>""</td><td></td><td>high</td></tr>
+<tr>
+<td>format</td><td>A SimpleDateFormat-compatible format for the timestamp. Used to generate the output when type=string or used to parse the input if the input is a string.</td><td>string</td><td>""</td><td></td><td>medium</td></tr>
+</tbody></table>
+</div>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/generated/consumer_config.html
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diff --git a/100/generated/consumer_config.html b/100/generated/consumer_config.html
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@@ -0,0 +1,122 @@
+<table class="data-table"><tbody>
+<tr>
+<th>Name</th>
+<th>Description</th>
+<th>Type</th>
+<th>Default</th>
+<th>Valid Values</th>
+<th>Importance</th>
+</tr>
+<tr>
+<td>bootstrap.servers</td><td>A list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping—this list only impacts the initial hosts used to discover the full set of servers. This list should be in the form <code>host1:port1,host2:port2,...</code>. Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down).</td><td>list</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>key.deserializer</td><td>Deserializer class for key that implements the <code>org.apache.kafka.common.serialization.Deserializer</code> interface.</td><td>class</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>value.deserializer</td><td>Deserializer class for value that implements the <code>org.apache.kafka.common.serialization.Deserializer</code> interface.</td><td>class</td><td></td><td></td><td>high</td></tr>
+<tr>
+<td>fetch.min.bytes</td><td>The minimum amount of data the server should return for a fetch request. If insufficient data is available the request will wait for that much data to accumulate before answering the request. The default setting of 1 byte means that fetch requests are answered as soon as a single byte of data is available or the fetch request times out waiting for data to arrive. Setting this to something greater than 1 will cause the server to wait for larger amounts of data to accumulate which can improve server throughput a bit at the cost of some additional latency.</td><td>int</td><td>1</td><td>[0,...]</td><td>high</td></tr>
+<tr>
+<td>group.id</td><td>A unique string that identifies the consumer group this consumer belongs to. This property is required if the consumer uses either the group management functionality by using <code>subscribe(topic)</code> or the Kafka-based offset management strategy.</td><td>string</td><td>""</td><td></td><td>high</td></tr>
+<tr>
+<td>heartbeat.interval.ms</td><td>The expected time between heartbeats to the consumer coordinator when using Kafka's group management facilities. Heartbeats are used to ensure that the consumer's session stays active and to facilitate rebalancing when new consumers join or leave the group. The value must be set lower than <code>session.timeout.ms</code>, but typically should be set no higher than 1/3 of that value. It can be adjusted even lower to control the expected time for normal rebalances.</td><td>int</td><td>3000</td><td></td><td>high</td></tr>
+<tr>
+<td>max.partition.fetch.bytes</td><td>The maximum amount of data per-partition the server will return. Records are fetched in batches by the consumer. If the first record batch in the first non-empty partition of the fetch is larger than this limit, the batch will still be returned to ensure that the consumer can make progress. The maximum record batch size accepted by the broker is defined via <code>message.max.bytes</code> (broker config) or <code>max.message.bytes</code> (topic config). See fetch.max.bytes for limiting the consumer request size.</td><td>int</td><td>1048576</td><td>[0,...]</td><td>high</td></tr>
+<tr>
+<td>session.timeout.ms</td><td>The timeout used to detect consumer failures when using Kafka's group management facility. The consumer sends periodic heartbeats to indicate its liveness to the broker. If no heartbeats are received by the broker before the expiration of this session timeout, then the broker will remove this consumer from the group and initiate a rebalance. Note that the value must be in the allowable range as configured in the broker configuration by <code>group.min.session.timeout.ms</code> and <code>group.max.session.timeout.ms</code>.</td><td>int</td><td>10000</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.key.password</td><td>The password of the private key in the key store file. This is optional for client.</td><td>password</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.keystore.location</td><td>The location of the key store file. This is optional for client and can be used for two-way authentication for client.</td><td>string</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.keystore.password</td><td>The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured. </td><td>password</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.truststore.location</td><td>The location of the trust store file. </td><td>string</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>ssl.truststore.password</td><td>The password for the trust store file. If a password is not set access to the truststore is still available, but integrity checking is disabled.</td><td>password</td><td>null</td><td></td><td>high</td></tr>
+<tr>
+<td>auto.offset.reset</td><td>What to do when there is no initial offset in Kafka or if the current offset does not exist any more on the server (e.g. because that data has been deleted): <ul><li>earliest: automatically reset the offset to the earliest offset<li>latest: automatically reset the offset to the latest offset</li><li>none: throw exception to the consumer if no previous offset is found for the consumer's group</li><li>anything else: throw exception to the consumer.</li></ul></td><td>string</td><td>latest</td><td>[latest, earliest, none]</td><td>medium</td></tr>
+<tr>
+<td>connections.max.idle.ms</td><td>Close idle connections after the number of milliseconds specified by this config.</td><td>long</td><td>540000</td><td></td><td>medium</td></tr>
+<tr>
+<td>enable.auto.commit</td><td>If true the consumer's offset will be periodically committed in the background.</td><td>boolean</td><td>true</td><td></td><td>medium</td></tr>
+<tr>
+<td>exclude.internal.topics</td><td>Whether records from internal topics (such as offsets) should be exposed to the consumer. If set to <code>true</code> the only way to receive records from an internal topic is subscribing to it.</td><td>boolean</td><td>true</td><td></td><td>medium</td></tr>
+<tr>
+<td>fetch.max.bytes</td><td>The maximum amount of data the server should return for a fetch request. Records are fetched in batches by the consumer, and if the first record batch in the first non-empty partition of the fetch is larger than this value, the record batch will still be returned to ensure that the consumer can make progress. As such, this is not a absolute maximum. The maximum record batch size accepted by the broker is defined via <code>message.max.bytes</code> (broker config) or <code>max.message.bytes</code> (topic config). Note that the consumer performs multiple fetches in parallel.</td><td>int</td><td>52428800</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>isolation.level</td><td><p>Controls how to read messages written transactionally. If set to <code>read_committed</code>, consumer.poll() will only return transactional messages which have been committed. If set to <code>read_uncommitted</code>' (the default), consumer.poll() will return all messages, even transactional messages which have been aborted. Non-transactional messages will be returned unconditionally in either mode.</p> <p>Messages will always be returned in offset order. Hence, in <code>read_committed</code> mode, consumer.poll() will only return messages up to the last stable offset (LSO), which is the one less than the offset of the first open transaction. In particular any messages appearing after messages belonging to ongoing transactions will be withheld until the relevant transaction has been completed. As a result, <code>read_committed</code> consumers will not be able to read up to the high watermark when there are in flight transactions.</p><p> Further, whe
n in <code>read_committed</mode> the seekToEnd method will return the LSO</td><td>string</td><td>read_uncommitted</td><td>[read_committed, read_uncommitted]</td><td>medium</td></tr>
+<tr>
+<td>max.poll.interval.ms</td><td>The maximum delay between invocations of poll() when using consumer group management. This places an upper bound on the amount of time that the consumer can be idle before fetching more records. If poll() is not called before expiration of this timeout, then the consumer is considered failed and the group will rebalance in order to reassign the partitions to another member. </td><td>int</td><td>300000</td><td>[1,...]</td><td>medium</td></tr>
+<tr>
+<td>max.poll.records</td><td>The maximum number of records returned in a single call to poll().</td><td>int</td><td>500</td><td>[1,...]</td><td>medium</td></tr>
+<tr>
+<td>partition.assignment.strategy</td><td>The class name of the partition assignment strategy that the client will use to distribute partition ownership amongst consumer instances when group management is used</td><td>list</td><td>class org.apache.kafka.clients.consumer.RangeAssignor</td><td></td><td>medium</td></tr>
+<tr>
+<td>receive.buffer.bytes</td><td>The size of the TCP receive buffer (SO_RCVBUF) to use when reading data. If the value is -1, the OS default will be used.</td><td>int</td><td>65536</td><td>[-1,...]</td><td>medium</td></tr>
+<tr>
+<td>request.timeout.ms</td><td>The configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted.</td><td>int</td><td>305000</td><td>[0,...]</td><td>medium</td></tr>
+<tr>
+<td>sasl.jaas.config</td><td>JAAS login context parameters for SASL connections in the format used by JAAS configuration files. JAAS configuration file format is described <a href="http://docs.oracle.com/javase/8/docs/technotes/guides/security/jgss/tutorials/LoginConfigFile.html">here</a>. The format for the value is: '<loginModuleClass> <controlFlag> (<optionName>=<optionValue>)*;'</td><td>password</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.kerberos.service.name</td><td>The Kerberos principal name that Kafka runs as. This can be defined either in Kafka's JAAS config or in Kafka's config.</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>sasl.mechanism</td><td>SASL mechanism used for client connections. This may be any mechanism for which a security provider is available. GSSAPI is the default mechanism.</td><td>string</td><td>GSSAPI</td><td></td><td>medium</td></tr>
+<tr>
+<td>security.protocol</td><td>Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.</td><td>string</td><td>PLAINTEXT</td><td></td><td>medium</td></tr>
+<tr>
+<td>send.buffer.bytes</td><td>The size of the TCP send buffer (SO_SNDBUF) to use when sending data. If the value is -1, the OS default will be used.</td><td>int</td><td>131072</td><td>[-1,...]</td><td>medium</td></tr>
+<tr>
+<td>ssl.enabled.protocols</td><td>The list of protocols enabled for SSL connections.</td><td>list</td><td>TLSv1.2,TLSv1.1,TLSv1</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.keystore.type</td><td>The file format of the key store file. This is optional for client.</td><td>string</td><td>JKS</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.protocol</td><td>The SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.</td><td>string</td><td>TLS</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.provider</td><td>The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.</td><td>string</td><td>null</td><td></td><td>medium</td></tr>
+<tr>
+<td>ssl.truststore.type</td><td>The file format of the trust store file.</td><td>string</td><td>JKS</td><td></td><td>medium</td></tr>
+<tr>
+<td>auto.commit.interval.ms</td><td>The frequency in milliseconds that the consumer offsets are auto-committed to Kafka if <code>enable.auto.commit</code> is set to <code>true</code>.</td><td>int</td><td>5000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>check.crcs</td><td>Automatically check the CRC32 of the records consumed. This ensures no on-the-wire or on-disk corruption to the messages occurred. This check adds some overhead, so it may be disabled in cases seeking extreme performance.</td><td>boolean</td><td>true</td><td></td><td>low</td></tr>
+<tr>
+<td>client.id</td><td>An id string to pass to the server when making requests. The purpose of this is to be able to track the source of requests beyond just ip/port by allowing a logical application name to be included in server-side request logging.</td><td>string</td><td>""</td><td></td><td>low</td></tr>
+<tr>
+<td>fetch.max.wait.ms</td><td>The maximum amount of time the server will block before answering the fetch request if there isn't sufficient data to immediately satisfy the requirement given by fetch.min.bytes.</td><td>int</td><td>500</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>interceptor.classes</td><td>A list of classes to use as interceptors. Implementing the <code>org.apache.kafka.clients.consumer.ConsumerInterceptor</code> interface allows you to intercept (and possibly mutate) records received by the consumer. By default, there are no interceptors.</td><td>list</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>metadata.max.age.ms</td><td>The period of time in milliseconds after which we force a refresh of metadata even if we haven't seen any partition leadership changes to proactively discover any new brokers or partitions.</td><td>long</td><td>300000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>metric.reporters</td><td>A list of classes to use as metrics reporters. Implementing the <code>org.apache.kafka.common.metrics.MetricsReporter</code> interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.</td><td>list</td><td>""</td><td></td><td>low</td></tr>
+<tr>
+<td>metrics.num.samples</td><td>The number of samples maintained to compute metrics.</td><td>int</td><td>2</td><td>[1,...]</td><td>low</td></tr>
+<tr>
+<td>metrics.recording.level</td><td>The highest recording level for metrics.</td><td>string</td><td>INFO</td><td>[INFO, DEBUG]</td><td>low</td></tr>
+<tr>
+<td>metrics.sample.window.ms</td><td>The window of time a metrics sample is computed over.</td><td>long</td><td>30000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>reconnect.backoff.max.ms</td><td>The maximum amount of time in milliseconds to wait when reconnecting to a broker that has repeatedly failed to connect. If provided, the backoff per host will increase exponentially for each consecutive connection failure, up to this maximum. After calculating the backoff increase, 20% random jitter is added to avoid connection storms.</td><td>long</td><td>1000</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>reconnect.backoff.ms</td><td>The base amount of time to wait before attempting to reconnect to a given host. This avoids repeatedly connecting to a host in a tight loop. This backoff applies to all connection attempts by the client to a broker.</td><td>long</td><td>50</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>retry.backoff.ms</td><td>The amount of time to wait before attempting to retry a failed request to a given topic partition. This avoids repeatedly sending requests in a tight loop under some failure scenarios.</td><td>long</td><td>100</td><td>[0,...]</td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.kinit.cmd</td><td>Kerberos kinit command path.</td><td>string</td><td>/usr/bin/kinit</td><td></td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.min.time.before.relogin</td><td>Login thread sleep time between refresh attempts.</td><td>long</td><td>60000</td><td></td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.ticket.renew.jitter</td><td>Percentage of random jitter added to the renewal time.</td><td>double</td><td>0.05</td><td></td><td>low</td></tr>
+<tr>
+<td>sasl.kerberos.ticket.renew.window.factor</td><td>Login thread will sleep until the specified window factor of time from last refresh to ticket's expiry has been reached, at which time it will try to renew the ticket.</td><td>double</td><td>0.8</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.cipher.suites</td><td>A list of cipher suites. This is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. By default all the available cipher suites are supported.</td><td>list</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.endpoint.identification.algorithm</td><td>The endpoint identification algorithm to validate server hostname using server certificate. </td><td>string</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.keymanager.algorithm</td><td>The algorithm used by key manager factory for SSL connections. Default value is the key manager factory algorithm configured for the Java Virtual Machine.</td><td>string</td><td>SunX509</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.secure.random.implementation</td><td>The SecureRandom PRNG implementation to use for SSL cryptography operations. </td><td>string</td><td>null</td><td></td><td>low</td></tr>
+<tr>
+<td>ssl.trustmanager.algorithm</td><td>The algorithm used by trust manager factory for SSL connections. Default value is the trust manager factory algorithm configured for the Java Virtual Machine.</td><td>string</td><td>PKIX</td><td></td><td>low</td></tr>
+</tbody></table>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/generated/consumer_metrics.html
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diff --git a/100/generated/consumer_metrics.html b/100/generated/consumer_metrics.html
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+++ b/100/generated/consumer_metrics.html
@@ -0,0 +1,64 @@
+<table class="data-table"><tbody>
+<tr>
+<td colspan=3 class="mbeanName" style="background-color:#ccc; font-weight: bold;">kafka.consumer:type=consumer-fetch-manager-metrics,client-id="{client-id}"</td></tr>
+<tr>
+<th style="width: 90px"></th>
+<th>Attribute name</th>
+<th>Description</th>
+</tr>
+<tr>
+<td></td><td>bytes-consumed-rate</td><td>The average number of bytes consumed per second</td></tr>
+<tr>
+<td></td><td>bytes-consumed-total</td><td>The total number of bytes consumed</td></tr>
+<tr>
+<td></td><td>fetch-latency-avg</td><td>The average time taken for a fetch request.</td></tr>
+<tr>
+<td></td><td>fetch-latency-max</td><td>The max time taken for any fetch request.</td></tr>
+<tr>
+<td></td><td>fetch-rate</td><td>The number of fetch requests per second.</td></tr>
+<tr>
+<td></td><td>fetch-size-avg</td><td>The average number of bytes fetched per request</td></tr>
+<tr>
+<td></td><td>fetch-size-max</td><td>The maximum number of bytes fetched per request</td></tr>
+<tr>
+<td></td><td>fetch-throttle-time-avg</td><td>The average throttle time in ms</td></tr>
+<tr>
+<td></td><td>fetch-throttle-time-max</td><td>The maximum throttle time in ms</td></tr>
+<tr>
+<td></td><td>fetch-total</td><td>The total number of fetch requests.</td></tr>
+<tr>
+<td></td><td>records-consumed-rate</td><td>The average number of records consumed per second</td></tr>
+<tr>
+<td></td><td>records-consumed-total</td><td>The total number of records consumed</td></tr>
+<tr>
+<td></td><td>records-lag-max</td><td>The maximum lag in terms of number of records for any partition in this window</td></tr>
+<tr>
+<td></td><td>records-per-request-avg</td><td>The average number of records in each request</td></tr>
+<tr>
+<td></td><td>{topic}-{partition}.records-lag</td><td>The latest lag of the partition</td></tr>
+<tr>
+<td></td><td>{topic}-{partition}.records-lag-avg</td><td>The average lag of the partition</td></tr>
+<tr>
+<td></td><td>{topic}-{partition}.records-lag-max</td><td>The max lag of the partition</td></tr>
+<tr>
+<td colspan=3 class="mbeanName" style="background-color:#ccc; font-weight: bold;">kafka.consumer:type=consumer-fetch-manager-metrics,client-id="{client-id}",topic="{topic}"</td></tr>
+<tr>
+<th style="width: 90px"></th>
+<th>Attribute name</th>
+<th>Description</th>
+</tr>
+<tr>
+<td></td><td>bytes-consumed-rate</td><td>The average number of bytes consumed per second for a topic</td></tr>
+<tr>
+<td></td><td>bytes-consumed-total</td><td>The total number of bytes consumed for a topic</td></tr>
+<tr>
+<td></td><td>fetch-size-avg</td><td>The average number of bytes fetched per request for a topic</td></tr>
+<tr>
+<td></td><td>fetch-size-max</td><td>The maximum number of bytes fetched per request for a topic</td></tr>
+<tr>
+<td></td><td>records-consumed-rate</td><td>The average number of records consumed per second for a topic</td></tr>
+<tr>
+<td></td><td>records-consumed-total</td><td>The total number of records consumed for a topic</td></tr>
+<tr>
+<td></td><td>records-per-request-avg</td><td>The average number of records in each request for a topic</td></tr>
+</tbody></table>
[10/14] kafka-site git commit: Update site for 1.0.0 release
Posted by ij...@apache.org.
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/generated/protocol_messages.html
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diff --git a/100/generated/protocol_messages.html b/100/generated/protocol_messages.html
new file mode 100644
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+++ b/100/generated/protocol_messages.html
@@ -0,0 +1,4345 @@
+<h5>Headers:</h5>
+<pre>Request Header => api_key api_version correlation_id client_id
+ api_key => INT16
+ api_version => INT16
+ correlation_id => INT32
+ client_id => NULLABLE_STRING
+</pre>
+<table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>api_key</td><td>The id of the request type.</td></tr>
+<tr>
+<td>api_version</td><td>The version of the API.</td></tr>
+<tr>
+<td>correlation_id</td><td>A user-supplied integer value that will be passed back with the response</td></tr>
+<tr>
+<td>client_id</td><td>A user specified identifier for the client making the request.</td></tr>
+</table>
+<pre>Response Header => correlation_id
+ correlation_id => INT32
+</pre>
+<table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>correlation_id</td><td>The user-supplied value passed in with the request</td></tr>
+</table>
+<h5><a name="The_Messages_Produce">Produce API (Key: 0):</a></h5>
+
+<b>Requests:</b><br>
+<p><pre>Produce Request (Version: 0) => acks timeout [topic_data]
+ acks => INT16
+ timeout => INT32
+ topic_data => topic [data]
+ topic => STRING
+ data => partition record_set
+ partition => INT32
+ record_set => RECORDS
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>acks</td><td>The number of acknowledgments the producer requires the leader to have received before considering a request complete. Allowed values: 0 for no acknowledgments, 1 for only the leader and -1 for the full ISR.</td></tr>
+<tr>
+<td>timeout</td><td>The time to await a response in ms.</td></tr>
+<tr>
+<td>topic_data</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>data</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>record_set</td><td>null</td></tr>
+</table>
+</p>
+<p><pre>Produce Request (Version: 1) => acks timeout [topic_data]
+ acks => INT16
+ timeout => INT32
+ topic_data => topic [data]
+ topic => STRING
+ data => partition record_set
+ partition => INT32
+ record_set => RECORDS
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>acks</td><td>The number of acknowledgments the producer requires the leader to have received before considering a request complete. Allowed values: 0 for no acknowledgments, 1 for only the leader and -1 for the full ISR.</td></tr>
+<tr>
+<td>timeout</td><td>The time to await a response in ms.</td></tr>
+<tr>
+<td>topic_data</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>data</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>record_set</td><td>null</td></tr>
+</table>
+</p>
+<p><pre>Produce Request (Version: 2) => acks timeout [topic_data]
+ acks => INT16
+ timeout => INT32
+ topic_data => topic [data]
+ topic => STRING
+ data => partition record_set
+ partition => INT32
+ record_set => RECORDS
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>acks</td><td>The number of acknowledgments the producer requires the leader to have received before considering a request complete. Allowed values: 0 for no acknowledgments, 1 for only the leader and -1 for the full ISR.</td></tr>
+<tr>
+<td>timeout</td><td>The time to await a response in ms.</td></tr>
+<tr>
+<td>topic_data</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>data</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>record_set</td><td>null</td></tr>
+</table>
+</p>
+<p><pre>Produce Request (Version: 3) => transactional_id acks timeout [topic_data]
+ transactional_id => NULLABLE_STRING
+ acks => INT16
+ timeout => INT32
+ topic_data => topic [data]
+ topic => STRING
+ data => partition record_set
+ partition => INT32
+ record_set => RECORDS
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>transactional_id</td><td>The transactional id or null if the producer is not transactional</td></tr>
+<tr>
+<td>acks</td><td>The number of acknowledgments the producer requires the leader to have received before considering a request complete. Allowed values: 0 for no acknowledgments, 1 for only the leader and -1 for the full ISR.</td></tr>
+<tr>
+<td>timeout</td><td>The time to await a response in ms.</td></tr>
+<tr>
+<td>topic_data</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>data</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>record_set</td><td>null</td></tr>
+</table>
+</p>
+<p><pre>Produce Request (Version: 4) => transactional_id acks timeout [topic_data]
+ transactional_id => NULLABLE_STRING
+ acks => INT16
+ timeout => INT32
+ topic_data => topic [data]
+ topic => STRING
+ data => partition record_set
+ partition => INT32
+ record_set => RECORDS
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>transactional_id</td><td>The transactional id or null if the producer is not transactional</td></tr>
+<tr>
+<td>acks</td><td>The number of acknowledgments the producer requires the leader to have received before considering a request complete. Allowed values: 0 for no acknowledgments, 1 for only the leader and -1 for the full ISR.</td></tr>
+<tr>
+<td>timeout</td><td>The time to await a response in ms.</td></tr>
+<tr>
+<td>topic_data</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>data</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>record_set</td><td>null</td></tr>
+</table>
+</p>
+<p><pre>Produce Request (Version: 5) => transactional_id acks timeout [topic_data]
+ transactional_id => NULLABLE_STRING
+ acks => INT16
+ timeout => INT32
+ topic_data => topic [data]
+ topic => STRING
+ data => partition record_set
+ partition => INT32
+ record_set => RECORDS
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>transactional_id</td><td>The transactional id or null if the producer is not transactional</td></tr>
+<tr>
+<td>acks</td><td>The number of acknowledgments the producer requires the leader to have received before considering a request complete. Allowed values: 0 for no acknowledgments, 1 for only the leader and -1 for the full ISR.</td></tr>
+<tr>
+<td>timeout</td><td>The time to await a response in ms.</td></tr>
+<tr>
+<td>topic_data</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>data</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>record_set</td><td>null</td></tr>
+</table>
+</p>
+<b>Responses:</b><br>
+<p><pre>Produce Response (Version: 0) => [responses]
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition error_code base_offset
+ partition => INT32
+ error_code => INT16
+ base_offset => INT64
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>base_offset</td><td>null</td></tr>
+</table>
+</p>
+<p><pre>Produce Response (Version: 1) => [responses] throttle_time_ms
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition error_code base_offset
+ partition => INT32
+ error_code => INT16
+ base_offset => INT64
+ throttle_time_ms => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>base_offset</td><td>null</td></tr>
+<tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+</table>
+</p>
+<p><pre>Produce Response (Version: 2) => [responses] throttle_time_ms
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition error_code base_offset log_append_time
+ partition => INT32
+ error_code => INT16
+ base_offset => INT64
+ log_append_time => INT64
+ throttle_time_ms => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>base_offset</td><td>null</td></tr>
+<tr>
+<td>log_append_time</td><td>The timestamp returned by broker after appending the messages. If CreateTime is used for the topic, the timestamp will be -1. If LogAppendTime is used for the topic, the timestamp will be the broker local time when the messages are appended.</td></tr>
+<tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+</table>
+</p>
+<p><pre>Produce Response (Version: 3) => [responses] throttle_time_ms
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition error_code base_offset log_append_time
+ partition => INT32
+ error_code => INT16
+ base_offset => INT64
+ log_append_time => INT64
+ throttle_time_ms => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>base_offset</td><td>null</td></tr>
+<tr>
+<td>log_append_time</td><td>The timestamp returned by broker after appending the messages. If CreateTime is used for the topic, the timestamp will be -1. If LogAppendTime is used for the topic, the timestamp will be the broker local time when the messages are appended.</td></tr>
+<tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+</table>
+</p>
+<p><pre>Produce Response (Version: 4) => [responses] throttle_time_ms
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition error_code base_offset log_append_time
+ partition => INT32
+ error_code => INT16
+ base_offset => INT64
+ log_append_time => INT64
+ throttle_time_ms => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>base_offset</td><td>null</td></tr>
+<tr>
+<td>log_append_time</td><td>The timestamp returned by broker after appending the messages. If CreateTime is used for the topic, the timestamp will be -1. If LogAppendTime is used for the topic, the timestamp will be the broker local time when the messages are appended.</td></tr>
+<tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+</table>
+</p>
+<p><pre>Produce Response (Version: 5) => [responses] throttle_time_ms
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition error_code base_offset log_append_time log_start_offset
+ partition => INT32
+ error_code => INT16
+ base_offset => INT64
+ log_append_time => INT64
+ log_start_offset => INT64
+ throttle_time_ms => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>base_offset</td><td>null</td></tr>
+<tr>
+<td>log_append_time</td><td>The timestamp returned by broker after appending the messages. If CreateTime is used for the topic, the timestamp will be -1. If LogAppendTime is used for the topic, the timestamp will be the broker local time when the messages are appended.</td></tr>
+<tr>
+<td>log_start_offset</td><td>The start offset of the log at the time this produce response was created</td></tr>
+<tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+</table>
+</p>
+<h5><a name="The_Messages_Fetch">Fetch API (Key: 1):</a></h5>
+
+<b>Requests:</b><br>
+<p><pre>Fetch Request (Version: 0) => replica_id max_wait_time min_bytes [topics]
+ replica_id => INT32
+ max_wait_time => INT32
+ min_bytes => INT32
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition fetch_offset max_bytes
+ partition => INT32
+ fetch_offset => INT64
+ max_bytes => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>replica_id</td><td>Broker id of the follower. For normal consumers, use -1.</td></tr>
+<tr>
+<td>max_wait_time</td><td>Maximum time in ms to wait for the response.</td></tr>
+<tr>
+<td>min_bytes</td><td>Minimum bytes to accumulate in the response.</td></tr>
+<tr>
+<td>topics</td><td>Topics to fetch.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to fetch.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>fetch_offset</td><td>Message offset.</td></tr>
+<tr>
+<td>max_bytes</td><td>Maximum bytes to fetch.</td></tr>
+</table>
+</p>
+<p><pre>Fetch Request (Version: 1) => replica_id max_wait_time min_bytes [topics]
+ replica_id => INT32
+ max_wait_time => INT32
+ min_bytes => INT32
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition fetch_offset max_bytes
+ partition => INT32
+ fetch_offset => INT64
+ max_bytes => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>replica_id</td><td>Broker id of the follower. For normal consumers, use -1.</td></tr>
+<tr>
+<td>max_wait_time</td><td>Maximum time in ms to wait for the response.</td></tr>
+<tr>
+<td>min_bytes</td><td>Minimum bytes to accumulate in the response.</td></tr>
+<tr>
+<td>topics</td><td>Topics to fetch.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to fetch.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>fetch_offset</td><td>Message offset.</td></tr>
+<tr>
+<td>max_bytes</td><td>Maximum bytes to fetch.</td></tr>
+</table>
+</p>
+<p><pre>Fetch Request (Version: 2) => replica_id max_wait_time min_bytes [topics]
+ replica_id => INT32
+ max_wait_time => INT32
+ min_bytes => INT32
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition fetch_offset max_bytes
+ partition => INT32
+ fetch_offset => INT64
+ max_bytes => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>replica_id</td><td>Broker id of the follower. For normal consumers, use -1.</td></tr>
+<tr>
+<td>max_wait_time</td><td>Maximum time in ms to wait for the response.</td></tr>
+<tr>
+<td>min_bytes</td><td>Minimum bytes to accumulate in the response.</td></tr>
+<tr>
+<td>topics</td><td>Topics to fetch.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to fetch.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>fetch_offset</td><td>Message offset.</td></tr>
+<tr>
+<td>max_bytes</td><td>Maximum bytes to fetch.</td></tr>
+</table>
+</p>
+<p><pre>Fetch Request (Version: 3) => replica_id max_wait_time min_bytes max_bytes [topics]
+ replica_id => INT32
+ max_wait_time => INT32
+ min_bytes => INT32
+ max_bytes => INT32
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition fetch_offset max_bytes
+ partition => INT32
+ fetch_offset => INT64
+ max_bytes => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>replica_id</td><td>Broker id of the follower. For normal consumers, use -1.</td></tr>
+<tr>
+<td>max_wait_time</td><td>Maximum time in ms to wait for the response.</td></tr>
+<tr>
+<td>min_bytes</td><td>Minimum bytes to accumulate in the response.</td></tr>
+<tr>
+<td>max_bytes</td><td>Maximum bytes to accumulate in the response. Note that this is not an absolute maximum, if the first message in the first non-empty partition of the fetch is larger than this value, the message will still be returned to ensure that progress can be made.</td></tr>
+<tr>
+<td>topics</td><td>Topics to fetch in the order provided.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to fetch.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>fetch_offset</td><td>Message offset.</td></tr>
+<tr>
+<td>max_bytes</td><td>Maximum bytes to fetch.</td></tr>
+</table>
+</p>
+<p><pre>Fetch Request (Version: 4) => replica_id max_wait_time min_bytes max_bytes isolation_level [topics]
+ replica_id => INT32
+ max_wait_time => INT32
+ min_bytes => INT32
+ max_bytes => INT32
+ isolation_level => INT8
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition fetch_offset max_bytes
+ partition => INT32
+ fetch_offset => INT64
+ max_bytes => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>replica_id</td><td>Broker id of the follower. For normal consumers, use -1.</td></tr>
+<tr>
+<td>max_wait_time</td><td>Maximum time in ms to wait for the response.</td></tr>
+<tr>
+<td>min_bytes</td><td>Minimum bytes to accumulate in the response.</td></tr>
+<tr>
+<td>max_bytes</td><td>Maximum bytes to accumulate in the response. Note that this is not an absolute maximum, if the first message in the first non-empty partition of the fetch is larger than this value, the message will still be returned to ensure that progress can be made.</td></tr>
+<tr>
+<td>isolation_level</td><td>This setting controls the visibility of transactional records. Using READ_UNCOMMITTED (isolation_level = 0) makes all records visible. With READ_COMMITTED (isolation_level = 1), non-transactional and COMMITTED transactional records are visible. To be more concrete, READ_COMMITTED returns all data from offsets smaller than the current LSO (last stable offset), and enables the inclusion of the list of aborted transactions in the result, which allows consumers to discard ABORTED transactional records</td></tr>
+<tr>
+<td>topics</td><td>Topics to fetch in the order provided.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to fetch.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>fetch_offset</td><td>Message offset.</td></tr>
+<tr>
+<td>max_bytes</td><td>Maximum bytes to fetch.</td></tr>
+</table>
+</p>
+<p><pre>Fetch Request (Version: 5) => replica_id max_wait_time min_bytes max_bytes isolation_level [topics]
+ replica_id => INT32
+ max_wait_time => INT32
+ min_bytes => INT32
+ max_bytes => INT32
+ isolation_level => INT8
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition fetch_offset log_start_offset max_bytes
+ partition => INT32
+ fetch_offset => INT64
+ log_start_offset => INT64
+ max_bytes => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>replica_id</td><td>Broker id of the follower. For normal consumers, use -1.</td></tr>
+<tr>
+<td>max_wait_time</td><td>Maximum time in ms to wait for the response.</td></tr>
+<tr>
+<td>min_bytes</td><td>Minimum bytes to accumulate in the response.</td></tr>
+<tr>
+<td>max_bytes</td><td>Maximum bytes to accumulate in the response. Note that this is not an absolute maximum, if the first message in the first non-empty partition of the fetch is larger than this value, the message will still be returned to ensure that progress can be made.</td></tr>
+<tr>
+<td>isolation_level</td><td>This setting controls the visibility of transactional records. Using READ_UNCOMMITTED (isolation_level = 0) makes all records visible. With READ_COMMITTED (isolation_level = 1), non-transactional and COMMITTED transactional records are visible. To be more concrete, READ_COMMITTED returns all data from offsets smaller than the current LSO (last stable offset), and enables the inclusion of the list of aborted transactions in the result, which allows consumers to discard ABORTED transactional records</td></tr>
+<tr>
+<td>topics</td><td>Topics to fetch in the order provided.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to fetch.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>fetch_offset</td><td>Message offset.</td></tr>
+<tr>
+<td>log_start_offset</td><td>Earliest available offset of the follower replica. The field is only used when request is sent by follower. </td></tr>
+<tr>
+<td>max_bytes</td><td>Maximum bytes to fetch.</td></tr>
+</table>
+</p>
+<p><pre>Fetch Request (Version: 6) => replica_id max_wait_time min_bytes max_bytes isolation_level [topics]
+ replica_id => INT32
+ max_wait_time => INT32
+ min_bytes => INT32
+ max_bytes => INT32
+ isolation_level => INT8
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition fetch_offset log_start_offset max_bytes
+ partition => INT32
+ fetch_offset => INT64
+ log_start_offset => INT64
+ max_bytes => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>replica_id</td><td>Broker id of the follower. For normal consumers, use -1.</td></tr>
+<tr>
+<td>max_wait_time</td><td>Maximum time in ms to wait for the response.</td></tr>
+<tr>
+<td>min_bytes</td><td>Minimum bytes to accumulate in the response.</td></tr>
+<tr>
+<td>max_bytes</td><td>Maximum bytes to accumulate in the response. Note that this is not an absolute maximum, if the first message in the first non-empty partition of the fetch is larger than this value, the message will still be returned to ensure that progress can be made.</td></tr>
+<tr>
+<td>isolation_level</td><td>This setting controls the visibility of transactional records. Using READ_UNCOMMITTED (isolation_level = 0) makes all records visible. With READ_COMMITTED (isolation_level = 1), non-transactional and COMMITTED transactional records are visible. To be more concrete, READ_COMMITTED returns all data from offsets smaller than the current LSO (last stable offset), and enables the inclusion of the list of aborted transactions in the result, which allows consumers to discard ABORTED transactional records</td></tr>
+<tr>
+<td>topics</td><td>Topics to fetch in the order provided.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to fetch.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>fetch_offset</td><td>Message offset.</td></tr>
+<tr>
+<td>log_start_offset</td><td>Earliest available offset of the follower replica. The field is only used when request is sent by follower. </td></tr>
+<tr>
+<td>max_bytes</td><td>Maximum bytes to fetch.</td></tr>
+</table>
+</p>
+<b>Responses:</b><br>
+<p><pre>Fetch Response (Version: 0) => [responses]
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition_header record_set
+ partition_header => partition error_code high_watermark
+ partition => INT32
+ error_code => INT16
+ high_watermark => INT64
+ record_set => RECORDS
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition_header</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>high_watermark</td><td>Last committed offset.</td></tr>
+<tr>
+<td>record_set</td><td>null</td></tr>
+</table>
+</p>
+<p><pre>Fetch Response (Version: 1) => throttle_time_ms [responses]
+ throttle_time_ms => INT32
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition_header record_set
+ partition_header => partition error_code high_watermark
+ partition => INT32
+ error_code => INT16
+ high_watermark => INT64
+ record_set => RECORDS
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+<tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition_header</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>high_watermark</td><td>Last committed offset.</td></tr>
+<tr>
+<td>record_set</td><td>null</td></tr>
+</table>
+</p>
+<p><pre>Fetch Response (Version: 2) => throttle_time_ms [responses]
+ throttle_time_ms => INT32
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition_header record_set
+ partition_header => partition error_code high_watermark
+ partition => INT32
+ error_code => INT16
+ high_watermark => INT64
+ record_set => RECORDS
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+<tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition_header</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>high_watermark</td><td>Last committed offset.</td></tr>
+<tr>
+<td>record_set</td><td>null</td></tr>
+</table>
+</p>
+<p><pre>Fetch Response (Version: 3) => throttle_time_ms [responses]
+ throttle_time_ms => INT32
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition_header record_set
+ partition_header => partition error_code high_watermark
+ partition => INT32
+ error_code => INT16
+ high_watermark => INT64
+ record_set => RECORDS
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+<tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition_header</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>high_watermark</td><td>Last committed offset.</td></tr>
+<tr>
+<td>record_set</td><td>null</td></tr>
+</table>
+</p>
+<p><pre>Fetch Response (Version: 4) => throttle_time_ms [responses]
+ throttle_time_ms => INT32
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition_header record_set
+ partition_header => partition error_code high_watermark last_stable_offset [aborted_transactions]
+ partition => INT32
+ error_code => INT16
+ high_watermark => INT64
+ last_stable_offset => INT64
+ aborted_transactions => producer_id first_offset
+ producer_id => INT64
+ first_offset => INT64
+ record_set => RECORDS
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+<tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition_header</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>high_watermark</td><td>Last committed offset.</td></tr>
+<tr>
+<td>last_stable_offset</td><td>The last stable offset (or LSO) of the partition. This is the last offset such that the state of all transactional records prior to this offset have been decided (ABORTED or COMMITTED)</td></tr>
+<tr>
+<td>aborted_transactions</td><td>null</td></tr>
+<tr>
+<td>producer_id</td><td>The producer id associated with the aborted transactions</td></tr>
+<tr>
+<td>first_offset</td><td>The first offset in the aborted transaction</td></tr>
+<tr>
+<td>record_set</td><td>null</td></tr>
+</table>
+</p>
+<p><pre>Fetch Response (Version: 5) => throttle_time_ms [responses]
+ throttle_time_ms => INT32
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition_header record_set
+ partition_header => partition error_code high_watermark last_stable_offset log_start_offset [aborted_transactions]
+ partition => INT32
+ error_code => INT16
+ high_watermark => INT64
+ last_stable_offset => INT64
+ log_start_offset => INT64
+ aborted_transactions => producer_id first_offset
+ producer_id => INT64
+ first_offset => INT64
+ record_set => RECORDS
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+<tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition_header</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>high_watermark</td><td>Last committed offset.</td></tr>
+<tr>
+<td>last_stable_offset</td><td>The last stable offset (or LSO) of the partition. This is the last offset such that the state of all transactional records prior to this offset have been decided (ABORTED or COMMITTED)</td></tr>
+<tr>
+<td>log_start_offset</td><td>Earliest available offset.</td></tr>
+<tr>
+<td>aborted_transactions</td><td>null</td></tr>
+<tr>
+<td>producer_id</td><td>The producer id associated with the aborted transactions</td></tr>
+<tr>
+<td>first_offset</td><td>The first offset in the aborted transaction</td></tr>
+<tr>
+<td>record_set</td><td>null</td></tr>
+</table>
+</p>
+<p><pre>Fetch Response (Version: 6) => throttle_time_ms [responses]
+ throttle_time_ms => INT32
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition_header record_set
+ partition_header => partition error_code high_watermark last_stable_offset log_start_offset [aborted_transactions]
+ partition => INT32
+ error_code => INT16
+ high_watermark => INT64
+ last_stable_offset => INT64
+ log_start_offset => INT64
+ aborted_transactions => producer_id first_offset
+ producer_id => INT64
+ first_offset => INT64
+ record_set => RECORDS
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+<tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition_header</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>high_watermark</td><td>Last committed offset.</td></tr>
+<tr>
+<td>last_stable_offset</td><td>The last stable offset (or LSO) of the partition. This is the last offset such that the state of all transactional records prior to this offset have been decided (ABORTED or COMMITTED)</td></tr>
+<tr>
+<td>log_start_offset</td><td>Earliest available offset.</td></tr>
+<tr>
+<td>aborted_transactions</td><td>null</td></tr>
+<tr>
+<td>producer_id</td><td>The producer id associated with the aborted transactions</td></tr>
+<tr>
+<td>first_offset</td><td>The first offset in the aborted transaction</td></tr>
+<tr>
+<td>record_set</td><td>null</td></tr>
+</table>
+</p>
+<h5><a name="The_Messages_ListOffsets">ListOffsets API (Key: 2):</a></h5>
+
+<b>Requests:</b><br>
+<p><pre>ListOffsets Request (Version: 0) => replica_id [topics]
+ replica_id => INT32
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition timestamp max_num_offsets
+ partition => INT32
+ timestamp => INT64
+ max_num_offsets => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>replica_id</td><td>Broker id of the follower. For normal consumers, use -1.</td></tr>
+<tr>
+<td>topics</td><td>Topics to list offsets.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to list offset.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>timestamp</td><td>Timestamp.</td></tr>
+<tr>
+<td>max_num_offsets</td><td>Maximum offsets to return.</td></tr>
+</table>
+</p>
+<p><pre>ListOffsets Request (Version: 1) => replica_id [topics]
+ replica_id => INT32
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition timestamp
+ partition => INT32
+ timestamp => INT64
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>replica_id</td><td>Broker id of the follower. For normal consumers, use -1.</td></tr>
+<tr>
+<td>topics</td><td>Topics to list offsets.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to list offset.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>timestamp</td><td>The target timestamp for the partition.</td></tr>
+</table>
+</p>
+<p><pre>ListOffsets Request (Version: 2) => replica_id isolation_level [topics]
+ replica_id => INT32
+ isolation_level => INT8
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition timestamp
+ partition => INT32
+ timestamp => INT64
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>replica_id</td><td>Broker id of the follower. For normal consumers, use -1.</td></tr>
+<tr>
+<td>isolation_level</td><td>This setting controls the visibility of transactional records. Using READ_UNCOMMITTED (isolation_level = 0) makes all records visible. With READ_COMMITTED (isolation_level = 1), non-transactional and COMMITTED transactional records are visible. To be more concrete, READ_COMMITTED returns all data from offsets smaller than the current LSO (last stable offset), and enables the inclusion of the list of aborted transactions in the result, which allows consumers to discard ABORTED transactional records</td></tr>
+<tr>
+<td>topics</td><td>Topics to list offsets.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to list offset.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>timestamp</td><td>The target timestamp for the partition.</td></tr>
+</table>
+</p>
+<b>Responses:</b><br>
+<p><pre>ListOffsets Response (Version: 0) => [responses]
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition error_code [offsets]
+ partition => INT32
+ error_code => INT16
+ offsets => INT64
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>offsets</td><td>A list of offsets.</td></tr>
+</table>
+</p>
+<p><pre>ListOffsets Response (Version: 1) => [responses]
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition error_code timestamp offset
+ partition => INT32
+ error_code => INT16
+ timestamp => INT64
+ offset => INT64
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>timestamp</td><td>The timestamp associated with the returned offset</td></tr>
+<tr>
+<td>offset</td><td>offset found</td></tr>
+</table>
+</p>
+<p><pre>ListOffsets Response (Version: 2) => throttle_time_ms [responses]
+ throttle_time_ms => INT32
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition error_code timestamp offset
+ partition => INT32
+ error_code => INT16
+ timestamp => INT64
+ offset => INT64
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+<tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>timestamp</td><td>The timestamp associated with the returned offset</td></tr>
+<tr>
+<td>offset</td><td>offset found</td></tr>
+</table>
+</p>
+<h5><a name="The_Messages_Metadata">Metadata API (Key: 3):</a></h5>
+
+<b>Requests:</b><br>
+<p><pre>Metadata Request (Version: 0) => [topics]
+ topics => STRING
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>topics</td><td>An array of topics to fetch metadata for. If no topics are specified fetch metadata for all topics.</td></tr>
+</table>
+</p>
+<p><pre>Metadata Request (Version: 1) => [topics]
+ topics => STRING
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>topics</td><td>An array of topics to fetch metadata for. If the topics array is null fetch metadata for all topics.</td></tr>
+</table>
+</p>
+<p><pre>Metadata Request (Version: 2) => [topics]
+ topics => STRING
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>topics</td><td>An array of topics to fetch metadata for. If the topics array is null fetch metadata for all topics.</td></tr>
+</table>
+</p>
+<p><pre>Metadata Request (Version: 3) => [topics]
+ topics => STRING
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>topics</td><td>An array of topics to fetch metadata for. If the topics array is null fetch metadata for all topics.</td></tr>
+</table>
+</p>
+<p><pre>Metadata Request (Version: 4) => [topics] allow_auto_topic_creation
+ topics => STRING
+ allow_auto_topic_creation => BOOLEAN
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>topics</td><td>An array of topics to fetch metadata for. If the topics array is null fetch metadata for all topics.</td></tr>
+<tr>
+<td>allow_auto_topic_creation</td><td>If this and the broker config 'auto.create.topics.enable' are true, topics that don't exist will be created by the broker. Otherwise, no topics will be created by the broker.</td></tr>
+</table>
+</p>
+<p><pre>Metadata Request (Version: 5) => [topics] allow_auto_topic_creation
+ topics => STRING
+ allow_auto_topic_creation => BOOLEAN
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>topics</td><td>An array of topics to fetch metadata for. If the topics array is null fetch metadata for all topics.</td></tr>
+<tr>
+<td>allow_auto_topic_creation</td><td>If this and the broker config 'auto.create.topics.enable' are true, topics that don't exist will be created by the broker. Otherwise, no topics will be created by the broker.</td></tr>
+</table>
+</p>
+<b>Responses:</b><br>
+<p><pre>Metadata Response (Version: 0) => [brokers] [topic_metadata]
+ brokers => node_id host port
+ node_id => INT32
+ host => STRING
+ port => INT32
+ topic_metadata => error_code topic [partition_metadata]
+ error_code => INT16
+ topic => STRING
+ partition_metadata => error_code partition leader [replicas] [isr]
+ error_code => INT16
+ partition => INT32
+ leader => INT32
+ replicas => INT32
+ isr => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>brokers</td><td>Host and port information for all brokers.</td></tr>
+<tr>
+<td>node_id</td><td>The broker id.</td></tr>
+<tr>
+<td>host</td><td>The hostname of the broker.</td></tr>
+<tr>
+<td>port</td><td>The port on which the broker accepts requests.</td></tr>
+<tr>
+<td>topic_metadata</td><td>null</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_metadata</td><td>Metadata for each partition of the topic.</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>leader</td><td>The id of the broker acting as leader for this partition.</td></tr>
+<tr>
+<td>replicas</td><td>The set of all nodes that host this partition.</td></tr>
+<tr>
+<td>isr</td><td>The set of nodes that are in sync with the leader for this partition.</td></tr>
+</table>
+</p>
+<p><pre>Metadata Response (Version: 1) => [brokers] controller_id [topic_metadata]
+ brokers => node_id host port rack
+ node_id => INT32
+ host => STRING
+ port => INT32
+ rack => NULLABLE_STRING
+ controller_id => INT32
+ topic_metadata => error_code topic is_internal [partition_metadata]
+ error_code => INT16
+ topic => STRING
+ is_internal => BOOLEAN
+ partition_metadata => error_code partition leader [replicas] [isr]
+ error_code => INT16
+ partition => INT32
+ leader => INT32
+ replicas => INT32
+ isr => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>brokers</td><td>Host and port information for all brokers.</td></tr>
+<tr>
+<td>node_id</td><td>The broker id.</td></tr>
+<tr>
+<td>host</td><td>The hostname of the broker.</td></tr>
+<tr>
+<td>port</td><td>The port on which the broker accepts requests.</td></tr>
+<tr>
+<td>rack</td><td>The rack of the broker.</td></tr>
+<tr>
+<td>controller_id</td><td>The broker id of the controller broker.</td></tr>
+<tr>
+<td>topic_metadata</td><td>null</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>is_internal</td><td>Indicates if the topic is considered a Kafka internal topic</td></tr>
+<tr>
+<td>partition_metadata</td><td>Metadata for each partition of the topic.</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>leader</td><td>The id of the broker acting as leader for this partition.</td></tr>
+<tr>
+<td>replicas</td><td>The set of all nodes that host this partition.</td></tr>
+<tr>
+<td>isr</td><td>The set of nodes that are in sync with the leader for this partition.</td></tr>
+</table>
+</p>
+<p><pre>Metadata Response (Version: 2) => [brokers] cluster_id controller_id [topic_metadata]
+ brokers => node_id host port rack
+ node_id => INT32
+ host => STRING
+ port => INT32
+ rack => NULLABLE_STRING
+ cluster_id => NULLABLE_STRING
+ controller_id => INT32
+ topic_metadata => error_code topic is_internal [partition_metadata]
+ error_code => INT16
+ topic => STRING
+ is_internal => BOOLEAN
+ partition_metadata => error_code partition leader [replicas] [isr]
+ error_code => INT16
+ partition => INT32
+ leader => INT32
+ replicas => INT32
+ isr => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>brokers</td><td>Host and port information for all brokers.</td></tr>
+<tr>
+<td>node_id</td><td>The broker id.</td></tr>
+<tr>
+<td>host</td><td>The hostname of the broker.</td></tr>
+<tr>
+<td>port</td><td>The port on which the broker accepts requests.</td></tr>
+<tr>
+<td>rack</td><td>The rack of the broker.</td></tr>
+<tr>
+<td>cluster_id</td><td>The cluster id that this broker belongs to.</td></tr>
+<tr>
+<td>controller_id</td><td>The broker id of the controller broker.</td></tr>
+<tr>
+<td>topic_metadata</td><td>null</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>is_internal</td><td>Indicates if the topic is considered a Kafka internal topic</td></tr>
+<tr>
+<td>partition_metadata</td><td>Metadata for each partition of the topic.</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>leader</td><td>The id of the broker acting as leader for this partition.</td></tr>
+<tr>
+<td>replicas</td><td>The set of all nodes that host this partition.</td></tr>
+<tr>
+<td>isr</td><td>The set of nodes that are in sync with the leader for this partition.</td></tr>
+</table>
+</p>
+<p><pre>Metadata Response (Version: 3) => throttle_time_ms [brokers] cluster_id controller_id [topic_metadata]
+ throttle_time_ms => INT32
+ brokers => node_id host port rack
+ node_id => INT32
+ host => STRING
+ port => INT32
+ rack => NULLABLE_STRING
+ cluster_id => NULLABLE_STRING
+ controller_id => INT32
+ topic_metadata => error_code topic is_internal [partition_metadata]
+ error_code => INT16
+ topic => STRING
+ is_internal => BOOLEAN
+ partition_metadata => error_code partition leader [replicas] [isr]
+ error_code => INT16
+ partition => INT32
+ leader => INT32
+ replicas => INT32
+ isr => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+<tr>
+<td>brokers</td><td>Host and port information for all brokers.</td></tr>
+<tr>
+<td>node_id</td><td>The broker id.</td></tr>
+<tr>
+<td>host</td><td>The hostname of the broker.</td></tr>
+<tr>
+<td>port</td><td>The port on which the broker accepts requests.</td></tr>
+<tr>
+<td>rack</td><td>The rack of the broker.</td></tr>
+<tr>
+<td>cluster_id</td><td>The cluster id that this broker belongs to.</td></tr>
+<tr>
+<td>controller_id</td><td>The broker id of the controller broker.</td></tr>
+<tr>
+<td>topic_metadata</td><td>null</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>is_internal</td><td>Indicates if the topic is considered a Kafka internal topic</td></tr>
+<tr>
+<td>partition_metadata</td><td>Metadata for each partition of the topic.</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>leader</td><td>The id of the broker acting as leader for this partition.</td></tr>
+<tr>
+<td>replicas</td><td>The set of all nodes that host this partition.</td></tr>
+<tr>
+<td>isr</td><td>The set of nodes that are in sync with the leader for this partition.</td></tr>
+</table>
+</p>
+<p><pre>Metadata Response (Version: 4) => throttle_time_ms [brokers] cluster_id controller_id [topic_metadata]
+ throttle_time_ms => INT32
+ brokers => node_id host port rack
+ node_id => INT32
+ host => STRING
+ port => INT32
+ rack => NULLABLE_STRING
+ cluster_id => NULLABLE_STRING
+ controller_id => INT32
+ topic_metadata => error_code topic is_internal [partition_metadata]
+ error_code => INT16
+ topic => STRING
+ is_internal => BOOLEAN
+ partition_metadata => error_code partition leader [replicas] [isr]
+ error_code => INT16
+ partition => INT32
+ leader => INT32
+ replicas => INT32
+ isr => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+<tr>
+<td>brokers</td><td>Host and port information for all brokers.</td></tr>
+<tr>
+<td>node_id</td><td>The broker id.</td></tr>
+<tr>
+<td>host</td><td>The hostname of the broker.</td></tr>
+<tr>
+<td>port</td><td>The port on which the broker accepts requests.</td></tr>
+<tr>
+<td>rack</td><td>The rack of the broker.</td></tr>
+<tr>
+<td>cluster_id</td><td>The cluster id that this broker belongs to.</td></tr>
+<tr>
+<td>controller_id</td><td>The broker id of the controller broker.</td></tr>
+<tr>
+<td>topic_metadata</td><td>null</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>is_internal</td><td>Indicates if the topic is considered a Kafka internal topic</td></tr>
+<tr>
+<td>partition_metadata</td><td>Metadata for each partition of the topic.</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>leader</td><td>The id of the broker acting as leader for this partition.</td></tr>
+<tr>
+<td>replicas</td><td>The set of all nodes that host this partition.</td></tr>
+<tr>
+<td>isr</td><td>The set of nodes that are in sync with the leader for this partition.</td></tr>
+</table>
+</p>
+<p><pre>Metadata Response (Version: 5) => throttle_time_ms [brokers] cluster_id controller_id [topic_metadata]
+ throttle_time_ms => INT32
+ brokers => node_id host port rack
+ node_id => INT32
+ host => STRING
+ port => INT32
+ rack => NULLABLE_STRING
+ cluster_id => NULLABLE_STRING
+ controller_id => INT32
+ topic_metadata => error_code topic is_internal [partition_metadata]
+ error_code => INT16
+ topic => STRING
+ is_internal => BOOLEAN
+ partition_metadata => error_code partition leader [replicas] [isr] [offline_replicas]
+ error_code => INT16
+ partition => INT32
+ leader => INT32
+ replicas => INT32
+ isr => INT32
+ offline_replicas => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+<tr>
+<td>brokers</td><td>Host and port information for all brokers.</td></tr>
+<tr>
+<td>node_id</td><td>The broker id.</td></tr>
+<tr>
+<td>host</td><td>The hostname of the broker.</td></tr>
+<tr>
+<td>port</td><td>The port on which the broker accepts requests.</td></tr>
+<tr>
+<td>rack</td><td>The rack of the broker.</td></tr>
+<tr>
+<td>cluster_id</td><td>The cluster id that this broker belongs to.</td></tr>
+<tr>
+<td>controller_id</td><td>The broker id of the controller broker.</td></tr>
+<tr>
+<td>topic_metadata</td><td>null</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>is_internal</td><td>Indicates if the topic is considered a Kafka internal topic</td></tr>
+<tr>
+<td>partition_metadata</td><td>Metadata for each partition of the topic.</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>leader</td><td>The id of the broker acting as leader for this partition.</td></tr>
+<tr>
+<td>replicas</td><td>The set of all nodes that host this partition.</td></tr>
+<tr>
+<td>isr</td><td>The set of nodes that are in sync with the leader for this partition.</td></tr>
+<tr>
+<td>offline_replicas</td><td>The set of offline replicas of this partition.</td></tr>
+</table>
+</p>
+<h5><a name="The_Messages_LeaderAndIsr">LeaderAndIsr API (Key: 4):</a></h5>
+
+<b>Requests:</b><br>
+<p><pre>LeaderAndIsr Request (Version: 0) => controller_id controller_epoch [partition_states] [live_leaders]
+ controller_id => INT32
+ controller_epoch => INT32
+ partition_states => topic partition controller_epoch leader leader_epoch [isr] zk_version [replicas]
+ topic => STRING
+ partition => INT32
+ controller_epoch => INT32
+ leader => INT32
+ leader_epoch => INT32
+ isr => INT32
+ zk_version => INT32
+ replicas => INT32
+ live_leaders => id host port
+ id => INT32
+ host => STRING
+ port => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>controller_id</td><td>The controller id.</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>partition_states</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>leader</td><td>The broker id for the leader.</td></tr>
+<tr>
+<td>leader_epoch</td><td>The leader epoch.</td></tr>
+<tr>
+<td>isr</td><td>The in sync replica ids.</td></tr>
+<tr>
+<td>zk_version</td><td>The ZK version.</td></tr>
+<tr>
+<td>replicas</td><td>The replica ids.</td></tr>
+<tr>
+<td>live_leaders</td><td>null</td></tr>
+<tr>
+<td>id</td><td>The broker id.</td></tr>
+<tr>
+<td>host</td><td>The hostname of the broker.</td></tr>
+<tr>
+<td>port</td><td>The port on which the broker accepts requests.</td></tr>
+</table>
+</p>
+<p><pre>LeaderAndIsr Request (Version: 1) => controller_id controller_epoch [partition_states] [live_leaders]
+ controller_id => INT32
+ controller_epoch => INT32
+ partition_states => topic partition controller_epoch leader leader_epoch [isr] zk_version [replicas] is_new
+ topic => STRING
+ partition => INT32
+ controller_epoch => INT32
+ leader => INT32
+ leader_epoch => INT32
+ isr => INT32
+ zk_version => INT32
+ replicas => INT32
+ is_new => BOOLEAN
+ live_leaders => id host port
+ id => INT32
+ host => STRING
+ port => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>controller_id</td><td>The controller id.</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>partition_states</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>leader</td><td>The broker id for the leader.</td></tr>
+<tr>
+<td>leader_epoch</td><td>The leader epoch.</td></tr>
+<tr>
+<td>isr</td><td>The in sync replica ids.</td></tr>
+<tr>
+<td>zk_version</td><td>The ZK version.</td></tr>
+<tr>
+<td>replicas</td><td>The replica ids.</td></tr>
+<tr>
+<td>is_new</td><td>Whether the replica should have existed on the broker or not</td></tr>
+<tr>
+<td>live_leaders</td><td>null</td></tr>
+<tr>
+<td>id</td><td>The broker id.</td></tr>
+<tr>
+<td>host</td><td>The hostname of the broker.</td></tr>
+<tr>
+<td>port</td><td>The port on which the broker accepts requests.</td></tr>
+</table>
+</p>
+<b>Responses:</b><br>
+<p><pre>LeaderAndIsr Response (Version: 0) => error_code [partitions]
+ error_code => INT16
+ partitions => topic partition error_code
+ topic => STRING
+ partition => INT32
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>partitions</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<p><pre>LeaderAndIsr Response (Version: 1) => error_code [partitions]
+ error_code => INT16
+ partitions => topic partition error_code
+ topic => STRING
+ partition => INT32
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>partitions</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<h5><a name="The_Messages_StopReplica">StopReplica API (Key: 5):</a></h5>
+
+<b>Requests:</b><br>
+<p><pre>StopReplica Request (Version: 0) => controller_id controller_epoch delete_partitions [partitions]
+ controller_id => INT32
+ controller_epoch => INT32
+ delete_partitions => BOOLEAN
+ partitions => topic partition
+ topic => STRING
+ partition => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>controller_id</td><td>The controller id.</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>delete_partitions</td><td>Boolean which indicates if replica's partitions must be deleted.</td></tr>
+<tr>
+<td>partitions</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+</table>
+</p>
+<b>Responses:</b><br>
+<p><pre>StopReplica Response (Version: 0) => error_code [partitions]
+ error_code => INT16
+ partitions => topic partition error_code
+ topic => STRING
+ partition => INT32
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>partitions</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<h5><a name="The_Messages_UpdateMetadata">UpdateMetadata API (Key: 6):</a></h5>
+
+<b>Requests:</b><br>
+<p><pre>UpdateMetadata Request (Version: 0) => controller_id controller_epoch [partition_states] [live_brokers]
+ controller_id => INT32
+ controller_epoch => INT32
+ partition_states => topic partition controller_epoch leader leader_epoch [isr] zk_version [replicas]
+ topic => STRING
+ partition => INT32
+ controller_epoch => INT32
+ leader => INT32
+ leader_epoch => INT32
+ isr => INT32
+ zk_version => INT32
+ replicas => INT32
+ live_brokers => id host port
+ id => INT32
+ host => STRING
+ port => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>controller_id</td><td>The controller id.</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>partition_states</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>leader</td><td>The broker id for the leader.</td></tr>
+<tr>
+<td>leader_epoch</td><td>The leader epoch.</td></tr>
+<tr>
+<td>isr</td><td>The in sync replica ids.</td></tr>
+<tr>
+<td>zk_version</td><td>The ZK version.</td></tr>
+<tr>
+<td>replicas</td><td>The replica ids.</td></tr>
+<tr>
+<td>live_brokers</td><td>null</td></tr>
+<tr>
+<td>id</td><td>The broker id.</td></tr>
+<tr>
+<td>host</td><td>The hostname of the broker.</td></tr>
+<tr>
+<td>port</td><td>The port on which the broker accepts requests.</td></tr>
+</table>
+</p>
+<p><pre>UpdateMetadata Request (Version: 1) => controller_id controller_epoch [partition_states] [live_brokers]
+ controller_id => INT32
+ controller_epoch => INT32
+ partition_states => topic partition controller_epoch leader leader_epoch [isr] zk_version [replicas]
+ topic => STRING
+ partition => INT32
+ controller_epoch => INT32
+ leader => INT32
+ leader_epoch => INT32
+ isr => INT32
+ zk_version => INT32
+ replicas => INT32
+ live_brokers => id [end_points]
+ id => INT32
+ end_points => port host security_protocol_type
+ port => INT32
+ host => STRING
+ security_protocol_type => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>controller_id</td><td>The controller id.</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>partition_states</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>leader</td><td>The broker id for the leader.</td></tr>
+<tr>
+<td>leader_epoch</td><td>The leader epoch.</td></tr>
+<tr>
+<td>isr</td><td>The in sync replica ids.</td></tr>
+<tr>
+<td>zk_version</td><td>The ZK version.</td></tr>
+<tr>
+<td>replicas</td><td>The replica ids.</td></tr>
+<tr>
+<td>live_brokers</td><td>null</td></tr>
+<tr>
+<td>id</td><td>The broker id.</td></tr>
+<tr>
+<td>end_points</td><td>null</td></tr>
+<tr>
+<td>port</td><td>The port on which the broker accepts requests.</td></tr>
+<tr>
+<td>host</td><td>The hostname of the broker.</td></tr>
+<tr>
+<td>security_protocol_type</td><td>The security protocol type.</td></tr>
+</table>
+</p>
+<p><pre>UpdateMetadata Request (Version: 2) => controller_id controller_epoch [partition_states] [live_brokers]
+ controller_id => INT32
+ controller_epoch => INT32
+ partition_states => topic partition controller_epoch leader leader_epoch [isr] zk_version [replicas]
+ topic => STRING
+ partition => INT32
+ controller_epoch => INT32
+ leader => INT32
+ leader_epoch => INT32
+ isr => INT32
+ zk_version => INT32
+ replicas => INT32
+ live_brokers => id [end_points] rack
+ id => INT32
+ end_points => port host security_protocol_type
+ port => INT32
+ host => STRING
+ security_protocol_type => INT16
+ rack => NULLABLE_STRING
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>controller_id</td><td>The controller id.</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>partition_states</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>leader</td><td>The broker id for the leader.</td></tr>
+<tr>
+<td>leader_epoch</td><td>The leader epoch.</td></tr>
+<tr>
+<td>isr</td><td>The in sync replica ids.</td></tr>
+<tr>
+<td>zk_version</td><td>The ZK version.</td></tr>
+<tr>
+<td>replicas</td><td>The replica ids.</td></tr>
+<tr>
+<td>live_brokers</td><td>null</td></tr>
+<tr>
+<td>id</td><td>The broker id.</td></tr>
+<tr>
+<td>end_points</td><td>null</td></tr>
+<tr>
+<td>port</td><td>The port on which the broker accepts requests.</td></tr>
+<tr>
+<td>host</td><td>The hostname of the broker.</td></tr>
+<tr>
+<td>security_protocol_type</td><td>The security protocol type.</td></tr>
+<tr>
+<td>rack</td><td>The rack</td></tr>
+</table>
+</p>
+<p><pre>UpdateMetadata Request (Version: 3) => controller_id controller_epoch [partition_states] [live_brokers]
+ controller_id => INT32
+ controller_epoch => INT32
+ partition_states => topic partition controller_epoch leader leader_epoch [isr] zk_version [replicas]
+ topic => STRING
+ partition => INT32
+ controller_epoch => INT32
+ leader => INT32
+ leader_epoch => INT32
+ isr => INT32
+ zk_version => INT32
+ replicas => INT32
+ live_brokers => id [end_points] rack
+ id => INT32
+ end_points => port host listener_name security_protocol_type
+ port => INT32
+ host => STRING
+ listener_name => STRING
+ security_protocol_type => INT16
+ rack => NULLABLE_STRING
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>controller_id</td><td>The controller id.</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>partition_states</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>leader</td><td>The broker id for the leader.</td></tr>
+<tr>
+<td>leader_epoch</td><td>The leader epoch.</td></tr>
+<tr>
+<td>isr</td><td>The in sync replica ids.</td></tr>
+<tr>
+<td>zk_version</td><td>The ZK version.</td></tr>
+<tr>
+<td>replicas</td><td>The replica ids.</td></tr>
+<tr>
+<td>live_brokers</td><td>null</td></tr>
+<tr>
+<td>id</td><td>The broker id.</td></tr>
+<tr>
+<td>end_points</td><td>null</td></tr>
+<tr>
+<td>port</td><td>The port on which the broker accepts requests.</td></tr>
+<tr>
+<td>host</td><td>The hostname of the broker.</td></tr>
+<tr>
+<td>listener_name</td><td>The listener name.</td></tr>
+<tr>
+<td>security_protocol_type</td><td>The security protocol type.</td></tr>
+<tr>
+<td>rack</td><td>The rack</td></tr>
+</table>
+</p>
+<p><pre>UpdateMetadata Request (Version: 4) => controller_id controller_epoch [partition_states] [live_brokers]
+ controller_id => INT32
+ controller_epoch => INT32
+ partition_states => topic partition controller_epoch leader leader_epoch [isr] zk_version [replicas] [offline_replicas]
+ topic => STRING
+ partition => INT32
+ controller_epoch => INT32
+ leader => INT32
+ leader_epoch => INT32
+ isr => INT32
+ zk_version => INT32
+ replicas => INT32
+ offline_replicas => INT32
+ live_brokers => id [end_points] rack
+ id => INT32
+ end_points => port host listener_name security_protocol_type
+ port => INT32
+ host => STRING
+ listener_name => STRING
+ security_protocol_type => INT16
+ rack => NULLABLE_STRING
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>controller_id</td><td>The controller id.</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>partition_states</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>controller_epoch</td><td>The controller epoch.</td></tr>
+<tr>
+<td>leader</td><td>The broker id for the leader.</td></tr>
+<tr>
+<td>leader_epoch</td><td>The leader epoch.</td></tr>
+<tr>
+<td>isr</td><td>The in sync replica ids.</td></tr>
+<tr>
+<td>zk_version</td><td>The ZK version.</td></tr>
+<tr>
+<td>replicas</td><td>The replica ids.</td></tr>
+<tr>
+<td>offline_replicas</td><td>The offline replica ids</td></tr>
+<tr>
+<td>live_brokers</td><td>null</td></tr>
+<tr>
+<td>id</td><td>The broker id.</td></tr>
+<tr>
+<td>end_points</td><td>null</td></tr>
+<tr>
+<td>port</td><td>The port on which the broker accepts requests.</td></tr>
+<tr>
+<td>host</td><td>The hostname of the broker.</td></tr>
+<tr>
+<td>listener_name</td><td>The listener name.</td></tr>
+<tr>
+<td>security_protocol_type</td><td>The security protocol type.</td></tr>
+<tr>
+<td>rack</td><td>The rack</td></tr>
+</table>
+</p>
+<b>Responses:</b><br>
+<p><pre>UpdateMetadata Response (Version: 0) => error_code
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<p><pre>UpdateMetadata Response (Version: 1) => error_code
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<p><pre>UpdateMetadata Response (Version: 2) => error_code
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<p><pre>UpdateMetadata Response (Version: 3) => error_code
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<p><pre>UpdateMetadata Response (Version: 4) => error_code
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<h5><a name="The_Messages_ControlledShutdown">ControlledShutdown API (Key: 7):</a></h5>
+
+<b>Requests:</b><br>
+<p><pre>ControlledShutdown Request (Version: 0) => broker_id
+ broker_id => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>broker_id</td><td>The id of the broker for which controlled shutdown has been requested.</td></tr>
+</table>
+</p>
+<p><pre>ControlledShutdown Request (Version: 1) => broker_id
+ broker_id => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>broker_id</td><td>The id of the broker for which controlled shutdown has been requested.</td></tr>
+</table>
+</p>
+<b>Responses:</b><br>
+<p><pre>ControlledShutdown Response (Version: 0) => error_code [partitions_remaining]
+ error_code => INT16
+ partitions_remaining => topic partition
+ topic => STRING
+ partition => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>partitions_remaining</td><td>The partitions that the broker still leads.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+</table>
+</p>
+<p><pre>ControlledShutdown Response (Version: 1) => error_code [partitions_remaining]
+ error_code => INT16
+ partitions_remaining => topic partition
+ topic => STRING
+ partition => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>partitions_remaining</td><td>The partitions that the broker still leads.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+</table>
+</p>
+<h5><a name="The_Messages_OffsetCommit">OffsetCommit API (Key: 8):</a></h5>
+
+<b>Requests:</b><br>
+<p><pre>OffsetCommit Request (Version: 0) => group_id [topics]
+ group_id => STRING
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition offset metadata
+ partition => INT32
+ offset => INT64
+ metadata => NULLABLE_STRING
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>group_id</td><td>The unique group identifier</td></tr>
+<tr>
+<td>topics</td><td>Topics to commit offsets.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to commit offsets.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>offset</td><td>Message offset to be committed.</td></tr>
+<tr>
+<td>metadata</td><td>Any associated metadata the client wants to keep.</td></tr>
+</table>
+</p>
+<p><pre>OffsetCommit Request (Version: 1) => group_id generation_id member_id [topics]
+ group_id => STRING
+ generation_id => INT32
+ member_id => STRING
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition offset timestamp metadata
+ partition => INT32
+ offset => INT64
+ timestamp => INT64
+ metadata => NULLABLE_STRING
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>group_id</td><td>The unique group identifier</td></tr>
+<tr>
+<td>generation_id</td><td>The generation of the group.</td></tr>
+<tr>
+<td>member_id</td><td>The member id assigned by the group coordinator or null if joining for the first time.</td></tr>
+<tr>
+<td>topics</td><td>Topics to commit offsets.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to commit offsets.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>offset</td><td>Message offset to be committed.</td></tr>
+<tr>
+<td>timestamp</td><td>Timestamp of the commit</td></tr>
+<tr>
+<td>metadata</td><td>Any associated metadata the client wants to keep.</td></tr>
+</table>
+</p>
+<p><pre>OffsetCommit Request (Version: 2) => group_id generation_id member_id retention_time [topics]
+ group_id => STRING
+ generation_id => INT32
+ member_id => STRING
+ retention_time => INT64
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition offset metadata
+ partition => INT32
+ offset => INT64
+ metadata => NULLABLE_STRING
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>group_id</td><td>The unique group identifier</td></tr>
+<tr>
+<td>generation_id</td><td>The generation of the group.</td></tr>
+<tr>
+<td>member_id</td><td>The member id assigned by the group coordinator or null if joining for the first time.</td></tr>
+<tr>
+<td>retention_time</td><td>Time period in ms to retain the offset.</td></tr>
+<tr>
+<td>topics</td><td>Topics to commit offsets.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to commit offsets.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>offset</td><td>Message offset to be committed.</td></tr>
+<tr>
+<td>metadata</td><td>Any associated metadata the client wants to keep.</td></tr>
+</table>
+</p>
+<p><pre>OffsetCommit Request (Version: 3) => group_id generation_id member_id retention_time [topics]
+ group_id => STRING
+ generation_id => INT32
+ member_id => STRING
+ retention_time => INT64
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition offset metadata
+ partition => INT32
+ offset => INT64
+ metadata => NULLABLE_STRING
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>group_id</td><td>The unique group identifier</td></tr>
+<tr>
+<td>generation_id</td><td>The generation of the group.</td></tr>
+<tr>
+<td>member_id</td><td>The member id assigned by the group coordinator or null if joining for the first time.</td></tr>
+<tr>
+<td>retention_time</td><td>Time period in ms to retain the offset.</td></tr>
+<tr>
+<td>topics</td><td>Topics to commit offsets.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to commit offsets.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>offset</td><td>Message offset to be committed.</td></tr>
+<tr>
+<td>metadata</td><td>Any associated metadata the client wants to keep.</td></tr>
+</table>
+</p>
+<b>Responses:</b><br>
+<p><pre>OffsetCommit Response (Version: 0) => [responses]
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition error_code
+ partition => INT32
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<p><pre>OffsetCommit Response (Version: 1) => [responses]
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition error_code
+ partition => INT32
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<p><pre>OffsetCommit Response (Version: 2) => [responses]
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition error_code
+ partition => INT32
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<p><pre>OffsetCommit Response (Version: 3) => throttle_time_ms [responses]
+ throttle_time_ms => INT32
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition error_code
+ partition => INT32
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+<tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<h5><a name="The_Messages_OffsetFetch">OffsetFetch API (Key: 9):</a></h5>
+
+<b>Requests:</b><br>
+<p><pre>OffsetFetch Request (Version: 0) => group_id [topics]
+ group_id => STRING
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition
+ partition => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>group_id</td><td>The unique group identifier</td></tr>
+<tr>
+<td>topics</td><td>Topics to fetch offsets.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to fetch offsets.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+</table>
+</p>
+<p><pre>OffsetFetch Request (Version: 1) => group_id [topics]
+ group_id => STRING
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition
+ partition => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>group_id</td><td>The unique group identifier</td></tr>
+<tr>
+<td>topics</td><td>Topics to fetch offsets.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to fetch offsets.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+</table>
+</p>
+<p><pre>OffsetFetch Request (Version: 2) => group_id [topics]
+ group_id => STRING
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition
+ partition => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>group_id</td><td>The unique group identifier</td></tr>
+<tr>
+<td>topics</td><td>Topics to fetch offsets. If the topic array is null fetch offsets for all topics.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to fetch offsets.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+</table>
+</p>
+<p><pre>OffsetFetch Request (Version: 3) => group_id [topics]
+ group_id => STRING
+ topics => topic [partitions]
+ topic => STRING
+ partitions => partition
+ partition => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>group_id</td><td>The unique group identifier</td></tr>
+<tr>
+<td>topics</td><td>Topics to fetch offsets. If the topic array is null fetch offsets for all topics.</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partitions</td><td>Partitions to fetch offsets.</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+</table>
+</p>
+<b>Responses:</b><br>
+<p><pre>OffsetFetch Response (Version: 0) => [responses]
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition offset metadata error_code
+ partition => INT32
+ offset => INT64
+ metadata => NULLABLE_STRING
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>offset</td><td>Last committed message offset.</td></tr>
+<tr>
+<td>metadata</td><td>Any associated metadata the client wants to keep.</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<p><pre>OffsetFetch Response (Version: 1) => [responses]
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition offset metadata error_code
+ partition => INT32
+ offset => INT64
+ metadata => NULLABLE_STRING
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>offset</td><td>Last committed message offset.</td></tr>
+<tr>
+<td>metadata</td><td>Any associated metadata the client wants to keep.</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<p><pre>OffsetFetch Response (Version: 2) => [responses] error_code
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition offset metadata error_code
+ partition => INT32
+ offset => INT64
+ metadata => NULLABLE_STRING
+ error_code => INT16
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>offset</td><td>Last committed message offset.</td></tr>
+<tr>
+<td>metadata</td><td>Any associated metadata the client wants to keep.</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<p><pre>OffsetFetch Response (Version: 3) => throttle_time_ms [responses] error_code
+ throttle_time_ms => INT32
+ responses => topic [partition_responses]
+ topic => STRING
+ partition_responses => partition offset metadata error_code
+ partition => INT32
+ offset => INT64
+ metadata => NULLABLE_STRING
+ error_code => INT16
+ error_code => INT16
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+<tr>
+<td>responses</td><td>null</td></tr>
+<tr>
+<td>topic</td><td>Name of topic</td></tr>
+<tr>
+<td>partition_responses</td><td>null</td></tr>
+<tr>
+<td>partition</td><td>Topic partition id</td></tr>
+<tr>
+<td>offset</td><td>Last committed message offset.</td></tr>
+<tr>
+<td>metadata</td><td>Any associated metadata the client wants to keep.</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+</table>
+</p>
+<h5><a name="The_Messages_FindCoordinator">FindCoordinator API (Key: 10):</a></h5>
+
+<b>Requests:</b><br>
+<p><pre>FindCoordinator Request (Version: 0) => group_id
+ group_id => STRING
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>group_id</td><td>The unique group identifier</td></tr>
+</table>
+</p>
+<p><pre>FindCoordinator Request (Version: 1) => coordinator_key coordinator_type
+ coordinator_key => STRING
+ coordinator_type => INT8
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>coordinator_key</td><td>Id to use for finding the coordinator (for groups, this is the groupId, for transactional producers, this is the transactional id)</td></tr>
+<tr>
+<td>coordinator_type</td><td>The type of coordinator to find (0 = group, 1 = transaction)</td></tr>
+</table>
+</p>
+<b>Responses:</b><br>
+<p><pre>FindCoordinator Response (Version: 0) => error_code coordinator
+ error_code => INT16
+ coordinator => node_id host port
+ node_id => INT32
+ host => STRING
+ port => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>coordinator</td><td>Host and port information for the coordinator for a consumer group.</td></tr>
+<tr>
+<td>node_id</td><td>The broker id.</td></tr>
+<tr>
+<td>host</td><td>The hostname of the broker.</td></tr>
+<tr>
+<td>port</td><td>The port on which the broker accepts requests.</td></tr>
+</table>
+</p>
+<p><pre>FindCoordinator Response (Version: 1) => throttle_time_ms error_code error_message coordinator
+ throttle_time_ms => INT32
+ error_code => INT16
+ error_message => NULLABLE_STRING
+ coordinator => node_id host port
+ node_id => INT32
+ host => STRING
+ port => INT32
+</pre><table class="data-table"><tbody>
+<tr><th>Field</th>
+<th>Description</th>
+</tr><tr>
+<td>throttle_time_ms</td><td>Duration in milliseconds for which the request was throttled due to quota violation (Zero if the request did not violate any quota)</td></tr>
+<tr>
+<td>error_code</td><td>Response error code</td></tr>
+<tr>
+<td>error_message</td><td>Response error message</td></tr>
+<tr>
+<td>coordinator</td><td>Host and por
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<script id="design-template" type="text/x-handlebars-template">
+ <h3><a id="majordesignelements" href="#majordesignelements">4.1 Motivation</a></h3>
+ <p>
+ We designed Kafka to be able to act as a unified platform for handling all the real-time data feeds <a href="#introduction">a large company might have</a>. To do this we had to think through a fairly broad set of use cases.
+ <p>
+ It would have to have high-throughput to support high volume event streams such as real-time log aggregation.
+ <p>
+ It would need to deal gracefully with large data backlogs to be able to support periodic data loads from offline systems.
+ <p>
+ It also meant the system would have to handle low-latency delivery to handle more traditional messaging use-cases.
+ <p>
+ We wanted to support partitioned, distributed, real-time processing of these feeds to create new, derived feeds. This motivated our partitioning and consumer model.
+ <p>
+ Finally in cases where the stream is fed into other data systems for serving, we knew the system would have to be able to guarantee fault-tolerance in the presence of machine failures.
+ <p>
+ Supporting these uses led us to a design with a number of unique elements, more akin to a database log than a traditional messaging system. We will outline some elements of the design in the following sections.
+
+ <h3><a id="persistence" href="#persistence">4.2 Persistence</a></h3>
+ <h4><a id="design_filesystem" href="#design_filesystem">Don't fear the filesystem!</a></h4>
+ <p>
+ Kafka relies heavily on the filesystem for storing and caching messages. There is a general perception that "disks are slow" which makes people skeptical that a persistent structure can offer competitive performance.
+ In fact disks are both much slower and much faster than people expect depending on how they are used; and a properly designed disk structure can often be as fast as the network.
+ <p>
+ The key fact about disk performance is that the throughput of hard drives has been diverging from the latency of a disk seek for the last decade. As a result the performance of linear writes on a <a href="http://en.wikipedia.org/wiki/Non-RAID_drive_architectures">JBOD</a>
+ configuration with six 7200rpm SATA RAID-5 array is about 600MB/sec but the performance of random writes is only about 100k/sec—a difference of over 6000X. These linear reads and writes are the most
+ predictable of all usage patterns, and are heavily optimized by the operating system. A modern operating system provides read-ahead and write-behind techniques that prefetch data in large block multiples and
+ group smaller logical writes into large physical writes. A further discussion of this issue can be found in this <a href="http://queue.acm.org/detail.cfm?id=1563874">ACM Queue article</a>; they actually find that
+ <a href="http://deliveryimages.acm.org/10.1145/1570000/1563874/jacobs3.jpg">sequential disk access can in some cases be faster than random memory access!</a>
+ <p>
+ To compensate for this performance divergence, modern operating systems have become increasingly aggressive in their use of main memory for disk caching. A modern OS will happily divert <i>all</i> free memory to
+ disk caching with little performance penalty when the memory is reclaimed. All disk reads and writes will go through this unified cache. This feature cannot easily be turned off without using direct I/O, so even
+ if a process maintains an in-process cache of the data, this data will likely be duplicated in OS pagecache, effectively storing everything twice.
+ <p>
+ Furthermore, we are building on top of the JVM, and anyone who has spent any time with Java memory usage knows two things:
+ <ol>
+ <li>The memory overhead of objects is very high, often doubling the size of the data stored (or worse).</li>
+ <li>Java garbage collection becomes increasingly fiddly and slow as the in-heap data increases.</li>
+ </ol>
+ <p>
+ As a result of these factors using the filesystem and relying on pagecache is superior to maintaining an in-memory cache or other structure—we at least double the available cache by having automatic access
+ to all free memory, and likely double again by storing a compact byte structure rather than individual objects. Doing so will result in a cache of up to 28-30GB on a 32GB machine without GC penalties.
+ Furthermore, this cache will stay warm even if the service is restarted, whereas the in-process cache will need to be rebuilt in memory (which for a 10GB cache may take 10 minutes) or else it will need to start
+ with a completely cold cache (which likely means terrible initial performance). This also greatly simplifies the code as all logic for maintaining coherency between the cache and filesystem is now in the OS,
+ which tends to do so more efficiently and more correctly than one-off in-process attempts. If your disk usage favors linear reads then read-ahead is effectively pre-populating this cache with useful data on each
+ disk read.
+ <p>
+ This suggests a design which is very simple: rather than maintain as much as possible in-memory and flush it all out to the filesystem in a panic when we run out of space, we invert that. All data is immediately
+ written to a persistent log on the filesystem without necessarily flushing to disk. In effect this just means that it is transferred into the kernel's pagecache.
+ <p>
+ This style of pagecache-centric design is described in an <a href="http://varnish-cache.org/wiki/ArchitectNotes">article</a> on the design of Varnish here (along with a healthy dose of arrogance).
+
+ <h4><a id="design_constanttime" href="#design_constanttime">Constant Time Suffices</a></h4>
+ <p>
+ The persistent data structure used in messaging systems are often a per-consumer queue with an associated BTree or other general-purpose random access data structures to maintain metadata about messages.
+ BTrees are the most versatile data structure available, and make it possible to support a wide variety of transactional and non-transactional semantics in the messaging system.
+ They do come with a fairly high cost, though: Btree operations are O(log N). Normally O(log N) is considered essentially equivalent to constant time, but this is not true for disk operations.
+ Disk seeks come at 10 ms a pop, and each disk can do only one seek at a time so parallelism is limited. Hence even a handful of disk seeks leads to very high overhead.
+ Since storage systems mix very fast cached operations with very slow physical disk operations, the observed performance of tree structures is often superlinear as data increases with fixed cache--i.e. doubling
+ your data makes things much worse than twice as slow.
+ <p>
+ Intuitively a persistent queue could be built on simple reads and appends to files as is commonly the case with logging solutions. This structure has the advantage that all operations are O(1) and reads do not
+ block writes or each other. This has obvious performance advantages since the performance is completely decoupled from the data size—one server can now take full advantage of a number of cheap,
+ low-rotational speed 1+TB SATA drives. Though they have poor seek performance, these drives have acceptable performance for large reads and writes and come at 1/3 the price and 3x the capacity.
+ <p>
+ Having access to virtually unlimited disk space without any performance penalty means that we can provide some features not usually found in a messaging system. For example, in Kafka, instead of attempting to
+ delete messages as soon as they are consumed, we can retain messages for a relatively long period (say a week). This leads to a great deal of flexibility for consumers, as we will describe.
+
+ <h3><a id="maximizingefficiency" href="#maximizingefficiency">4.3 Efficiency</a></h3>
+ <p>
+ We have put significant effort into efficiency. One of our primary use cases is handling web activity data, which is very high volume: each page view may generate dozens of writes. Furthermore, we assume each
+ message published is read by at least one consumer (often many), hence we strive to make consumption as cheap as possible.
+ <p>
+ We have also found, from experience building and running a number of similar systems, that efficiency is a key to effective multi-tenant operations. If the downstream infrastructure service can easily become a
+ bottleneck due to a small bump in usage by the application, such small changes will often create problems. By being very fast we help ensure that the application will tip-over under load before the infrastructure.
+ This is particularly important when trying to run a centralized service that supports dozens or hundreds of applications on a centralized cluster as changes in usage patterns are a near-daily occurrence.
+ <p>
+ We discussed disk efficiency in the previous section. Once poor disk access patterns have been eliminated, there are two common causes of inefficiency in this type of system: too many small I/O operations, and
+ excessive byte copying.
+ <p>
+ The small I/O problem happens both between the client and the server and in the server's own persistent operations.
+ <p>
+ To avoid this, our protocol is built around a "message set" abstraction that naturally groups messages together. This allows network requests to group messages together and amortize the overhead of the network
+ roundtrip rather than sending a single message at a time. The server in turn appends chunks of messages to its log in one go, and the consumer fetches large linear chunks at a time.
+ <p>
+ This simple optimization produces orders of magnitude speed up. Batching leads to larger network packets, larger sequential disk operations, contiguous memory blocks, and so on, all of which allows Kafka to turn
+ a bursty stream of random message writes into linear writes that flow to the consumers.
+ <p>
+ The other inefficiency is in byte copying. At low message rates this is not an issue, but under load the impact is significant. To avoid this we employ a standardized binary message format that is shared by the
+ producer, the broker, and the consumer (so data chunks can be transferred without modification between them).
+ <p>
+ The message log maintained by the broker is itself just a directory of files, each populated by a sequence of message sets that have been written to disk in the same format used by the producer and consumer.
+ Maintaining this common format allows optimization of the most important operation: network transfer of persistent log chunks. Modern unix operating systems offer a highly optimized code path for transferring data
+ out of pagecache to a socket; in Linux this is done with the <a href="http://man7.org/linux/man-pages/man2/sendfile.2.html">sendfile system call</a>.
+ <p>
+ To understand the impact of sendfile, it is important to understand the common data path for transfer of data from file to socket:
+ <ol>
+ <li>The operating system reads data from the disk into pagecache in kernel space</li>
+ <li>The application reads the data from kernel space into a user-space buffer</li>
+ <li>The application writes the data back into kernel space into a socket buffer</li>
+ <li>The operating system copies the data from the socket buffer to the NIC buffer where it is sent over the network</li>
+ </ol>
+ <p>
+ This is clearly inefficient, there are four copies and two system calls. Using sendfile, this re-copying is avoided by allowing the OS to send the data from pagecache to the network directly. So in this optimized
+ path, only the final copy to the NIC buffer is needed.
+ <p>
+ We expect a common use case to be multiple consumers on a topic. Using the zero-copy optimization above, data is copied into pagecache exactly once and reused on each consumption instead of being stored in memory
+ and copied out to user-space every time it is read. This allows messages to be consumed at a rate that approaches the limit of the network connection.
+ <p>
+ This combination of pagecache and sendfile means that on a Kafka cluster where the consumers are mostly caught up you will see no read activity on the disks whatsoever as they will be serving data entirely from cache.
+ <p>
+ For more background on the sendfile and zero-copy support in Java, see this <a href="http://www.ibm.com/developerworks/linux/library/j-zerocopy">article</a>.
+
+ <h4><a id="design_compression" href="#design_compression">End-to-end Batch Compression</a></h4>
+ <p>
+ In some cases the bottleneck is actually not CPU or disk but network bandwidth. This is particularly true for a data pipeline that needs to send messages between data centers over a wide-area network. Of course,
+ the user can always compress its messages one at a time without any support needed from Kafka, but this can lead to very poor compression ratios as much of the redundancy is due to repetition between messages of
+ the same type (e.g. field names in JSON or user agents in web logs or common string values). Efficient compression requires compressing multiple messages together rather than compressing each message individually.
+ <p>
+ Kafka supports this with an efficient batching format. A batch of messages can be clumped together compressed and sent to the server in this form. This batch of messages will be written in compressed form and will
+ remain compressed in the log and will only be decompressed by the consumer.
+ <p>
+ Kafka supports GZIP, Snappy and LZ4 compression protocols. More details on compression can be found <a href="https://cwiki.apache.org/confluence/display/KAFKA/Compression">here</a>.
+
+ <h3><a id="theproducer" href="#theproducer">4.4 The Producer</a></h3>
+
+ <h4><a id="design_loadbalancing" href="#design_loadbalancing">Load balancing</a></h4>
+ <p>
+ The producer sends data directly to the broker that is the leader for the partition without any intervening routing tier. To help the producer do this all Kafka nodes can answer a request for metadata about which
+ servers are alive and where the leaders for the partitions of a topic are at any given time to allow the producer to appropriately direct its requests.
+ <p>
+ The client controls which partition it publishes messages to. This can be done at random, implementing a kind of random load balancing, or it can be done by some semantic partitioning function. We expose the interface
+ for semantic partitioning by allowing the user to specify a key to partition by and using this to hash to a partition (there is also an option to override the partition function if need be). For example if the key
+ chosen was a user id then all data for a given user would be sent to the same partition. This in turn will allow consumers to make locality assumptions about their consumption. This style of partitioning is explicitly
+ designed to allow locality-sensitive processing in consumers.
+
+ <h4><a id="design_asyncsend" href="#design_asyncsend">Asynchronous send</a></h4>
+ <p>
+ Batching is one of the big drivers of efficiency, and to enable batching the Kafka producer will attempt to accumulate data in memory and to send out larger batches in a single request. The batching can be configured
+ to accumulate no more than a fixed number of messages and to wait no longer than some fixed latency bound (say 64k or 10 ms). This allows the accumulation of more bytes to send, and few larger I/O operations on the
+ servers. This buffering is configurable and gives a mechanism to trade off a small amount of additional latency for better throughput.
+ <p>
+ Details on <a href="#producerconfigs">configuration</a> and the <a href="http://kafka.apache.org/082/javadoc/index.html?org/apache/kafka/clients/producer/KafkaProducer.html">api</a> for the producer can be found
+ elsewhere in the documentation.
+
+ <h3><a id="theconsumer" href="#theconsumer">4.5 The Consumer</a></h3>
+
+ The Kafka consumer works by issuing "fetch" requests to the brokers leading the partitions it wants to consume. The consumer specifies its offset in the log with each request and receives back a chunk of log
+ beginning from that position. The consumer thus has significant control over this position and can rewind it to re-consume data if need be.
+
+ <h4><a id="design_pull" href="#design_pull">Push vs. pull</a></h4>
+ <p>
+ An initial question we considered is whether consumers should pull data from brokers or brokers should push data to the consumer. In this respect Kafka follows a more traditional design, shared by most messaging
+ systems, where data is pushed to the broker from the producer and pulled from the broker by the consumer. Some logging-centric systems, such as <a href="http://github.com/facebook/scribe">Scribe</a> and
+ <a href="http://flume.apache.org/">Apache Flume</a>, follow a very different push-based path where data is pushed downstream. There are pros and cons to both approaches. However, a push-based system has difficulty
+ dealing with diverse consumers as the broker controls the rate at which data is transferred. The goal is generally for the consumer to be able to consume at the maximum possible rate; unfortunately, in a push
+ system this means the consumer tends to be overwhelmed when its rate of consumption falls below the rate of production (a denial of service attack, in essence). A pull-based system has the nicer property that
+ the consumer simply falls behind and catches up when it can. This can be mitigated with some kind of backoff protocol by which the consumer can indicate it is overwhelmed, but getting the rate of transfer to
+ fully utilize (but never over-utilize) the consumer is trickier than it seems. Previous attempts at building systems in this fashion led us to go with a more traditional pull model.
+ <p>
+ Another advantage of a pull-based system is that it lends itself to aggressive batching of data sent to the consumer. A push-based system must choose to either send a request immediately or accumulate more data
+ and then send it later without knowledge of whether the downstream consumer will be able to immediately process it. If tuned for low latency, this will result in sending a single message at a time only for the
+ transfer to end up being buffered anyway, which is wasteful. A pull-based design fixes this as the consumer always pulls all available messages after its current position in the log (or up to some configurable max
+ size). So one gets optimal batching without introducing unnecessary latency.
+ <p>
+ The deficiency of a naive pull-based system is that if the broker has no data the consumer may end up polling in a tight loop, effectively busy-waiting for data to arrive. To avoid this we have parameters in our
+ pull request that allow the consumer request to block in a "long poll" waiting until data arrives (and optionally waiting until a given number of bytes is available to ensure large transfer sizes).
+ <p>
+ You could imagine other possible designs which would be only pull, end-to-end. The producer would locally write to a local log, and brokers would pull from that with consumers pulling from them. A similar type of
+ "store-and-forward" producer is often proposed. This is intriguing but we felt not very suitable for our target use cases which have thousands of producers. Our experience running persistent data systems at
+ scale led us to feel that involving thousands of disks in the system across many applications would not actually make things more reliable and would be a nightmare to operate. And in practice we have found that we
+ can run a pipeline with strong SLAs at large scale without a need for producer persistence.
+
+ <h4><a id="design_consumerposition" href="#design_consumerposition">Consumer Position</a></h4>
+ Keeping track of <i>what</i> has been consumed is, surprisingly, one of the key performance points of a messaging system.
+ <p>
+ Most messaging systems keep metadata about what messages have been consumed on the broker. That is, as a message is handed out to a consumer, the broker either records that fact locally immediately or it may wait
+ for acknowledgement from the consumer. This is a fairly intuitive choice, and indeed for a single machine server it is not clear where else this state could go. Since the data structures used for storage in many
+ messaging systems scale poorly, this is also a pragmatic choice--since the broker knows what is consumed it can immediately delete it, keeping the data size small.
+ <p>
+ What is perhaps not obvious is that getting the broker and consumer to come into agreement about what has been consumed is not a trivial problem. If the broker records a message as <b>consumed</b> immediately every
+ time it is handed out over the network, then if the consumer fails to process the message (say because it crashes or the request times out or whatever) that message will be lost. To solve this problem, many messaging
+ systems add an acknowledgement feature which means that messages are only marked as <b>sent</b> not <b>consumed</b> when they are sent; the broker waits for a specific acknowledgement from the consumer to record the
+ message as <b>consumed</b>. This strategy fixes the problem of losing messages, but creates new problems. First of all, if the consumer processes the message but fails before it can send an acknowledgement then the
+ message will be consumed twice. The second problem is around performance, now the broker must keep multiple states about every single message (first to lock it so it is not given out a second time, and then to mark
+ it as permanently consumed so that it can be removed). Tricky problems must be dealt with, like what to do with messages that are sent but never acknowledged.
+ <p>
+ Kafka handles this differently. Our topic is divided into a set of totally ordered partitions, each of which is consumed by exactly one consumer within each subscribing consumer group at any given time. This means
+ that the position of a consumer in each partition is just a single integer, the offset of the next message to consume. This makes the state about what has been consumed very small, just one number for each partition.
+ This state can be periodically checkpointed. This makes the equivalent of message acknowledgements very cheap.
+ <p>
+ There is a side benefit of this decision. A consumer can deliberately <i>rewind</i> back to an old offset and re-consume data. This violates the common contract of a queue, but turns out to be an essential feature
+ for many consumers. For example, if the consumer code has a bug and is discovered after some messages are consumed, the consumer can re-consume those messages once the bug is fixed.
+
+ <h4><a id="design_offlineload" href="#design_offlineload">Offline Data Load</a></h4>
+
+ Scalable persistence allows for the possibility of consumers that only periodically consume such as batch data loads that periodically bulk-load data into an offline system such as Hadoop or a relational data
+ warehouse.
+ <p>
+ In the case of Hadoop we parallelize the data load by splitting the load over individual map tasks, one for each node/topic/partition combination, allowing full parallelism in the loading. Hadoop provides the task
+ management, and tasks which fail can restart without danger of duplicate data—they simply restart from their original position.
+
+ <h3><a id="semantics" href="#semantics">4.6 Message Delivery Semantics</a></h3>
+ <p>
+ Now that we understand a little about how producers and consumers work, let's discuss the semantic guarantees Kafka provides between producer and consumer. Clearly there are multiple possible message delivery
+ guarantees that could be provided:
+ <ul>
+ <li>
+ <i>At most once</i>—Messages may be lost but are never redelivered.
+ </li>
+ <li>
+ <i>At least once</i>—Messages are never lost but may be redelivered.
+ </li>
+ <li>
+ <i>Exactly once</i>—this is what people actually want, each message is delivered once and only once.
+ </li>
+ </ul>
+
+ It's worth noting that this breaks down into two problems: the durability guarantees for publishing a message and the guarantees when consuming a message.
+ <p>
+ Many systems claim to provide "exactly once" delivery semantics, but it is important to read the fine print, most of these claims are misleading (i.e. they don't translate to the case where consumers or producers
+ can fail, cases where there are multiple consumer processes, or cases where data written to disk can be lost).
+ <p>
+ Kafka's semantics are straight-forward. When publishing a message we have a notion of the message being "committed" to the log. Once a published message is committed it will not be lost as long as one broker that
+ replicates the partition to which this message was written remains "alive". The definition of committed message, alive partition as well as a description of which types of failures we attempt to handle will be
+ described in more detail in the next section. For now let's assume a perfect, lossless broker and try to understand the guarantees to the producer and consumer. If a producer attempts to publish a message and
+ experiences a network error it cannot be sure if this error happened before or after the message was committed. This is similar to the semantics of inserting into a database table with an autogenerated key.
+ <p>
+ Prior to 0.11.0.0, if a producer failed to receive a response indicating that a message was committed, it had little choice but to resend the message. This provides at-least-once delivery semantics since the
+ message may be written to the log again during resending if the original request had in fact succeeded. Since 0.11.0.0, the Kafka producer also supports an idempotent delivery option which guarantees that resending
+ will not result in duplicate entries in the log. To achieve this, the broker assigns each producer an ID and deduplicates messages using a sequence number that is sent by the producer along with every message.
+ Also beginning with 0.11.0.0, the producer supports the ability to send messages to multiple topic partitions using transaction-like semantics: i.e. either all messages are successfully written or none of them are.
+ The main use case for this is exactly-once processing between Kafka topics (described below).
+ <p>
+ Not all use cases require such strong guarantees. For uses which are latency sensitive we allow the producer to specify the durability level it desires. If the producer specifies that it wants to wait on the message
+ being committed this can take on the order of 10 ms. However the producer can also specify that it wants to perform the send completely asynchronously or that it wants to wait only until the leader (but not
+ necessarily the followers) have the message.
+ <p>
+ Now let's describe the semantics from the point-of-view of the consumer. All replicas have the exact same log with the same offsets. The consumer controls its position in this log. If the consumer never crashed it
+ could just store this position in memory, but if the consumer fails and we want this topic partition to be taken over by another process the new process will need to choose an appropriate position from which to start
+ processing. Let's say the consumer reads some messages -- it has several options for processing the messages and updating its position.
+ <ol>
+ <li>It can read the messages, then save its position in the log, and finally process the messages. In this case there is a possibility that the consumer process crashes after saving its position but before saving
+ the output of its message processing. In this case the process that took over processing would start at the saved position even though a few messages prior to that position had not been processed. This corresponds
+ to "at-most-once" semantics as in the case of a consumer failure messages may not be processed.
+ <li>It can read the messages, process the messages, and finally save its position. In this case there is a possibility that the consumer process crashes after processing messages but before saving its position.
+ In this case when the new process takes over the first few messages it receives will already have been processed. This corresponds to the "at-least-once" semantics in the case of consumer failure. In many cases
+ messages have a primary key and so the updates are idempotent (receiving the same message twice just overwrites a record with another copy of itself).
+ </ol>
+ <p>
+ So what about exactly once semantics (i.e. the thing you actually want)? When consuming from a Kafka topic and producing to another topic (as in a <a href="https://kafka.apache.org/documentation/streams">Kafka Streams</a>
+ application), we can leverage the new transactional producer capabilities in 0.11.0.0 that were mentioned above. The consumer's position is stored as a message in a topic, so we can write the offset to Kafka in the
+ same transaction as the output topics receiving the processed data. If the transaction is aborted, the consumer's position will revert to its old value and the produced data on the output topics will not be visible
+ to other consumers, depending on their "isolation level." In the default "read_uncommitted" isolation level, all messages are visible to consumers even if they were part of an aborted transaction,
+ but in "read_committed," the consumer will only return messages from transactions which were committed (and any messages which were not part of a transaction).
+ <p>
+ When writing to an external system, the limitation is in the need to coordinate the consumer's position with what is actually stored as output. The classic way of achieving this would be to introduce a two-phase
+ commit between the storage of the consumer position and the storage of the consumers output. But this can be handled more simply and generally by letting the consumer store its offset in the same place as
+ its output. This is better because many of the output systems a consumer might want to write to will not support a two-phase commit. As an example of this, consider a
+ <a href="https://kafka.apache.org/documentation/#connect">Kafka Connect</a> connector which populates data in HDFS along with the offsets of the data it reads so that it is guaranteed that either data and
+ offsets are both updated or neither is. We follow similar patterns for many other data systems which require these stronger semantics and for which the messages do not have a primary key to allow for deduplication.
+ <p>
+ So effectively Kafka supports exactly-once delivery in <a href="https://kafka.apache.org/documentation/streams">Kafka Streams</a>, and the transactional producer/consumer can be used generally to provide
+ exactly-once delivery when transfering and processing data between Kafka topics. Exactly-once delivery for other destination systems generally requires cooperation with such systems, but Kafka provides the
+ offset which makes implementing this feasible (see also <a href="https://kafka.apache.org/documentation/#connect">Kafka Connect</a>). Otherwise, Kafka guarantees at-least-once delivery by default, and allows
+ the user to implement at-most-once delivery by disabling retries on the producer and committing offsets in the consumer prior to processing a batch of messages.
+
+ <h3><a id="replication" href="#replication">4.7 Replication</a></h3>
+ <p>
+ Kafka replicates the log for each topic's partitions across a configurable number of servers (you can set this replication factor on a topic-by-topic basis). This allows automatic failover to these replicas when a
+ server in the cluster fails so messages remain available in the presence of failures.
+ <p>
+ Other messaging systems provide some replication-related features, but, in our (totally biased) opinion, this appears to be a tacked-on thing, not heavily used, and with large downsides: slaves are inactive,
+ throughput is heavily impacted, it requires fiddly manual configuration, etc. Kafka is meant to be used with replication by default—in fact we implement un-replicated topics as replicated topics where the
+ replication factor is one.
+ <p>
+ The unit of replication is the topic partition. Under non-failure conditions, each partition in Kafka has a single leader and zero or more followers. The total number of replicas including the leader constitute the
+ replication factor. All reads and writes go to the leader of the partition. Typically, there are many more partitions than brokers and the leaders are evenly distributed among brokers. The logs on the followers are
+ identical to the leader's log—all have the same offsets and messages in the same order (though, of course, at any given time the leader may have a few as-yet unreplicated messages at the end of its log).
+ <p>
+ Followers consume messages from the leader just as a normal Kafka consumer would and apply them to their own log. Having the followers pull from the leader has the nice property of allowing the follower to naturally
+ batch together log entries they are applying to their log.
+ <p>
+ As with most distributed systems automatically handling failures requires having a precise definition of what it means for a node to be "alive". For Kafka node liveness has two conditions
+ <ol>
+ <li>A node must be able to maintain its session with ZooKeeper (via ZooKeeper's heartbeat mechanism)
+ <li>If it is a slave it must replicate the writes happening on the leader and not fall "too far" behind
+ </ol>
+ We refer to nodes satisfying these two conditions as being "in sync" to avoid the vagueness of "alive" or "failed". The leader keeps track of the set of "in sync" nodes. If a follower dies, gets stuck, or falls
+ behind, the leader will remove it from the list of in sync replicas. The determination of stuck and lagging replicas is controlled by the replica.lag.time.max.ms configuration.
+ <p>
+ In distributed systems terminology we only attempt to handle a "fail/recover" model of failures where nodes suddenly cease working and then later recover (perhaps without knowing that they have died). Kafka does not
+ handle so-called "Byzantine" failures in which nodes produce arbitrary or malicious responses (perhaps due to bugs or foul play).
+ <p>
+ We can now more precisely define that a message is considered committed when all in sync replicas for that partition have applied it to their log.
+ Only committed messages are ever given out to the consumer. This means that the consumer need not worry about potentially seeing a message that could be lost if the leader fails. Producers, on the other hand,
+ have the option of either waiting for the message to be committed or not, depending on their preference for tradeoff between latency and durability. This preference is controlled by the acks setting that the
+ producer uses.
+ Note that topics have a setting for the "minimum number" of in-sync replicas that is checked when the producer requests acknowledgment that a message
+ has been written to the full set of in-sync replicas. If a less stringent acknowledgement is requested by the producer, then the message can be committed, and consumed,
+ even if the number of in-sync replicas is lower than the minimum (e.g. it can be as low as just the leader).
+ <p>
+ The guarantee that Kafka offers is that a committed message will not be lost, as long as there is at least one in sync replica alive, at all times.
+ <p>
+ Kafka will remain available in the presence of node failures after a short fail-over period, but may not remain available in the presence of network partitions.
+
+ <h4><a id="design_replicatedlog" href="#design_replicatedlog">Replicated Logs: Quorums, ISRs, and State Machines (Oh my!)</a></h4>
+
+ At its heart a Kafka partition is a replicated log. The replicated log is one of the most basic primitives in distributed data systems, and there are many approaches for implementing one. A replicated log can be
+ used by other systems as a primitive for implementing other distributed systems in the <a href="http://en.wikipedia.org/wiki/State_machine_replication">state-machine style</a>.
+ <p>
+ A replicated log models the process of coming into consensus on the order of a series of values (generally numbering the log entries 0, 1, 2, ...). There are many ways to implement this, but the simplest and fastest
+ is with a leader who chooses the ordering of values provided to it. As long as the leader remains alive, all followers need to only copy the values and ordering the leader chooses.
+ <p>
+ Of course if leaders didn't fail we wouldn't need followers! When the leader does die we need to choose a new leader from among the followers. But followers themselves may fall behind or crash so we must ensure we
+ choose an up-to-date follower. The fundamental guarantee a log replication algorithm must provide is that if we tell the client a message is committed, and the leader fails, the new leader we elect must also have
+ that message. This yields a tradeoff: if the leader waits for more followers to acknowledge a message before declaring it committed then there will be more potentially electable leaders.
+ <p>
+ If you choose the number of acknowledgements required and the number of logs that must be compared to elect a leader such that there is guaranteed to be an overlap, then this is called a Quorum.
+ <p>
+ A common approach to this tradeoff is to use a majority vote for both the commit decision and the leader election. This is not what Kafka does, but let's explore it anyway to understand the tradeoffs. Let's say we
+ have 2<i>f</i>+1 replicas. If <i>f</i>+1 replicas must receive a message prior to a commit being declared by the leader, and if we elect a new leader by electing the follower with the most complete log from at least
+ <i>f</i>+1 replicas, then, with no more than <i>f</i> failures, the leader is guaranteed to have all committed messages. This is because among any <i>f</i>+1 replicas, there must be at least one replica that contains
+ all committed messages. That replica's log will be the most complete and therefore will be selected as the new leader. There are many remaining details that each algorithm must handle (such as precisely defined what
+ makes a log more complete, ensuring log consistency during leader failure or changing the set of servers in the replica set) but we will ignore these for now.
+ <p>
+ This majority vote approach has a very nice property: the latency is dependent on only the fastest servers. That is, if the replication factor is three, the latency is determined by the faster slave not the slower one.
+ <p>
+ There are a rich variety of algorithms in this family including ZooKeeper's
+ <a href="http://web.archive.org/web/20140602093727/http://www.stanford.edu/class/cs347/reading/zab.pdf">Zab</a>,
+ <a href="https://ramcloud.stanford.edu/wiki/download/attachments/11370504/raft.pdf">Raft</a>,
+ and <a href="http://pmg.csail.mit.edu/papers/vr-revisited.pdf">Viewstamped Replication</a>.
+ The most similar academic publication we are aware of to Kafka's actual implementation is
+ <a href="http://research.microsoft.com/apps/pubs/default.aspx?id=66814">PacificA</a> from Microsoft.
+ <p>
+ The downside of majority vote is that it doesn't take many failures to leave you with no electable leaders. To tolerate one failure requires three copies of the data, and to tolerate two failures requires five copies
+ of the data. In our experience having only enough redundancy to tolerate a single failure is not enough for a practical system, but doing every write five times, with 5x the disk space requirements and 1/5th the
+ throughput, is not very practical for large volume data problems. This is likely why quorum algorithms more commonly appear for shared cluster configuration such as ZooKeeper but are less common for primary data
+ storage. For example in HDFS the namenode's high-availability feature is built on a <a href="http://blog.cloudera.com/blog/2012/10/quorum-based-journaling-in-cdh4-1">majority-vote-based journal</a>, but this more
+ expensive approach is not used for the data itself.
+ <p>
+ Kafka takes a slightly different approach to choosing its quorum set. Instead of majority vote, Kafka dynamically maintains a set of in-sync replicas (ISR) that are caught-up to the leader. Only members of this set
+ are eligible for election as leader. A write to a Kafka partition is not considered committed until <i>all</i> in-sync replicas have received the write. This ISR set is persisted to ZooKeeper whenever it changes.
+ Because of this, any replica in the ISR is eligible to be elected leader. This is an important factor for Kafka's usage model where there are many partitions and ensuring leadership balance is important.
+ With this ISR model and <i>f+1</i> replicas, a Kafka topic can tolerate <i>f</i> failures without losing committed messages.
+ <p>
+ For most use cases we hope to handle, we think this tradeoff is a reasonable one. In practice, to tolerate <i>f</i> failures, both the majority vote and the ISR approach will wait for the same number of replicas to
+ acknowledge before committing a message (e.g. to survive one failure a majority quorum needs three replicas and one acknowledgement and the ISR approach requires two replicas and one acknowledgement).
+ The ability to commit without the slowest servers is an advantage of the majority vote approach. However, we think it is ameliorated by allowing the client to choose whether they block on the message commit or not,
+ and the additional throughput and disk space due to the lower required replication factor is worth it.
+ <p>
+ Another important design distinction is that Kafka does not require that crashed nodes recover with all their data intact. It is not uncommon for replication algorithms in this space to depend on the existence of
+ "stable storage" that cannot be lost in any failure-recovery scenario without potential consistency violations. There are two primary problems with this assumption. First, disk errors are the most common problem we
+ observe in real operation of persistent data systems and they often do not leave data intact. Secondly, even if this were not a problem, we do not want to require the use of fsync on every write for our consistency
+ guarantees as this can reduce performance by two to three orders of magnitude. Our protocol for allowing a replica to rejoin the ISR ensures that before rejoining, it must fully re-sync again even if it lost unflushed
+ data in its crash.
+
+ <h4><a id="design_uncleanleader" href="#design_uncleanleader">Unclean leader election: What if they all die?</a></h4>
+
+ Note that Kafka's guarantee with respect to data loss is predicated on at least one replica remaining in sync. If all the nodes replicating a partition die, this guarantee no longer holds.
+ <p>
+ However a practical system needs to do something reasonable when all the replicas die. If you are unlucky enough to have this occur, it is important to consider what will happen. There are two behaviors that could be
+ implemented:
+ <ol>
+ <li>Wait for a replica in the ISR to come back to life and choose this replica as the leader (hopefully it still has all its data).
+ <li>Choose the first replica (not necessarily in the ISR) that comes back to life as the leader.
+ </ol>
+ <p>
+ This is a simple tradeoff between availability and consistency. If we wait for replicas in the ISR, then we will remain unavailable as long as those replicas are down. If such replicas were destroyed or their data
+ was lost, then we are permanently down. If, on the other hand, a non-in-sync replica comes back to life and we allow it to become leader, then its log becomes the source of truth even though it is not guaranteed to
+ have every committed message. By default, Kafka chooses the second strategy and favor choosing a potentially inconsistent replica when all replicas in the ISR are dead. This behavior can be disabled using
+ configuration property unclean.leader.election.enable, to support use cases where downtime is preferable to inconsistency.
+ <p>
+ This dilemma is not specific to Kafka. It exists in any quorum-based scheme. For example in a majority voting scheme, if a majority of servers suffer a permanent failure, then you must either choose to lose 100% of
+ your data or violate consistency by taking what remains on an existing server as your new source of truth.
+
+
+ <h4><a id="design_ha" href="#design_ha">Availability and Durability Guarantees</a></h4>
+
+ When writing to Kafka, producers can choose whether they wait for the message to be acknowledged by 0,1 or all (-1) replicas.
+ Note that "acknowledgement by all replicas" does not guarantee that the full set of assigned replicas have received the message. By default, when acks=all, acknowledgement happens as soon as all the current in-sync
+ replicas have received the message. For example, if a topic is configured with only two replicas and one fails (i.e., only one in sync replica remains), then writes that specify acks=all will succeed. However, these
+ writes could be lost if the remaining replica also fails.
+
+ Although this ensures maximum availability of the partition, this behavior may be undesirable to some users who prefer durability over availability. Therefore, we provide two topic-level configurations that can be
+ used to prefer message durability over availability:
+ <ol>
+ <li> Disable unclean leader election - if all replicas become unavailable, then the partition will remain unavailable until the most recent leader becomes available again. This effectively prefers unavailability
+ over the risk of message loss. See the previous section on Unclean Leader Election for clarification. </li>
+ <li> Specify a minimum ISR size - the partition will only accept writes if the size of the ISR is above a certain minimum, in order to prevent the loss of messages that were written to just a single replica,
+ which subsequently becomes unavailable. This setting only takes effect if the producer uses acks=all and guarantees that the message will be acknowledged by at least this many in-sync replicas.
+ This setting offers a trade-off between consistency and availability. A higher setting for minimum ISR size guarantees better consistency since the message is guaranteed to be written to more replicas which reduces
+ the probability that it will be lost. However, it reduces availability since the partition will be unavailable for writes if the number of in-sync replicas drops below the minimum threshold. </li>
+ </ol>
+
+
+ <h4><a id="design_replicamanagment" href="#design_replicamanagment">Replica Management</a></h4>
+
+ The above discussion on replicated logs really covers only a single log, i.e. one topic partition. However a Kafka cluster will manage hundreds or thousands of these partitions. We attempt to balance partitions
+ within a cluster in a round-robin fashion to avoid clustering all partitions for high-volume topics on a small number of nodes. Likewise we try to balance leadership so that each node is the leader for a proportional
+ share of its partitions.
+ <p>
+ It is also important to optimize the leadership election process as that is the critical window of unavailability. A naive implementation of leader election would end up running an election per partition for all
+ partitions a node hosted when that node failed. Instead, we elect one of the brokers as the "controller". This controller detects failures at the broker level and is responsible for changing the leader of all
+ affected partitions in a failed broker. The result is that we are able to batch together many of the required leadership change notifications which makes the election process far cheaper and faster for a large number
+ of partitions. If the controller fails, one of the surviving brokers will become the new controller.
+
+ <h3><a id="compaction" href="#compaction">4.8 Log Compaction</a></h3>
+
+ Log compaction ensures that Kafka will always retain at least the last known value for each message key within the log of data for a single topic partition. It addresses use cases and scenarios such as restoring
+ state after application crashes or system failure, or reloading caches after application restarts during operational maintenance. Let's dive into these use cases in more detail and then describe how compaction works.
+ <p>
+ So far we have described only the simpler approach to data retention where old log data is discarded after a fixed period of time or when the log reaches some predetermined size. This works well for temporal event
+ data such as logging where each record stands alone. However an important class of data streams are the log of changes to keyed, mutable data (for example, the changes to a database table).
+ <p>
+ Let's discuss a concrete example of such a stream. Say we have a topic containing user email addresses; every time a user updates their email address we send a message to this topic using their user id as the
+ primary key. Now say we send the following messages over some time period for a user with id 123, each message corresponding to a change in email address (messages for other ids are omitted):
+ <pre class="brush: text;">
+ 123 => bill@microsoft.com
+ .
+ .
+ .
+ 123 => bill@gatesfoundation.org
+ .
+ .
+ .
+ 123 => bill@gmail.com
+ </pre>
+ Log compaction gives us a more granular retention mechanism so that we are guaranteed to retain at least the last update for each primary key (e.g. <code>bill@gmail.com</code>). By doing this we guarantee that the
+ log contains a full snapshot of the final value for every key not just keys that changed recently. This means downstream consumers can restore their own state off this topic without us having to retain a complete
+ log of all changes.
+ <p>
+ Let's start by looking at a few use cases where this is useful, then we'll see how it can be used.
+ <ol>
+ <li><i>Database change subscription</i>. It is often necessary to have a data set in multiple data systems, and often one of these systems is a database of some kind (either a RDBMS or perhaps a new-fangled key-value
+ store). For example you might have a database, a cache, a search cluster, and a Hadoop cluster. Each change to the database will need to be reflected in the cache, the search cluster, and eventually in Hadoop.
+ In the case that one is only handling the real-time updates you only need recent log. But if you want to be able to reload the cache or restore a failed search node you may need a complete data set.
+ <li><i>Event sourcing</i>. This is a style of application design which co-locates query processing with application design and uses a log of changes as the primary store for the application.
+ <li><i>Journaling for high-availability</i>. A process that does local computation can be made fault-tolerant by logging out changes that it makes to its local state so another process can reload these changes and
+ carry on if it should fail. A concrete example of this is handling counts, aggregations, and other "group by"-like processing in a stream query system. Samza, a real-time stream-processing framework,
+ <a href="http://samza.apache.org/learn/documentation/0.7.0/container/state-management.html">uses this feature</a> for exactly this purpose.
+ </ol>
+ In each of these cases one needs primarily to handle the real-time feed of changes, but occasionally, when a machine crashes or data needs to be re-loaded or re-processed, one needs to do a full load.
+ Log compaction allows feeding both of these use cases off the same backing topic.
+
+ This style of usage of a log is described in more detail in <a href="http://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying">this blog post</a>.
+ <p>
+ The general idea is quite simple. If we had infinite log retention, and we logged each change in the above cases, then we would have captured the state of the system at each time from when it first began.
+ Using this complete log, we could restore to any point in time by replaying the first N records in the log. This hypothetical complete log is not very practical for systems that update a single record many times
+ as the log will grow without bound even for a stable dataset. The simple log retention mechanism which throws away old updates will bound space but the log is no longer a way to restore the current state—now
+ restoring from the beginning of the log no longer recreates the current state as old updates may not be captured at all.
+ <p>
+ Log compaction is a mechanism to give finer-grained per-record retention, rather than the coarser-grained time-based retention. The idea is to selectively remove records where we have a more recent update with the
+ same primary key. This way the log is guaranteed to have at least the last state for each key.
+ <p>
+ This retention policy can be set per-topic, so a single cluster can have some topics where retention is enforced by size or time and other topics where retention is enforced by compaction.
+ <p>
+ This functionality is inspired by one of LinkedIn's oldest and most successful pieces of infrastructure—a database changelog caching service called <a href="https://github.com/linkedin/databus">Databus</a>.
+ Unlike most log-structured storage systems Kafka is built for subscription and organizes data for fast linear reads and writes. Unlike Databus, Kafka acts as a source-of-truth store so it is useful even in
+ situations where the upstream data source would not otherwise be replayable.
+
+ <h4><a id="design_compactionbasics" href="#design_compactionbasics">Log Compaction Basics</a></h4>
+
+ Here is a high-level picture that shows the logical structure of a Kafka log with the offset for each message.
+ <p>
+ <img class="centered" src="/{{version}}/images/log_cleaner_anatomy.png">
+ <p>
+ The head of the log is identical to a traditional Kafka log. It has dense, sequential offsets and retains all messages. Log compaction adds an option for handling the tail of the log. The picture above shows a log
+ with a compacted tail. Note that the messages in the tail of the log retain the original offset assigned when they were first written—that never changes. Note also that all offsets remain valid positions in
+ the log, even if the message with that offset has been compacted away; in this case this position is indistinguishable from the next highest offset that does appear in the log. For example, in the picture above the
+ offsets 36, 37, and 38 are all equivalent positions and a read beginning at any of these offsets would return a message set beginning with 38.
+ <p>
+ Compaction also allows for deletes. A message with a key and a null payload will be treated as a delete from the log. This delete marker will cause any prior message with that key to be removed (as would any new
+ message with that key), but delete markers are special in that they will themselves be cleaned out of the log after a period of time to free up space. The point in time at which deletes are no longer retained is
+ marked as the "delete retention point" in the above diagram.
+ <p>
+ The compaction is done in the background by periodically recopying log segments. Cleaning does not block reads and can be throttled to use no more than a configurable amount of I/O throughput to avoid impacting
+ producers and consumers. The actual process of compacting a log segment looks something like this:
+ <p>
+ <img class="centered" src="/{{version}}/images/log_compaction.png">
+ <p>
+ <h4><a id="design_compactionguarantees" href="#design_compactionguarantees">What guarantees does log compaction provide?</a></h4>
+
+ Log compaction guarantees the following:
+ <ol>
+ <li>Any consumer that stays caught-up to within the head of the log will see every message that is written; these messages will have sequential offsets. The topic's <code>min.compaction.lag.ms</code> can be used to
+ guarantee the minimum length of time must pass after a message is written before it could be compacted. I.e. it provides a lower bound on how long each message will remain in the (uncompacted) head.
+ <li>Ordering of messages is always maintained. Compaction will never re-order messages, just remove some.
+ <li>The offset for a message never changes. It is the permanent identifier for a position in the log.
+ <li>Any consumer progressing from the start of the log will see at least the final state of all records in the order they were written. Additionally, all delete markers for deleted records will be seen, provided
+ the consumer reaches the head of the log in a time period less than the topic's <code>delete.retention.ms</code> setting (the default is 24 hours). In other words: since the removal of delete markers happens
+ concurrently with reads, it is possible for a consumer to miss delete markers if it lags by more than <code>delete.retention.ms</code>.
+ </ol>
+
+ <h4><a id="design_compactiondetails" href="#design_compactiondetails">Log Compaction Details</a></h4>
+
+ Log compaction is handled by the log cleaner, a pool of background threads that recopy log segment files, removing records whose key appears in the head of the log. Each compactor thread works as follows:
+ <ol>
+ <li>It chooses the log that has the highest ratio of log head to log tail
+ <li>It creates a succinct summary of the last offset for each key in the head of the log
+ <li>It recopies the log from beginning to end removing keys which have a later occurrence in the log. New, clean segments are swapped into the log immediately so the additional disk space required is just one
+ additional log segment (not a fully copy of the log).
+ <li>The summary of the log head is essentially just a space-compact hash table. It uses exactly 24 bytes per entry. As a result with 8GB of cleaner buffer one cleaner iteration can clean around 366GB of log head
+ (assuming 1k messages).
+ </ol>
+ <p>
+ <h4><a id="design_compactionconfig" href="#design_compactionconfig">Configuring The Log Cleaner</a></h4>
+
+ The log cleaner is enabled by default. This will start the pool of cleaner threads.
+ To enable log cleaning on a particular topic you can add the log-specific property
+ <pre class="brush: text;"> log.cleanup.policy=compact</pre>
+ This can be done either at topic creation time or using the alter topic command.
+ <p>
+ The log cleaner can be configured to retain a minimum amount of the uncompacted "head" of the log. This is enabled by setting the compaction time lag.
+ <pre class="brush: text;"> log.cleaner.min.compaction.lag.ms</pre>
+
+ This can be used to prevent messages newer than a minimum message age from being subject to compaction. If not set, all log segments are eligible for compaction except for the last segment, i.e. the one currently
+ being written to. The active segment will not be compacted even if all of its messages are older than the minimum compaction time lag.
+ </p>
+ <p>
+ Further cleaner configurations are described <a href="/documentation.html#brokerconfigs">here</a>.
+
+ <h3><a id="design_quotas" href="#design_quotas">4.9 Quotas</a></h3>
+ <p>
+ Kafka cluster has the ability to enforce quotas on requests to control the broker resources used by clients. Two types
+ of client quotas can be enforced by Kafka brokers for each group of clients sharing a quota:
+ <ol>
+ <li>Network bandwidth quotas define byte-rate thresholds (since 0.9)</li>
+ <li>Request rate quotas define CPU utilization thresholds as a percentage of network and I/O threads (since 0.11)</li>
+ </ol>
+ </p>
+
+ <h4><a id="design_quotasnecessary" href="#design_quotasnecessary">Why are quotas necessary?</a></h4>
+ <p>
+ It is possible for producers and consumers to produce/consume very high volumes of data or generate requests at a very high
+ rate and thus monopolize broker resources, cause network saturation and generally DOS other clients and the brokers themselves.
+ Having quotas protects against these issues and is all the more important in large multi-tenant clusters where a small set of badly behaved clients can degrade user experience for the well behaved ones.
+ In fact, when running Kafka as a service this even makes it possible to enforce API limits according to an agreed upon contract.
+ </p>
+ <h4><a id="design_quotasgroups" href="#design_quotasgroups">Client groups</a></h4>
+ The identity of Kafka clients is the user principal which represents an authenticated user in a secure cluster. In a cluster that supports unauthenticated clients, user principal is a grouping of unauthenticated
+ users
+ chosen by the broker using a configurable <code>PrincipalBuilder</code>. Client-id is a logical grouping of clients with a meaningful name chosen by the client application. The tuple (user, client-id) defines
+ a secure logical group of clients that share both user principal and client-id.
+ <p>
+ Quotas can be applied to (user, client-id), user or client-id groups. For a given connection, the most specific quota matching the connection is applied. All connections of a quota group share the quota configured for the group.
+ For example, if (user="test-user", client-id="test-client") has a produce quota of 10MB/sec, this is shared across all producer instances of user "test-user" with the client-id "test-client".
+ </p>
+ <h4><a id="design_quotasconfig" href="#design_quotasconfig">Quota Configuration</a></h4>
+ <p>
+ Quota configuration may be defined for (user, client-id), user and client-id groups. It is possible to override the default quota at any of the quota levels that needs a higher (or even lower) quota.
+ The mechanism is similar to the per-topic log config overrides.
+ User and (user, client-id) quota overrides are written to ZooKeeper under <i><b>/config/users</b></i> and client-id quota overrides are written under <i><b>/config/clients</b></i>.
+ These overrides are read by all brokers and are effective immediately. This lets us change quotas without having to do a rolling restart of the entire cluster. See <a href="#quotas">here</a> for details.
+ Default quotas for each group may also be updated dynamically using the same mechanism.
+ </p>
+ <p>
+ The order of precedence for quota configuration is:
+ <ol>
+ <li>/config/users/<user>/clients/<client-id></li>
+ <li>/config/users/<user>/clients/<default></li>
+ <li>/config/users/<user></li>
+ <li>/config/users/<default>/clients/<client-id></li>
+ <li>/config/users/<default>/clients/<default></li>
+ <li>/config/users/<default></li>
+ <li>/config/clients/<client-id></li>
+ <li>/config/clients/<default></li>
+ </ol>
+
+ Broker properties (quota.producer.default, quota.consumer.default) can also be used to set defaults of network bandwidth quotas for client-id groups. These properties are being deprecated and will be removed in a later release.
+ Default quotas for client-id can be set in Zookeeper similar to the other quota overrides and defaults.
+ </p>
+ <h4><a id="design_quotasbandwidth" href="#design_quotasbandwidth">Network Bandwidth Quotas</a></h4>
+ <p>
+ Network bandwidth quotas are defined as the byte rate threshold for each group of clients sharing a quota.
+ By default, each unique client group receives a fixed quota in bytes/sec as configured by the cluster.
+ This quota is defined on a per-broker basis. Each group of clients can publish/fetch a maximum of X bytes/sec
+ per broker before clients are throttled.
+ </p>
+ <h4><a id="design_quotascpu" href="#design_quotascpu">Request Rate Quotas</a></h4>
+ <p>
+ Request rate quotas are defined as the percentage of time a client can utilize on request handler I/O
+ threads and network threads of each broker within a quota window. A quota of <tt>n%</tt> represents
+ <tt>n%</tt> of one thread, so the quota is out of a total capacity of <tt>((num.io.threads + num.network.threads) * 100)%</tt>.
+ Each group of clients may use a total percentage of upto <tt>n%</tt> across all I/O and network threads in a quota
+ window before being throttled. Since the number of threads allocated for I/O and network threads are typically based
+ on the number of cores available on the broker host, request rate quotas represent the total percentage of CPU
+ that may be used by each group of clients sharing the quota.
+ </p>
+ <h4><a id="design_quotasenforcement" href="#design_quotasenforcement">Enforcement</a></h4>
+ <p>
+ By default, each unique client group receives a fixed quota as configured by the cluster.
+ This quota is defined on a per-broker basis. Each client can utilize this quota per broker before it gets throttled. We decided that defining these quotas per broker is much better than
+ having a fixed cluster wide bandwidth per client because that would require a mechanism to share client quota usage among all the brokers. This can be harder to get right than the quota implementation itself!
+ </p>
+ <p>
+ How does a broker react when it detects a quota violation? In our solution, the broker does not return an error rather it attempts to slow down a client exceeding its quota.
+ It computes the amount of delay needed to bring a guilty client under its quota and delays the response for that time. This approach keeps the quota violation transparent to clients
+ (outside of client-side metrics). This also keeps them from having to implement any special backoff and retry behavior which can get tricky. In fact, bad client behavior (retry without backoff)
+ can exacerbate the very problem quotas are trying to solve.
+ </p>
+ <p>
+ Byte-rate and thread utilization are measured over multiple small windows (e.g. 30 windows of 1 second each) in order to detect and correct quota violations quickly. Typically, having large measurement windows
+ (for e.g. 10 windows of 30 seconds each) leads to large bursts of traffic followed by long delays which is not great in terms of user experience.
+ </p>
+</script>
+
+<div class="p-design"></div>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/documentation.html
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+++ b/100/documentation.html
@@ -0,0 +1,83 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<script><!--#include virtual="js/templateData.js" --></script>
+
+<!--#include virtual="../includes/_header.htm" -->
+<!--#include virtual="../includes/_top.htm" -->
+
+
+<div class="content documentation documentation--current">
+ <!--#include virtual="../includes/_nav.htm" -->
+ <div class="right">
+ <!--#include virtual="../includes/_docs_banner.htm" -->
+ <h1>Documentation</h1>
+ <h3>Kafka 0.11.0 Documentation</h3>
+ Prior releases: <a href="/07/documentation.html">0.7.x</a>, <a href="/08/documentation.html">0.8.0</a>, <a href="/081/documentation.html">0.8.1.X</a>, <a href="/082/documentation.html">0.8.2.X</a>, <a href="/090/documentation.html">0.9.0.X</a>, <a href="/0100/documentation.html">0.10.0.X</a>, <a href="/0101/documentation.html">0.10.1.X</a>, <a href="/0102/documentation.html">0.10.2.X</a>.
+
+ <!--#include virtual="toc.html" -->
+
+ <h2><a id="gettingStarted" href="#gettingStarted">1. Getting Started</a></h2>
+ <h3><a id="introduction" href="#introduction">1.1 Introduction</a></h3>
+ <!--#include virtual="introduction.html" -->
+ <h3><a id="uses" href="#uses">1.2 Use Cases</a></h3>
+ <!--#include virtual="uses.html" -->
+ <h3><a id="quickstart" href="#quickstart">1.3 Quick Start</a></h3>
+ <!--#include virtual="quickstart.html" -->
+ <h3><a id="ecosystem" href="#ecosystem">1.4 Ecosystem</a></h3>
+ <!--#include virtual="ecosystem.html" -->
+ <h3><a id="upgrade" href="#upgrade">1.5 Upgrading From Previous Versions</a></h3>
+ <!--#include virtual="upgrade.html" -->
+
+ <h2><a id="api" href="#api">2. APIs</a></h2>
+
+ <!--#include virtual="api.html" -->
+
+ <h2><a id="configuration" href="#configuration">3. Configuration</a></h2>
+
+ <!--#include virtual="configuration.html" -->
+
+ <h2><a id="design" href="#design">4. Design</a></h2>
+
+ <!--#include virtual="design.html" -->
+
+ <h2><a id="implementation" href="#implementation">5. Implementation</a></h2>
+
+ <!--#include virtual="implementation.html" -->
+
+ <h2><a id="operations" href="#operations">6. Operations</a></h2>
+
+ <!--#include virtual="ops.html" -->
+
+ <h2><a id="security" href="#security">7. Security</a></h2>
+ <!--#include virtual="security.html" -->
+
+ <h2><a id="connect" href="#connect">8. Kafka Connect</a></h2>
+ <!--#include virtual="connect.html" -->
+
+ <h2><a id="streams" href="/100/documentation/streams">9. Kafka Streams</a></h2>
+ <p>
+ Kafka Streams is a client library for processing and analyzing data stored in Kafka. It builds upon important stream processing concepts such as properly distinguishing between event time and processing time, windowing support, exactly-once processing semantics and simple yet efficient management of application state.
+ </p>
+ <p>
+ Kafka Streams has a <b>low barrier to entry</b>: You can quickly write and run a small-scale proof-of-concept on a single machine; and you only need to run additional instances of your application on multiple machines to scale up to high-volume production workloads. Kafka Streams transparently handles the load balancing of multiple instances of the same application by leveraging Kafka's parallelism model.
+ </p>
+
+ <p>Learn More about Kafka Streams read <a href="/100/documentation/streams">this</a> Section.</p>
+
+<!--#include virtual="../includes/_footer.htm" -->
+<!--#include virtual="../includes/_docs_footer.htm" -->
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/documentation/index.html
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diff --git a/100/documentation/index.html b/100/documentation/index.html
new file mode 100644
index 0000000..1d7507f
--- /dev/null
+++ b/100/documentation/index.html
@@ -0,0 +1,18 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<!--#include virtual="../documentation.html" -->
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/documentation/streams/architecture.html
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diff --git a/100/documentation/streams/architecture.html b/100/documentation/streams/architecture.html
new file mode 100644
index 0000000..6ba69f3
--- /dev/null
+++ b/100/documentation/streams/architecture.html
@@ -0,0 +1,19 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<!-- should always link the the latest release's documentation -->
+<!--#include virtual="../../streams/architecture.html" -->
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/documentation/streams/core-concepts.html
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diff --git a/100/documentation/streams/core-concepts.html b/100/documentation/streams/core-concepts.html
new file mode 100644
index 0000000..ff46c53
--- /dev/null
+++ b/100/documentation/streams/core-concepts.html
@@ -0,0 +1,19 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<!-- should always link the the latest release's documentation -->
+<!--#include virtual="../../streams/core-concepts.html" -->
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/documentation/streams/developer-guide.html
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diff --git a/100/documentation/streams/developer-guide.html b/100/documentation/streams/developer-guide.html
new file mode 100644
index 0000000..e258331
--- /dev/null
+++ b/100/documentation/streams/developer-guide.html
@@ -0,0 +1,19 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<!-- should always link the the latest release's documentation -->
+<!--#include virtual="../../streams/developer-guide.html" -->
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/documentation/streams/index.html
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diff --git a/100/documentation/streams/index.html b/100/documentation/streams/index.html
new file mode 100644
index 0000000..1aaaff4
--- /dev/null
+++ b/100/documentation/streams/index.html
@@ -0,0 +1,19 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<!-- should always link the the latest release's documentation -->
+<!--#include virtual="../../streams/index.html" -->
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/documentation/streams/quickstart.html
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diff --git a/100/documentation/streams/quickstart.html b/100/documentation/streams/quickstart.html
new file mode 100644
index 0000000..f69c0d5
--- /dev/null
+++ b/100/documentation/streams/quickstart.html
@@ -0,0 +1,19 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<!-- should always link the the latest release's documentation -->
+<!--#include virtual="../../streams/quickstart.html" -->
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/documentation/streams/tutorial.html
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diff --git a/100/documentation/streams/tutorial.html b/100/documentation/streams/tutorial.html
new file mode 100644
index 0000000..90f408d
--- /dev/null
+++ b/100/documentation/streams/tutorial.html
@@ -0,0 +1,19 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<!-- should always link the the latest release's documentation -->
+<!--#include virtual="../../streams/tutorial.html" -->
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/documentation/streams/upgrade-guide.html
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diff --git a/100/documentation/streams/upgrade-guide.html b/100/documentation/streams/upgrade-guide.html
new file mode 100644
index 0000000..0c68795
--- /dev/null
+++ b/100/documentation/streams/upgrade-guide.html
@@ -0,0 +1,19 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<!-- should always link the the latest release's documentation -->
+<!--#include virtual="../../streams/upgrade-guide.html" -->
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/ecosystem.html
----------------------------------------------------------------------
diff --git a/100/ecosystem.html b/100/ecosystem.html
new file mode 100644
index 0000000..5fbcec5
--- /dev/null
+++ b/100/ecosystem.html
@@ -0,0 +1,18 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+There are a plethora of tools that integrate with Kafka outside the main distribution. The <a href="https://cwiki.apache.org/confluence/display/KAFKA/Ecosystem"> ecosystem page</a> lists many of these, including stream processing systems, Hadoop integration, monitoring, and deployment tools.
[14/14] kafka-site git commit: Update site for 1.0.0 release
Posted by ij...@apache.org.
Update site for 1.0.0 release
Javadocs will be in a subsequent commit.
Project: http://git-wip-us.apache.org/repos/asf/kafka-site/repo
Commit: http://git-wip-us.apache.org/repos/asf/kafka-site/commit/cb024c13
Tree: http://git-wip-us.apache.org/repos/asf/kafka-site/tree/cb024c13
Diff: http://git-wip-us.apache.org/repos/asf/kafka-site/diff/cb024c13
Branch: refs/heads/asf-site
Commit: cb024c13523430daf107a332efeac33da80f6a37
Parents: e977ecc
Author: Ismael Juma <is...@juma.me.uk>
Authored: Wed Nov 1 12:58:15 2017 +0000
Committer: Ismael Juma <is...@juma.me.uk>
Committed: Wed Nov 1 12:58:15 2017 +0000
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100/connect.html | 514 +++
100/design.html | 624 +++
100/documentation.html | 83 +
100/documentation/index.html | 18 +
100/documentation/streams/architecture.html | 19 +
100/documentation/streams/core-concepts.html | 19 +
100/documentation/streams/developer-guide.html | 19 +
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100/documentation/streams/quickstart.html | 19 +
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100/generated/producer_config.html | 116 +
100/generated/producer_metrics.html | 78 +
100/generated/protocol_api_keys.html | 81 +
100/generated/protocol_errors.html | 70 +
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+<script id="api-template" type="text/x-handlebars-template">
+ Kafka includes five core apis:
+ <ol>
+ <li>The <a href="#producerapi">Producer</a> API allows applications to send streams of data to topics in the Kafka cluster.
+ <li>The <a href="#consumerapi">Consumer</a> API allows applications to read streams of data from topics in the Kafka cluster.
+ <li>The <a href="#streamsapi">Streams</a> API allows transforming streams of data from input topics to output topics.
+ <li>The <a href="#connectapi">Connect</a> API allows implementing connectors that continually pull from some source system or application into Kafka or push from Kafka into some sink system or application.
+ <li>The <a href="#adminapi">AdminClient</a> API allows managing and inspecting topics, brokers, and other Kafka objects.
+ </ol>
+
+ Kafka exposes all its functionality over a language independent protocol which has clients available in many programming languages. However only the Java clients are maintained as part of the main Kafka project, the others are available as independent open source projects. A list of non-Java clients is available <a href="https://cwiki.apache.org/confluence/display/KAFKA/Clients">here</a>.
+
+ <h3><a id="producerapi" href="#producerapi">2.1 Producer API</a></h3>
+
+ The Producer API allows applications to send streams of data to topics in the Kafka cluster.
+ <p>
+ Examples showing how to use the producer are given in the
+ <a href="/{{version}}/javadoc/index.html?org/apache/kafka/clients/producer/KafkaProducer.html" title="Kafka {{dotVersion}} Javadoc">javadocs</a>.
+ <p>
+ To use the producer, you can use the following maven dependency:
+
+ <pre class="brush: xml;">
+ <dependency>
+ <groupId>org.apache.kafka</groupId>
+ <artifactId>kafka-clients</artifactId>
+ <version>{{fullDotVersion}}</version>
+ </dependency>
+ </pre>
+
+ <h3><a id="consumerapi" href="#consumerapi">2.2 Consumer API</a></h3>
+
+ The Consumer API allows applications to read streams of data from topics in the Kafka cluster.
+ <p>
+ Examples showing how to use the consumer are given in the
+ <a href="/{{version}}/javadoc/index.html?org/apache/kafka/clients/consumer/KafkaConsumer.html" title="Kafka {{dotVersion}} Javadoc">javadocs</a>.
+ <p>
+ To use the consumer, you can use the following maven dependency:
+ <pre class="brush: xml;">
+ <dependency>
+ <groupId>org.apache.kafka</groupId>
+ <artifactId>kafka-clients</artifactId>
+ <version>{{fullDotVersion}}</version>
+ </dependency>
+ </pre>
+
+ <h3><a id="streamsapi" href="#streamsapi">2.3 Streams API</a></h3>
+
+ The <a href="#streamsapi">Streams</a> API allows transforming streams of data from input topics to output topics.
+ <p>
+ Examples showing how to use this library are given in the
+ <a href="/{{version}}/javadoc/index.html?org/apache/kafka/streams/KafkaStreams.html" title="Kafka {{dotVersion}} Javadoc">javadocs</a>
+ <p>
+ Additional documentation on using the Streams API is available <a href="/{{version}}/documentation/streams">here</a>.
+ <p>
+ To use Kafka Streams you can use the following maven dependency:
+
+ <pre class="brush: xml;">
+ <dependency>
+ <groupId>org.apache.kafka</groupId>
+ <artifactId>kafka-streams</artifactId>
+ <version>{{fullDotVersion}}</version>
+ </dependency>
+ </pre>
+
+ <h3><a id="connectapi" href="#connectapi">2.4 Connect API</a></h3>
+
+ The Connect API allows implementing connectors that continually pull from some source data system into Kafka or push from Kafka into some sink data system.
+ <p>
+ Many users of Connect won't need to use this API directly, though, they can use pre-built connectors without needing to write any code. Additional information on using Connect is available <a href="/documentation.html#connect">here</a>.
+ <p>
+ Those who want to implement custom connectors can see the <a href="/{{version}}/javadoc/index.html?org/apache/kafka/connect" title="Kafka {{dotVersion}} Javadoc">javadoc</a>.
+ <p>
+
+ <h3><a id="adminapi" href="#adminapi">2.5 AdminClient API</a></h3>
+
+ The AdminClient API supports managing and inspecting topics, brokers, acls, and other Kafka objects.
+ <p>
+ To use the AdminClient API, add the following Maven dependency:
+ <pre class="brush: xml;">
+ <dependency>
+ <groupId>org.apache.kafka</groupId>
+ <artifactId>kafka-clients</artifactId>
+ <version>{{fullDotVersion}}</version>
+ </dependency>
+ </pre>
+ For more information about the AdminClient APIs, see the <a href="/{{version}}/javadoc/index.html?org/apache/kafka/clients/admin/AdminClient.html" title="Kafka {{dotVersion}} Javadoc">javadoc</a>.
+ <p>
+
+ <h3><a id="legacyapis" href="#legacyapis">2.6 Legacy APIs</a></h3>
+
+ <p>
+ A more limited legacy producer and consumer api is also included in Kafka. These old Scala APIs are deprecated and only still available for compatibility purposes. Information on them can be found here <a href="/081/documentation.html#producerapi" title="Kafka 0.8.1 Docs">
+ here</a>.
+ </p>
+</script>
+
+<div class="p-api"></div>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/configuration.html
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<script id="configuration-template" type="text/x-handlebars-template">
+ Kafka uses key-value pairs in the <a href="http://en.wikipedia.org/wiki/.properties">property file format</a> for configuration. These values can be supplied either from a file or programmatically.
+
+ <h3><a id="brokerconfigs" href="#brokerconfigs">3.1 Broker Configs</a></h3>
+
+ The essential configurations are the following:
+ <ul>
+ <li><code>broker.id</code>
+ <li><code>log.dirs</code>
+ <li><code>zookeeper.connect</code>
+ </ul>
+
+ Topic-level configurations and defaults are discussed in more detail <a href="#topicconfigs">below</a>.
+
+ <!--#include virtual="generated/kafka_config.html" -->
+
+ <p>More details about broker configuration can be found in the scala class <code>kafka.server.KafkaConfig</code>.</p>
+
+ <h3><a id="topicconfigs" href="#topicconfigs">3.2 Topic-Level Configs</a></h3>
+
+ Configurations pertinent to topics have both a server default as well an optional per-topic override. If no per-topic configuration is given the server default is used. The override can be set at topic creation time by giving one or more <code>--config</code> options. This example creates a topic named <i>my-topic</i> with a custom max message size and flush rate:
+ <pre class="brush: bash;">
+ > bin/kafka-topics.sh --zookeeper localhost:2181 --create --topic my-topic --partitions 1
+ --replication-factor 1 --config max.message.bytes=64000 --config flush.messages=1
+ </pre>
+ Overrides can also be changed or set later using the alter configs command. This example updates the max message size for <i>my-topic</i>:
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --entity-type topics --entity-name my-topic
+ --alter --add-config max.message.bytes=128000
+ </pre>
+
+ To check overrides set on the topic you can do
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --entity-type topics --entity-name my-topic --describe
+ </pre>
+
+ To remove an override you can do
+ <pre class="brush: bash;">
+ > bin/kafka-configs.sh --zookeeper localhost:2181 --entity-type topics --entity-name my-topic --alter --delete-config max.message.bytes
+ </pre>
+
+ The following are the topic-level configurations. The server's default configuration for this property is given under the Server Default Property heading. A given server default config value only applies to a topic if it does not have an explicit topic config override.
+
+ <!--#include virtual="generated/topic_config.html" -->
+
+ <h3><a id="producerconfigs" href="#producerconfigs">3.3 Producer Configs</a></h3>
+
+ Below is the configuration of the Java producer:
+ <!--#include virtual="generated/producer_config.html" -->
+
+ <p>
+ For those interested in the legacy Scala producer configs, information can be found <a href="http://kafka.apache.org/082/documentation.html#producerconfigs">
+ here</a>.
+ </p>
+
+ <h3><a id="consumerconfigs" href="#consumerconfigs">3.4 Consumer Configs</a></h3>
+
+ In 0.9.0.0 we introduced the new Java consumer as a replacement for the older Scala-based simple and high-level consumers.
+ The configs for both new and old consumers are described below.
+
+ <h4><a id="newconsumerconfigs" href="#newconsumerconfigs">3.4.1 New Consumer Configs</a></h4>
+ Below is the configuration for the new consumer:
+ <!--#include virtual="generated/consumer_config.html" -->
+
+ <h4><a id="oldconsumerconfigs" href="#oldconsumerconfigs">3.4.2 Old Consumer Configs</a></h4>
+
+ The essential old consumer configurations are the following:
+ <ul>
+ <li><code>group.id</code>
+ <li><code>zookeeper.connect</code>
+ </ul>
+
+ <table class="data-table">
+ <tbody><tr>
+ <th>Property</th>
+ <th>Default</th>
+ <th>Description</th>
+ </tr>
+ <tr>
+ <td>group.id</td>
+ <td colspan="1"></td>
+ <td>A string that uniquely identifies the group of consumer processes to which this consumer belongs. By setting the same group id multiple processes indicate that they are all part of the same consumer group.</td>
+ </tr>
+ <tr>
+ <td>zookeeper.connect</td>
+ <td colspan="1"></td>
+ <td>Specifies the ZooKeeper connection string in the form <code>hostname:port</code> where host and port are the host and port of a ZooKeeper server. To allow connecting through other ZooKeeper nodes when that ZooKeeper machine is down you can also specify multiple hosts in the form <code>hostname1:port1,hostname2:port2,hostname3:port3</code>.
+ <p>
+ The server may also have a ZooKeeper chroot path as part of its ZooKeeper connection string which puts its data under some path in the global ZooKeeper namespace. If so the consumer should use the same chroot path in its connection string. For example to give a chroot path of <code>/chroot/path</code> you would give the connection string as <code>hostname1:port1,hostname2:port2,hostname3:port3/chroot/path</code>.</td>
+ </tr>
+ <tr>
+ <td>consumer.id</td>
+ <td colspan="1">null</td>
+ <td>
+ <p>Generated automatically if not set.</p>
+ </td>
+ </tr>
+ <tr>
+ <td>socket.timeout.ms</td>
+ <td colspan="1">30 * 1000</td>
+ <td>The socket timeout for network requests. The actual timeout set will be max.fetch.wait + socket.timeout.ms.</td>
+ </tr>
+ <tr>
+ <td>socket.receive.buffer.bytes</td>
+ <td colspan="1">64 * 1024</td>
+ <td>The socket receive buffer for network requests</td>
+ </tr>
+ <tr>
+ <td>fetch.message.max.bytes</td>
+ <td nowrap>1024 * 1024</td>
+ <td>The number of bytes of messages to attempt to fetch for each topic-partition in each fetch request. These bytes will be read into memory for each partition, so this helps control the memory used by the consumer. The fetch request size must be at least as large as the maximum message size the server allows or else it is possible for the producer to send messages larger than the consumer can fetch.</td>
+ </tr>
+ <tr>
+ <td>num.consumer.fetchers</td>
+ <td colspan="1">1</td>
+ <td>The number fetcher threads used to fetch data.</td>
+ </tr>
+ <tr>
+ <td>auto.commit.enable</td>
+ <td colspan="1">true</td>
+ <td>If true, periodically commit to ZooKeeper the offset of messages already fetched by the consumer. This committed offset will be used when the process fails as the position from which the new consumer will begin.</td>
+ </tr>
+ <tr>
+ <td>auto.commit.interval.ms</td>
+ <td colspan="1">60 * 1000</td>
+ <td>The frequency in ms that the consumer offsets are committed to zookeeper.</td>
+ </tr>
+ <tr>
+ <td>queued.max.message.chunks</td>
+ <td colspan="1">2</td>
+ <td>Max number of message chunks buffered for consumption. Each chunk can be up to fetch.message.max.bytes.</td>
+ </tr>
+ <tr>
+ <td>rebalance.max.retries</td>
+ <td colspan="1">4</td>
+ <td>When a new consumer joins a consumer group the set of consumers attempt to "rebalance" the load to assign partitions to each consumer. If the set of consumers changes while this assignment is taking place the rebalance will fail and retry. This setting controls the maximum number of attempts before giving up.</td>
+ </tr>
+ <tr>
+ <td>fetch.min.bytes</td>
+ <td colspan="1">1</td>
+ <td>The minimum amount of data the server should return for a fetch request. If insufficient data is available the request will wait for that much data to accumulate before answering the request.</td>
+ </tr>
+ <tr>
+ <td>fetch.wait.max.ms</td>
+ <td colspan="1">100</td>
+ <td>The maximum amount of time the server will block before answering the fetch request if there isn't sufficient data to immediately satisfy fetch.min.bytes</td>
+ </tr>
+ <tr>
+ <td>rebalance.backoff.ms</td>
+ <td>2000</td>
+ <td>Backoff time between retries during rebalance. If not set explicitly, the value in zookeeper.sync.time.ms is used.
+ </td>
+ </tr>
+ <tr>
+ <td>refresh.leader.backoff.ms</td>
+ <td colspan="1">200</td>
+ <td>Backoff time to wait before trying to determine the leader of a partition that has just lost its leader.</td>
+ </tr>
+ <tr>
+ <td>auto.offset.reset</td>
+ <td colspan="1">largest</td>
+ <td>
+ <p>What to do when there is no initial offset in ZooKeeper or if an offset is out of range:<br/>* smallest : automatically reset the offset to the smallest offset<br/>* largest : automatically reset the offset to the largest offset<br/>* anything else: throw exception to the consumer</p>
+ </td>
+ </tr>
+ <tr>
+ <td>consumer.timeout.ms</td>
+ <td colspan="1">-1</td>
+ <td>Throw a timeout exception to the consumer if no message is available for consumption after the specified interval</td>
+ </tr>
+ <tr>
+ <td>exclude.internal.topics</td>
+ <td colspan="1">true</td>
+ <td>Whether messages from internal topics (such as offsets) should be exposed to the consumer.</td>
+ </tr>
+ <tr>
+ <td>client.id</td>
+ <td colspan="1">group id value</td>
+ <td>The client id is a user-specified string sent in each request to help trace calls. It should logically identify the application making the request.</td>
+ </tr>
+ <tr>
+ <td>zookeeper.session.timeout.ms </td>
+ <td colspan="1">6000</td>
+ <td>ZooKeeper session timeout. If the consumer fails to heartbeat to ZooKeeper for this period of time it is considered dead and a rebalance will occur.</td>
+ </tr>
+ <tr>
+ <td>zookeeper.connection.timeout.ms</td>
+ <td colspan="1">6000</td>
+ <td>The max time that the client waits while establishing a connection to zookeeper.</td>
+ </tr>
+ <tr>
+ <td>zookeeper.sync.time.ms </td>
+ <td colspan="1">2000</td>
+ <td>How far a ZK follower can be behind a ZK leader</td>
+ </tr>
+ <tr>
+ <td>offsets.storage</td>
+ <td colspan="1">zookeeper</td>
+ <td>Select where offsets should be stored (zookeeper or kafka).</td>
+ </tr>
+ <tr>
+ <td>offsets.channel.backoff.ms</td>
+ <td colspan="1">1000</td>
+ <td>The backoff period when reconnecting the offsets channel or retrying failed offset fetch/commit requests.</td>
+ </tr>
+ <tr>
+ <td>offsets.channel.socket.timeout.ms</td>
+ <td colspan="1">10000</td>
+ <td>Socket timeout when reading responses for offset fetch/commit requests. This timeout is also used for ConsumerMetadata requests that are used to query for the offset manager.</td>
+ </tr>
+ <tr>
+ <td>offsets.commit.max.retries</td>
+ <td colspan="1">5</td>
+ <td>Retry the offset commit up to this many times on failure. This retry count only applies to offset commits during shut-down. It does not apply to commits originating from the auto-commit thread. It also does not apply to attempts to query for the offset coordinator before committing offsets. i.e., if a consumer metadata request fails for any reason, it will be retried and that retry does not count toward this limit.</td>
+ </tr>
+ <tr>
+ <td>dual.commit.enabled</td>
+ <td colspan="1">true</td>
+ <td>If you are using "kafka" as offsets.storage, you can dual commit offsets to ZooKeeper (in addition to Kafka). This is required during migration from zookeeper-based offset storage to kafka-based offset storage. With respect to any given consumer group, it is safe to turn this off after all instances within that group have been migrated to the new version that commits offsets to the broker (instead of directly to ZooKeeper).</td>
+ </tr>
+ <tr>
+ <td>partition.assignment.strategy</td>
+ <td colspan="1">range</td>
+ <td><p>Select between the "range" or "roundrobin" strategy for assigning partitions to consumer streams.<p>The round-robin partition assignor lays out all the available partitions and all the available consumer threads. It then proceeds to do a round-robin assignment from partition to consumer thread. If the subscriptions of all consumer instances are identical, then the partitions will be uniformly distributed. (i.e., the partition ownership counts will be within a delta of exactly one across all consumer threads.) Round-robin assignment is permitted only if: (a) Every topic has the same number of streams within a consumer instance (b) The set of subscribed topics is identical for every consumer instance within the group.<p> Range partitioning works on a per-topic basis. For each topic, we lay out the available partitions in numeric order and the consumer threads in lexicographic order. We then divide the number of partitions by the total number of consumer streams (threads
) to determine the number of partitions to assign to each consumer. If it does not evenly divide, then the first few consumers will have one extra partition.</td>
+ </tr>
+ </tbody>
+ </table>
+
+
+ <p>More details about consumer configuration can be found in the scala class <code>kafka.consumer.ConsumerConfig</code>.</p>
+
+ <h3><a id="connectconfigs" href="#connectconfigs">3.5 Kafka Connect Configs</a></h3>
+ Below is the configuration of the Kafka Connect framework.
+ <!--#include virtual="generated/connect_config.html" -->
+
+ <h3><a id="streamsconfigs" href="#streamsconfigs">3.6 Kafka Streams Configs</a></h3>
+ Below is the configuration of the Kafka Streams client library.
+ <!--#include virtual="generated/streams_config.html" -->
+
+ <h3><a id="adminclientconfigs" href="#adminclientconfigs">3.7 AdminClient Configs</a></h3>
+ Below is the configuration of the Kafka Admin client library.
+ <!--#include virtual="generated/admin_client_config.html" -->
+</script>
+
+<div class="p-configuration"></div>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/connect.html
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+<!--~
+ ~ Licensed to the Apache Software Foundation (ASF) under one or more
+ ~ contributor license agreements. See the NOTICE file distributed with
+ ~ this work for additional information regarding copyright ownership.
+ ~ The ASF licenses this file to You under the Apache License, Version 2.0
+ ~ (the "License"); you may not use this file except in compliance with
+ ~ the License. You may obtain a copy of the License at
+ ~
+ ~ http://www.apache.org/licenses/LICENSE-2.0
+ ~
+ ~ Unless required by applicable law or agreed to in writing, software
+ ~ distributed under the License is distributed on an "AS IS" BASIS,
+ ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ ~ See the License for the specific language governing permissions and
+ ~ limitations under the License.
+ ~-->
+
+<script id="connect-template" type="text/x-handlebars-template">
+ <h3><a id="connect_overview" href="#connect_overview">8.1 Overview</a></h3>
+
+ <p>Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems. It makes it simple to quickly define <i>connectors</i> that move large collections of data into and out of Kafka. Kafka Connect can ingest entire databases or collect metrics from all your application servers into Kafka topics, making the data available for stream processing with low latency. An export job can deliver data from Kafka topics into secondary storage and query systems or into batch systems for offline analysis.</p>
+
+ <p>Kafka Connect features include:</p>
+ <ul>
+ <li><b>A common framework for Kafka connectors</b> - Kafka Connect standardizes integration of other data systems with Kafka, simplifying connector development, deployment, and management</li>
+ <li><b>Distributed and standalone modes</b> - scale up to a large, centrally managed service supporting an entire organization or scale down to development, testing, and small production deployments</li>
+ <li><b>REST interface</b> - submit and manage connectors to your Kafka Connect cluster via an easy to use REST API</li>
+ <li><b>Automatic offset management</b> - with just a little information from connectors, Kafka Connect can manage the offset commit process automatically so connector developers do not need to worry about this error prone part of connector development</li>
+ <li><b>Distributed and scalable by default</b> - Kafka Connect builds on the existing group management protocol. More workers can be added to scale up a Kafka Connect cluster.</li>
+ <li><b>Streaming/batch integration</b> - leveraging Kafka's existing capabilities, Kafka Connect is an ideal solution for bridging streaming and batch data systems</li>
+ </ul>
+
+ <h3><a id="connect_user" href="#connect_user">8.2 User Guide</a></h3>
+
+ <p>The quickstart provides a brief example of how to run a standalone version of Kafka Connect. This section describes how to configure, run, and manage Kafka Connect in more detail.</p>
+
+ <h4><a id="connect_running" href="#connect_running">Running Kafka Connect</a></h4>
+
+ <p>Kafka Connect currently supports two modes of execution: standalone (single process) and distributed.</p>
+
+ <p>In standalone mode all work is performed in a single process. This configuration is simpler to setup and get started with and may be useful in situations where only one worker makes sense (e.g. collecting log files), but it does not benefit from some of the features of Kafka Connect such as fault tolerance. You can start a standalone process with the following command:</p>
+
+ <pre class="brush: bash;">
+ > bin/connect-standalone.sh config/connect-standalone.properties connector1.properties [connector2.properties ...]
+ </pre>
+
+ <p>The first parameter is the configuration for the worker. This includes settings such as the Kafka connection parameters, serialization format, and how frequently to commit offsets. The provided example should work well with a local cluster running with the default configuration provided by <code>config/server.properties</code>. It will require tweaking to use with a different configuration or production deployment. All workers (both standalone and distributed) require a few configs:</p>
+ <ul>
+ <li><code>bootstrap.servers</code> - List of Kafka servers used to bootstrap connections to Kafka</li>
+ <li><code>key.converter</code> - Converter class used to convert between Kafka Connect format and the serialized form that is written to Kafka. This controls the format of the keys in messages written to or read from Kafka, and since this is independent of connectors it allows any connector to work with any serialization format. Examples of common formats include JSON and Avro.</li>
+ <li><code>value.converter</code> - Converter class used to convert between Kafka Connect format and the serialized form that is written to Kafka. This controls the format of the values in messages written to or read from Kafka, and since this is independent of connectors it allows any connector to work with any serialization format. Examples of common formats include JSON and Avro.</li>
+ </ul>
+
+ <p>The important configuration options specific to standalone mode are:</p>
+ <ul>
+ <li><code>offset.storage.file.filename</code> - File to store offset data in</li>
+ </ul>
+
+ <p>The parameters that are configured here are intended for producers and consumers used by Kafka Connect to access the configuration, offset and status topics. For configuration of Kafka source and Kafka sink tasks, the same parameters can be used but need to be prefixed with <code>consumer.</code> and <code>producer.</code> respectively. The only parameter that is inherited from the worker configuration is <code>bootstrap.servers</code>, which in most cases will be sufficient, since the same cluster is often used for all purposes. A notable exeption is a secured cluster, which requires extra parameters to allow connections. These parameters will need to be set up to three times in the worker configuration, once for management access, once for Kafka sinks and once for Kafka sources.</p>
+
+ <p>The remaining parameters are connector configuration files. You may include as many as you want, but all will execute within the same process (on different threads).</p>
+
+ <p>Distributed mode handles automatic balancing of work, allows you to scale up (or down) dynamically, and offers fault tolerance both in the active tasks and for configuration and offset commit data. Execution is very similar to standalone mode:</p>
+
+ <pre class="brush: bash;">
+ > bin/connect-distributed.sh config/connect-distributed.properties
+ </pre>
+
+ <p>The difference is in the class which is started and the configuration parameters which change how the Kafka Connect process decides where to store configurations, how to assign work, and where to store offsets and task statues. In the distributed mode, Kafka Connect stores the offsets, configs and task statuses in Kafka topics. It is recommended to manually create the topics for offset, configs and statuses in order to achieve the desired the number of partitions and replication factors. If the topics are not yet created when starting Kafka Connect, the topics will be auto created with default number of partitions and replication factor, which may not be best suited for its usage.</p>
+
+ <p>In particular, the following configuration parameters, in addition to the common settings mentioned above, are critical to set before starting your cluster:</p>
+ <ul>
+ <li><code>group.id</code> (default <code>connect-cluster</code>) - unique name for the cluster, used in forming the Connect cluster group; note that this <b>must not conflict</b> with consumer group IDs</li>
+ <li><code>config.storage.topic</code> (default <code>connect-configs</code>) - topic to use for storing connector and task configurations; note that this should be a single partition, highly replicated, compacted topic. You may need to manually create the topic to ensure the correct configuration as auto created topics may have multiple partitions or be automatically configured for deletion rather than compaction</li>
+ <li><code>offset.storage.topic</code> (default <code>connect-offsets</code>) - topic to use for storing offsets; this topic should have many partitions, be replicated, and be configured for compaction</li>
+ <li><code>status.storage.topic</code> (default <code>connect-status</code>) - topic to use for storing statuses; this topic can have multiple partitions, and should be replicated and configured for compaction</li>
+ </ul>
+
+ <p>Note that in distributed mode the connector configurations are not passed on the command line. Instead, use the REST API described below to create, modify, and destroy connectors.</p>
+
+
+ <h4><a id="connect_configuring" href="#connect_configuring">Configuring Connectors</a></h4>
+
+ <p>Connector configurations are simple key-value mappings. For standalone mode these are defined in a properties file and passed to the Connect process on the command line. In distributed mode, they will be included in the JSON payload for the request that creates (or modifies) the connector.</p>
+
+ <p>Most configurations are connector dependent, so they can't be outlined here. However, there are a few common options:</p>
+
+ <ul>
+ <li><code>name</code> - Unique name for the connector. Attempting to register again with the same name will fail.</li>
+ <li><code>connector.class</code> - The Java class for the connector</li>
+ <li><code>tasks.max</code> - The maximum number of tasks that should be created for this connector. The connector may create fewer tasks if it cannot achieve this level of parallelism.</li>
+ <li><code>key.converter</code> - (optional) Override the default key converter set by the worker.</li>
+ <li><code>value.converter</code> - (optional) Override the default value converter set by the worker.</li>
+ </ul>
+
+ <p>The <code>connector.class</code> config supports several formats: the full name or alias of the class for this connector. If the connector is org.apache.kafka.connect.file.FileStreamSinkConnector, you can either specify this full name or use FileStreamSink or FileStreamSinkConnector to make the configuration a bit shorter.</p>
+
+ <p>Sink connectors also have one additional option to control their input:</p>
+ <ul>
+ <li><code>topics</code> - A list of topics to use as input for this connector</li>
+ </ul>
+
+ <p>For any other options, you should consult the documentation for the connector.</p>
+
+ <h4><a id="connect_transforms" href="#connect_transforms">Transformations</a></h4>
+
+ <p>Connectors can be configured with transformations to make lightweight message-at-a-time modifications. They can be convenient for data massaging and event routing.</p>
+
+ <p>A transformation chain can be specified in the connector configuration.</p>
+
+ <ul>
+ <li><code>transforms</code> - List of aliases for the transformation, specifying the order in which the transformations will be applied.</li>
+ <li><code>transforms.$alias.type</code> - Fully qualified class name for the transformation.</li>
+ <li><code>transforms.$alias.$transformationSpecificConfig</code> Configuration properties for the transformation</li>
+ </ul>
+
+ <p>For example, lets take the built-in file source connector and use a transformation to add a static field.</p>
+
+ <p>Throughout the example we'll use schemaless JSON data format. To use schemaless format, we changed the following two lines in <code>connect-standalone.properties</code> from true to false:</p>
+
+ <pre class="brush: text;">
+ key.converter.schemas.enable
+ value.converter.schemas.enable
+ </pre>
+
+ <p>The file source connector reads each line as a String. We will wrap each line in a Map and then add a second field to identify the origin of the event. To do this, we use two transformations:</p>
+ <ul>
+ <li><b>HoistField</b> to place the input line inside a Map</li>
+ <li><b>InsertField</b> to add the static field. In this example we'll indicate that the record came from a file connector</li>
+ </ul>
+
+ <p>After adding the transformations, <code>connect-file-source.properties</code> file looks as following:</p>
+
+ <pre class="brush: text;">
+ name=local-file-source
+ connector.class=FileStreamSource
+ tasks.max=1
+ file=test.txt
+ topic=connect-test
+ transforms=MakeMap, InsertSource
+ transforms.MakeMap.type=org.apache.kafka.connect.transforms.HoistField$Value
+ transforms.MakeMap.field=line
+ transforms.InsertSource.type=org.apache.kafka.connect.transforms.InsertField$Value
+ transforms.InsertSource.static.field=data_source
+ transforms.InsertSource.static.value=test-file-source
+ </pre>
+
+ <p>All the lines starting with <code>transforms</code> were added for the transformations. You can see the two transformations we created: "InsertSource" and "MakeMap" are aliases that we chose to give the transformations. The transformation types are based on the list of built-in transformations you can see below. Each transformation type has additional configuration: HoistField requires a configuration called "field", which is the name of the field in the map that will include the original String from the file. InsertField transformation lets us specify the field name and the value that we are adding.</p>
+
+ <p>When we ran the file source connector on my sample file without the transformations, and then read them using <code>kafka-console-consumer.sh</code>, the results were:</p>
+
+ <pre class="brush: text;">
+ "foo"
+ "bar"
+ "hello world"
+ </pre>
+
+ <p>We then create a new file connector, this time after adding the transformations to the configuration file. This time, the results will be:</p>
+
+ <pre class="brush: json;">
+ {"line":"foo","data_source":"test-file-source"}
+ {"line":"bar","data_source":"test-file-source"}
+ {"line":"hello world","data_source":"test-file-source"}
+ </pre>
+
+ <p>You can see that the lines we've read are now part of a JSON map, and there is an extra field with the static value we specified. This is just one example of what you can do with transformations.</p>
+
+ <p>Several widely-applicable data and routing transformations are included with Kafka Connect:</p>
+
+ <ul>
+ <li>InsertField - Add a field using either static data or record metadata</li>
+ <li>ReplaceField - Filter or rename fields</li>
+ <li>MaskField - Replace field with valid null value for the type (0, empty string, etc)</li>
+ <li>ValueToKey</li>
+ <li>HoistField - Wrap the entire event as a single field inside a Struct or a Map</li>
+ <li>ExtractField - Extract a specific field from Struct and Map and include only this field in results</li>
+ <li>SetSchemaMetadata - modify the schema name or version</li>
+ <li>TimestampRouter - Modify the topic of a record based on original topic and timestamp. Useful when using a sink that needs to write to different tables or indexes based on timestamps</li>
+ <li>RegexRouter - modify the topic of a record based on original topic, replacement string and a regular expression</li>
+ </ul>
+
+ <p>Details on how to configure each transformation are listed below:</p>
+
+
+ <!--#include virtual="generated/connect_transforms.html" -->
+
+ <h4><a id="connect_rest" href="#connect_rest">REST API</a></h4>
+
+ <p>Since Kafka Connect is intended to be run as a service, it also provides a REST API for managing connectors. By default, this service runs on port 8083. The following are the currently supported endpoints:</p>
+
+ <ul>
+ <li><code>GET /connectors</code> - return a list of active connectors</li>
+ <li><code>POST /connectors</code> - create a new connector; the request body should be a JSON object containing a string <code>name</code> field and an object <code>config</code> field with the connector configuration parameters</li>
+ <li><code>GET /connectors/{name}</code> - get information about a specific connector</li>
+ <li><code>GET /connectors/{name}/config</code> - get the configuration parameters for a specific connector</li>
+ <li><code>PUT /connectors/{name}/config</code> - update the configuration parameters for a specific connector</li>
+ <li><code>GET /connectors/{name}/status</code> - get current status of the connector, including if it is running, failed, paused, etc., which worker it is assigned to, error information if it has failed, and the state of all its tasks</li>
+ <li><code>GET /connectors/{name}/tasks</code> - get a list of tasks currently running for a connector</li>
+ <li><code>GET /connectors/{name}/tasks/{taskid}/status</code> - get current status of the task, including if it is running, failed, paused, etc., which worker it is assigned to, and error information if it has failed</li>
+ <li><code>PUT /connectors/{name}/pause</code> - pause the connector and its tasks, which stops message processing until the connector is resumed</li>
+ <li><code>PUT /connectors/{name}/resume</code> - resume a paused connector (or do nothing if the connector is not paused)</li>
+ <li><code>POST /connectors/{name}/restart</code> - restart a connector (typically because it has failed)</li>
+ <li><code>POST /connectors/{name}/tasks/{taskId}/restart</code> - restart an individual task (typically because it has failed)</li>
+ <li><code>DELETE /connectors/{name}</code> - delete a connector, halting all tasks and deleting its configuration</li>
+ </ul>
+
+ <p>Kafka Connect also provides a REST API for getting information about connector plugins:</p>
+
+ <ul>
+ <li><code>GET /connector-plugins</code>- return a list of connector plugins installed in the Kafka Connect cluster. Note that the API only checks for connectors on the worker that handles the request, which means you may see inconsistent results, especially during a rolling upgrade if you add new connector jars</li>
+ <li><code>PUT /connector-plugins/{connector-type}/config/validate</code> - validate the provided configuration values against the configuration definition. This API performs per config validation, returns suggested values and error messages during validation.</li>
+ </ul>
+
+ <h3><a id="connect_development" href="#connect_development">8.3 Connector Development Guide</a></h3>
+
+ <p>This guide describes how developers can write new connectors for Kafka Connect to move data between Kafka and other systems. It briefly reviews a few key concepts and then describes how to create a simple connector.</p>
+
+ <h4><a id="connect_concepts" href="#connect_concepts">Core Concepts and APIs</a></h4>
+
+ <h5><a id="connect_connectorsandtasks" href="#connect_connectorsandtasks">Connectors and Tasks</a></h5>
+
+ <p>To copy data between Kafka and another system, users create a <code>Connector</code> for the system they want to pull data from or push data to. Connectors come in two flavors: <code>SourceConnectors</code> import data from another system (e.g. <code>JDBCSourceConnector</code> would import a relational database into Kafka) and <code>SinkConnectors</code> export data (e.g. <code>HDFSSinkConnector</code> would export the contents of a Kafka topic to an HDFS file).</p>
+
+ <p><code>Connectors</code> do not perform any data copying themselves: their configuration describes the data to be copied, and the <code>Connector</code> is responsible for breaking that job into a set of <code>Tasks</code> that can be distributed to workers. These <code>Tasks</code> also come in two corresponding flavors: <code>SourceTask</code> and <code>SinkTask</code>.</p>
+
+ <p>With an assignment in hand, each <code>Task</code> must copy its subset of the data to or from Kafka. In Kafka Connect, it should always be possible to frame these assignments as a set of input and output streams consisting of records with consistent schemas. Sometimes this mapping is obvious: each file in a set of log files can be considered a stream with each parsed line forming a record using the same schema and offsets stored as byte offsets in the file. In other cases it may require more effort to map to this model: a JDBC connector can map each table to a stream, but the offset is less clear. One possible mapping uses a timestamp column to generate queries incrementally returning new data, and the last queried timestamp can be used as the offset.</p>
+
+
+ <h5><a id="connect_streamsandrecords" href="#connect_streamsandrecords">Streams and Records</a></h5>
+
+ <p>Each stream should be a sequence of key-value records. Both the keys and values can have complex structure -- many primitive types are provided, but arrays, objects, and nested data structures can be represented as well. The runtime data format does not assume any particular serialization format; this conversion is handled internally by the framework.</p>
+
+ <p>In addition to the key and value, records (both those generated by sources and those delivered to sinks) have associated stream IDs and offsets. These are used by the framework to periodically commit the offsets of data that have been processed so that in the event of failures, processing can resume from the last committed offsets, avoiding unnecessary reprocessing and duplication of events.</p>
+
+ <h5><a id="connect_dynamicconnectors" href="#connect_dynamicconnectors">Dynamic Connectors</a></h5>
+
+ <p>Not all jobs are static, so <code>Connector</code> implementations are also responsible for monitoring the external system for any changes that might require reconfiguration. For example, in the <code>JDBCSourceConnector</code> example, the <code>Connector</code> might assign a set of tables to each <code>Task</code>. When a new table is created, it must discover this so it can assign the new table to one of the <code>Tasks</code> by updating its configuration. When it notices a change that requires reconfiguration (or a change in the number of <code>Tasks</code>), it notifies the framework and the framework updates any corresponding <code>Tasks</code>.</p>
+
+
+ <h4><a id="connect_developing" href="#connect_developing">Developing a Simple Connector</a></h4>
+
+ <p>Developing a connector only requires implementing two interfaces, the <code>Connector</code> and <code>Task</code>. A simple example is included with the source code for Kafka in the <code>file</code> package. This connector is meant for use in standalone mode and has implementations of a <code>SourceConnector</code>/<code>SourceTask</code> to read each line of a file and emit it as a record and a <code>SinkConnector</code>/<code>SinkTask</code> that writes each record to a file.</p>
+
+ <p>The rest of this section will walk through some code to demonstrate the key steps in creating a connector, but developers should also refer to the full example source code as many details are omitted for brevity.</p>
+
+ <h5><a id="connect_connectorexample" href="#connect_connectorexample">Connector Example</a></h5>
+
+ <p>We'll cover the <code>SourceConnector</code> as a simple example. <code>SinkConnector</code> implementations are very similar. Start by creating the class that inherits from <code>SourceConnector</code> and add a couple of fields that will store parsed configuration information (the filename to read from and the topic to send data to):</p>
+
+ <pre class="brush: java;">
+ public class FileStreamSourceConnector extends SourceConnector {
+ private String filename;
+ private String topic;
+ </pre>
+
+ <p>The easiest method to fill in is <code>taskClass()</code>, which defines the class that should be instantiated in worker processes to actually read the data:</p>
+
+ <pre class="brush: java;">
+ @Override
+ public Class<? extends Task> taskClass() {
+ return FileStreamSourceTask.class;
+ }
+ </pre>
+
+ <p>We will define the <code>FileStreamSourceTask</code> class below. Next, we add some standard lifecycle methods, <code>start()</code> and <code>stop()</code></p>:
+
+ <pre class="brush: java;">
+ @Override
+ public void start(Map<String, String> props) {
+ // The complete version includes error handling as well.
+ filename = props.get(FILE_CONFIG);
+ topic = props.get(TOPIC_CONFIG);
+ }
+
+ @Override
+ public void stop() {
+ // Nothing to do since no background monitoring is required.
+ }
+ </pre>
+
+ <p>Finally, the real core of the implementation is in <code>taskConfigs()</code>. In this case we are only
+ handling a single file, so even though we may be permitted to generate more tasks as per the
+ <code>maxTasks</code> argument, we return a list with only one entry:</p>
+
+ <pre class="brush: java;">
+ @Override
+ public List<Map<String, String>> taskConfigs(int maxTasks) {
+ ArrayList<Map<String, String>> configs = new ArrayList<>();
+ // Only one input stream makes sense.
+ Map<String, String> config = new HashMap<>();
+ if (filename != null)
+ config.put(FILE_CONFIG, filename);
+ config.put(TOPIC_CONFIG, topic);
+ configs.add(config);
+ return configs;
+ }
+ </pre>
+
+ <p>Although not used in the example, <code>SourceTask</code> also provides two APIs to commit offsets in the source system: <code>commit</code> and <code>commitRecord</code>. The APIs are provided for source systems which have an acknowledgement mechanism for messages. Overriding these methods allows the source connector to acknowledge messages in the source system, either in bulk or individually, once they have been written to Kafka.
+ The <code>commit</code> API stores the offsets in the source system, up to the offsets that have been returned by <code>poll</code>. The implementation of this API should block until the commit is complete. The <code>commitRecord</code> API saves the offset in the source system for each <code>SourceRecord</code> after it is written to Kafka. As Kafka Connect will record offsets automatically, <code>SourceTask</code>s are not required to implement them. In cases where a connector does need to acknowledge messages in the source system, only one of the APIs is typically required.</p>
+
+ <p>Even with multiple tasks, this method implementation is usually pretty simple. It just has to determine the number of input tasks, which may require contacting the remote service it is pulling data from, and then divvy them up. Because some patterns for splitting work among tasks are so common, some utilities are provided in <code>ConnectorUtils</code> to simplify these cases.</p>
+
+ <p>Note that this simple example does not include dynamic input. See the discussion in the next section for how to trigger updates to task configs.</p>
+
+ <h5><a id="connect_taskexample" href="#connect_taskexample">Task Example - Source Task</a></h5>
+
+ <p>Next we'll describe the implementation of the corresponding <code>SourceTask</code>. The implementation is short, but too long to cover completely in this guide. We'll use pseudo-code to describe most of the implementation, but you can refer to the source code for the full example.</p>
+
+ <p>Just as with the connector, we need to create a class inheriting from the appropriate base <code>Task</code> class. It also has some standard lifecycle methods:</p>
+
+
+ <pre class="brush: java;">
+ public class FileStreamSourceTask extends SourceTask {
+ String filename;
+ InputStream stream;
+ String topic;
+
+ @Override
+ public void start(Map<String, String> props) {
+ filename = props.get(FileStreamSourceConnector.FILE_CONFIG);
+ stream = openOrThrowError(filename);
+ topic = props.get(FileStreamSourceConnector.TOPIC_CONFIG);
+ }
+
+ @Override
+ public synchronized void stop() {
+ stream.close();
+ }
+ </pre>
+
+ <p>These are slightly simplified versions, but show that that these methods should be relatively simple and the only work they should perform is allocating or freeing resources. There are two points to note about this implementation. First, the <code>start()</code> method does not yet handle resuming from a previous offset, which will be addressed in a later section. Second, the <code>stop()</code> method is synchronized. This will be necessary because <code>SourceTasks</code> are given a dedicated thread which they can block indefinitely, so they need to be stopped with a call from a different thread in the Worker.</p>
+
+ <p>Next, we implement the main functionality of the task, the <code>poll()</code> method which gets events from the input system and returns a <code>List<SourceRecord></code>:</p>
+
+ <pre class="brush: java;">
+ @Override
+ public List<SourceRecord> poll() throws InterruptedException {
+ try {
+ ArrayList<SourceRecord> records = new ArrayList<>();
+ while (streamValid(stream) && records.isEmpty()) {
+ LineAndOffset line = readToNextLine(stream);
+ if (line != null) {
+ Map<String, Object> sourcePartition = Collections.singletonMap("filename", filename);
+ Map<String, Object> sourceOffset = Collections.singletonMap("position", streamOffset);
+ records.add(new SourceRecord(sourcePartition, sourceOffset, topic, Schema.STRING_SCHEMA, line));
+ } else {
+ Thread.sleep(1);
+ }
+ }
+ return records;
+ } catch (IOException e) {
+ // Underlying stream was killed, probably as a result of calling stop. Allow to return
+ // null, and driving thread will handle any shutdown if necessary.
+ }
+ return null;
+ }
+ </pre>
+
+ <p>Again, we've omitted some details, but we can see the important steps: the <code>poll()</code> method is going to be called repeatedly, and for each call it will loop trying to read records from the file. For each line it reads, it also tracks the file offset. It uses this information to create an output <code>SourceRecord</code> with four pieces of information: the source partition (there is only one, the single file being read), source offset (byte offset in the file), output topic name, and output value (the line, and we include a schema indicating this value will always be a string). Other variants of the <code>SourceRecord</code> constructor can also include a specific output partition and a key.</p>
+
+ <p>Note that this implementation uses the normal Java <code>InputStream</code> interface and may sleep if data is not available. This is acceptable because Kafka Connect provides each task with a dedicated thread. While task implementations have to conform to the basic <code>poll()</code> interface, they have a lot of flexibility in how they are implemented. In this case, an NIO-based implementation would be more efficient, but this simple approach works, is quick to implement, and is compatible with older versions of Java.</p>
+
+ <h5><a id="connect_sinktasks" href="#connect_sinktasks">Sink Tasks</a></h5>
+
+ <p>The previous section described how to implement a simple <code>SourceTask</code>. Unlike <code>SourceConnector</code> and <code>SinkConnector</code>, <code>SourceTask</code> and <code>SinkTask</code> have very different interfaces because <code>SourceTask</code> uses a pull interface and <code>SinkTask</code> uses a push interface. Both share the common lifecycle methods, but the <code>SinkTask</code> interface is quite different:</p>
+
+ <pre class="brush: java;">
+ public abstract class SinkTask implements Task {
+ public void initialize(SinkTaskContext context) {
+ this.context = context;
+ }
+
+ public abstract void put(Collection<SinkRecord> records);
+
+ public void flush(Map<TopicPartition, OffsetAndMetadata> currentOffsets) {
+ }
+ </pre>
+
+ <p>The <code>SinkTask</code> documentation contains full details, but this interface is nearly as simple as the <code>SourceTask</code>. The <code>put()</code> method should contain most of the implementation, accepting sets of <code>SinkRecords</code>, performing any required translation, and storing them in the destination system. This method does not need to ensure the data has been fully written to the destination system before returning. In fact, in many cases internal buffering will be useful so an entire batch of records can be sent at once, reducing the overhead of inserting events into the downstream data store. The <code>SinkRecords</code> contain essentially the same information as <code>SourceRecords</code>: Kafka topic, partition, offset and the event key and value.</p>
+
+ <p>The <code>flush()</code> method is used during the offset commit process, which allows tasks to recover from failures and resume from a safe point such that no events will be missed. The method should push any outstanding data to the destination system and then block until the write has been acknowledged. The <code>offsets</code> parameter can often be ignored, but is useful in some cases where implementations want to store offset information in the destination store to provide exactly-once
+ delivery. For example, an HDFS connector could do this and use atomic move operations to make sure the <code>flush()</code> operation atomically commits the data and offsets to a final location in HDFS.</p>
+
+
+ <h5><a id="connect_resuming" href="#connect_resuming">Resuming from Previous Offsets</a></h5>
+
+ <p>The <code>SourceTask</code> implementation included a stream ID (the input filename) and offset (position in the file) with each record. The framework uses this to commit offsets periodically so that in the case of a failure, the task can recover and minimize the number of events that are reprocessed and possibly duplicated (or to resume from the most recent offset if Kafka Connect was stopped gracefully, e.g. in standalone mode or due to a job reconfiguration). This commit process is completely automated by the framework, but only the connector knows how to seek back to the right position in the input stream to resume from that location.</p>
+
+ <p>To correctly resume upon startup, the task can use the <code>SourceContext</code> passed into its <code>initialize()</code> method to access the offset data. In <code>initialize()</code>, we would add a bit more code to read the offset (if it exists) and seek to that position:</p>
+
+ <pre class="brush: java;">
+ stream = new FileInputStream(filename);
+ Map<String, Object> offset = context.offsetStorageReader().offset(Collections.singletonMap(FILENAME_FIELD, filename));
+ if (offset != null) {
+ Long lastRecordedOffset = (Long) offset.get("position");
+ if (lastRecordedOffset != null)
+ seekToOffset(stream, lastRecordedOffset);
+ }
+ </pre>
+
+ <p>Of course, you might need to read many keys for each of the input streams. The <code>OffsetStorageReader</code> interface also allows you to issue bulk reads to efficiently load all offsets, then apply them by seeking each input stream to the appropriate position.</p>
+
+ <h4><a id="connect_dynamicio" href="#connect_dynamicio">Dynamic Input/Output Streams</a></h4>
+
+ <p>Kafka Connect is intended to define bulk data copying jobs, such as copying an entire database rather than creating many jobs to copy each table individually. One consequence of this design is that the set of input or output streams for a connector can vary over time.</p>
+
+ <p>Source connectors need to monitor the source system for changes, e.g. table additions/deletions in a database. When they pick up changes, they should notify the framework via the <code>ConnectorContext</code> object that reconfiguration is necessary. For example, in a <code>SourceConnector</code>:</p>
+
+ <pre class="brush: java;">
+ if (inputsChanged())
+ this.context.requestTaskReconfiguration();
+ </pre>
+
+ <p>The framework will promptly request new configuration information and update the tasks, allowing them to gracefully commit their progress before reconfiguring them. Note that in the <code>SourceConnector</code> this monitoring is currently left up to the connector implementation. If an extra thread is required to perform this monitoring, the connector must allocate it itself.</p>
+
+ <p>Ideally this code for monitoring changes would be isolated to the <code>Connector</code> and tasks would not need to worry about them. However, changes can also affect tasks, most commonly when one of their input streams is destroyed in the input system, e.g. if a table is dropped from a database. If the <code>Task</code> encounters the issue before the <code>Connector</code>, which will be common if the <code>Connector</code> needs to poll for changes, the <code>Task</code> will need to handle the subsequent error. Thankfully, this can usually be handled simply by catching and handling the appropriate exception.</p>
+
+ <p><code>SinkConnectors</code> usually only have to handle the addition of streams, which may translate to new entries in their outputs (e.g., a new database table). The framework manages any changes to the Kafka input, such as when the set of input topics changes because of a regex subscription. <code>SinkTasks</code> should expect new input streams, which may require creating new resources in the downstream system, such as a new table in a database. The trickiest situation to handle in these cases may be conflicts between multiple <code>SinkTasks</code> seeing a new input stream for the first time and simultaneously trying to create the new resource. <code>SinkConnectors</code>, on the other hand, will generally require no special code for handling a dynamic set of streams.</p>
+
+ <h4><a id="connect_configs" href="#connect_configs">Connect Configuration Validation</a></h4>
+
+ <p>Kafka Connect allows you to validate connector configurations before submitting a connector to be executed and can provide feedback about errors and recommended values. To take advantage of this, connector developers need to provide an implementation of <code>config()</code> to expose the configuration definition to the framework.</p>
+
+ <p>The following code in <code>FileStreamSourceConnector</code> defines the configuration and exposes it to the framework.</p>
+
+ <pre class="brush: java;">
+ private static final ConfigDef CONFIG_DEF = new ConfigDef()
+ .define(FILE_CONFIG, Type.STRING, Importance.HIGH, "Source filename.")
+ .define(TOPIC_CONFIG, Type.STRING, Importance.HIGH, "The topic to publish data to");
+
+ public ConfigDef config() {
+ return CONFIG_DEF;
+ }
+ </pre>
+
+ <p><code>ConfigDef</code> class is used for specifying the set of expected configurations. For each configuration, you can specify the name, the type, the default value, the documentation, the group information, the order in the group, the width of the configuration value and the name suitable for display in the UI. Plus, you can provide special validation logic used for single configuration validation by overriding the <code>Validator</code> class. Moreover, as there may be dependencies between configurations, for example, the valid values and visibility of a configuration may change according to the values of other configurations. To handle this, <code>ConfigDef</code> allows you to specify the dependents of a configuration and to provide an implementation of <code>Recommender</code> to get valid values and set visibility of a configuration given the current configuration values.</p>
+
+ <p>Also, the <code>validate()</code> method in <code>Connector</code> provides a default validation implementation which returns a list of allowed configurations together with configuration errors and recommended values for each configuration. However, it does not use the recommended values for configuration validation. You may provide an override of the default implementation for customized configuration validation, which may use the recommended values.</p>
+
+ <h4><a id="connect_schemas" href="#connect_schemas">Working with Schemas</a></h4>
+
+ <p>The FileStream connectors are good examples because they are simple, but they also have trivially structured data -- each line is just a string. Almost all practical connectors will need schemas with more complex data formats.</p>
+
+ <p>To create more complex data, you'll need to work with the Kafka Connect <code>data</code> API. Most structured records will need to interact with two classes in addition to primitive types: <code>Schema</code> and <code>Struct</code>.</p>
+
+ <p>The API documentation provides a complete reference, but here is a simple example creating a <code>Schema</code> and <code>Struct</code>:</p>
+
+ <pre class="brush: java;">
+ Schema schema = SchemaBuilder.struct().name(NAME)
+ .field("name", Schema.STRING_SCHEMA)
+ .field("age", Schema.INT_SCHEMA)
+ .field("admin", new SchemaBuilder.boolean().defaultValue(false).build())
+ .build();
+
+ Struct struct = new Struct(schema)
+ .put("name", "Barbara Liskov")
+ .put("age", 75);
+ </pre>
+
+ <p>If you are implementing a source connector, you'll need to decide when and how to create schemas. Where possible, you should avoid recomputing them as much as possible. For example, if your connector is guaranteed to have a fixed schema, create it statically and reuse a single instance.</p>
+
+ <p>However, many connectors will have dynamic schemas. One simple example of this is a database connector. Considering even just a single table, the schema will not be predefined for the entire connector (as it varies from table to table). But it also may not be fixed for a single table over the lifetime of the connector since the user may execute an <code>ALTER TABLE</code> command. The connector must be able to detect these changes and react appropriately.</p>
+
+ <p>Sink connectors are usually simpler because they are consuming data and therefore do not need to create schemas. However, they should take just as much care to validate that the schemas they receive have the expected format. When the schema does not match -- usually indicating the upstream producer is generating invalid data that cannot be correctly translated to the destination system -- sink connectors should throw an exception to indicate this error to the system.</p>
+
+ <h4><a id="connect_administration" href="#connect_administration">Kafka Connect Administration</a></h4>
+
+ <p>
+ Kafka Connect's <a href="#connect_rest">REST layer</a> provides a set of APIs to enable administration of the cluster. This includes APIs to view the configuration of connectors and the status of their tasks, as well as to alter their current behavior (e.g. changing configuration and restarting tasks).
+ </p>
+
+ <p>
+ When a connector is first submitted to the cluster, the workers rebalance the full set of connectors in the cluster and their tasks so that each worker has approximately the same amount of work. This same rebalancing procedure is also used when connectors increase or decrease the number of tasks they require, or when a connector's configuration is changed. You can use the REST API to view the current status of a connector and its tasks, including the id of the worker to which each was assigned. For example, querying the status of a file source (using <code>GET /connectors/file-source/status</code>) might produce output like the following:
+ </p>
+
+ <pre class="brush: json;">
+ {
+ "name": "file-source",
+ "connector": {
+ "state": "RUNNING",
+ "worker_id": "192.168.1.208:8083"
+ },
+ "tasks": [
+ {
+ "id": 0,
+ "state": "RUNNING",
+ "worker_id": "192.168.1.209:8083"
+ }
+ ]
+ }
+ </pre>
+
+ <p>
+ Connectors and their tasks publish status updates to a shared topic (configured with <code>status.storage.topic</code>) which all workers in the cluster monitor. Because the workers consume this topic asynchronously, there is typically a (short) delay before a state change is visible through the status API. The following states are possible for a connector or one of its tasks:
+ </p>
+
+ <ul>
+ <li><b>UNASSIGNED:</b> The connector/task has not yet been assigned to a worker.</li>
+ <li><b>RUNNING:</b> The connector/task is running.</li>
+ <li><b>PAUSED:</b> The connector/task has been administratively paused.</li>
+ <li><b>FAILED:</b> The connector/task has failed (usually by raising an exception, which is reported in the status output).</li>
+ </ul>
+
+ <p>
+ In most cases, connector and task states will match, though they may be different for short periods of time when changes are occurring or if tasks have failed. For example, when a connector is first started, there may be a noticeable delay before the connector and its tasks have all transitioned to the RUNNING state. States will also diverge when tasks fail since Connect does not automatically restart failed tasks. To restart a connector/task manually, you can use the restart APIs listed above. Note that if you try to restart a task while a rebalance is taking place, Connect will return a 409 (Conflict) status code. You can retry after the rebalance completes, but it might not be necessary since rebalances effectively restart all the connectors and tasks in the cluster.
+ </p>
+
+ <p>
+ It's sometimes useful to temporarily stop the message processing of a connector. For example, if the remote system is undergoing maintenance, it would be preferable for source connectors to stop polling it for new data instead of filling logs with exception spam. For this use case, Connect offers a pause/resume API. While a source connector is paused, Connect will stop polling it for additional records. While a sink connector is paused, Connect will stop pushing new messages to it. The pause state is persistent, so even if you restart the cluster, the connector will not begin message processing again until the task has been resumed. Note that there may be a delay before all of a connector's tasks have transitioned to the PAUSED state since it may take time for them to finish whatever processing they were in the middle of when being paused. Additionally, failed tasks will not transition to the PAUSED state until they have been restarted.
+ </p>
+</script>
+
+<div class="p-connect"></div>
[04/14] kafka-site git commit: Update site for 1.0.0 release
Posted by ij...@apache.org.
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/streams/developer-guide.html
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<script><!--#include virtual="../js/templateData.js" --></script>
+
+<script id="content-template" type="text/x-handlebars-template">
+ <h1>Developer Guide for Kafka Streams API</h1>
+
+ <p>
+ This developer guide describes how to write, configure, and execute a Kafka Streams application. There is a <a href="/{{version}}/documentation/#quickstart_kafkastreams">quickstart</a> example that provides how to run a stream processing program coded in the Kafka Streams library.
+ </p>
+
+ <p>
+ The computational logic of a Kafka Streams application is defined as a <a href="/{{version}}/documentation/streams/core-concepts#streams_topology">processor topology</a>. Kafka Streams provide two sets of APIs to define the processor topology, Low-Level Processor API and High-Level Streams DSL.
+ </p>
+
+ <ul class="toc">
+ <li><a href="#streams_processor">1. Low-level Processor API</a>
+ <ul>
+ <li><a href="#streams_processor_process">1.1 Processor</a>
+ <li><a href="#streams_processor_topology">1.2 Processor Topology</a>
+ <li><a href="#streams_processor_statestore">1.3 State Stores</a>
+ <li><a href="#restoration_progress">1.4 Monitoring the Restoration Progress of Fault-tolerant State Store</a>
+ <li><a href="#disable-changelogs">1.5 Enable / Disable Fault Tolerance of State Stores (Store Changelogs)</a>
+ <li><a href="#implementing-custom-state-stores">1.6 Implementing Custom State Stores</a>
+ <li><a href="#connecting-processors-and-state-stores">1.7 Connecting Processors and State Stores</a>
+ <li><a href="#streams_processor_describe">1.5 Describe a Topology</a>
+ </ul>
+ </li>
+ <li><a href="#streams_dsl">2. High-Level Streams DSL</a>
+ <ul>
+ <li><a href="#streams_duality">2.1 Duality of Streams and Tables</a>
+ <li><a href="#streams_dsl_source">2.2 Creating Source Streams from Kafka</a>
+ <li><a href="#streams_dsl_transform">2.3 Transform a stream</a>
+ <li><a href="#streams_dsl_sink">2.4 Write streams back to Kafka</a>
+ <li><a href="#streams_dsl_build">2.5 Generate the processor topology</a>
+ </ul>
+ </li>
+ <li><a href="#streams_interactive_queries">3. Interactive Queries</a>
+ <ul>
+ <li><a href="#streams_developer-guide_interactive-queries_your_app">3.1 Your application and interactive queries</a>
+ <li><a href="#streams_developer-guide_interactive-queries_local-stores">3.2 Querying local state stores (for an application instance)</a>
+ <li><a href="#streams_developer-guide_interactive-queries_local-key-value-stores">3.3 Querying local key-value stores</a>
+ <li><a href="#streams_developer-guide_interactive-queries_local-window-stores">3.4 Querying local window stores</a>
+ <li><a href="#streams_developer-guide_interactive-queries_custom-stores">3.5 Querying local custom state stores</a>
+ <li><a href="#streams_developer-guide_interactive-queries_discovery">3.6 Querying remote state stores (for the entire application)</a>
+ <li><a href="#streams_developer-guide_interactive-queries_rpc-layer">3.7 Adding an RPC layer to your application</a>
+ <li><a href="#streams_developer-guide_interactive-queries_expose-rpc">3.8 Exposing the RPC endpoints of your application</a>
+ <li><a href="#streams_developer-guide_interactive-queries_discover-app-instances-and-stores">3.9 Discovering and accessing application instances and their respective local state stores</a>
+ </ul>
+ </li>
+ <li><a href="#streams_developer-guide_memory-management">4. Memory Management</a>
+ <ul>
+ <li><a href="#streams_developer-guide_memory-management_record-cache">4.1 Record caches in the DSL</a>
+ <li><a href="#streams_developer-guide_memory-management_state-store-cache">4.2 State store caches in the Processor API</a>
+ <li><a href="#streams_developer-guide_memory-management_other_memory_usage">4.3 Other memory usage</a>
+ </ul>
+ </li>
+ <li><a href="#streams_configure_execute">5. Application Configuration and Execution</a>
+ <ul>
+ <li><a href="#streams_client_config">5.1 Producer and Consumer Configuration</a>
+ <li><a href="#streams_broker_config">5.2 Broker Configuration</a>
+ <li><a href="#streams_topic_config">5.3 Internal Topic Configuration</a>
+ <li><a href="#streams_execute">5.4 Executing Your Kafka Streams Application</a>
+ </ul>
+ </li>
+ </ul>
+ <p>
+ There is a <a href="/{{version}}/documentation/#quickstart_kafkastreams">quickstart</a> example that provides how to run a stream processing program coded in the Kafka Streams library.
+ This section focuses on how to write, configure, and execute a Kafka Streams application.
+ </p>
+
+ <p>
+ As we have mentioned above, the computational logic of a Kafka Streams application is defined as a <a href="/{{version}}/documentation/streams/core-concepts#streams_topology">processor topology</a>.
+ Currently Kafka Streams provides two sets of APIs to define the processor topology, which will be described in the subsequent sections.
+ </p>
+
+ <h3><a id="streams_processor" href="#streams_processor">Low-Level Processor API</a></h3>
+
+ <h4><a id="streams_processor_process" href="#streams_processor_process">Processor</a></h4>
+
+ <p>
+ A <a href="/{{version}}/documentation/streams/core-concepts"><b>stream processor</b></a> is a node in the processor topology that represents a single processing step.
+ With the <code>Processor</code> API, you can define arbitrary stream processors that process one received record at a time, and connect these processors with
+ their associated state stores to compose the processor topology that represents their customized processing logic.
+ </p>
+
+ <p>
+ The <code>Processor</code> interface provides the <code>process</code> method API, which is performed on each record that is received.
+ The processor can maintain the current <code>ProcessorContext</code> instance variable initialized in the <code>init</code> method
+ and use the context to schedule a periodically called punctuation function (<code>context().schedule</code>),
+ to forward the new or modified key-value pair to downstream processors (<code>context().forward</code>),
+ to commit the current processing progress (<code>context().commit</code>), and so on.
+ </p>
+
+ <p>
+ The following example <code>Processor</code> implementation defines a simple word-count algorithm:
+ </p>
+
+ <pre class="brush: java;">
+ public class MyProcessor implements Processor<String, String> {
+ private ProcessorContext context;
+ private KeyValueStore<String, Long> kvStore;
+
+ @Override
+ @SuppressWarnings("unchecked")
+ public void init(ProcessorContext context) {
+ // keep the processor context locally because we need it in punctuate() and commit()
+ this.context = context;
+
+ // schedule a punctuation method every 1000 milliseconds.
+ this.context.schedule(1000, PunctuationType.WALL_CLOCK_TIME, new Punctuator() {
+ @Override
+ public void punctuate(long timestamp) {
+ KeyValueIterator<String, Long> iter = this.kvStore.all();
+
+ while (iter.hasNext()) {
+ KeyValue<String, Long> entry = iter.next();
+ context.forward(entry.key, entry.value.toString());
+ }
+
+ // it is the caller's responsibility to close the iterator on state store;
+ // otherwise it may lead to memory and file handlers leak depending on the
+ // underlying state store implementation.
+ iter.close();
+
+ // commit the current processing progress
+ context.commit();
+ }
+ });
+
+ // retrieve the key-value store named "Counts"
+ this.kvStore = (KeyValueStore<String, Long>) context.getStateStore("Counts");
+ }
+
+ @Override
+ public void process(String dummy, String line) {
+ String[] words = line.toLowerCase().split(" ");
+
+ for (String word : words) {
+ Long oldValue = this.kvStore.get(word);
+
+ if (oldValue == null) {
+ this.kvStore.put(word, 1L);
+ } else {
+ this.kvStore.put(word, oldValue + 1L);
+ }
+ }
+ }
+
+ @Override
+ public void punctuate(long timestamp) {
+ KeyValueIterator<String, Long> iter = this.kvStore.all();
+
+ while (iter.hasNext()) {
+ KeyValue<String, Long> entry = iter.next();
+ context.forward(entry.key, entry.value.toString());
+ }
+
+ iter.close(); // avoid OOM
+ // commit the current processing progress
+ context.commit();
+ }
+
+ @Override
+ public void close() {
+ // close any resources managed by this processor.
+ // Note: Do not close any StateStores as these are managed
+ // by the library
+ }
+ };
+ </pre>
+
+ <p>
+ In the previous example, the following actions are performed:
+ </p>
+
+ <ul>
+ <li>In the <code>init</code> method, schedule the punctuation every 1 second and retrieve the local state store by its name "Counts".</li>
+ <li>In the <code>process</code> method, upon each received record, split the value string into words, and update their counts into the state store (we will talk about this feature later in the section).</li>
+ <li>In the scheduled <code>punctuate</code> method, iterate the local state store and send the aggregated counts to the downstream processor, and commit the current stream state.</li>
+ <li>When done with the <code>KeyValueIterator<String, Long></code> you <em>must</em> close the iterator, as shown above or use the try-with-resources statement.</li>
+ </ul>
+
+
+ <h4><a id="streams_processor_topology" href="#streams_processor_topology">Processor Topology</a></h4>
+
+ <p>
+ With the customized processors defined in the Processor API, you can use <code>Topology</code> to build a processor topology
+ by connecting these processors together:
+ </p>
+
+ <pre class="brush: java;">
+ Topology topology = new Topology();
+
+ topology.addSource("SOURCE", "src-topic")
+ // add "PROCESS1" node which takes the source processor "SOURCE" as its upstream processor
+ .addProcessor("PROCESS1", () -> new MyProcessor1(), "SOURCE")
+
+ // add "PROCESS2" node which takes "PROCESS1" as its upstream processor
+ .addProcessor("PROCESS2", () -> new MyProcessor2(), "PROCESS1")
+
+ // add "PROCESS3" node which takes "PROCESS1" as its upstream processor
+ .addProcessor("PROCESS3", () -> new MyProcessor3(), "PROCESS1")
+
+ // add the sink processor node "SINK1" that takes Kafka topic "sink-topic1"
+ // as output and the "PROCESS1" node as its upstream processor
+ .addSink("SINK1", "sink-topic1", "PROCESS1")
+
+ // add the sink processor node "SINK2" that takes Kafka topic "sink-topic2"
+ // as output and the "PROCESS2" node as its upstream processor
+ .addSink("SINK2", "sink-topic2", "PROCESS2")
+
+ // add the sink processor node "SINK3" that takes Kafka topic "sink-topic3"
+ // as output and the "PROCESS3" node as its upstream processor
+ .addSink("SINK3", "sink-topic3", "PROCESS3");
+ </pre>
+
+ Here is a quick walk through of the previous code to build the topology:
+
+ <ul>
+ <li>A source node (<code>"SOURCE"</code>) is added to the topology using the <code>addSource</code> method, with one Kafka topic (<code>"src-topic"</code>) fed to it.</li>
+ <li>Three processor nodes are then added using the <code>addProcessor</code> method; here the first processor is a child of the source node, but is the parent of the other two processors.</li>
+ <li>Three sink nodes are added to complete the topology using the <code>addSink</code> method, each piping from a different parent processor node and writing to a separate topic.</li>
+ </ul>
+
+<h4><a id="streams_processor_statestore" href="#streams_processor_statestore">State Stores</a></h4>
+
+<p>
+To make state stores fault-tolerant (e.g., to recover from machine crashes) as well as to allow for state store migration without data loss (e.g., to migrate a stateful stream task from one machine to another when elastically adding or removing capacity from your application), a state store can be <strong>continuously backed up</strong> to a Kafka topic behind the scenes.
+We sometimes refer to this topic as the state store's associated <em>changelog topic</em> or simply its <em>changelog</em>.
+In the case of a machine failure, for example, the state store and thus the application's state can be fully restored from its changelog.
+You can enable or disable this backup feature for a state store, and thus its fault tolerance.
+</p>
+
+<p>
+By default, persistent <strong>key-value stores</strong> are fault-tolerant.
+They are backed by a <a href="https://kafka.apache.org/documentation.html#compaction">compacted</a> changelog topic.
+The purpose of compacting this topic is to prevent the topic from growing indefinitely, to reduce the storage consumed in the associated Kafka cluster, and to minimize recovery time if a state store needs to be restored from its changelog topic.
+</p>
+
+<p>
+Similarly, persistent <strong>window stores</strong> are fault-tolerant.
+They are backed by a topic that uses both <em>compaction</em> and <em>deletion</em>.
+Using deletion in addition to compaction is required for the changelog topics of window stores because of the structure of the message keys that are being sent to the changelog topics: for window stores, the message keys are composite keys that include not only the "normal" key but also window timestamps.
+For such composite keys it would not be sufficient to enable just compaction in order to prevent a changelog topic from growing out of bounds.
+With deletion enabled, old windows that have expired will be cleaned up by Kafka's log cleaner as the log segments expire.
+The default retention setting is <code>Windows#maintainMs()</code> + 1 day. This setting can be overriden by specifying <code>StreamsConfig.WINDOW_STORE_CHANGE_LOG_ADDITIONAL_RETENTION_MS_CONFIG</code> in the <code>StreamsConfig</code>.
+</p>
+
+<p>
+One additional note regarding the use of state stores. Any time you open an <code>Iterator</code> from a state store you <em>must</em> call <code>close()</code> on the iterator
+when you are done working with it to reclaim resources. Or you can use the iterator from within a try-with-resources statement.
+ By not closing an iterator, you may likely encounter an OOM error.
+</p>
+
+
+<h4><a id="restoration_progress" href="#restoration_progress">Monitoring the Restoration Progress of Fault-tolerant State Stores</a></h4>
+
+<p>
+When starting up your application any fault-tolerant state stores don't need a restoration process as the persisted state is read from local disk.
+But there could be situations when a full restore from the backing changelog topic is required (e.g., a failure wiped out the local state or your application runs in a stateless environment and persisted data is lost on re-starts).
+</p>
+
+<p>
+If you have a significant amount of data in the changelog topic, the restoration process could take a non-negligible amount of time.
+Given that processing of new data won't start until the restoration process is completed, having a window into the progress of restoration is useful.
+</p>
+
+<p>
+In order to observe the restoration of all state stores you provide your application an instance of the <code>org.apache.kafka.streams.processor.StateRestoreListener</code> interface.
+You set the <code>org.apache.kafka.streams.processor.StateRestoreListener</code> by calling the <code>KafkaStreams#setGlobalStateRestoreListener</code> method.
+</p>
+
+<p>
+ A basic implementation example that prints restoration status to the console:
+</p>
+
+<pre class="brush: java;">
+ import org.apache.kafka.common.TopicPartition;
+ import org.apache.kafka.streams.processor.StateRestoreListener;
+
+ public class ConsoleGlobalRestoreListerner implements StateRestoreListener {
+
+ @Override
+ public void onRestoreStart(final TopicPartition topicPartition,
+ final String storeName,
+ final long startingOffset,
+ final long endingOffset) {
+
+ System.out.print("Started restoration of " + storeName + " partition " + topicPartition.partition());
+ System.out.println(" total records to be restored " + (endingOffset - startingOffset));
+ }
+
+ @Override
+ public void onBatchRestored(final TopicPartition topicPartition,
+ final String storeName,
+ final long batchEndOffset,
+ final long numRestored) {
+
+ System.out.println("Restored batch " + numRestored + " for " + storeName + " partition " + topicPartition.partition());
+
+ }
+
+ @Override
+ public void onRestoreEnd(final TopicPartition topicPartition,
+ final String storeName,
+ final long totalRestored) {
+
+ System.out.println("Restoration complete for " + storeName + " partition " + topicPartition.partition());
+ }
+ }
+</pre>
+
+<blockquote>
+<p>
+ The <code>StateRestoreListener</code> instance is shared across all <code>org.apache.kafka.streams.processor.internals.StreamThread</code> instances and it is assumed all methods are stateless.
+ If any stateful operations are desired, then the user will need to provide synchronization internally.
+</p>
+</blockquote>
+
+<h4> <a id="disable-changelogs" href="#disable-changelogs">Enable / Disable Fault Tolerance of State Stores (Store Changelogs)</a></h4>
+
+<p>
+ You can enable or disable fault tolerance for a state store by enabling or disabling, respectively ,the changelogging of the store through <code>StateStoreBuilder#withLoggingEnabled(Map<String, String>)</code>
+ and <code>StateStoreBuilder#withLoggingDisabled()</code>.
+ You can also fine-tune the associated topic’s configuration if needed.
+</p>
+
+<p>Example for disabling fault-tolerance:</p>
+
+<pre class="brush: java;">
+
+ import org.apache.kafka.streams.processor.state.KeyValueBytesStoreSupplier;
+ import org.apache.kafka.streams.processor.state.StateStoreBuilder;
+ import org.apache.kafka.streams.state.Stores;
+
+ KeyValueBytesStoreSupplier countStoreSupplier = Stores.inMemoryKeyValueStore("Counts");
+ StateStoreBuilder builder = Stores.keyValueStoreBuilder(countStoreSupplier,
+ Serdes.String(),
+ Serdes.Long())
+ .withLoggingDisabled(); // disable backing up the store to a changelog topic
+
+</pre>
+
+<blockquote>
+<p>If the changelog is disabled then the attached state store is no longer fault tolerant and it can't have any standby replicas</p>
+</blockquote>
+
+<p>
+ Example for enabling fault tolerance, with additional changelog-topic configuration: You can add any log config
+ from kafka.log.LogConfig|core/src/main/scala/kafka/log/LogConfig.scala#L61. Unrecognized configs will be ignored.
+</p>
+
+<pre class="brush: java;">
+
+ import org.apache.kafka.streams.processor.state.KeyValueBytesStoreSupplier;
+ import org.apache.kafka.streams.processor.state.StateStoreBuilder;
+ import org.apache.kafka.streams.state.Stores;
+
+ Map<String, String> changelogConfig = new HashMap();
+ // override min.insync.replicas
+ changelogConfig.put("min.insyc.replicas", "1")
+
+ KeyValueBytesStoreSupplier countStoreSupplier = Stores.inMemoryKeyValueStore("Counts");
+ StateStoreBuilder builder = Stores.keyValueStoreBuilder(countStoreSupplier,
+ Serdes.String(),
+ Serdes.Long())
+ .withLoggingEnabled(changelogConfig); // enable changelogging, with custom changelog settings
+
+
+</pre>
+
+<h4><a id="implementing-custom-state-stores" href="#implementing-custom-state-stores">Implementing custom State Stores</a></h4>
+
+<p>
+ Apart from using the built-in state store types, you can also implement your own.
+ The primary interface to implement for the store is <code>org.apache.kafka.streams.processor.StateStore</code>.
+ Beyond that, Kafka Streams also has a few extended interfaces such as <code>KeyValueStore</code>.
+</p>
+
+<p>
+ In addition to the actual store, you also need to provide a "factory" for the store by implementing the <code>org.apache.kafka.streams.processor.state.StoreSupplier</code> interface, which Kafka Streams uses to create instances of your store.
+</p>
+
+<p>
+ You also have the option of providing a <code>org.apache.kafka.streams.processor.StateRestoreCallback</code> instance used to restore the state store from its backing changelog topic.
+ This is done via the <code>org.apache.kafka.streams.processor.ProcessorContext#register</code> call inside the <code>StateStore#init</code> all.
+</p>
+
+<pre class="brush: java;">
+ public void init(ProcessorContext context, StateStore store) {
+ context.register(store, false, stateRestoreCallBackIntance);
+ }
+</pre>
+
+<p>
+ There is an additional interface <code>org.apache.kafka.streams.processor.BatchingStateRestoreCallback</code> that provides bulk restoration semantics vs. the single record-at-a-time restoration semantics offered by the <code>StateRestoreCallback</code> interface.
+</p>
+
+<p>
+ Addtionally there are two abstract classes that implement <code>StateRestoreCallback</code> or <code>BatchingStateRestoreCallback</code> in conjuntion with the <code>org.apache.kafka.streams.processor.StateRestoreListener</code> interface (<code>org.apache.kafka.streams.processor.AbstractNotifyingRestoreCallback</code> and <code>org.apache.kafka.streams.processor.AbstractNotifyingBatchingRestoreCallback</code> respectively) that provide the ability for the state store to recieve notification of restoration progress for that store.
+ The <code>StateRestoreListener</code> in this case is per state store instance and is used for internal purposes such as updating config settings based on the status of the restoration process.
+</p>
+
+<h4><a id="connecting-processors-and-state-stores" href="#connecting-processors-and-state-stores">Connecting Processors and State Stores</a></h4>
+
+<p>
+Now that we have defined a processor (WordCountProcessor) and the state stores, we can now construct the processor topology by connecting these processors and state stores together by using the <code>Topology</code> instance.
+In addition, users can add <em>source processors</em> with the specified Kafka topics to generate input data streams into the topology, and <em>sink processors</em> with the specified Kafka topics to generate output data streams out of the topology.
+</p>
+
+<pre class="brush: java;">
+ Topology topology = new Topology();
+
+ // add the source processor node that takes Kafka topic "source-topic" as input
+ topology.addSource("Source", "source-topic")
+
+ // add the WordCountProcessor node which takes the source processor as its upstream processor
+ .addProcessor("Process", () -> new WordCountProcessor(), "Source")
+
+ // add the count store associated with the WordCountProcessor processor
+ .addStateStore(countStoreSupplier, "Process")
+
+ // add the sink processor node that takes Kafka topic "sink-topic" as output
+ // and the WordCountProcessor node as its upstream processor
+ .addSink("Sink", "sink-topic", "Process");
+</pre>
+
+<p>There are several steps in the above implementation to build the topology, and here is a quick walk-through:</p>
+<ul>
+ <li>A source processor node named "Source" is added to the topology using the <code>addSource</code> method, with one Kafka topic "source-topic" fed to it.</li>
+ <li>A processor node named "Process" with the pre-defined <code>WordCountProcessor</code> logic is then added as the downstream processor of the "Source" node using the <code>addProcessor</code> method.</li>
+ <li>A predefined persistent key-value state store is created and associated with the "Process" node, using <code>countStoreSupplier</code>.</li>
+ <li>A sink processor node is then added to complete the topology using the <code>addSink</code> method, taking the "Process" node as its upstream processor and writing to a separate "sink-topic" Kafka topic.</li>
+</ul>
+
+<p>
+In this topology, the "Process" stream processor node is considered a downstream processor of the "Source" node, and an upstream processor of the "Sink" node.
+As a result, whenever the "Source" node forward a newly fetched record from Kafka to its downstream "Process" node, <code>WordCountProcessor#process()</code> method is triggered to process the record and update the associated state store; and whenever <code>context#forward()</code> is called in the <code>Punctuator#punctuate()</code> method, the aggregate key-value pair will be sent via the "Sink" processor node to the Kafka topic "sink-topic".
+Note that in the <code>WordCountProcessor</code> implementation, users need to refer to the same store name "Counts" when accessing the key-value store; otherwise an exception will be thrown at runtime, indicating that the state store cannot be found; also, if the state store itself is not associated with the processor in the <code>Topology</code> code, accessing it in the processor's <code>init()</code> method will also throw an exception at runtime, indicating the state store is not accessible from this processor.
+</p>
+
+
+ <h4><a id="streams_processor_describe" href="#streams_processor_describe">Describe a <code>Topology</code></a></h4>
+
+ <p>
+ After a <code>Topology</code> is specified, it is possible to retrieve a description of the corresponding DAG via <code>#describe()</code> that returns a <code>TopologyDescription</code>.
+ A <code>TopologyDescription</code> contains all added source, processor, and sink nodes as well as all attached stores.
+ You can access the specified input and output topic names and patterns for source and sink nodes.
+ For processor nodes, the attached stores are added to the description.
+ Additionally, all nodes have a list to all their connected successor and predecessor nodes.
+ Thus, <code>TopologyDescritpion</code> allows to retrieve the DAG structure of the specified topology.
+ <br />
+ Note that global stores are listed explicitly because they are accessible by all nodes without the need to explicitly connect them.
+ Furthermore, nodes are grouped by <code>Sub-topologies</code>, where each sub-topology is a group of processor nodes that are directly connected to each other (i.e., either by a direct connection—but not a topic—or by sharing a store).
+ During execution, each <code>Sub-topology</code> will be processed by <a href="/{{version}}/documentation/streams/architecture#streams_architecture_tasks">one or multiple tasks</a>.
+ Thus, each <code>Sub-topology</code> describes an independent unit of works that can be executed by different threads in parallel.
+ <br />
+ Describing a <code>Topology</code> before starting your streams application with the specified topology is helpful to reason about tasks and thus maximum parallelism (we will talk about how to execute your written application later in this section).
+ It is also helpful to get insight into a <code>Topology</code> if it is not specified directly as described above but via Kafka Streams DSL (we will describe the DSL in the next section.
+ </p>
+
+ In the next section we present another way to build the processor topology: the Kafka Streams DSL.
+ <br>
+
+ <h3><a id="streams_dsl" href="#streams_dsl">High-Level Streams DSL</a></h3>
+
+ To build a <code>Topology</code> using the Streams DSL, developers can apply the <code>StreamsBuilder</code> class.
+ A simple example is included with the source code for Kafka in the <code>streams/examples</code> package. The rest of this section will walk
+ through some code to demonstrate the key steps in creating a topology using the Streams DSL, but we recommend developers to read the full example source
+ codes for details.
+
+ <h4><a id="streams_duality" href="#streams_duality">Duality of Streams and Tables</a></h4>
+
+ <p>
+ Before we discuss concepts such as aggregations in Kafka Streams we must first introduce tables, and most importantly the relationship between tables and streams:
+ the so-called <a href="https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying/">stream-table duality</a>.
+ Essentially, this duality means that a stream can be viewed as a table, and vice versa. Kafka's log compaction feature, for example, exploits this duality.
+ </p>
+
+ <p>
+ A simple form of a table is a collection of key-value pairs, also called a map or associative array. Such a table may look as follows:
+ </p>
+ <img class="centered" src="/{{version}}/images/streams-table-duality-01.png">
+
+ The <b>stream-table duality</b> describes the close relationship between streams and tables.
+ <ul>
+ <li><b>Stream as Table</b>: A stream can be considered a changelog of a table, where each data record in the stream captures a state change of the table. A stream is thus a table in disguise, and it can be easily turned into a "real" table by replaying the changelog from beginning to end to reconstruct the table. Similarly, in a more general analogy, aggregating data records in a stream - such as computing the total number of pageviews by user from a stream of pageview events - will return a table (here with the key and the value being the user and its corresponding pageview count, respectively).</li>
+ <li><b>Table as Stream</b>: A table can be considered a snapshot, at a point in time, of the latest value for each key in a stream (a stream's data records are key-value pairs). A table is thus a stream in disguise, and it can be easily turned into a "real" stream by iterating over each key-value entry in the table.</li>
+ </ul>
+
+ <p>
+ Let's illustrate this with an example. Imagine a table that tracks the total number of pageviews by user (first column of diagram below). Over time, whenever a new pageview event is processed, the state of the table is updated accordingly. Here, the state changes between different points in time - and different revisions of the table - can be represented as a changelog stream (second column).
+ </p>
+ <img class="centered" src="/{{version}}/images/streams-table-duality-02.png" style="width:300px">
+
+ <p>
+ Interestingly, because of the stream-table duality, the same stream can be used to reconstruct the original table (third column):
+ </p>
+ <img class="centered" src="/{{version}}/images/streams-table-duality-03.png" style="width:600px">
+
+ <p>
+ The same mechanism is used, for example, to replicate databases via change data capture (CDC) and, within Kafka Streams, to replicate its so-called state stores across machines for fault-tolerance.
+ The stream-table duality is such an important concept that Kafka Streams models it explicitly via the <a href="#streams_kstream_ktable">KStream, KTable, and GlobalKTable</a> interfaces, which we describe in the next sections.
+ </p>
+
+ <h5><a id="streams_kstream_ktable" href="#streams_kstream_ktable">KStream, KTable, and GlobalKTable</a></h5>
+ The DSL uses three main abstractions. A <b>KStream</b> is an abstraction of a record stream, where each data record represents a self-contained datum in the unbounded data set.
+ A <b>KTable</b> is an abstraction of a changelog stream, where each data record represents an update. More precisely, the value in a data record is considered to be an update of the last value for the same record key,
+ if any (if a corresponding key doesn't exist yet, the update will be considered a create).
+ Like a <b>KTable</b>, a <b>GlobalKTable</b> is an abstraction of a changelog stream, where each data record represents an update.
+ However, a <b>GlobalKTable</b> is different from a <b>KTable</b> in that it is fully replicated on each KafkaStreams instance.
+ <b>GlobalKTable</b> also provides the ability to look up current values of data records by keys.
+ This table-lookup functionality is available through <a href="#streams_dsl_joins">join operations</a>.
+
+ To illustrate the difference between KStreams and KTables/GlobalKTables, let's imagine the following two data records are being sent to the stream:
+
+ <pre>
+ ("alice", 1) --> ("alice", 3)
+ </pre>
+
+ If the stream is defined as a KStream and the stream processing application were to sum the values it would return <code>4</code>. If the stream is defined as a KTable or GlobalKTable, the return would be <code>3</code>, since the last record would be considered as an update.
+
+ <h4><a id="streams_dsl_source" href="#streams_dsl_source">Creating Source Streams from Kafka</a></h4>
+
+ <p>
+ You can easily read data from Kafka topics into your application. We support the following operations.
+ </p>
+ <table class="data-table" border="1">
+ <tbody><tr>
+ <th>Reading from Kafka</th>
+ <th>Description</th>
+ </tr>
+ <tr>
+ <td><b>Stream</b>: input topic(s) → <code>KStream</code></td>
+ <td>Create a <code>KStream</code> from the specified Kafka input topic(s), interpreting the data as a record stream.
+ A <code>KStream</code> represents a partitioned record stream.
+ <p>
+ Slightly simplified, in the case of a KStream, the local KStream instance of every application instance will be populated
+ with data from only a <b>subset</b> of the partitions of the input topic. Collectively, i.e. across all application instances,
+ all the partitions of the input topic will be read and processed.
+ </p>
+ <pre class="brush: java;">
+ import org.apache.kafka.common.serialization.Serdes;
+ import org.apache.kafka.streams.StreamsBuilder;
+ import org.apache.kafka.streams.kstream.KStream;
+
+ StreamsBuilder builder = new StreamsBuilder();
+
+ KStream<String, Long> wordCounts = builder.stream(
+ "word-counts-input-topic" /* input topic */,
+ Consumed.with(Serdes.String(), Serdes.Long()); // define key and value serdes
+ </pre>
+ When to provide serdes explicitly:
+ <ul>
+ <li>If you do not specify serdes explicitly, the default serdes from the configuration are used.</li>
+ <li>You must specificy serdes explicitly if the key and/or value types of the records in the Kafka input topic(s) do not match
+ the configured default serdes. </li>
+ </ul>
+ Several variants of <code>stream</code> exist to e.g. specify a regex pattern for input topics to read from.</td>
+ </tr>
+ <tr>
+ <td><b>Table</b>: input topic(s) → <code>KTable</code></td>
+ <td>
+ Reads the specified Kafka input topic into a <code>KTable</code>. The topic is interpreted as a changelog stream,
+ where records with the same key are interpreted as UPSERT aka INSERT/UPDATE (when the record value is not <code>null</code>) or
+ as DELETE (when the value is null) for that key.
+ <p>
+ Slightly simplified, in the case of a KTable, the local KTable instance of every application instance will be populated
+ with data from only a subset of the partitions of the input topic. Collectively, i.e. across all application instances, all
+ the partitions of the input topic will be read and processed.
+ </p>
+ <p>
+ You may provide an optional name for the table (more precisely, for the internal state store that backs the table).
+ When a name is provided, the table can be queryied using <a href="#streams_interactive_queries">interactive queries</a>.
+ When a name is not provided the table will not queryable and an internal name will be provided for the state store.
+ </p>
+ <pre class="brush: java;">
+ import org.apache.kafka.common.serialization.Serdes;
+ import org.apache.kafka.streams.StreamsBuilder;
+ import org.apache.kafka.streams.kstream.KTable;
+
+ StreamsBuilder builder = new StreamsBuilder();
+
+ KTable<String, Long> wordCounts = builder.table(
+ Serdes.String(), /* key serde */
+ Serdes.Long(), /* value serde */
+ "word-counts-input-topic", /* input topic */
+ "word-counts-partitioned-store" /* table/store name */);
+ </pre>
+
+ When to provide serdes explicitly:
+ <ul>
+ <li>If you do not specify serdes explicitly, the default serdes from the configuration are used.</li>
+ <li>You must specificy serdes explicitly if the key and/or value types of the records in the Kafka input topic do not
+ match the configured default serdes.</li>
+ </ul>
+
+ Several variants of <code>table</code> exist to e.g. specify the <code>auto.offset.reset</code>
+ policy to be used when reading from the input topic.
+ </td>
+ <tr>
+ <td><b>Global Table</b>: input topic → <code>GlobalKTable</code></td>
+ <td>
+ Reads the specified Kafka input topic into a <code>GlobalKTable</code>. The topic is interpreted as a changelog stream, where records
+ with the same key are interpreted as UPSERT aka INSERT/UPDATE (when the record value is not <code>null</code>) or as DELETE (when the
+ value is <code>null</code>) for that key.
+ <p>
+ Slightly simplified, in the case of a GlobalKTable, the local GlobalKTable instance of every application instance will be
+ populated with data from all the partitions of the input topic. In other words, when using a global table, every application
+ instance will get its own, full copy of the topic's data.
+ </p>
+ <p>
+ You may provide an optional name for the table (more precisely, for the internal state store that backs the table).
+ When a name is provided, the table can be queryied using <a href="#streams_interactive_queries">interactive queries</a>.
+ When a name is not provided the table will not queryable and an internal name will be provided for the state store.
+ </p>
+ <pre class="brush: java;">
+ import org.apache.kafka.common.serialization.Serdes;
+ import org.apache.kafka.streams.StreamsBuilder;
+ import org.apache.kafka.streams.kstream.GlobalKTable;
+
+ StreamsBuilder builder = new StreamsBuilder();
+
+ GlobalKTable<String, Long> wordCounts = builder.globalTable(
+ Serdes.String(), /* key serde */
+ Serdes.Long(), /* value serde */
+ "word-counts-input-topic", /* input topic */
+ "word-counts-global-store" /* table/store name */);
+ </pre>
+
+ When to provide serdes explicitly:
+ <ul>
+ <li>If you do not specify serdes explicitly, the default serdes from the configuration are used.</li>
+ <li>You must specificy serdes explicitly if the key and/or value types of the records in the Kafka input topic do not
+ match the configured default serdes.</li>
+ </ul>
+ Several variants of <code>globalTable</code> exist to e.g. specify explicit serdes.
+
+ </td>
+ </tbody>
+ </table>
+
+ <h4><a id="streams_dsl_transform" href="#streams_dsl_transform">Transform a stream</a></h4>
+ <p>
+ <code>KStream</code> and <code>KTable</code> support a variety of transformation operations. Each of these operations
+ can be translated into one or more connected processors into the underlying processor topology. Since <code>KStream</code>
+ and <code>KTable</code> are strongly typed, all these transformation operations are defined as generic functions where
+ users could specify the input and output data types.
+ </p>
+ <p>
+ Some <code>KStream</code> transformations may generate one or more <code>KStream</code> objects (e.g., filter and
+ map on <code>KStream</code> generate another <code>KStream</code>, while branch on <code>KStream</code> can generate
+ multiple <code>KStream</code> instances) while some others may generate a <code>KTable</code> object (e.g., aggregation) interpreted
+ as the changelog stream to the resulted relation. This allows Kafka Streams to continuously update the computed value upon arrival
+ of late records after it has already been produced to the downstream transformation operators. As for <code>KTable</code>,
+ all its transformation operations can only generate another <code>KTable</code> (though the Kafka Streams DSL does
+ provide a special function to convert a <code>KTable</code> representation into a <code>KStream</code>, which we will
+ describe later). Nevertheless, all these transformation methods can be chained together to compose a complex processor topology.
+ </p>
+ <p>
+ We describe these transformation operations in the following subsections, categorizing them into two categories:
+ stateless and stateful transformations.
+ </p>
+ <h5><a id="streams_dsl_transformations_stateless" href="#streams_dsl_transformations_stateless">Stateless transformations</a></h5>
+ <p>
+ Stateless transformations, by definition, do not depend on any state for processing, and hence implementation-wise they do not
+ require a state store associated with the stream processor.
+ </p>
+ <table class="data-table" border="1">
+ <tbody><tr>
+ <th>Transformation</th>
+ <th>Description</th>
+ </tr>
+ <tr>
+ <td><b>Branch</b>: <code>KStream → KStream</code></td>
+ <td>
+ <p>
+ Branch (or split) a <code>KStream</code> based on the supplied predicates into one or more <code>KStream</code> instances.
+ </p>
+ <p>
+ Predicates are evaluated in order. A record is placed to one and only one output stream on the first match:
+ if the n-th predicate evaluates to true, the record is placed to n-th stream. If no predicate matches,
+ the record is dropped.
+ </p>
+ <p>
+ Branching is useful, for example, to route records to different downstream topics.
+ </p>
+ <pre class="brush: java;">
+ KStream<String, Long> stream = ...;
+ KStream<String, Long>[] branches = stream.branch(
+ (key, value) -> key.startsWith("A"), /* first predicate */
+ (key, value) -> key.startsWith("B"), /* second predicate */
+ (key, value) -> true /* third predicate */
+ );
+ // KStream branches[0] contains all records whose keys start with "A"
+ // KStream branches[1] contains all records whose keys start with "B"
+ // KStream branches[2] contains all other records
+ // Java 7 example: cf. `filter` for how to create `Predicate` instances
+ </pre>
+ </td>
+ </tr>
+ <tr>
+ <td><b>Filter</b>: <code>KStream → KStream or KTable → KTable</code></td>
+ <td>
+ <p>
+ Evaluates a boolean function for each element and retains those for which the function returns true.
+ </p>
+ <pre class="brush: java;">
+ KStream<String, Long> stream = ...;
+ KTable<String, Long> table = ...;
+ // A filter that selects (keeps) only positive numbers
+ // Java 8+ example, using lambda expressions
+ KStream<String, Long> onlyPositives = stream.filter((key, value) -> value > 0);
+
+ // Java 7 example
+ KStream<String, Long> onlyPositives = stream.filter(
+ new Predicate<String, Long>() {
+ @Override
+ public boolean test(String key, Long value) {
+ return value > 0;
+ }
+ });
+
+ // A filter on a KTable that materializes the result into a StateStore
+ table.filter((key, value) -> value != 0, Materialized.<String, Long, KeyValueStore<Bytes, byte[]>>as("filtered"));
+ </pre>
+ </td>
+ </tr>
+ <tr>
+ <td><b>Inverse Filter</b>: <code>KStream → KStream or KTable → KTable</code></td>
+ <td>
+ <p>
+ Evaluates a boolean function for each element and drops those for which the function returns true.
+ </p>
+ <pre class="brush: java;">
+ KStream<String, Long> stream = ...;
+
+ // An inverse filter that discards any negative numbers or zero
+ // Java 8+ example, using lambda expressions
+ KStream<String, Long> onlyPositives = stream.filterNot((key, value) -> value <= 0);
+
+ // Java 7 example
+ KStream<String, Long> onlyPositives = stream.filterNot(
+ new Predicate<String, Long>() {
+ @Override
+ public boolean test(String key, Long value) {
+ return value <= 0;
+ }
+ });
+ </pre>
+ </td>
+ </tr>
+ <tr>
+ <td><b>FlatMap</b>: <code>KStream → KStream </code></td>
+ <td>
+ <p>
+ Takes one record and produces zero, one, or more records. You can modify the record keys and values,
+ including their types.
+ </p>
+
+ <p>
+ Marks the stream for data re-partitioning: Applying a grouping or a join after <code>flatMap</code> will result in
+ re-partitioning of the records. If possible use <code>flatMapValues</code> instead, which will not cause data re-partitioning.
+ </p>
+ <pre class="brush: java;">
+ KStream<Long, String> stream = ...;
+ KStream<String, Integer> transformed = stream.flatMap(
+ // Here, we generate two output records for each input record.
+ // We also change the key and value types.
+ // Example: (345L, "Hello") -> ("HELLO", 1000), ("hello", 9000)
+ (key, value) -> {
+ List<KeyValue<String, Integer>> result = new LinkedList<>();
+ result.add(KeyValue.pair(value.toUpperCase(), 1000));
+ result.add(KeyValue.pair(value.toLowerCase(), 9000));
+ return result;
+ }
+ );
+ // Java 7 example: cf. `map` for how to create `KeyValueMapper` instances
+ </pre>
+ </td>
+ </tr>
+ <tr>
+ <td><b>FlatMap (values only)</b>: <code>KStream → KStream </code></td>
+ <td>
+ <p>
+ Takes one record and produces zero, one, or more records, while retaining the key of the original record.
+ You can modify the record values and the value type.
+ </p>
+ <p>
+ <code>flatMapValues</code> is preferable to <code>flatMap</code> because it will not cause data re-partitioning. However,
+ it does not allow you to modify the key or key type like <code>flatMap</code> does.
+ </p>
+ <pre class="brush: java;">
+ // Split a sentence into words.
+ KStream<byte[], String> sentences = ...;
+ KStream<byte[], String> words = sentences.flatMapValues(value -> Arrays.asList(value.split("\\s+")));
+
+ // Java 7 example: cf. `mapValues` for how to create `ValueMapper` instances
+ </pre>
+ </td>
+ </tr>
+ <tr>
+ <td><b>Foreach</b>: <code>KStream → void </code></td>
+ <td>
+ <p>
+ Terminal operation. Performs a stateless action on each record.
+ </p>
+ <p>
+ Note on processing guarantees: Any side effects of an action (such as writing to external systems)
+ are not trackable by Kafka, which means they will typically not benefit from Kafka's processing guarantees.
+ </p>
+ <pre class="brush: java;">
+ KStream<String, Long> stream = ...;
+
+ // Print the contents of the KStream to the local console.
+ // Java 8+ example, using lambda expressions
+ stream.foreach((key, value) -> System.out.println(key + " => " + value));
+
+ // Java 7 example
+ stream.foreach(
+ new ForeachAction<String, Long>() {
+ @Override
+ public void apply(String key, Long value) {
+ System.out.println(key + " => " + value);
+ }
+ });
+ </pre>
+ </td>
+ </tr>
+ <tr>
+ <td><b>GroupByKey</b>: <code>KStream → KGroupedStream </code></td>
+ <td>
+ <p>
+ Groups the records by the existing key.
+ </p>
+ <p>
+ Grouping is a prerequisite for aggregating a stream or a table and ensures that data is properly
+ partitioned ("keyed") for subsequent operations.
+ </p>
+ <p>
+ <b>When to set explicit serdes</b>: Variants of <code>groupByKey</code> exist to override the configured default serdes of
+ your application, which you must do if the key and/or value types of the resulting <code>KGroupedStream</code> do
+ not match the configured default serdes.
+ </p>
+ <p>
+ <b>Note:</b>
+ Grouping vs. Windowing: A related operation is windowing, which lets you control how to "sub-group" the
+ grouped records of the same key into so-called windows for stateful operations such as windowed aggregations
+ or windowed joins.
+ </p>
+ <p>
+ Causes data re-partitioning if and only if the stream was marked for re-partitioning. <code>groupByKey</code> is
+ preferable to <code>groupBy</code> because it re-partitions data only if the stream was already marked for re-partitioning.
+ However, <code>groupByKey</code> does not allow you to modify the key or key type like <code>groupBy</code> does.
+ </p>
+ <pre class="brush: java;">
+ KStream<byte[], String> stream = ...;
+
+ // Group by the existing key, using the application's configured
+ // default serdes for keys and values.
+ KGroupedStream<byte[], String> groupedStream = stream.groupByKey();
+
+ // When the key and/or value types do not match the configured
+ // default serdes, we must explicitly specify serdes.
+ KGroupedStream<byte[], String> groupedStream = stream.groupByKey(
+ Serialized.with(
+ Serdes.ByteArray(), /* key */
+ Serdes.String()) /* value */
+ );
+ </pre>
+ </td>
+ </tr>
+ <tr>
+ <td><b>GroupBy</b>: <code>KStream → KGroupedStream or KTable → KGroupedTable</code></td>
+ <td>
+ <p>
+ Groups the records by a new key, which may be of a different key type. When grouping a table,
+ you may also specify a new value and value type. groupBy is a shorthand for selectKey(...).groupByKey().
+ </p>
+ <p>
+ Grouping is a prerequisite for aggregating a stream or a table and ensures that data is properly
+ partitioned ("keyed") for subsequent operations.
+ </p>
+ <p>
+ <b>When to set explicit serdes</b>: Variants of groupBy exist to override the configured default serdes of your
+ application, which you must do if the key and/or value types of the resulting KGroupedStream or
+ KGroupedTable do not match the configured default serdes.
+ </p>
+ <p>
+ <b>Note:</b>
+ Grouping vs. Windowing: A related operation is windowing, which lets you control how to “sub-group” the
+ grouped records of the same key into so-called windows for stateful operations such as windowed aggregations
+ or windowed joins.
+ </p>
+ <p>
+ <b>Always causes data re-partitioning:</b> groupBy always causes data re-partitioning. If possible use groupByKey
+ instead, which will re-partition data only if required.
+ </p>
+ <pre class="brush: java;">
+ KStream<byte[], String> stream = ...;
+ KTable<byte[], String> table = ...;
+
+ // Java 8+ examples, using lambda expressions
+
+ // Group the stream by a new key and key type
+ KGroupedStream<String, String> groupedStream = stream.groupBy(
+ (key, value) -> value,
+ Serialize.with(
+ Serdes.String(), /* key (note: type was modified) */
+ Serdes.String()) /* value */
+ );
+
+ // Group the table by a new key and key type, and also modify the value and value type.
+ KGroupedTable<String, Integer> groupedTable = table.groupBy(
+ (key, value) -> KeyValue.pair(value, value.length()),
+ Serialized.with(
+ Serdes.String(), /* key (note: type was modified) */
+ Serdes.Integer()) /* value (note: type was modified) */
+ );
+
+
+ // Java 7 examples
+
+ // Group the stream by a new key and key type
+ KGroupedStream<String, String> groupedStream = stream.groupBy(
+ new KeyValueMapper<byte[], String, String>>() {
+ @Override
+ public String apply(byte[] key, String value) {
+ return value;
+ }
+ },
+ Serialized.with(
+ Serdes.String(), /* key (note: type was modified) */
+ Serdes.String()) /* value */
+ );
+
+ // Group the table by a new key and key type, and also modify the value and value type.
+ KGroupedTable<String, Integer> groupedTable = table.groupBy(
+ new KeyValueMapper<byte[], String, KeyValue<String, Integer>>() {
+ @Override
+ public KeyValue<String, Integer> apply(byte[] key, String value) {
+ return KeyValue.pair(value, value.length());
+ }
+ },
+ Serialized.with(
+ Serdes.String(), /* key (note: type was modified) */
+ Serdes.Integer()) /* value (note: type was modified) */
+ );
+ </pre>
+ </td>
+ </tr>
+ <tr>
+ <td><b>Map</b>: <code>KStream → KStream</code></td>
+ <td>
+ <p>
+ Takes one record and produces one record. You can modify the record key and value, including their types.
+ </p>
+
+ <p>
+ <b>Marks the stream for data re-partitioning:</b> Applying a grouping or a join after <code>flatMap</code> will result in
+ re-partitioning of the records. If possible use <code>mapValues</code> instead, which will not cause data re-partitioning.
+ </p>
+
+ <pre class="brush: java;">
+ KStream<byte[], String> stream = ...;
+
+ // Java 8+ example, using lambda expressions
+ // Note how we change the key and the key type (similar to `selectKey`)
+ // as well as the value and the value type.
+ KStream<String, Integer> transformed = stream.map(
+ (key, value) -> KeyValue.pair(value.toLowerCase(), value.length()));
+
+ // Java 7 example
+ KStream<String, Integer> transformed = stream.map(
+ new KeyValueMapper<byte[], String, KeyValue<String, Integer>>() {
+ @Override
+ public KeyValue<String, Integer> apply(byte[] key, String value) {
+ return new KeyValue<>(value.toLowerCase(), value.length());
+ }
+ });
+ </pre>
+ </td>
+ </tr>
+ <tr>
+ <td><b>Map (values only)</b>: <code>KStream → KStream or KTable → KTable</code></td>
+ <td>
+ <p>
+ Takes one record and produces one record, while retaining the key of the original record. You can modify
+ the record value and the value type.
+ </p>
+ <p>
+ <code>mapValues</code> is preferable to <code>map</code> because it will not cause data re-partitioning. However, it does not
+ allow you to modify the key or key type like <code>map</code> does.
+ </p>
+
+ <pre class="brush: java;">
+ KStream<byte[], String> stream = ...;
+ KTable<String, String> table = ...;
+
+ // Java 8+ example, using lambda expressions
+ KStream<byte[], String> uppercased = stream.mapValues(value -> value.toUpperCase());
+
+ // Java 7 example
+ KStream<byte[], String> uppercased = stream.mapValues(
+ new ValueMapper<String>() {
+ @Override
+ public String apply(String s) {
+ return s.toUpperCase();
+ }
+ });
+
+ // mapValues on a KTable and also materialize the results into a statestore
+ table.mapValue(value -> value.toUpperCase(), Materialized.<String, String, KeyValueStore<Bytes, byte[]>>as("uppercased"));
+ </pre>
+ </td>
+ </tr>
+ <tr>
+ <td><b>Print</b>: <code>KStream → void </code></td>
+ <td>
+ <p>
+ Terminal operation. Prints the records to <code>System.out</code>. See Javadocs for serde and <code>toString()</code> caveats.
+ </p>
+ <pre class="brush: java;">
+ KStream<byte[], String> stream = ...;
+ stream.print();
+
+ // You can also override how and where the data is printed, i.e, to file:
+ stream.print(Printed.toFile("stream.out"));
+
+ // with a custom KeyValueMapper and label
+ stream.print(Printed.toSysOut()
+ .withLabel("my-stream")
+ .withKeyValueMapper((key, value) -> key + " -> " + value));
+ </pre>
+ </td>
+ </tr>
+ <tr>
+ <td><b>SelectKey</b>: <code>KStream → KStream</code></td>
+ <td>
+ <p>
+ Assigns a new key, possibly of a new key type, to each record.
+ </p>
+ <p>
+ Marks the stream for data re-partitioning: Applying a grouping or a join after <code>flatMap</code> will result in
+ re-partitioning of the records.
+ </p>
+
+ <pre class="brush: java;">
+ KStream<byte[], String> stream = ...;
+
+ // Derive a new record key from the record's value. Note how the key type changes, too.
+ // Java 8+ example, using lambda expressions
+ KStream<String, String> rekeyed = stream.selectKey((key, value) -> value.split(" ")[0])
+
+ // Java 7 example
+ KStream<String, String> rekeyed = stream.selectKey(
+ new KeyValueMapper<byte[], String, String>() {
+ @Override
+ public String apply(byte[] key, String value) {
+ return value.split(" ")[0];
+ }
+ });
+ </pre>
+ </td>
+ </tr>
+ <tr>
+ <td><b>Table to Stream</b>: <code>KTable → KStream</code></td>
+ <td>
+ <p>
+ Converts this table into a stream.
+ </p>
+ <pre class="brush: java;">
+ KTable<byte[], String> table = ...;
+
+ // Also, a variant of `toStream` exists that allows you
+ // to select a new key for the resulting stream.
+ KStream<byte[], String> stream = table.toStream();
+ </pre>
+ </td>
+ </tr>
+ <tr>
+ <td><b>WriteAsText</b>: <code>KStream → void </code></td>
+ <td>
+ <p>
+ Terminal operation. Write the records to a file. See Javadocs for serde and <code>toString()</code> caveats.
+ </p>
+ <pre class="brush: java;">
+ KStream<byte[], String> stream = ...;
+ stream.writeAsText("/path/to/local/output.txt");
+
+ // Several variants of `writeAsText` exist to e.g. override the
+ // default serdes for record keys and record values.
+ stream.writeAsText("/path/to/local/output.txt", Serdes.ByteArray(), Serdes.String());
+ </pre>
+ </td>
+ </tr>
+ </tbody>
+ </table>
+
+
+ <h5><a id="streams_dsl_transformations_stateful" href="#streams_dsl_transformations_stateful">Stateful transformations</a></h5>
+ <h6><a id="streams_dsl_transformations_stateful_overview" href="#streams_dsl_transformations_stateful_overview">Overview</a></h6>
+ <p>
+ Stateful transformations, by definition, depend on state for processing inputs and producing outputs, and
+ hence implementation-wise they require a state store associated with the stream processor. For example,
+ in aggregating operations, a windowing state store is used to store the latest aggregation results per window;
+ in join operations, a windowing state store is used to store all the records received so far within the
+ defined window boundary.
+ </p>
+ <p>
+ Note, that state stores are fault-tolerant. In case of failure, Kafka Streams guarantees to fully restore
+ all state stores prior to resuming the processing.
+ </p>
+ <p>
+ Available stateful transformations in the DSL include:
+ <ul>
+ <li><a href=#streams_dsl_aggregations>Aggregating</a></li>
+ <li><a href="#streams_dsl_joins">Joining</a></li>
+ <li><a href="#streams_dsl_windowing">Windowing (as part of aggregations and joins)</a></li>
+ <li>Applying custom processors and transformers, which may be stateful, for Processor API integration</li>
+ </ul>
+ </p>
+ <p>
+ The following diagram shows their relationships:
+ </p>
+ <figure>
+ <img class="centered" src="/{{version}}/images/streams-stateful_operations.png" style="width:500pt;">
+ <figcaption style="text-align: center;"><i>Stateful transformations in the DSL</i></figcaption>
+ </figure>
+
+ <p>
+ We will discuss the various stateful transformations in detail in the subsequent sections. However, let's start
+ with a first example of a stateful application: the canonical WordCount algorithm.
+ </p>
+ <p>
+ WordCount example in Java 8+, using lambda expressions:
+ </p>
+ <pre class="brush: java;">
+ // We assume record values represent lines of text. For the sake of this example, we ignore
+ // whatever may be stored in the record keys.
+ KStream<String, String> textLines = ...;
+
+ KStream<String, Long> wordCounts = textLines
+ // Split each text line, by whitespace, into words. The text lines are the record
+ // values, i.e. we can ignore whatever data is in the record keys and thus invoke
+ // `flatMapValues` instead of the more generic `flatMap`.
+ .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
+ // Group the stream by word to ensure the key of the record is the word.
+ .groupBy((key, word) -> word)
+ // Count the occurrences of each word (record key).
+ //
+ // This will change the stream type from `KGroupedStream<String, String>` to
+ // `KTable<String, Long>` (word -> count). We must provide a name for
+ // the resulting KTable, which will be used to name e.g. its associated
+ // state store and changelog topic.
+ .count("Counts")
+ // Convert the `KTable<String, Long>` into a `KStream<String, Long>`.
+ .toStream();
+ </pre>
+ <p>
+ WordCount example in Java 7:
+ </p>
+ <pre class="brush: java;">
+ // Code below is equivalent to the previous Java 8+ example above.
+ KStream<String, String> textLines = ...;
+
+ KStream<String, Long> wordCounts = textLines
+ .flatMapValues(new ValueMapper<String, Iterable<String>>() {
+ @Override
+ public Iterable<String> apply(String value) {
+ return Arrays.asList(value.toLowerCase().split("\\W+"));
+ }
+ })
+ .groupBy(new KeyValueMapper<String, String, String>>() {
+ @Override
+ public String apply(String key, String word) {
+ return word;
+ }
+ })
+ .count("Counts")
+ .toStream();
+ </pre>
+
+ <h6><a id="streams_dsl_aggregations" href="#streams_dsl_aggregations">Aggregate a stream</a></h6>
+ <p>
+ Once records are grouped by key via <code>groupByKey</code> or <code>groupBy</code> -- and
+ thus represented as either a <code>KGroupedStream</code> or a
+ <code>KGroupedTable</code> -- they can be aggregated via an operation such as
+ <code>reduce</code>.
+ For windowed aggregations use <code>windowedBy(Windows).reduce(Reducer)</code>.
+ Aggregations are <i>key-based</i> operations, i.e.they always operate over records (notably record values) <i>of the same key</i>.
+ You maychoose to perform aggregations on
+ <a href="#streams_dsl_windowing">windowed</a> or non-windowed data.
+ </p>
+ <table class="data-table" border="1">
+ <tbody>
+ <tr>
+ <th>Transformation</th>
+ <th>Description</th>
+ </tr>
+ <tr>
+ <td><b>Aggregate</b>: <code>KGroupedStream → KTable</code> or <code>KGroupedTable
+ → KTable</code></td>
+ <td>
+ <p>
+ <b>Rolling aggregation</b>. Aggregates the values of (non-windowed) records by
+ the grouped key. Aggregating is a generalization of <code>reduce</code> and allows, for example, the
+ aggregate value to have a different type than the input values.
+ </p>
+ <p>
+ When aggregating a grouped stream, you must provide an initializer (think:
+ <code>aggValue = 0</code>) and an "adder"
+ aggregator (think: <code>aggValue + curValue</code>). When aggregating a <i>grouped</i>
+ table, you must additionally provide a "subtractor" aggregator (think: <code>aggValue - oldValue</code>).
+ </p>
+ <p>
+ Several variants of <code>aggregate</code> exist, see Javadocs for details.
+ </p>
+ <pre class="brush: java;">
+ KGroupedStream<Bytes, String> groupedStream = ...;
+ KGroupedTable<Bytes, String> groupedTable = ...;
+
+ // Java 8+ examples, using lambda expressions
+
+ // Aggregating a KGroupedStream (note how the value type changes from String to Long)
+ KTable<Bytes, Long> aggregatedStream = groupedStream.aggregate(
+ () -> 0L, /* initializer */
+ (aggKey, newValue, aggValue) -> aggValue + newValue.length(), /* adder */
+ Serdes.Long(), /* serde for aggregate value */
+ "aggregated-stream-store" /* state store name */);
+
+ // Aggregating a KGroupedTable (note how the value type changes from String to Long)
+ KTable<Bytes, Long> aggregatedTable = groupedTable.aggregate(
+ () -> 0L, /* initializer */
+ (aggKey, newValue, aggValue) -> aggValue + newValue.length(), /* adder */
+ (aggKey, oldValue, aggValue) -> aggValue - oldValue.length(), /* subtractor */
+ Serdes.Long(), /* serde for aggregate value */
+ "aggregated-table-store" /* state store name */);
+
+
+ // windowed aggregation
+ KTable<Windowed<Bytes>, Long> windowedAggregate = groupedStream.windowedBy(TimeWindows.of(TimeUnit.MINUTES(5).toMillis())
+ .aggregate(() -> 0L, /* initializer */
+ (aggKey, newValue, aggValue) -> aggValue + newValue.length(), /* aggregator */
+ Serdes.Long()) /* serde for aggregate value */
+
+
+ // Java 7 examples
+
+ // Aggregating a KGroupedStream (note how the value type changes from String to Long)
+ KTable<Bytes, Long> aggregatedStream = groupedStream.aggregate(
+ new Initializer<Long>() { /* initializer */
+ @Override
+ public Long apply() {
+ return 0L;
+ }
+ },
+ new Aggregator<Bytes, String, Long>() { /* adder */
+ @Override
+ public Long apply(Bytes aggKey, String newValue, Long aggValue) {
+ return aggValue + newValue.length();
+ }
+ },
+ Serdes.Long(),
+ "aggregated-stream-store");
+
+ // Aggregating a KGroupedTable (note how the value type changes from String to Long)
+ KTable<Bytes, Long> aggregatedTable = groupedTable.aggregate(
+ new Initializer<Long>() { /* initializer */
+ @Override
+ public Long apply() {
+ return 0L;
+ }
+ },
+ new Aggregator<Bytes, String, Long>() { /* adder */
+ @Override
+ public Long apply(Bytes aggKey, String newValue, Long aggValue) {
+ return aggValue + newValue.length();
+ }
+ },
+ new Aggregator<Bytes, String, Long>() { /* subtractor */
+ @Override
+ public Long apply(Bytes aggKey, String oldValue, Long aggValue) {
+ return aggValue - oldValue.length();
+ }
+ },
+ Serdes.Long(),
+ "aggregated-table-store");
+
+ // Windowed aggregation
+ KTable<Bytes, Long> aggregatedStream = groupedStream.windowedBy(TimeWindows.of(TimeUnit.MINUTES(5).toMillis())
+ .aggregate(
+ new Initializer<Long>() { /* initializer */
+ @Override
+ public Long apply() {
+ return 0L;
+ }
+ },
+ new Aggregator<Bytes, String, Long>() { /* adder */
+ @Override
+ public Long apply(Bytes aggKey, String newValue, Long aggValue) {
+ return aggValue + newValue.length();
+ }
+ },
+ Serdes.Long());
+ </pre>
+ <p>
+ Detailed behavior of <code>KGroupedStream</code>:
+ </p>
+ <ul>
+ <li>Input records with <code>null</code> keys are ignored in general.</li>
+ <li>When a record key is received for the first time, the initializer is called
+ (and called before the adder).</li>
+ <li>Whenever a record with a non-null value is received, the adder is called.</li>
+ </ul>
+ <p>
+ Detailed behavior of KGroupedTable:
+ </p>
+ <ul>
+ <li>Input records with null keys are ignored in general.</li>
+ <li>When a record key is received for the first time, the initializer is called
+ (and called before the adder and subtractor). Note that, in contrast to <code>KGroupedStream</code>, over
+ time the initializer may be called more
+ than once for a key as a result of having received input tombstone records
+ for that key (see below).</li>
+ <li>When the first non-<code>null</code> value is received for a key (think:
+ INSERT), then only the adder is called.</li>
+ <li>When subsequent non-<code>null</code> values are received for a key (think:
+ UPDATE), then (1) the subtractor is called
+ with the old value as stored in the table and (2) the adder is called with
+ the new value of the input record
+ that was just received. The order of execution for the subtractor and adder
+ is not defined.</li>
+ <li>When a tombstone record -- i.e. a record with a <code>null</code> value -- is
+ received for a key (think: DELETE), then
+ only the subtractor is called. Note that, whenever the subtractor returns a
+ <code>null</code> value itself, then the
+ corresponding key is removed from the resulting KTable. If that happens, any
+ next input record for that key will trigger the initializer again.</li>
+ </ul>
+ <p>
+ See the example at the bottom of this section for a visualization of the
+ aggregation semantics.
+ </p>
+ </td>
+ </tr>
+ <tr>
+ <td><b>Aggregate (windowed)</b>: <code>KGroupedStream → KTable</code></td>
+ <td>
+ <p>
+ <b>Windowed aggregation</b>. Aggregates the values of records, per window, by
+ the grouped key. Aggregating is a generalization of
+ <code>reduce</code> and allows, for example, the aggregate value to have a
+ different type than the input values.
+ </p>
+ <p>
+ You must provide an initializer (think: <code>aggValue = 0</code>), "adder"
+ aggregator (think: <code>aggValue + curValue</code>),
+ and a window. When windowing based on sessions, you must additionally provide a
+ "session merger" aggregator (think:
+ <code>mergedAggValue = leftAggValue + rightAggValue</code>).
+ </p>
+ <p>
+ The windowed <code>aggregate</code> turns a <code>KGroupedStream
+ <K , V></code> into a windowed <code>KTable<Windowed<K>, V></code>.
+ </p>
+ <p>
+ Several variants of <code>aggregate</code> exist, see Javadocs for details.
+ </p>
+
+ <pre class="brush: java;">
+ import java.util.concurrent.TimeUnit;
+ KGroupedStream<String, Long> groupedStream = ...;
+
+ // Java 8+ examples, using lambda expressions
+
+ // Aggregating with time-based windowing (here: with 5-minute tumbling windows)
+ KTable<Windowed<String>, Long> timeWindowedAggregatedStream = groupedStream
+ .windowedBy(TimeWindows.of(TimeUnit.MINUTES.toMillis(5))) /* time-based window */
+ .aggregate(
+ () -> 0L, /* initializer */
+ (aggKey, newValue, aggValue) -> aggValue + newValue, /* adder */
+ Materialized.<String, Long, WindowStore<Bytes, byte[]>>as("time-windowed-aggregated-stream-store") /* state store name */
+ .withValueSerde(Serdes.Long())); /* serde for aggregate value */
+
+
+ // Aggregating with session-based windowing (here: with an inactivity gap of 5 minutes)
+ KTable<Windowed<String>, Long> sessionizedAggregatedStream = groupedStream
+ .windowedBy(SessionWindows.with(TimeUnit.MINUTES.toMillis(5))) /* session window */
+ .aggregate(
+ () -> 0L, /* initializer */
+ (aggKey, newValue, aggValue) -> aggValue + newValue, /* adder */
+ (aggKey, leftAggValue, rightAggValue) -> leftAggValue + rightAggValue, /* session merger */
+ Materialized.<String, Long, SessionStore<Bytes, byte[]>>as("sessionized-aggregated-stream-store") /* state store name */
+ .withValueSerde(Serdes.Long())); /* serde for aggregate value */
+
+ // Java 7 examples
+
+ // Aggregating with time-based windowing (here: with 5-minute tumbling windows)
+ KTable<Windowed<String>, Long> timeWindowedAggregatedStream = groupedStream
+ .windowedBy(TimeWindows.of(TimeUnit.MINUTES.toMillis(5))) /* time-based window */
+ .aggregate(
+ new Initializer<Long>() { /* initializer */
+ @Override
+ public Long apply() {
+ return 0L;
+ }
+ },
+ new Aggregator<String, Long, Long>() { /* adder */
+ @Override
+ public Long apply(String aggKey, Long newValue, Long aggValue) {
+ return aggValue + newValue;
+ }
+ },
+ Materialized.<String, Long, WindowStore<Bytes, byte[]>>as("time-windowed-aggregated-stream-store") /* state store name */
+ .withValueSerde(Serdes.Long()) /* serde for aggregate value */
+ );
+
+ // Aggregating with session-based windowing (here: with an inactivity gap of 5 minutes)
+ KTable<Windowed<String>, Long> sessionizedAggregatedStream = groupedStream
+ .windowedBy(SessionWindows.with(TimeUnit.MINUTES.toMillis(5))) /* session window */
+ .aggregate(
+ new Initializer<Long>() { /* initializer */
+ @Override
+ public Long apply() {
+ return 0L;
+ }
+ },
+ new Aggregator<String, Long, Long>() { /* adder */
+ @Override
+ public Long apply(String aggKey, Long newValue, Long aggValue) {
+ return aggValue + newValue;
+ }
+ },
+ new Merger<String, Long>() { /* session merger */
+ @Override
+ public Long apply(String aggKey, Long leftAggValue, Long rightAggValue) {
+ return rightAggValue + leftAggValue;
+ }
+ },
+ Materialized.<String, Long, SessionStore<Bytes, byte[]>>as("sessionized-aggregated-stream-store") /* state store name */
+ .withValueSerde(Serdes.Long()) /* serde for aggregate value */
+ );
+ </pre>
+
+ <p>
+ Detailed behavior:
+ </p>
+ <ul>
+ <li>The windowed aggregate behaves similar to the rolling aggregate described
+ above. The additional twist is that the behavior applies per window.</li>
+ <li>Input records with <code>null</code> keys are ignored in general.</li>
+ <li>When a record key is received for the first time for a given window, the
+ initializer is called (and called before the adder).</li>
+ <li>Whenever a record with a non-<code>null</code> value is received for a given window, the
+ adder is called.
+ (Note: As a result of a known bug in Kafka 0.11.0.0, the adder is currently
+ also called for <code>null</code> values. You can work around this, for example, by
+ manually filtering out <code>null</code> values prior to grouping the stream.)</li>
+ <li>When using session windows: the session merger is called whenever two
+ sessions are being merged.</li>
+ </ul>
+ <p>
+ See the example at the bottom of this section for a visualization of the aggregation semantics.
+ </p>
+ </td>
+ </tr>
+ <tr>
+ <td><b>Count</b>: <code>KGroupedStream → KTable or KGroupedTable → KTable</code></td>
+ <td>
+ <p>
+ <b>Rolling aggregation</b>. Counts the number of records by the grouped key.
+ Several variants of <code>count</code> exist, see Javadocs for details.
+ </p>
+ <pre class="brush: java;">
+ KGroupedStream<S
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[07/14] kafka-site git commit: Update site for 1.0.0 release
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<!--#include virtual="../includes/_header.htm" -->
+<!--#include virtual="../includes/_top.htm" -->
+<div class="content">
+ <!--#include virtual="../includes/_nav.htm" -->
+ <div class="right">
+ <h1>Kafka protocol guide</h1>
+
+<p>This document covers the wire protocol implemented in Kafka. It is meant to give a readable guide to the protocol that covers the available requests, their binary format, and the proper way to make use of them to implement a client. This document assumes you understand the basic design and terminology described <a href="https://kafka.apache.org/documentation.html#design">here</a></p>
+
+<ul class="toc">
+ <li><a href="#protocol_preliminaries">Preliminaries</a>
+ <ul>
+ <li><a href="#protocol_network">Network</a>
+ <li><a href="#protocol_partitioning">Partitioning and bootstrapping</a>
+ <li><a href="#protocol_partitioning_strategies">Partitioning Strategies</a>
+ <li><a href="#protocol_batching">Batching</a>
+ <li><a href="#protocol_compatibility">Versioning and Compatibility</a>
+ </ul>
+ </li>
+ <li><a href="#protocol_details">The Protocol</a>
+ <ul>
+ <li><a href="#protocol_types">Protocol Primitive Types</a>
+ <li><a href="#protocol_grammar">Notes on reading the request format grammars</a>
+ <li><a href="#protocol_common">Common Request and Response Structure</a>
+ <li><a href="#protocol_message_sets">Message Sets</a>
+ </ul>
+ </li>
+ <li><a href="#protocol_constants">Constants</a>
+ <ul>
+ <li><a href="#protocol_error_codes">Error Codes</a>
+ <li><a href="#protocol_api_keys">Api Keys</a>
+ </ul>
+ </li>
+ <li><a href="#protocol_messages">The Messages</a></li>
+ <li><a href="#protocol_philosophy">Some Common Philosophical Questions</a></li>
+</ul>
+
+<h4><a id="protocol_preliminaries" href="#protocol_preliminaries">Preliminaries</a></h4>
+
+<h5><a id="protocol_network" href="#protocol_network">Network</a></h5>
+
+<p>Kafka uses a binary protocol over TCP. The protocol defines all apis as request response message pairs. All messages are size delimited and are made up of the following primitive types.</p>
+
+<p>The client initiates a socket connection and then writes a sequence of request messages and reads back the corresponding response message. No handshake is required on connection or disconnection. TCP is happier if you maintain persistent connections used for many requests to amortize the cost of the TCP handshake, but beyond this penalty connecting is pretty cheap.</p>
+
+<p>The client will likely need to maintain a connection to multiple brokers, as data is partitioned and the clients will need to talk to the server that has their data. However it should not generally be necessary to maintain multiple connections to a single broker from a single client instance (i.e. connection pooling).</p>
+
+<p>The server guarantees that on a single TCP connection, requests will be processed in the order they are sent and responses will return in that order as well. The broker's request processing allows only a single in-flight request per connection in order to guarantee this ordering. Note that clients can (and ideally should) use non-blocking IO to implement request pipelining and achieve higher throughput. i.e., clients can send requests even while awaiting responses for preceding requests since the outstanding requests will be buffered in the underlying OS socket buffer. All requests are initiated by the client, and result in a corresponding response message from the server except where noted.</p>
+
+<p>The server has a configurable maximum limit on request size and any request that exceeds this limit will result in the socket being disconnected.</p>
+
+<h5><a id="protocol_partitioning" href="#protocol_partitioning">Partitioning and bootstrapping</a></h5>
+
+<p>Kafka is a partitioned system so not all servers have the complete data set. Instead recall that topics are split into a pre-defined number of partitions, P, and each partition is replicated with some replication factor, N. Topic partitions themselves are just ordered "commit logs" numbered 0, 1, ..., P.</p>
+
+<p>All systems of this nature have the question of how a particular piece of data is assigned to a particular partition. Kafka clients directly control this assignment, the brokers themselves enforce no particular semantics of which messages should be published to a particular partition. Rather, to publish messages the client directly addresses messages to a particular partition, and when fetching messages, fetches from a particular partition. If two clients want to use the same partitioning scheme they must use the same method to compute the mapping of key to partition.</p>
+
+<p>These requests to publish or fetch data must be sent to the broker that is currently acting as the leader for a given partition. This condition is enforced by the broker, so a request for a particular partition to the wrong broker will result in an the NotLeaderForPartition error code (described below).</p>
+
+<p>How can the client find out which topics exist, what partitions they have, and which brokers currently host those partitions so that it can direct its requests to the right hosts? This information is dynamic, so you can't just configure each client with some static mapping file. Instead all Kafka brokers can answer a metadata request that describes the current state of the cluster: what topics there are, which partitions those topics have, which broker is the leader for those partitions, and the host and port information for these brokers.</p>
+
+<p>In other words, the client needs to somehow find one broker and that broker will tell the client about all the other brokers that exist and what partitions they host. This first broker may itself go down so the best practice for a client implementation is to take a list of two or three urls to bootstrap from. The user can then choose to use a load balancer or just statically configure two or three of their kafka hosts in the clients.</p>
+
+<p>The client does not need to keep polling to see if the cluster has changed; it can fetch metadata once when it is instantiated cache that metadata until it receives an error indicating that the metadata is out of date. This error can come in two forms: (1) a socket error indicating the client cannot communicate with a particular broker, (2) an error code in the response to a request indicating that this broker no longer hosts the partition for which data was requested.</p>
+<ol>
+ <li>Cycle through a list of "bootstrap" kafka urls until we find one we can connect to. Fetch cluster metadata.</li>
+ <li>Process fetch or produce requests, directing them to the appropriate broker based on the topic/partitions they send to or fetch from.</li>
+ <li>If we get an appropriate error, refresh the metadata and try again.</li>
+</ol>
+
+<h5><a id="protocol_partitioning_strategies" href="#protocol_partitioning_strategies">Partitioning Strategies</a></h5>
+
+<p>As mentioned above the assignment of messages to partitions is something the producing client controls. That said, how should this functionality be exposed to the end-user?</p>
+
+<p>Partitioning really serves two purposes in Kafka:</p>
+<ol>
+ <li>It balances data and request load over brokers</li>
+ <li>It serves as a way to divvy up processing among consumer processes while allowing local state and preserving order within the partition. We call this semantic partitioning.</li>
+</ol>
+
+<p>For a given use case you may care about only one of these or both.</p>
+
+<p>To accomplish simple load balancing a simple approach would be for the client to just round robin requests over all brokers. Another alternative, in an environment where there are many more producers than brokers, would be to have each client chose a single partition at random and publish to that. This later strategy will result in far fewer TCP connections.</p>
+
+<p>Semantic partitioning means using some key in the message to assign messages to partitions. For example if you were processing a click message stream you might want to partition the stream by the user id so that all data for a particular user would go to a single consumer. To accomplish this the client can take a key associated with the message and use some hash of this key to choose the partition to which to deliver the message.</p>
+
+<h5><a id="protocol_batching" href="#protocol_batching">Batching</a></h5>
+
+<p>Our apis encourage batching small things together for efficiency. We have found this is a very significant performance win. Both our API to send messages and our API to fetch messages always work with a sequence of messages not a single message to encourage this. A clever client can make use of this and support an "asynchronous" mode in which it batches together messages sent individually and sends them in larger clumps. We go even further with this and allow the batching across multiple topics and partitions, so a produce request may contain data to append to many partitions and a fetch request may pull data from many partitions all at once.</p>
+
+<p>The client implementer can choose to ignore this and send everything one at a time if they like.</p>
+
+<h5><a id="protocol_compatibility" href="#protocol_compatibility">Versioning and Compatibility</a></h5>
+
+<p>The protocol is designed to enable incremental evolution in a backward compatible fashion. Our versioning is on a per API basis, each version consisting of a request and response pair. Each request contains an API key that identifies the API being invoked and a version number that indicates the format of the request and the expected format of the response.</p>
+
+<p>The intention is that clients will support a range of API versions. When communicating with a particular broker, a given client should use the highest API version supported by both and indicate this version in their requests.</p>
+
+<p>The server will reject requests with a version it does not support, and will always respond to the client with exactly the protocol format it expects based on the version it included in its request. The intended upgrade path is that new features would first be rolled out on the server (with the older clients not making use of them) and then as newer clients are deployed these new features would gradually be taken advantage of.</p>
+
+<p>Our goal is primarily to allow API evolution in an environment where downtime is not allowed and clients and servers cannot all be changed at once.</p>
+
+<p>Currently all versions are baselined at 0, as we evolve these APIs we will indicate the format for each version individually.</p>
+
+<h5><a id="api_versions" href="#api_versions">Retrieving Supported API versions</a></h5>
+<p>In order to work against multiple broker versions, clients need to know what versions of various APIs a
+ broker supports. The broker exposes this information since 0.10.0.0 as described in <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-35+-+Retrieving+protocol+version">KIP-35</a>.
+ Clients should use the supported API versions information to choose the highest API version supported by both client and broker. If no such version
+ exists, an error should be reported to the user.</p>
+<p>The following sequence may be used by a client to obtain supported API versions from a broker.</p>
+<ol>
+ <li>Client sends <code>ApiVersionsRequest</code> to a broker after connection has been established with the broker. If SSL is enabled,
+ this happens after SSL connection has been established.</li>
+ <li>On receiving <code>ApiVersionsRequest</code>, a broker returns its full list of supported ApiKeys and
+ versions regardless of current authentication state (e.g., before SASL authentication on an SASL listener, do note that no
+ Kafka protocol requests may take place on a SSL listener before the SSL handshake is finished). If this is considered to
+ leak information about the broker version a workaround is to use SSL with client authentication which is performed at an
+ earlier stage of the connection where the <code>ApiVersionRequest</code> is not available. Also, note that broker versions older
+ than 0.10.0.0 do not support this API and will either ignore the request or close connection in response to the request.</li>
+ <li>If multiple versions of an API are supported by broker and client, clients are recommended to use the latest version supported
+ by the broker and itself.</li>
+ <li>Deprecation of a protocol version is done by marking an API version as deprecated in the protocol documentation.</li>
+ <li>Supported API versions obtained from a broker are only valid for the connection on which that information is obtained.
+ In the event of disconnection, the client should obtain the information from the broker again, as the broker might have been
+ upgraded/downgraded in the mean time.</li>
+</ol>
+
+<h5><a id="sasl_handshake" href="#sasl_handshake">SASL Authentication Sequence</a></h5>
+<p>The following sequence is used for SASL authentication:
+<ol>
+ <li>Kafka <code>ApiVersionsRequest</code> may be sent by the client to obtain the version ranges of requests supported by the broker. This is optional.</li>
+ <li>Kafka <code>SaslHandshakeRequest</code> containing the SASL mechanism for authentication is sent by the client. If the requested mechanism is not enabled
+ in the server, the server responds with the list of supported mechanisms and closes the client connection. If the mechanism is enabled
+ in the server, the server sends a successful response and continues with SASL authentication.
+ <li>The actual SASL authentication is now performed. A series of SASL client and server tokens corresponding to the mechanism are sent as opaque
+ packets. These packets contain a 32-bit size followed by the token as defined by the protocol for the SASL mechanism.
+ <li>If authentication succeeds, subsequent packets are handled as Kafka API requests. Otherwise, the client connection is closed.
+</ol>
+<p>For interoperability with 0.9.0.x clients, the first packet received by the server is handled as a SASL/GSSAPI client token if it is not a valid
+Kafka request. SASL/GSSAPI authentication is performed starting with this packet, skipping the first two steps above.</p>
+
+
+<h4><a id="protocol_details" href="#protocol_details">The Protocol</a></h4>
+
+<h5><a id="protocol_types" href="#protocol_types">Protocol Primitive Types</a></h5>
+
+<p>The protocol is built out of the following primitive types.</p>
+
+<p><b>Fixed Width Primitives</b><p>
+
+<p>int8, int16, int32, int64 - Signed integers with the given precision (in bits) stored in big endian order.</p>
+
+<p><b>Variable Length Primitives</b><p>
+
+<p>bytes, string - These types consist of a signed integer giving a length N followed by N bytes of content. A length of -1 indicates null. string uses an int16 for its size, and bytes uses an int32.</p>
+
+<p><b>Arrays</b><p>
+
+<p>This is a notation for handling repeated structures. These will always be encoded as an int32 size containing the length N followed by N repetitions of the structure which can itself be made up of other primitive types. In the BNF grammars below we will show an array of a structure foo as [foo].</p>
+
+<h5><a id="protocol_grammar" href="#protocol_grammar">Notes on reading the request format grammars</a></h5>
+
+<p>The <a href="https://en.wikipedia.org/wiki/Backus%E2%80%93Naur_Form">BNF</a>s below give an exact context free grammar for the request and response binary format. The BNF is intentionally not compact in order to give human-readable name. As always in a BNF a sequence of productions indicates concatenation. When there are multiple possible productions these are separated with '|' and may be enclosed in parenthesis for grouping. The top-level definition is always given first and subsequent sub-parts are indented.</p>
+
+<h5><a id="protocol_common" href="#protocol_common">Common Request and Response Structure</a></h5>
+
+<p>All requests and responses originate from the following grammar which will be incrementally describe through the rest of this document:</p>
+
+<pre>
+RequestOrResponse => Size (RequestMessage | ResponseMessage)
+Size => int32
+</pre>
+
+<table class="data-table"><tbody>
+<tr><th>Field</th><th>Description</th></tr>
+<tr><td>message_size</td><td>The message_size field gives the size of the subsequent request or response message in bytes. The client can read requests by first reading this 4 byte size as an integer N, and then reading and parsing the subsequent N bytes of the request.</td></tr>
+</table>
+
+<h5><a id="protocol_message_sets" href="#protocol_message_sets">Message Sets</a></h5>
+
+<p>A description of the message set format can be found <a href="https://cwiki.apache.org/confluence/display/KAFKA/A+Guide+To+The+Kafka+Protocol#AGuideToTheKafkaProtocol-Messagesets">here</a>. (KAFKA-3368)</p>
+
+<h4><a id="protocol_constants" href="#protocol_constants">Constants</a></h4>
+
+<h5><a id="protocol_error_codes" href="#protocol_error_codes">Error Codes</a></h5>
+<p>We use numeric codes to indicate what problem occurred on the server. These can be translated by the client into exceptions or whatever the appropriate error handling mechanism in the client language. Here is a table of the error codes currently in use:</p>
+<!--#include virtual="generated/protocol_errors.html" -->
+
+<h5><a id="protocol_api_keys" href="#protocol_api_keys">Api Keys</a></h5>
+<p>The following are the numeric codes that the ApiKey in the request can take for each of the below request types.</p>
+<!--#include virtual="generated/protocol_api_keys.html" -->
+
+<h4><a id="protocol_messages" href="#protocol_messages">The Messages</a></h4>
+
+<p>This section gives details on each of the individual API Messages, their usage, their binary format, and the meaning of their fields.</p>
+<!--#include virtual="generated/protocol_messages.html" -->
+
+<h4><a id="protocol_philosophy" href="#protocol_philosophy">Some Common Philosophical Questions</a></h4>
+
+<p>Some people have asked why we don't use HTTP. There are a number of reasons, the best is that client implementors can make use of some of the more advanced TCP features--the ability to multiplex requests, the ability to simultaneously poll many connections, etc. We have also found HTTP libraries in many languages to be surprisingly shabby.</p>
+
+<p>Others have asked if maybe we shouldn't support many different protocols. Prior experience with this was that it makes it very hard to add and test new features if they have to be ported across many protocol implementations. Our feeling is that most users don't really see multiple protocols as a feature, they just want a good reliable client in the language of their choice.</p>
+
+<p>Another question is why we don't adopt XMPP, STOMP, AMQP or an existing protocol. The answer to this varies by protocol, but in general the problem is that the protocol does determine large parts of the implementation and we couldn't do what we are doing if we didn't have control over the protocol. Our belief is that it is possible to do better than existing messaging systems have in providing a truly distributed messaging system, and to do this we need to build something that works differently.</p>
+
+<p>A final question is why we don't use a system like Protocol Buffers or Thrift to define our request messages. These packages excel at helping you to managing lots and lots of serialized messages. However we have only a few messages. Support across languages is somewhat spotty (depending on the package). Finally the mapping between binary log format and wire protocol is something we manage somewhat carefully and this would not be possible with these systems. Finally we prefer the style of versioning APIs explicitly and checking this to inferring new values as nulls as it allows more nuanced control of compatibility.</p>
+
+ <script>
+ // Show selected style on nav item
+ $(function() { $('.b-nav__project').addClass('selected'); });
+ </script>
+
+<!--#include virtual="../includes/_footer.htm" -->
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/quickstart.html
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<script><!--#include virtual="js/templateData.js" --></script>
+
+<script id="quickstart-template" type="text/x-handlebars-template">
+<p>
+This tutorial assumes you are starting fresh and have no existing Kafka or ZooKeeper data.
+Since Kafka console scripts are different for Unix-based and Windows platforms, on Windows platforms use <code>bin\windows\</code> instead of <code>bin/</code>, and change the script extension to <code>.bat</code>.
+</p>
+
+<h4><a id="quickstart_download" href="#quickstart_download">Step 1: Download the code</a></h4>
+
+<a href="https://www.apache.org/dyn/closer.cgi?path=/kafka/{{fullDotVersion}}/kafka_2.11-{{fullDotVersion}}.tgz" title="Kafka downloads">Download</a> the {{fullDotVersion}} release and un-tar it.
+
+<pre class="brush: bash;">
+> tar -xzf kafka_2.11-{{fullDotVersion}}.tgz
+> cd kafka_2.11-{{fullDotVersion}}
+</pre>
+
+<h4><a id="quickstart_startserver" href="#quickstart_startserver">Step 2: Start the server</a></h4>
+
+<p>
+Kafka uses <a href="https://zookeeper.apache.org/">ZooKeeper</a> so you need to first start a ZooKeeper server if you don't already have one. You can use the convenience script packaged with kafka to get a quick-and-dirty single-node ZooKeeper instance.
+</p>
+
+<pre class="brush: bash;">
+> bin/zookeeper-server-start.sh config/zookeeper.properties
+[2013-04-22 15:01:37,495] INFO Reading configuration from: config/zookeeper.properties (org.apache.zookeeper.server.quorum.QuorumPeerConfig)
+...
+</pre>
+
+<p>Now start the Kafka server:</p>
+<pre class="brush: bash;">
+> bin/kafka-server-start.sh config/server.properties
+[2013-04-22 15:01:47,028] INFO Verifying properties (kafka.utils.VerifiableProperties)
+[2013-04-22 15:01:47,051] INFO Property socket.send.buffer.bytes is overridden to 1048576 (kafka.utils.VerifiableProperties)
+...
+</pre>
+
+<h4><a id="quickstart_createtopic" href="#quickstart_createtopic">Step 3: Create a topic</a></h4>
+
+<p>Let's create a topic named "test" with a single partition and only one replica:</p>
+<pre class="brush: bash;">
+> bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic test
+</pre>
+
+<p>We can now see that topic if we run the list topic command:</p>
+<pre class="brush: bash;">
+> bin/kafka-topics.sh --list --zookeeper localhost:2181
+test
+</pre>
+<p>Alternatively, instead of manually creating topics you can also configure your brokers to auto-create topics when a non-existent topic is published to.</p>
+
+<h4><a id="quickstart_send" href="#quickstart_send">Step 4: Send some messages</a></h4>
+
+<p>Kafka comes with a command line client that will take input from a file or from standard input and send it out as messages to the Kafka cluster. By default, each line will be sent as a separate message.</p>
+<p>
+Run the producer and then type a few messages into the console to send to the server.</p>
+
+<pre class="brush: bash;">
+> bin/kafka-console-producer.sh --broker-list localhost:9092 --topic test
+This is a message
+This is another message
+</pre>
+
+<h4><a id="quickstart_consume" href="#quickstart_consume">Step 5: Start a consumer</a></h4>
+
+<p>Kafka also has a command line consumer that will dump out messages to standard output.</p>
+
+<pre class="brush: bash;">
+> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic test --from-beginning
+This is a message
+This is another message
+</pre>
+<p>
+If you have each of the above commands running in a different terminal then you should now be able to type messages into the producer terminal and see them appear in the consumer terminal.
+</p>
+<p>
+All of the command line tools have additional options; running the command with no arguments will display usage information documenting them in more detail.
+</p>
+
+<h4><a id="quickstart_multibroker" href="#quickstart_multibroker">Step 6: Setting up a multi-broker cluster</a></h4>
+
+<p>So far we have been running against a single broker, but that's no fun. For Kafka, a single broker is just a cluster of size one, so nothing much changes other than starting a few more broker instances. But just to get feel for it, let's expand our cluster to three nodes (still all on our local machine).</p>
+<p>
+First we make a config file for each of the brokers (on Windows use the <code>copy</code> command instead):
+</p>
+<pre class="brush: bash;">
+> cp config/server.properties config/server-1.properties
+> cp config/server.properties config/server-2.properties
+</pre>
+
+<p>
+Now edit these new files and set the following properties:
+</p>
+<pre class="brush: text;">
+
+config/server-1.properties:
+ broker.id=1
+ listeners=PLAINTEXT://:9093
+ log.dir=/tmp/kafka-logs-1
+
+config/server-2.properties:
+ broker.id=2
+ listeners=PLAINTEXT://:9094
+ log.dir=/tmp/kafka-logs-2
+</pre>
+<p>The <code>broker.id</code> property is the unique and permanent name of each node in the cluster. We have to override the port and log directory only because we are running these all on the same machine and we want to keep the brokers from all trying to register on the same port or overwrite each other's data.</p>
+<p>
+We already have Zookeeper and our single node started, so we just need to start the two new nodes:
+</p>
+<pre class="brush: bash;">
+> bin/kafka-server-start.sh config/server-1.properties &
+...
+> bin/kafka-server-start.sh config/server-2.properties &
+...
+</pre>
+
+<p>Now create a new topic with a replication factor of three:</p>
+<pre class="brush: bash;">
+> bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 3 --partitions 1 --topic my-replicated-topic
+</pre>
+
+<p>Okay but now that we have a cluster how can we know which broker is doing what? To see that run the "describe topics" command:</p>
+<pre class="brush: bash;">
+> bin/kafka-topics.sh --describe --zookeeper localhost:2181 --topic my-replicated-topic
+Topic:my-replicated-topic PartitionCount:1 ReplicationFactor:3 Configs:
+ Topic: my-replicated-topic Partition: 0 Leader: 1 Replicas: 1,2,0 Isr: 1,2,0
+</pre>
+<p>Here is an explanation of output. The first line gives a summary of all the partitions, each additional line gives information about one partition. Since we have only one partition for this topic there is only one line.</p>
+<ul>
+ <li>"leader" is the node responsible for all reads and writes for the given partition. Each node will be the leader for a randomly selected portion of the partitions.
+ <li>"replicas" is the list of nodes that replicate the log for this partition regardless of whether they are the leader or even if they are currently alive.
+ <li>"isr" is the set of "in-sync" replicas. This is the subset of the replicas list that is currently alive and caught-up to the leader.
+</ul>
+<p>Note that in my example node 1 is the leader for the only partition of the topic.</p>
+<p>
+We can run the same command on the original topic we created to see where it is:
+</p>
+<pre class="brush: bash;">
+> bin/kafka-topics.sh --describe --zookeeper localhost:2181 --topic test
+Topic:test PartitionCount:1 ReplicationFactor:1 Configs:
+ Topic: test Partition: 0 Leader: 0 Replicas: 0 Isr: 0
+</pre>
+<p>So there is no surprise there—the original topic has no replicas and is on server 0, the only server in our cluster when we created it.</p>
+<p>
+Let's publish a few messages to our new topic:
+</p>
+<pre class="brush: bash;">
+> bin/kafka-console-producer.sh --broker-list localhost:9092 --topic my-replicated-topic
+...
+my test message 1
+my test message 2
+^C
+</pre>
+<p>Now let's consume these messages:</p>
+<pre class="brush: bash;">
+> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --from-beginning --topic my-replicated-topic
+...
+my test message 1
+my test message 2
+^C
+</pre>
+
+<p>Now let's test out fault-tolerance. Broker 1 was acting as the leader so let's kill it:</p>
+<pre class="brush: bash;">
+> ps aux | grep server-1.properties
+7564 ttys002 0:15.91 /System/Library/Frameworks/JavaVM.framework/Versions/1.8/Home/bin/java...
+> kill -9 7564
+</pre>
+
+On Windows use:
+<pre class="brush: bash;">
+> wmic process where "caption = 'java.exe' and commandline like '%server-1.properties%'" get processid
+ProcessId
+6016
+> taskkill /pid 6016 /f
+</pre>
+
+<p>Leadership has switched to one of the slaves and node 1 is no longer in the in-sync replica set:</p>
+
+<pre class="brush: bash;">
+> bin/kafka-topics.sh --describe --zookeeper localhost:2181 --topic my-replicated-topic
+Topic:my-replicated-topic PartitionCount:1 ReplicationFactor:3 Configs:
+ Topic: my-replicated-topic Partition: 0 Leader: 2 Replicas: 1,2,0 Isr: 2,0
+</pre>
+<p>But the messages are still available for consumption even though the leader that took the writes originally is down:</p>
+<pre class="brush: bash;">
+> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --from-beginning --topic my-replicated-topic
+...
+my test message 1
+my test message 2
+^C
+</pre>
+
+
+<h4><a id="quickstart_kafkaconnect" href="#quickstart_kafkaconnect">Step 7: Use Kafka Connect to import/export data</a></h4>
+
+<p>Writing data from the console and writing it back to the console is a convenient place to start, but you'll probably want
+to use data from other sources or export data from Kafka to other systems. For many systems, instead of writing custom
+integration code you can use Kafka Connect to import or export data.</p>
+
+<p>Kafka Connect is a tool included with Kafka that imports and exports data to Kafka. It is an extensible tool that runs
+<i>connectors</i>, which implement the custom logic for interacting with an external system. In this quickstart we'll see
+how to run Kafka Connect with simple connectors that import data from a file to a Kafka topic and export data from a
+Kafka topic to a file.</p>
+
+<p>First, we'll start by creating some seed data to test with:</p>
+
+<pre class="brush: bash;">
+> echo -e "foo\nbar" > test.txt
+</pre>
+Or on Windows:
+<pre class="brush: bash;">
+> echo foo> test.txt
+> echo bar>> test.txt
+</pre>
+
+<p>Next, we'll start two connectors running in <i>standalone</i> mode, which means they run in a single, local, dedicated
+process. We provide three configuration files as parameters. The first is always the configuration for the Kafka Connect
+process, containing common configuration such as the Kafka brokers to connect to and the serialization format for data.
+The remaining configuration files each specify a connector to create. These files include a unique connector name, the connector
+class to instantiate, and any other configuration required by the connector.</p>
+
+<pre class="brush: bash;">
+> bin/connect-standalone.sh config/connect-standalone.properties config/connect-file-source.properties config/connect-file-sink.properties
+</pre>
+
+<p>
+These sample configuration files, included with Kafka, use the default local cluster configuration you started earlier
+and create two connectors: the first is a source connector that reads lines from an input file and produces each to a Kafka topic
+and the second is a sink connector that reads messages from a Kafka topic and produces each as a line in an output file.
+</p>
+
+<p>
+During startup you'll see a number of log messages, including some indicating that the connectors are being instantiated.
+Once the Kafka Connect process has started, the source connector should start reading lines from <code>test.txt</code> and
+producing them to the topic <code>connect-test</code>, and the sink connector should start reading messages from the topic <code>connect-test</code>
+and write them to the file <code>test.sink.txt</code>. We can verify the data has been delivered through the entire pipeline
+by examining the contents of the output file:
+</p>
+
+
+<pre class="brush: bash;">
+> more test.sink.txt
+foo
+bar
+</pre>
+
+<p>
+Note that the data is being stored in the Kafka topic <code>connect-test</code>, so we can also run a console consumer to see the
+data in the topic (or use custom consumer code to process it):
+</p>
+
+
+<pre class="brush: bash;">
+> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic connect-test --from-beginning
+{"schema":{"type":"string","optional":false},"payload":"foo"}
+{"schema":{"type":"string","optional":false},"payload":"bar"}
+...
+</pre>
+
+<p>The connectors continue to process data, so we can add data to the file and see it move through the pipeline:</p>
+
+<pre class="brush: bash;">
+> echo Another line>> test.txt
+</pre>
+
+<p>You should see the line appear in the console consumer output and in the sink file.</p>
+
+<h4><a id="quickstart_kafkastreams" href="#quickstart_kafkastreams">Step 8: Use Kafka Streams to process data</a></h4>
+
+<p>
+ Kafka Streams is a client library for building mission-critical real-time applications and microservices,
+ where the input and/or output data is stored in Kafka clusters. Kafka Streams combines the simplicity of
+ writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's
+ server-side cluster technology to make these applications highly scalable, elastic, fault-tolerant, distributed,
+ and much more. This <a href="/{{version}}/documentation/streams/quickstart">quickstart example</a> will demonstrate how
+ to run a streaming application coded in this library.
+</p>
+
+
+</script>
+
+<div class="p-quickstart"></div>
[02/14] kafka-site git commit: Update site for 1.0.0 release
Posted by ij...@apache.org.
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/streams/upgrade-guide.html
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<script><!--#include virtual="../js/templateData.js" --></script>
+
+<script id="content-template" type="text/x-handlebars-template">
+ <h1>Upgrade Guide & API Changes</h1>
+
+ <p>
+ If you want to upgrade from 0.11.0.x to 1.0.0 you don't need to do any code changes as the public API is fully backward compatible.
+ However, some public APIs were deprecated and thus it is recommended to update your code eventually to allow for future upgrades.
+ See <a href="#streams_api_changes_100">below</a> a complete list of 1.0.0 API and semantic changes that allow you to advance your application and/or simplify your code base, including the usage of new features.
+ </p>
+
+ <p>
+ If you want to upgrade from 0.10.2.x to 0.11.0 you don't need to do any code changes as the public API is fully backward compatible.
+ However, some configuration parameters were deprecated and thus it is recommended to update your code eventually to allow for future upgrades.
+ See <a href="#streams_api_changes_0110">below</a> a complete list of 0.11.0 API and semantic changes that allow you to advance your application and/or simplify your code base, including the usage of new features.
+ </p>
+
+ <p>
+ If you want to upgrade from 0.10.1.x to 0.10.2, see the <a href="/{{version}}/documentation/#upgrade_1020_streams"><b>Upgrade Section for 0.10.2</b></a>.
+ It highlights incompatible changes you need to consider to upgrade your code and application.
+ See <a href="#streams_api_changes_0102">below</a> a complete list of 0.10.2 API and semantic changes that allow you to advance your application and/or simplify your code base, including the usage of new features.
+ </p>
+
+ <p>
+ If you want to upgrade from 0.10.0.x to 0.10.1, see the <a href="/{{version}}/documentation/#upgrade_1010_streams"><b>Upgrade Section for 0.10.1</b></a>.
+ It highlights incompatible changes you need to consider to upgrade your code and application.
+ See <a href="#streams_api_changes_0101">below</a> a complete list of 0.10.1 API changes that allow you to advance your application and/or simplify your code base, including the usage of new features.
+ </p>
+
+ <h3><a id="streams_api_changes_100" href="#streams_api_changes_100">Streams API changes in 1.0.0</a></h3>
+
+ <p>
+ With 1.0 a major API refactoring was accomplished and the new API is cleaner and easier to use.
+ This change includes the five main classes <code>KafkaStreams</code>, <code>KStreamBuilder</code>,
+ <code>KStream</code>, <code>KTable</code>, and <code>TopologyBuilder</code> (and some more others).
+ All changes are fully backward compatible as old API is only deprecated but not removed.
+ We recommend to move to the new API as soon as you can.
+ We will summarize all API changes in the next paragraphs.
+ </p>
+
+ <p>
+ The two main classes to specify a topology via the DSL (<code>KStreamBuilder</code>)
+ or the Processor API (<code>TopologyBuilder</code>) were deprecated and replaced by
+ <code>StreamsBuilder</code> and <code>Topology</code> (both new classes are located in
+ package <code>org.apache.kafka.streams</code>).
+ Note, that <code>StreamsBuilder</code> does not extend <code>Topology</code>, i.e.,
+ the class hierarchy is different now.
+ The new classes have basically the same methods as the old ones to build a topology via DSL or Processor API.
+ However, some internal methods that were public in <code>KStreamBuilder</code>
+ and <code>TopologyBuilder</code> but not part of the actual API are not present
+ in the new classes any longer.
+ Furthermore, some overloads were simplified compared to the original classes.
+ See <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-120%3A+Cleanup+Kafka+Streams+builder+API">KIP-120</a>
+ and <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-182%3A+Reduce+Streams+DSL+overloads+and+allow+easier+use+of+custom+storage+engines">KIP-182</a>
+ for full details.
+ </p>
+
+ <p>
+ Changing how a topology is specified also affects <code>KafkaStreams</code> constructors,
+ that now only accept a <code>Topology</code>.
+ Using the DSL builder class <code>StreamsBuilder</code> one can get the constructed
+ <code>Topology</code> via <code>StreamsBuilder#build()</code>.
+ Additionally, a new class <code>org.apache.kafka.streams.TopologyDescription</code>
+ (and some more dependent classes) were added.
+ Those can be used to get a detailed description of the specified topology
+ and can be obtained by calling <code>Topology#describe()</code>.
+ An example using this new API is shown in the <a href="/{{version}}/documentation/streams/quickstart">quickstart section</a>.
+ </p>
+
+ <p>
+ New methods in <code>KStream</code>:
+ </p>
+ <ul>
+ <li>With the introduction of <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-202+Move+merge%28%29+from+StreamsBuilder+to+KStream">KIP-202</a>
+ a new method <code>merge()</code> has been created in <code>KStream</code> as the StreamsBuilder class's <code>StreamsBuilder#merge()</code> has been removed.
+ The method signature was also changed, too: instead of providing multiple <code>KStream</code>s into the method at the once, only a single <code>KStream</code> is accepted.
+ </li>
+ </ul>
+
+ <p>
+ New methods in <code>KafkaStreams</code>:
+ </p>
+ <ul>
+ <li>retrieve the current runtime information about the local threads via <code>#localThreadsMetadata()</code> </li>
+ <li>observe the restoration of all state stores via <code>#setGlobalStateRestoreListener()</code>, in which users can provide their customized implementation of the <code>org.apache.kafka.streams.processor.StateRestoreListener</code> interface</li>
+ </ul>
+
+ <p>
+ Deprecated / modified methods in <code>KafkaStreams</code>:
+ </p>
+ <ul>
+ <li>
+ <code>toString()</code>, <code>toString(final String indent)</code> were previously used to return static and runtime information.
+ They have been deprecated in favor of using the new classes/methods <code>#localThreadsMetadata()</code> / <code>ThreadMetadata</code> (returning runtime information) and
+ <code>TopologyDescription</code> / <code>Topology#describe()</code> (returning static information).
+ </li>
+ <li>
+ With the introduction of <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-182%3A+Reduce+Streams+DSL+overloads+and+allow+easier+use+of+custom+storage+engines">KIP-182</a>
+ you should no longer pass in <code>Serde</code> to <code>KStream#print</code> operations.
+ If you can't rely on using <code>toString</code> to print your keys an values, you should instead you provide a custom <code>KeyValueMapper</code> via the <code>Printed#withKeyValueMapper</code> call.
+ </li>
+ <li>
+ <code>setStateListener()</code> now can only be set before the application start running, i.e. before <code>KafkaStreams.start()</code> is called.
+ </li>
+ </ul>
+
+ <p>
+ Deprecated methods in <code>KGroupedStream</code>
+ </p>
+ <ul>
+ <li>
+ Windowed aggregations have been deprecated from <code>KGroupedStream</code> and moved to <code>WindowedKStream</code>.
+ You can now perform a windowed aggregation by, for example, using <code>KGroupedStream#windowedBy(Windows)#reduce(Reducer)</code>.
+ </li>
+ </ul>
+
+ <p>
+ Modified methods in <code>Processor</code>:
+ </p>
+ <ul>
+ <li>
+ <p>
+ The Processor API was extended to allow users to schedule <code>punctuate</code> functions either based on data-driven <b>stream time</b> or wall-clock time.
+ As a result, the original <code>ProcessorContext#schedule</code> is deprecated with a new overloaded function that accepts a user customizable <code>Punctuator</code> callback interface, which triggers its <code>punctuate</code> API method periodically based on the <code>PunctuationType</code>.
+ The <code>PunctuationType</code> determines what notion of time is used for the punctuation scheduling: either <a href="/{{version}}/documentation/streams/core-concepts#streams_time">stream time</a> or wall-clock time (by default, <b>stream time</b> is configured to represent event time via <code>TimestampExtractor</code>).
+ In addition, the <code>punctuate</code> function inside <code>Processor</code> is also deprecated.
+ </p>
+ <p>
+ Before this, users could only schedule based on stream time (i.e. <code>PunctuationType.STREAM_TIME</code>) and hence the <code>punctuate</code> function was data-driven only because stream time is determined (and advanced forward) by the timestamps derived from the input data.
+ If there is no data arriving at the processor, the stream time would not advance and hence punctuation will not be triggered.
+ On the other hand, When wall-clock time (i.e. <code>PunctuationType.WALL_CLOCK_TIME</code>) is used, <code>punctuate</code> will be triggered purely based on wall-clock time.
+ So for example if the <code>Punctuator</code> function is scheduled based on <code>PunctuationType.WALL_CLOCK_TIME</code>, if these 60 records were processed within 20 seconds,
+ <code>punctuate</code> would be called 2 times (one time every 10 seconds);
+ if these 60 records were processed within 5 seconds, then no <code>punctuate</code> would be called at all.
+ Users can schedule multiple <code>Punctuator</code> callbacks with different <code>PunctuationType</code>s within the same processor by simply calling <code>ProcessorContext#schedule</code> multiple times inside processor's <code>init()</code> method.
+ </p>
+ </li>
+ </ul>
+
+ <p>
+ If you are monitoring on task level or processor-node / state store level Streams metrics, please note that the metrics sensor name and hierarchy was changed:
+ The task ids, store names and processor names are no longer in the sensor metrics names, but instead are added as tags of the sensors to achieve consistent metrics hierarchy.
+ As a result you may need to make corresponding code changes on your metrics reporting and monitoring tools when upgrading to 1.0.0.
+ Detailed metrics sensor can be found in the <a href="#kafka_streams_monitoring">Streams Monitoring</a> section.
+ </p>
+
+ <p>
+ The introduction of <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+deserialization+exception+handlers">KIP-161</a>
+ enables you to provide a default exception handler for deserialization errors when reading data from Kafka rather than throwing the exception all the way out of your streams application.
+ You can provide the configs via the <code>StreamsConfig</code> as <code>StreamsConfig#DEFAULT_DESERIALIZATION_EXCEPTION_HANDLER_CLASS_CONFIG</code>.
+ The specified handler must implement the <code>org.apache.kafka.streams.errors.DeserializationExceptionHandler</code> interface.
+ </p>
+
+ <p>
+ The introduction of <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-173%3A+Add+prefix+to+StreamsConfig+to+enable+setting+default+internal+topic+configs">KIP-173</a>
+ enables you to provide topic configuration parameters for any topics created by Kafka Streams.
+ This includes repartition and changelog topics.
+ You can provide the configs via the <code>StreamsConfig</code> by adding the configs with the prefix as defined by <code>StreamsConfig#topicPrefix(String)</code>.
+ Any properties in the <code>StreamsConfig</code> with the prefix will be applied when creating internal topics.
+ Any configs that aren't topic configs will be ignored.
+ If you already use <code>StateStoreSupplier</code> or <code>Materialized</code> to provide configs for changelogs, then they will take precedence over those supplied in the config.
+ </p>
+
+ <h3><a id="streams_api_changes_0110" href="#streams_api_changes_0110">Streams API changes in 0.11.0.0</a></h3>
+
+ <p> Updates in <code>StreamsConfig</code>: </p>
+ <ul>
+ <li> new configuration parameter <code>processing.guarantee</code> is added </li>
+ <li> configuration parameter <code>key.serde</code> was deprecated and replaced by <code>default.key.serde</code> </li>
+ <li> configuration parameter <code>value.serde</code> was deprecated and replaced by <code>default.value.serde</code> </li>
+ <li> configuration parameter <code>timestamp.extractor</code> was deprecated and replaced by <code>default.timestamp.extractor</code> </li>
+ <li> method <code>#keySerde()</code> was deprecated and replaced by <code>#defaultKeySerde()</code> </li>
+ <li> method <code>#valueSerde()</code> was deprecated and replaced by <code>#defaultValueSerde()</code> </li>
+ <li> new method <code>#defaultTimestampExtractor()</code> was added </li>
+ </ul>
+
+ <p> New methods in <code>TopologyBuilder</code>: </p>
+ <ul>
+ <li> added overloads for <code>#addSource()</code> that allow to define a <code>TimestampExtractor</code> per source node </li>
+ <li> added overloads for <code>#addGlobalStore()</code> that allow to define a <code>TimestampExtractor</code> per source node associated with the global store </li>
+ </ul>
+
+ <p> New methods in <code>KStreamBuilder</code>: </p>
+ <ul>
+ <li> added overloads for <code>#stream()</code> that allow to define a <code>TimestampExtractor</code> per input stream </li>
+ <li> added overloads for <code>#table()</code> that allow to define a <code>TimestampExtractor</code> per input table </li>
+ <li> added overloads for <code>#globalKTable()</code> that allow to define a <code>TimestampExtractor</code> per global table </li>
+ </ul>
+
+ <p> Deprecated methods in <code>KTable</code>: </p>
+ <ul>
+ <li> <code>void foreach(final ForeachAction<? super K, ? super V> action)</code> </li>
+ <li> <code>void print()</code> </li>
+ <li> <code>void print(final String streamName)</code> </li>
+ <li> <code>void print(final Serde<K> keySerde, final Serde<V> valSerde)</code> </li>
+ <li> <code>void print(final Serde<K> keySerde, final Serde<V> valSerde, final String streamName)</code> </li>
+ <li> <code>void writeAsText(final String filePath)</code> </li>
+ <li> <code>void writeAsText(final String filePath, final String streamName)</code> </li>
+ <li> <code>void writeAsText(final String filePath, final Serde<K> keySerde, final Serde<V> valSerde)</code> </li>
+ <li> <code>void writeAsText(final String filePath, final String streamName, final Serde<K> keySerde, final Serde<V> valSerde)</code> </li>
+ </ul>
+
+ <p>
+ The above methods have been deprecated in favor of using the Interactive Queries API.
+ If you want to query the current content of the state store backing the KTable, use the following approach:
+ </p>
+ <ul>
+ <li> Make a call to <code>KafkaStreams.store(final String storeName, final QueryableStoreType<T> queryableStoreType)</code> </li>
+ <li> Then make a call to <code>ReadOnlyKeyValueStore.all()</code> to iterate over the keys of a <code>KTable</code>. </li>
+ </ul>
+ <p>
+ If you want to view the changelog stream of the <code>KTable</code> then you could call <code>KTable.toStream().print(Printed.toSysOut)</code>.
+ </p>
+
+ <p> Metrics using exactly-once semantics: </p>
+ <p>
+ If exactly-once processing is enabled via the <code>processing.guarantees</code> parameter, internally Streams switches from a producer per thread to a producer per task runtime model.
+ In order to distinguish the different producers, the producer's <code>client.id</code> additionally encodes the task-ID for this case.
+ Because the producer's <code>client.id</code> is used to report JMX metrics, it might be required to update tools that receive those metrics.
+
+ </p>
+
+ <p> Producer's <code>client.id</code> naming schema: </p>
+ <ul>
+ <li> at-least-once (default): <code>[client.Id]-StreamThread-[sequence-number]</code> </li>
+ <li> exactly-once: <code>[client.Id]-StreamThread-[sequence-number]-[taskId]</code> </li>
+ </ul>
+ <p> <code>[client.Id]</code> is either set via Streams configuration parameter <code>client.id</code> or defaults to <code>[application.id]-[processId]</code> (<code>[processId]</code> is a random UUID). </p>
+
+ <h3><a id="streams_api_changes_01021" href="#streams_api_changes_01021">Notable changes in 0.10.2.1</a></h3>
+
+ <p>
+ Parameter updates in <code>StreamsConfig</code>:
+ </p>
+ <ul>
+ <li> The default config values of embedded producer's <code>retries</code> and consumer's <code>max.poll.interval.ms</code> have been changed to improve the resiliency of a Kafka Streams application </li>
+ </ul>
+
+ <h3><a id="streams_api_changes_0102" href="#streams_api_changes_0102">Streams API changes in 0.10.2.0</a></h3>
+
+ <p>
+ New methods in <code>KafkaStreams</code>:
+ </p>
+ <ul>
+ <li> set a listener to react on application state change via <code>#setStateListener(StateListener listener)</code> </li>
+ <li> retrieve the current application state via <code>#state()</code> </li>
+ <li> retrieve the global metrics registry via <code>#metrics()</code> </li>
+ <li> apply a timeout when closing an application via <code>#close(long timeout, TimeUnit timeUnit)</code> </li>
+ <li> specify a custom indent when retrieving Kafka Streams information via <code>#toString(String indent)</code> </li>
+ </ul>
+
+ <p>
+ Parameter updates in <code>StreamsConfig</code>:
+ </p>
+ <ul>
+ <li> parameter <code>zookeeper.connect</code> was deprecated; a Kafka Streams application does no longer interact with ZooKeeper for topic management but uses the new broker admin protocol
+ (cf. <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-4+-+Command+line+and+centralized+administrative+operations#KIP-4-Commandlineandcentralizedadministrativeoperations-TopicAdminSchema.1">KIP-4, Section "Topic Admin Schema"</a>) </li>
+ <li> added many new parameters for metrics, security, and client configurations </li>
+ </ul>
+
+ <p> Changes in <code>StreamsMetrics</code> interface: </p>
+ <ul>
+ <li> removed methods: <code>#addLatencySensor()</code> </li>
+ <li> added methods: <code>#addLatencyAndThroughputSensor()</code>, <code>#addThroughputSensor()</code>, <code>#recordThroughput()</code>,
+ <code>#addSensor()</code>, <code>#removeSensor()</code> </li>
+ </ul>
+
+ <p> New methods in <code>TopologyBuilder</code>: </p>
+ <ul>
+ <li> added overloads for <code>#addSource()</code> that allow to define a <code>auto.offset.reset</code> policy per source node </li>
+ <li> added methods <code>#addGlobalStore()</code> to add global <code>StateStore</code>s </li>
+ </ul>
+
+ <p> New methods in <code>KStreamBuilder</code>: </p>
+ <ul>
+ <li> added overloads for <code>#stream()</code> and <code>#table()</code> that allow to define a <code>auto.offset.reset</code> policy per input stream/table </li>
+ <li> added method <code>#globalKTable()</code> to create a <code>GlobalKTable</code> </li>
+ </ul>
+
+ <p> New joins for <code>KStream</code>: </p>
+ <ul>
+ <li> added overloads for <code>#join()</code> to join with <code>KTable</code> </li>
+ <li> added overloads for <code>#join()</code> and <code>leftJoin()</code> to join with <code>GlobalKTable</code> </li>
+ <li> note, join semantics in 0.10.2 were improved and thus you might see different result compared to 0.10.0.x and 0.10.1.x
+ (cf. <a href="https://cwiki.apache.org/confluence/display/KAFKA/Kafka+Streams+Join+Semantics">Kafka Streams Join Semantics</a> in the Apache Kafka wiki)
+ </ul>
+
+ <p> Aligned <code>null</code>-key handling for <code>KTable</code> joins: </p>
+ <ul>
+ <li> like all other KTable operations, <code>KTable-KTable</code> joins do not throw an exception on <code>null</code> key records anymore, but drop those records silently </li>
+ </ul>
+
+ <p> New window type <em>Session Windows</em>: </p>
+ <ul>
+ <li> added class <code>SessionWindows</code> to specify session windows </li>
+ <li> added overloads for <code>KGroupedStream</code> methods <code>#count()</code>, <code>#reduce()</code>, and <code>#aggregate()</code>
+ to allow session window aggregations </li>
+ </ul>
+
+ <p> Changes to <code>TimestampExtractor</code>: </p>
+ <ul>
+ <li> method <code>#extract()</code> has a second parameter now </li>
+ <li> new default timestamp extractor class <code>FailOnInvalidTimestamp</code>
+ (it gives the same behavior as old (and removed) default extractor <code>ConsumerRecordTimestampExtractor</code>) </li>
+ <li> new alternative timestamp extractor classes <code>LogAndSkipOnInvalidTimestamp</code> and <code>UsePreviousTimeOnInvalidTimestamps</code> </li>
+ </ul>
+
+ <p> Relaxed type constraints of many DSL interfaces, classes, and methods (cf. <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-100+-+Relax+Type+constraints+in+Kafka+Streams+API">KIP-100</a>). </p>
+
+ <h3><a id="streams_api_changes_0101" href="#streams_api_changes_0101">Streams API changes in 0.10.1.0</a></h3>
+
+ <p> Stream grouping and aggregation split into two methods: </p>
+ <ul>
+ <li> old: KStream #aggregateByKey(), #reduceByKey(), and #countByKey() </li>
+ <li> new: KStream#groupByKey() plus KGroupedStream #aggregate(), #reduce(), and #count() </li>
+ <li> Example: stream.countByKey() changes to stream.groupByKey().count() </li>
+ </ul>
+
+ <p> Auto Repartitioning: </p>
+ <ul>
+ <li> a call to through() after a key-changing operator and before an aggregation/join is no longer required </li>
+ <li> Example: stream.selectKey(...).through(...).countByKey() changes to stream.selectKey().groupByKey().count() </li>
+ </ul>
+
+ <p> TopologyBuilder: </p>
+ <ul>
+ <li> methods #sourceTopics(String applicationId) and #topicGroups(String applicationId) got simplified to #sourceTopics() and #topicGroups() </li>
+ </ul>
+
+ <p> DSL: new parameter to specify state store names: </p>
+ <ul>
+ <li> The new Interactive Queries feature requires to specify a store name for all source KTables and window aggregation result KTables (previous parameter "operator/window name" is now the storeName) </li>
+ <li> KStreamBuilder#table(String topic) changes to #topic(String topic, String storeName) </li>
+ <li> KTable#through(String topic) changes to #through(String topic, String storeName) </li>
+ <li> KGroupedStream #aggregate(), #reduce(), and #count() require additional parameter "String storeName"</li>
+ <li> Example: stream.countByKey(TimeWindows.of("windowName", 1000)) changes to stream.groupByKey().count(TimeWindows.of(1000), "countStoreName") </li>
+ </ul>
+
+ <p> Windowing: </p>
+ <ul>
+ <li> Windows are not named anymore: TimeWindows.of("name", 1000) changes to TimeWindows.of(1000) (cf. DSL: new parameter to specify state store names) </li>
+ <li> JoinWindows has no default size anymore: JoinWindows.of("name").within(1000) changes to JoinWindows.of(1000) </li>
+ </ul>
+
+ <div class="pagination">
+ <a href="/{{version}}/documentation/streams/architecture" class="pagination__btn pagination__btn__prev">Previous</a>
+ <a href="#" class="pagination__btn pagination__btn__next pagination__btn--disabled">Next</a>
+ </div>
+</script>
+
+<!--#include virtual="../../includes/_header.htm" -->
+<!--#include virtual="../../includes/_top.htm" -->
+<div class="content documentation documentation--current">
+ <!--#include virtual="../../includes/_nav.htm" -->
+ <div class="right">
+ <!--#include virtual="../../includes/_docs_banner.htm" -->
+ <ul class="breadcrumbs">
+ <li><a href="/documentation">Documentation</a></li>
+ <li><a href="/documentation/streams">Kafka Streams API</a></li>
+ </ul>
+ <div class="p-content"></div>
+ </div>
+</div>
+<!--#include virtual="../../includes/_footer.htm" -->
+<script>
+$(function() {
+ // Show selected style on nav item
+ $('.b-nav__streams').addClass('selected');
+
+ // Display docs subnav items
+ $('.b-nav__docs').parent().toggleClass('nav__item__with__subs--expanded');
+});
+</script>
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/toc.html
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--- /dev/null
+++ b/100/toc.html
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+<script><!--#include virtual="js/templateData.js" --></script>
+
+<script id="toc-template" type="text/x-handlebars-template">
+
+ <ul class="toc">
+ <li><a href="#gettingStarted">1. Getting Started</a>
+ <ul>
+ <li><a href="#introduction">1.1 Introduction</a>
+ <li><a href="#uses">1.2 Use Cases</a>
+ <li><a href="#quickstart">1.3 Quick Start</a>
+ <li><a href="#ecosystem">1.4 Ecosystem</a>
+ <li><a href="#upgrade">1.5 Upgrading</a>
+ </ul>
+ </li>
+ <li><a href="#api">2. APIs</a>
+ <ul>
+ <li><a href="#producerapi">2.1 Producer API</a>
+ <li><a href="#consumerapi">2.2 Consumer API</a>
+ <li><a href="/{{version}}/documentation/streams">2.3 Streams API</a>
+ <li><a href="#connectapi">2.4 Connect API</a>
+ <li><a href="#adminapi">2.5 AdminClient API</a>
+ <li><a href="#legacyapis">2.6 Legacy APIs</a>
+ </ul>
+ </li>
+ <li><a href="#configuration">3. Configuration</a>
+ <ul>
+ <li><a href="#brokerconfigs">3.1 Broker Configs</a>
+ <li><a href="#topicconfigs">3.2 Topic Configs</a>
+ <li><a href="#producerconfigs">3.3 Producer Configs</a>
+ <li><a href="#consumerconfigs">3.4 Consumer Configs</a>
+ <ul>
+ <li><a href="#newconsumerconfigs">3.4.1 New Consumer Configs</a>
+ <li><a href="#oldconsumerconfigs">3.4.2 Old Consumer Configs</a>
+ </ul>
+ <li><a href="#connectconfigs">3.5 Kafka Connect Configs</a>
+ <li><a href="#streamsconfigs">3.6 Kafka Streams Configs</a>
+ <li><a href="#adminclientconfigs">3.7 AdminClient Configs</a>
+ </ul>
+ </li>
+ <li><a href="#design">4. Design</a>
+ <ul>
+ <li><a href="#majordesignelements">4.1 Motivation</a>
+ <li><a href="#persistence">4.2 Persistence</a>
+ <li><a href="#maximizingefficiency">4.3 Efficiency</a>
+ <li><a href="#theproducer">4.4 The Producer</a>
+ <li><a href="#theconsumer">4.5 The Consumer</a>
+ <li><a href="#semantics">4.6 Message Delivery Semantics</a>
+ <li><a href="#replication">4.7 Replication</a>
+ <li><a href="#compaction">4.8 Log Compaction</a>
+ <li><a href="#design_quotas">4.9 Quotas</a>
+ </ul>
+ </li>
+ <li><a href="#implementation">5. Implementation</a>
+ <ul>
+ <li><a href="#networklayer">5.1 Network Layer</a>
+ <li><a href="#messages">5.2 Messages</a>
+ <li><a href="#messageformat">5.3 Message format</a>
+ <li><a href="#log">5.4 Log</a>
+ <li><a href="#distributionimpl">5.5 Distribution</a>
+ </ul>
+ </li>
+ <li><a href="#operations">6. Operations</a>
+ <ul>
+ <li><a href="#basic_ops">6.1 Basic Kafka Operations</a>
+ <ul>
+ <li><a href="#basic_ops_add_topic">Adding and removing topics</a>
+ <li><a href="#basic_ops_modify_topic">Modifying topics</a>
+ <li><a href="#basic_ops_restarting">Graceful shutdown</a>
+ <li><a href="#basic_ops_leader_balancing">Balancing leadership</a>
+ <li><a href="#basic_ops_consumer_lag">Checking consumer position</a>
+ <li><a href="#basic_ops_mirror_maker">Mirroring data between clusters</a>
+ <li><a href="#basic_ops_cluster_expansion">Expanding your cluster</a>
+ <li><a href="#basic_ops_decommissioning_brokers">Decommissioning brokers</a>
+ <li><a href="#basic_ops_increase_replication_factor">Increasing replication factor</a>
+ </ul>
+ <li><a href="#datacenters">6.2 Datacenters</a>
+ <li><a href="#config">6.3 Important Configs</a>
+ <ul>
+ <li><a href="#clientconfig">Important Client Configs</a>
+ <li><a href="#prodconfig">A Production Server Configs</a>
+ </ul>
+ <li><a href="#java">6.4 Java Version</a>
+ <li><a href="#hwandos">6.5 Hardware and OS</a>
+ <ul>
+ <li><a href="#os">OS</a>
+ <li><a href="#diskandfs">Disks and Filesystems</a>
+ <li><a href="#appvsosflush">Application vs OS Flush Management</a>
+ <li><a href="#linuxflush">Linux Flush Behavior</a>
+ <li><a href="#ext4">Ext4 Notes</a>
+ </ul>
+ <li><a href="#monitoring">6.6 Monitoring</a>
+ <li><a href="#zk">6.7 ZooKeeper</a>
+ <ul>
+ <li><a href="#zkversion">Stable Version</a>
+ <li><a href="#zkops">Operationalization</a>
+ </ul>
+ </ul>
+ </li>
+ <li><a href="#security">7. Security</a>
+ <ul>
+ <li><a href="#security_overview">7.1 Security Overview</a></li>
+ <li><a href="#security_ssl">7.2 Encryption and Authentication using SSL</a></li>
+ <li><a href="#security_sasl">7.3 Authentication using SASL</a></li>
+ <li><a href="#security_authz">7.4 Authorization and ACLs</a></li>
+ <li><a href="#security_rolling_upgrade">7.5 Incorporating Security Features in a Running Cluster</a></li>
+ <li><a href="#zk_authz">7.6 ZooKeeper Authentication</a></li>
+ <ul>
+ <li><a href="#zk_authz_new">New Clusters</a></li>
+ <li><a href="#zk_authz_migration">Migrating Clusters</a></li>
+ <li><a href="#zk_authz_ensemble">Migrating the ZooKeeper Ensemble</a></li>
+ </ul>
+ </ul>
+ </li>
+ <li><a href="#connect">8. Kafka Connect</a>
+ <ul>
+ <li><a href="#connect_overview">8.1 Overview</a></li>
+ <li><a href="#connect_user">8.2 User Guide</a></li>
+ <ul>
+ <li><a href="#connect_running">Running Kafka Connect</a></li>
+ <li><a href="#connect_configuring">Configuring Connectors</a></li>
+ <li><a href="#connect_transforms">Transformations</a></li>
+ <li><a href="#connect_rest">REST API</a></li>
+ </ul>
+ <li><a href="#connect_development">8.3 Connector Development Guide</a></li>
+ </ul>
+ </li>
+ <li><a href="/{{version}}/documentation/streams">9. Kafka Streams</a>
+ <ul>
+ <li><a href="/{{version}}/documentation/streams/quickstart">9.1 Play with a Streams Application</a></li>
+ <li><a href="/{{version}}/documentation/streams/tutorial">9.2 Write your own Streams Applications</a></li>
+ <li><a href="/{{version}}/documentation/streams/developer-guide">9.3 Developer Manual</a></li>
+ <li><a href="/{{version}}/documentation/streams/core-concepts">9.4 Core Concepts</a></li>
+ <li><a href="/{{version}}/documentation/streams/architecture">9.5 Architecture</a></li>
+ <li><a href="/{{version}}/documentation/streams/upgrade-guide">9.6 Upgrade Guide</a></li>
+ </ul>
+ </li>
+ </ul>
+
+</script>
+
+<div class="p-toc"></div>
[03/14] kafka-site git commit: Update site for 1.0.0 release
Posted by ij...@apache.org.
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/streams/index.html
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diff --git a/100/streams/index.html b/100/streams/index.html
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+ http://www.apache.org/licenses/LICENSE-2.0
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ -->
+<script>
+ <!--#include virtual="../js/templateData.js" -->
+</script>
+<script id="streams-template" type="text/x-handlebars-template">
+ <h1>Kafka Streams API</h1>
+ <div class="sub-nav-sticky">
+ <div class="sticky-top">
+ <div style="height:35px">
+ <a class="active-menu-item" href="#">Introduction</a>
+ <a href="/{{version}}/documentation/streams/developer-guide">Developers Guide</a>
+ <a href="/{{version}}/documentation/streams/core-concepts">Concepts</a>
+ <a href="/{{version}}/documentation/streams/quickstart">Run Demo App</a>
+ <a href="/{{version}}/documentation/streams/tutorial">Tutorial: Write App</a>
+ </div>
+ </div>
+ </div>
+ <h3 class="streams_intro">The easiest way to write mission-critical real-time applications and microservices</h3>
+ <p class="streams__description">Kafka Streams is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.</p>
+ <div class="video__series__grid">
+ <div class="yt__video__block">
+ <div class="yt__video__inner__block">
+ <iframe class="yt_series video_1 active" style="display:block" src="https://www.youtube.com/embed/Z3JKCLG3VP4?rel=0&showinfo=0&end=602" frameborder="0" allowfullscreen></iframe>
+ <iframe class="yt_series video_2" src="https://www.youtube.com/embed/LxxeXI1mPKo?rel=0&showinfo=0&end=622" frameborder="0" allowfullscreen></iframe>
+ <iframe class="yt_series video_3" src="https://www.youtube.com/embed/7JYEEx7SBuE?rel=0&showinfo=0end=557" frameborder="0" allowfullscreen></iframe>
+ <iframe class="yt_series video_4" src="https://www.youtube.com/embed/3kJgYIkAeHs?rel=0&showinfo=0&end=564" frameborder="0" allowfullscreen></iframe>
+ </div>
+ </div>
+ <div class="video__block">
+ <h3>TOUR OF THE STREAMS API</h3>
+ <div class="video__list">
+ <p class="video__item video_list_1 active" onclick="$('.video__item').removeClass('active'); $(this).addClass('active');$('.yt_series').hide();$('.video_1').show();">
+ <span class="number">1</span><span class="video__text">Intro to Streams</span>
+ </p>
+ <p class="video__item video_list_2" onclick="$('.video__item').removeClass('active'); $(this).addClass('active');$('.yt_series').hide();$('.video_2').show();">
+ <span class="number">2</span><span class="video__text">Creating a Streams Application</span>
+ </p>
+ <p class="video__item video_list_3" onclick="$('.video__item').removeClass('active'); $(this).addClass('active');$('.yt_series').hide();$('.video_3').show();">
+ <span class="number">3</span><span class="video__text">Transforming Data Pt. 1</span>
+ </p>
+ <p class="video__item video_list_4" onclick="$('.video__item').removeClass('active'); $(this).addClass('active');$('.yt_series').hide();$('.video_4').show();">
+ <span class="number">4</span><span class="video__text">Transforming Data Pt. 11</span>
+ </p>
+ </div>
+ </div>
+ </div>
+ <hr class="separator">
+ <div class="use-item-section">
+ <div class="use__list__sec">
+ <h3>Why you'll love using Kafka Streams!</h3>
+ <ul class="use-feature-list">
+ <li>Elastic, highly scalable, fault-tolerant</li>
+ <li>Deploy to containers, VMs, bare metal, cloud</li>
+ <li>Equally viable for small, medium, & large use cases</li>
+ <li>Fully integrated with Kafka security</li>
+ <li>Write standard Java applications</li>
+ <li>Exactly-once processing semantics</li>
+ <li>No seperate processing cluster required</li>
+ <li>Develop on Mac, Linux, Windows</li>
+
+ </ul>
+ </div>
+ <div class="first__app__cta">
+ <a href="/{{version}}/documentation/streams/tutorial" class="first__app__btn">Write your first app</a>
+ </div>
+ </div>
+ <hr class="separator">
+ <h3 class="stream__text">Streams API use cases</h3>
+ <div class="customers__grid">
+ <div class="customer__grid">
+ <div class="customer__item streams_logo_grid streams__ny__grid">
+ <a href="https://www.nytimes.com" target="_blank" class="grid__logo__link">
+ <span class="grid__item__logo" style="background-image: url('/images/powered-by/NYT.jpg');"></span>
+ </a>
+ <p class="grid__item__customer__description">
+ <a href="https://www.confluent.io/blog/publishing-apache-kafka-new-york-times/">The New York Times uses Apache Kafka </a>and the Kafka Streams API to store and distribute, in real-time, published content to the various applications and systems that make it available to the readers.
+ </p>
+ </div>
+ </div>
+ <div class="customer__grid">
+ <div class="customer__item streams_logo_grid streams__line__grid">
+ <a href="https://linecorp.com/" target="_blank" class="grid__logo__link">
+ <span class="grid__item__logo" style="background-image: url('/images/powered-by/line.svg');width:9rem"></span>
+ </a>
+ <p class="grid__item__customer__description">LINE uses Apache Kafka as a central datahub for our services to communicate to one another. Hundreds of billions of messages are produced daily and are used to execute various business logic, threat detection, search indexing and data analysis. LINE leverages Kafka Streams to reliably transform and filter topics enabling sub topics consumers can efficiently consume, meanwhile retaining easy maintainability thanks to its sophisticated yet minimal code base.</p>
+ </div>
+ </div>
+ <div class="customer__grid">
+ <div class="customer__item streams_logo_grid streams__zalando__grid">
+ <a href="http://www.zalando.com" target="_blank" class="grid__logo__link">
+ <span class="grid__item__logo" style="background-image: url('/images/powered-by/zalando.jpg');"></span>
+ </a>
+ <p class="grid__item__customer__description">As the leading online fashion retailer in Europe, Zalando uses Kafka as an ESB (Enterprise Service Bus), which helps us in transitioning from a monolithic to a micro services architecture. Using Kafka for processing
+ <a href="" 'https:="" kafka-summit.org="" sessions="" using-kstreams-ktables-calculate-real-time-domain-rankings="" '="" target="blank'"> event streams</a> enables our technical team to do near-real time business intelligence.
+ </p>
+ </div>
+ </div>
+ <div class="customer__grid">
+ <div class="customer__item streams_logo_grid streams__rabobank__grid">
+ <a href="https://www.rabobank.com" target="_blank" class="grid__logo__link">
+ <span class="grid__item__logo" style="background-image: url('/images/powered-by/rabobank.jpg');"></span>
+ </a>
+ <p class="grid__item__customer__description">Rabobank is one of the 3 largest banks in the Netherlands. Its digital nervous system, the Business Event Bus, is powered by Apache Kafka. It is used by an increasing amount of financial processes and services, one of which is Rabo Alerts. This service alerts customers in real-time upon financial events and is built using Kafka Streams.</p>
+ </div>
+ </div>
+ </div>
+ <h3 style="margin-top: 5.3rem;">Hello Kafka Streams</h3>
+ <p>The code example below implements a WordCount application that is elastic, highly scalable, fault-tolerant, stateful, and ready to run in production at large scale</p>
+
+ <div class="code-example">
+ <div class="btn-group">
+ <a class="selected b-java-8" data-section="java-8">Java 8+</a>
+ <a class="b-java-7" data-section="java-7">Java 7</a>
+ <a class="b-scala" data-section="scala">Scala</a>
+ </div>
+
+ <div class="code-example__snippet b-java-8 selected">
+ <pre class="brush: java;">
+ import org.apache.kafka.common.serialization.Serdes;
+ import org.apache.kafka.streams.KafkaStreams;
+ import org.apache.kafka.streams.StreamsConfig;
+ import org.apache.kafka.streams.kstream.KStream;
+ import org.apache.kafka.streams.kstream.KStreamBuilder;
+ import org.apache.kafka.streams.kstream.KTable;
+
+ import java.util.Arrays;
+ import java.util.Properties;
+
+ public class WordCountApplication {
+
+ public static void main(final String[] args) throws Exception {
+ Properties config = new Properties();
+ config.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-application");
+ config.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker1:9092");
+ config.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
+ config.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
+
+ KStreamBuilder builder = new KStreamBuilder();
+ KStream<String, String> textLines = builder.stream("TextLinesTopic");
+ KTable<String, Long> wordCounts = textLines
+ .flatMapValues(textLine -> Arrays.asList(textLine.toLowerCase().split("\\W+")))
+ .groupBy((key, word) -> word)
+ .count("Counts");
+ wordCounts.to(Serdes.String(), Serdes.Long(), "WordsWithCountsTopic");
+
+ KafkaStreams streams = new KafkaStreams(builder, config);
+ streams.start();
+ }
+
+ }
+ </pre>
+ </div>
+
+ <div class="code-example__snippet b-java-7">
+ <pre class="brush: java;">
+ import org.apache.kafka.common.serialization.Serdes;
+ import org.apache.kafka.streams.KafkaStreams;
+ import org.apache.kafka.streams.StreamsConfig;
+ import org.apache.kafka.streams.kstream.KStream;
+ import org.apache.kafka.streams.kstream.KStreamBuilder;
+ import org.apache.kafka.streams.kstream.KTable;
+ import org.apache.kafka.streams.kstream.KeyValueMapper;
+ import org.apache.kafka.streams.kstream.ValueMapper;
+
+ import java.util.Arrays;
+ import java.util.Properties;
+
+ public class WordCountApplication {
+
+ public static void main(final String[] args) throws Exception {
+ Properties config = new Properties();
+ config.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-application");
+ config.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker1:9092");
+ config.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
+ config.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
+
+ KStreamBuilder builder = new KStreamBuilder();
+ KStream<String, String> textLines = builder.stream("TextLinesTopic");
+ KTable<String, Long> wordCounts = textLines
+ .flatMapValues(new ValueMapper<String, Iterable<String>>() {
+ @Override
+ public Iterable<String> apply(String textLine) {
+ return Arrays.asList(textLine.toLowerCase().split("\\W+"));
+ }
+ })
+ .groupBy(new KeyValueMapper<String, String, String>() {
+ @Override
+ public String apply(String key, String word) {
+ return word;
+ }
+ })
+ .count("Counts");
+ wordCounts.to(Serdes.String(), Serdes.Long(), "WordsWithCountsTopic");
+
+ KafkaStreams streams = new KafkaStreams(builder, config);
+ streams.start();
+ }
+
+ }
+ </pre>
+ </div>
+
+ <div class="code-example__snippet b-scala">
+ <pre class="brush: scala;">
+ import java.lang.Long
+ import java.util.Properties
+ import java.util.concurrent.TimeUnit
+
+ import org.apache.kafka.common.serialization._
+ import org.apache.kafka.streams._
+ import org.apache.kafka.streams.kstream.{KStream, KStreamBuilder, KTable}
+
+ import scala.collection.JavaConverters.asJavaIterableConverter
+
+ object WordCountApplication {
+
+ def main(args: Array[String]) {
+ val config: Properties = {
+ val p = new Properties()
+ p.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-application")
+ p.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker1:9092")
+ p.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass)
+ p.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass)
+ p
+ }
+
+ val builder: KStreamBuilder = new KStreamBuilder()
+ val textLines: KStream[String, String] = builder.stream("TextLinesTopic")
+ val wordCounts: KTable[String, Long] = textLines
+ .flatMapValues(textLine => textLine.toLowerCase.split("\\W+").toIterable.asJava)
+ .groupBy((_, word) => word)
+ .count("Counts")
+ wordCounts.to(Serdes.String(), Serdes.Long(), "WordsWithCountsTopic")
+
+ val streams: KafkaStreams = new KafkaStreams(builder, config)
+ streams.start()
+
+ Runtime.getRuntime.addShutdownHook(new Thread(() => {
+ streams.close(10, TimeUnit.SECONDS)
+ }))
+ }
+
+ }
+ </pre>
+ </div>
+ </div>
+
+ <div class="pagination">
+ <a href="#" class="pagination__btn pagination__btn__prev pagination__btn--disabled">Previous</a>
+ <a href="/{{version}}/documentation/streams/quickstart" class="pagination__btn pagination__btn__next">Next</a>
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+
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\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/cb024c13/100/streams/quickstart.html
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diff --git a/100/streams/quickstart.html b/100/streams/quickstart.html
new file mode 100644
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--- /dev/null
+++ b/100/streams/quickstart.html
@@ -0,0 +1,361 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+<script><!--#include virtual="../js/templateData.js" --></script>
+
+<script id="content-template" type="text/x-handlebars-template">
+
+ <h1>Run Streams Demo Application</h1>
+
+<p>
+ This tutorial assumes you are starting fresh and have no existing Kafka or ZooKeeper data. However, if you have already started Kafka and
+ ZooKeeper, feel free to skip the first two steps.
+</p>
+
+ <p>
+ Kafka Streams is a client library for building mission-critical real-time applications and microservices,
+ where the input and/or output data is stored in Kafka clusters. Kafka Streams combines the simplicity of
+ writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's
+ server-side cluster technology to make these applications highly scalable, elastic, fault-tolerant, distributed,
+ and much more.
+ </p>
+ <p>
+This quickstart example will demonstrate how to run a streaming application coded in this library. Here is the gist
+of the <code><a href="https://github.com/apache/kafka/blob/{{dotVersion}}/streams/examples/src/main/java/org/apache/kafka/streams/examples/wordcount/WordCountDemo.java">WordCountDemo</a></code> example code (converted to use Java 8 lambda expressions for easy reading).
+</p>
+<pre class="brush: java;">
+// Serializers/deserializers (serde) for String and Long types
+final Serde<String> stringSerde = Serdes.String();
+final Serde<Long> longSerde = Serdes.Long();
+
+// Construct a `KStream` from the input topic "streams-plaintext-input", where message values
+// represent lines of text (for the sake of this example, we ignore whatever may be stored
+// in the message keys).
+KStream<String, String> textLines = builder.stream("streams-plaintext-input",
+ Consumed.with(stringSerde, stringSerde);
+
+KTable<String, Long> wordCounts = textLines
+ // Split each text line, by whitespace, into words.
+ .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
+
+ // Group the text words as message keys
+ .groupBy((key, value) -> value)
+
+ // Count the occurrences of each word (message key).
+ .count()
+
+// Store the running counts as a changelog stream to the output topic.
+wordCounts.toStream().to("streams-wordcount-output", Produced.with(Serdes.String(), Serdes.Long()));
+</pre>
+
+<p>
+It implements the WordCount
+algorithm, which computes a word occurrence histogram from the input text. However, unlike other WordCount examples
+you might have seen before that operate on bounded data, the WordCount demo application behaves slightly differently because it is
+designed to operate on an <b>infinite, unbounded stream</b> of data. Similar to the bounded variant, it is a stateful algorithm that
+tracks and updates the counts of words. However, since it must assume potentially
+unbounded input data, it will periodically output its current state and results while continuing to process more data
+because it cannot know when it has processed "all" the input data.
+</p>
+<p>
+ As the first step, we will start Kafka (unless you already have it started) and then we will
+ prepare input data to a Kafka topic, which will subsequently be processed by a Kafka Streams application.
+</p>
+
+<h4><a id="quickstart_streams_download" href="#quickstart_streams_download">Step 1: Download the code</a></h4>
+
+<a href="https://www.apache.org/dyn/closer.cgi?path=/kafka/{{fullDotVersion}}/kafka_{{scalaVersion}}-{{fullDotVersion}}.tgz" title="Kafka downloads">Download</a> the {{fullDotVersion}} release and un-tar it.
+Note that there are multiple downloadable Scala versions and we choose to use the recommended version ({{scalaVersion}}) here:
+
+<pre class="brush: bash;">
+> tar -xzf kafka_{{scalaVersion}}-{{fullDotVersion}}.tgz
+> cd kafka_{{scalaVersion}}-{{fullDotVersion}}
+</pre>
+
+<h4><a id="quickstart_streams_startserver" href="#quickstart_streams_startserver">Step 2: Start the Kafka server</a></h4>
+
+<p>
+Kafka uses <a href="https://zookeeper.apache.org/">ZooKeeper</a> so you need to first start a ZooKeeper server if you don't already have one. You can use the convenience script packaged with kafka to get a quick-and-dirty single-node ZooKeeper instance.
+</p>
+
+<pre class="brush: bash;">
+> bin/zookeeper-server-start.sh config/zookeeper.properties
+[2013-04-22 15:01:37,495] INFO Reading configuration from: config/zookeeper.properties (org.apache.zookeeper.server.quorum.QuorumPeerConfig)
+...
+</pre>
+
+<p>Now start the Kafka server:</p>
+<pre class="brush: bash;">
+> bin/kafka-server-start.sh config/server.properties
+[2013-04-22 15:01:47,028] INFO Verifying properties (kafka.utils.VerifiableProperties)
+[2013-04-22 15:01:47,051] INFO Property socket.send.buffer.bytes is overridden to 1048576 (kafka.utils.VerifiableProperties)
+...
+</pre>
+
+
+<h4><a id="quickstart_streams_prepare" href="#quickstart_streams_prepare">Step 3: Prepare input topic and start Kafka producer</a></h4>
+
+<!--
+
+<pre class="brush: bash;">
+> echo -e "all streams lead to kafka\nhello kafka streams\njoin kafka summit" > file-input.txt
+</pre>
+Or on Windows:
+<pre class="brush: bash;">
+> echo all streams lead to kafka> file-input.txt
+> echo hello kafka streams>> file-input.txt
+> echo|set /p=join kafka summit>> file-input.txt
+</pre>
+
+-->
+
+Next, we create the input topic named <b>streams-plaintext-input</b> and the output topic named <b>streams-wordcount-output</b>:
+
+<pre class="brush: bash;">
+> bin/kafka-topics.sh --create \
+ --zookeeper localhost:2181 \
+ --replication-factor 1 \
+ --partitions 1 \
+ --topic streams-plaintext-input
+Created topic "streams-plaintext-input".
+</pre>
+
+Note: we create the output topic with compaction enabled because the output stream is a changelog stream
+(cf. <a href="#anchor-changelog-output">explanation of application output</a> below).
+
+<pre class="brush: bash;">
+> bin/kafka-topics.sh --create \
+ --zookeeper localhost:2181 \
+ --replication-factor 1 \
+ --partitions 1 \
+ --topic streams-wordcount-output \
+ --config cleanup.policy=compact
+Created topic "streams-wordcount-output".
+</pre>
+
+The created topic can be described with the same <b>kafka-topics</b> tool:
+
+<pre class="brush: bash;">
+> bin/kafka-topics.sh --zookeeper localhost:2181 --describe
+
+Topic:streams-plaintext-input PartitionCount:1 ReplicationFactor:1 Configs:
+ Topic: streams-plaintext-input Partition: 0 Leader: 0 Replicas: 0 Isr: 0
+Topic:streams-wordcount-output PartitionCount:1 ReplicationFactor:1 Configs:
+ Topic: streams-wordcount-output Partition: 0 Leader: 0 Replicas: 0 Isr: 0
+</pre>
+
+<h4><a id="quickstart_streams_start" href="#quickstart_streams_start">Step 4: Start the Wordcount Application</a></h4>
+
+The following command starts the WordCount demo application:
+
+<pre class="brush: bash;">
+> bin/kafka-run-class.sh org.apache.kafka.streams.examples.wordcount.WordCountDemo
+</pre>
+
+<p>
+The demo application will read from the input topic <b>streams-plaintext-input</b>, perform the computations of the WordCount algorithm on each of the read messages,
+and continuously write its current results to the output topic <b>streams-wordcount-output</b>.
+Hence there won't be any STDOUT output except log entries as the results are written back into in Kafka.
+</p>
+
+Now we can start the console producer in a separate terminal to write some input data to this topic:
+
+<pre class="brush: bash;">
+> bin/kafka-console-producer.sh --broker-list localhost:9092 --topic streams-plaintext-input
+</pre>
+
+and inspect the output of the WordCount demo application by reading from its output topic with the console consumer in a separate terminal:
+
+<pre class="brush: bash;">
+> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 \
+ --topic streams-wordcount-output \
+ --from-beginning \
+ --formatter kafka.tools.DefaultMessageFormatter \
+ --property print.key=true \
+ --property print.value=true \
+ --property key.deserializer=org.apache.kafka.common.serialization.StringDeserializer \
+ --property value.deserializer=org.apache.kafka.common.serialization.LongDeserializer
+</pre>
+
+
+<h4><a id="quickstart_streams_process" href="#quickstart_streams_process">Step 5: Process some data</a></h4>
+
+Now let's write some message with the console producer into the input topic <b>streams-plaintext-input</b> by entering a single line of text and then hit <RETURN>.
+This will send a new message to the input topic, where the message key is null and the message value is the string encoded text line that you just entered
+(in practice, input data for applications will typically be streaming continuously into Kafka, rather than being manually entered as we do in this quickstart):
+
+<pre class="brush: bash;">
+> bin/kafka-console-producer.sh --broker-list localhost:9092 --topic streams-plaintext-input
+all streams lead to kafka
+</pre>
+
+<p>
+This message will be processed by the Wordcount application and the following output data will be written to the <b>streams-wordcount-output</b> topic and printed by the console consumer:
+</p>
+
+<pre class="brush: bash;">
+> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092
+ --topic streams-wordcount-output \
+ --from-beginning \
+ --formatter kafka.tools.DefaultMessageFormatter \
+ --property print.key=true \
+ --property print.value=true \
+ --property key.deserializer=org.apache.kafka.common.serialization.StringDeserializer \
+ --property value.deserializer=org.apache.kafka.common.serialization.LongDeserializer
+
+all 1
+streams 1
+lead 1
+to 1
+kafka 1
+</pre>
+
+<p>
+Here, the first column is the Kafka message key in <code>java.lang.String</code> format and represents a word that is being counted, and the second column is the message value in <code>java.lang.Long</code>format, representing the word's latest count.
+</p>
+
+Now let's continue writing one more message with the console producer into the input topic <b>streams-plaintext-input</b>.
+Enter the text line "hello kafka streams" and hit <RETURN>.
+Your terminal should look as follows:
+
+<pre class="brush: bash;">
+> bin/kafka-console-producer.sh --broker-list localhost:9092 --topic streams-plaintext-input
+all streams lead to kafka
+hello kafka streams
+</pre>
+
+In your other terminal in which the console consumer is running, you will observe that the WordCount application wrote new output data:
+
+<pre class="brush: bash;">
+> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092
+ --topic streams-wordcount-output \
+ --from-beginning \
+ --formatter kafka.tools.DefaultMessageFormatter \
+ --property print.key=true \
+ --property print.value=true \
+ --property key.deserializer=org.apache.kafka.common.serialization.StringDeserializer \
+ --property value.deserializer=org.apache.kafka.common.serialization.LongDeserializer
+
+all 1
+streams 1
+lead 1
+to 1
+kafka 1
+hello 1
+kafka 2
+streams 2
+</pre>
+
+Here the last printed lines <b>kafka 2</b> and <b>streams 2</b> indicate updates to the keys <b>kafka</b> and <b>streams</b> whose counts have been incremented from <b>1</b> to <b>2</b>.
+Whenever you write further input messages to the input topic, you will observe new messages being added to the <b>streams-wordcount-output</b> topic,
+representing the most recent word counts as computed by the WordCount application.
+Let's enter one final input text line "join kafka summit" and hit <RETURN> in the console producer to the input topic <b>streams-wordcount-input</b> before we wrap up this quickstart:
+
+<pre class="brush: bash;">
+> bin/kafka-console-producer.sh --broker-list localhost:9092 --topic streams-wordcount-input
+all streams lead to kafka
+hello kafka streams
+join kafka summit
+</pre>
+
+<a name="anchor-changelog-output"></a>
+The <b>streams-wordcount-output</b> topic will subsequently show the corresponding updated word counts (see last three lines):
+
+<pre class="brush: bash;">
+> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092
+ --topic streams-wordcount-output \
+ --from-beginning \
+ --formatter kafka.tools.DefaultMessageFormatter \
+ --property print.key=true \
+ --property print.value=true \
+ --property key.deserializer=org.apache.kafka.common.serialization.StringDeserializer \
+ --property value.deserializer=org.apache.kafka.common.serialization.LongDeserializer
+
+all 1
+streams 1
+lead 1
+to 1
+kafka 1
+hello 1
+kafka 2
+streams 2
+join 1
+kafka 3
+summit 1
+</pre>
+
+As one can see, outputs of the Wordcount application is actually a continuous stream of updates, where each output record (i.e. each line in the original output above) is
+an updated count of a single word, aka record key such as "kafka". For multiple records with the same key, each later record is an update of the previous one.
+
+<p>
+The two diagrams below illustrate what is essentially happening behind the scenes.
+The first column shows the evolution of the current state of the <code>KTable<String, Long></code> that is counting word occurrences for <code>count</code>.
+The second column shows the change records that result from state updates to the KTable and that are being sent to the output Kafka topic <b>streams-wordcount-output</b>.
+</p>
+
+<img src="/{{version}}/images/streams-table-updates-02.png" style="float: right; width: 25%;">
+<img src="/{{version}}/images/streams-table-updates-01.png" style="float: right; width: 25%;">
+
+<p>
+First the text line "all streams lead to kafka" is being processed.
+The <code>KTable</code> is being built up as each new word results in a new table entry (highlighted with a green background), and a corresponding change record is sent to the downstream <code>KStream</code>.
+</p>
+<p>
+When the second text line "hello kafka streams" is processed, we observe, for the first time, that existing entries in the <code>KTable</code> are being updated (here: for the words "kafka" and for "streams"). And again, change records are being sent to the output topic.
+</p>
+<p>
+And so on (we skip the illustration of how the third line is being processed). This explains why the output topic has the contents we showed above, because it contains the full record of changes.
+</p>
+
+<p>
+Looking beyond the scope of this concrete example, what Kafka Streams is doing here is to leverage the duality between a table and a changelog stream (here: table = the KTable, changelog stream = the downstream KStream): you can publish every change of the table to a stream, and if you consume the entire changelog stream from beginning to end, you can reconstruct the contents of the table.
+</p>
+
+<h4><a id="quickstart_streams_stop" href="#quickstart_streams_stop">Step 6: Teardown the application</a></h4>
+
+<p>You can now stop the console consumer, the console producer, the Wordcount application, the Kafka broker and the ZooKeeper server in order via <b>Ctrl-C</b>.</p>
+
+ <div class="pagination">
+ <a href="/{{version}}/documentation/streams" class="pagination__btn pagination__btn__prev">Previous</a>
+ <a href="/{{version}}/documentation/streams/tutorial" class="pagination__btn pagination__btn__next">Next</a>
+ </div>
+</script>
+
+<div class="p-quickstart-streams"></div>
+
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+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+<script><!--#include virtual="../js/templateData.js" --></script>
+
+<script id="content-template" type="text/x-handlebars-template">
+ <h1>Tutorial: Write a Streams Application</h1>
+
+ <p>
+ In this guide we will start from scratch on setting up your own project to write a stream processing application using Kafka Streams.
+ It is highly recommended to read the <a href="/{{version}}/documentation/streams/quickstart">quickstart</a> first on how to run a Streams application written in Kafka Streams if you have not done so.
+ </p>
+
+ <h4><a id="tutorial_maven_setup" href="#tutorial_maven_setup">Setting up a Maven Project</a></h4>
+
+ <p>
+ We are going to use a Kafka Streams Maven Archetype for creating a Streams project structure with the following commands:
+ </p>
+
+ <pre class="brush: bash;">
+ mvn archetype:generate \
+ -DarchetypeGroupId=org.apache.kafka \
+ -DarchetypeArtifactId=streams-quickstart-java \
+ -DarchetypeVersion={{fullDotVersion}} \
+ -DgroupId=streams.examples \
+ -DartifactId=streams.examples \
+ -Dversion=0.1 \
+ -Dpackage=myapps
+ </pre>
+
+ <p>
+ You can use a different value for <code>groupId</code>, <code>artifactId</code> and <code>package</code> parameters if you like.
+ Assuming the above parameter values are used, this command will create a project structure that looks like this:
+ </p>
+
+ <pre class="brush: bash;">
+ > tree streams.examples
+ streams-quickstart
+ |-- pom.xml
+ |-- src
+ |-- main
+ |-- java
+ | |-- myapps
+ | |-- LineSplit.java
+ | |-- Pipe.java
+ | |-- WordCount.java
+ |-- resources
+ |-- log4j.properties
+ </pre>
+
+ <p>
+ The <code>pom.xml</code> file included in the project already has the Streams dependency defined,
+ and there are already several example programs written with Streams library under <code>src/main/java</code>.
+ Since we are going to start writing such programs from scratch, we can now delete these examples:
+ </p>
+
+ <pre class="brush: bash;">
+ > cd streams-quickstart
+ > rm src/main/java/myapps/*.java
+ </pre>
+
+ <h4><a id="tutorial_code_pipe" href="#tutorial_code_pipe">Writing a first Streams application: Pipe</a></h4>
+
+ It's coding time now! Feel free to open your favorite IDE and import this Maven project, or simply open a text editor and create a java file under <code>src/main/java</code>.
+ Let's name it <code>Pipe.java</code>:
+
+ <pre class="brush: java;">
+ package myapps;
+
+ public class Pipe {
+
+ public static void main(String[] args) throws Exception {
+
+ }
+ }
+ </pre>
+
+ <p>
+ We are going to fill in the <code>main</code> function to write this pipe program. Note that we will not list the import statements as we go since IDEs can usually add them automatically.
+ However if you are using a text editor you need to manually add the imports, and at the end of this section we'll show the complete code snippet with import statement for you.
+ </p>
+
+ <p>
+ The first step to write a Streams application is to create a <code>java.util.Properties</code> map to specify different Streams execution configuration values as defined in <code>StreamsConfig</code>.
+ A couple of important configuration values you need to set are: <code>StreamsConfig.BOOTSTRAP_SERVERS_CONFIG</code>, which specifies a list of host/port pairs to use for establishing the initial connection to the Kafka cluster,
+ and <code>StreamsConfig.APPLICATION_ID_CONFIG</code>, which gives the unique identifier of your Streams application to distinguish itself with other applications talking to the same Kafka cluster:
+ </p>
+
+ <pre class="brush: java;">
+ Properties props = new Properties();
+ props.put(StreamsConfig.APPLICATION_ID_CONFIG, "streams-pipe");
+ props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092"); // assuming that the Kafka broker this application is talking to runs on local machine with port 9092
+ </pre>
+
+ <p>
+ In addition, you can customize other configurations in the same map, for example, default serialization and deserialization libraries for the record key-value pairs:
+ </p>
+
+ <pre class="brush: java;">
+ props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
+ props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
+ </pre>
+
+ <p>
+ For a full list of configurations of Kafka Streams please refer to this <a href="/{{version}}/documentation/#streamsconfigs">table</a>.
+ </p>
+
+ <p>
+ Next we will define the computational logic of our Streams application.
+ In Kafka Streams this computational logic is defined as a <code>topology</code> of connected processor nodes.
+ We can use a topology builder to construct such a topology,
+ </p>
+
+ <pre class="brush: java;">
+ final StreamsBuilder builder = new StreamsBuilder();
+ </pre>
+
+ <p>
+ And then create a source stream from a Kafka topic named <code>streams-plaintext-input</code> using this topology builder:
+ </p>
+
+ <pre class="brush: java;">
+ KStream<String, String> source = builder.stream("streams-plaintext-input");
+ </pre>
+
+ <p>
+ Now we get a <code>KStream</code> that is continuously generating records from its source Kafka topic <code>streams-plaintext-input</code>.
+ The records are organized as <code>String</code> typed key-value pairs.
+ The simplest thing we can do with this stream is to write it into another Kafka topic, say it's named <code>streams-pipe-output</code>:
+ </p>
+
+ <pre class="brush: java;">
+ source.to("streams-pipe-output");
+ </pre>
+
+ <p>
+ Note that we can also concatenate the above two lines into a single line as:
+ </p>
+
+ <pre class="brush: java;">
+ builder.stream("streams-plaintext-input").to("streams-pipe-output");
+ </pre>
+
+ <p>
+ We can inspect what kind of <code>topology</code> is created from this builder by doing the following:
+ </p>
+
+ <pre class="brush: java;">
+ final Topology topology = builder.build();
+ </pre>
+
+ <p>
+ And print its description to standard output as:
+ </p>
+
+ <pre class="brush: java;">
+ System.out.println(topology.describe());
+ </pre>
+
+ <p>
+ If we just stop here, compile and run the program, it will output the following information:
+ </p>
+
+ <pre class="brush: bash;">
+ > mvn clean package
+ > mvn exec:java -Dexec.mainClass=myapps.Pipe
+ Sub-topologies:
+ Sub-topology: 0
+ Source: KSTREAM-SOURCE-0000000000(topics: streams-plaintext-input) --> KSTREAM-SINK-0000000001
+ Sink: KSTREAM-SINK-0000000001(topic: streams-pipe-output) <-- KSTREAM-SOURCE-0000000000
+ Global Stores:
+ none
+ </pre>
+
+ <p>
+ As shown above, it illustrates that the constructed topology has two processor nodes, a source node <code>KSTREAM-SOURCE-0000000000</code> and a sink node <code>KSTREAM-SINK-0000000001</code>.
+ <code>KSTREAM-SOURCE-0000000000</code> continuously read records from Kafka topic <code>streams-plaintext-input</code> and pipe them to its downstream node <code>KSTREAM-SINK-0000000001</code>;
+ <code>KSTREAM-SINK-0000000001</code> will write each of its received record in order to another Kafka topic <code>streams-pipe-output</code>
+ (the <code>--></code> and <code><--</code> arrows dictates the downstream and upstream processor nodes of this node, i.e. "children" and "parents" within the topology graph).
+ It also illustrates that this simple topology has no global state stores associated with it (we will talk about state stores more in the following sections).
+ </p>
+
+ <p>
+ Note that we can always describe the topology as we did above at any given point while we are building it in the code, so as a user you can interactively "try and taste" your computational logic defined in the topology until you are happy with it.
+ Suppose we are already done with this simple topology that just pipes data from one Kafka topic to another in an endless streaming manner,
+ we can now construct the Streams client with the two components we have just constructed above: the configuration map and the topology object
+ (one can also construct a <code>StreamsConfig</code> object from the <code>props</code> map and then pass that object to the constructor,
+ <code>KafkaStreams</code> have overloaded constructor functions to takes either type).
+ </p>
+
+ <pre class="brush: java;">
+ final KafkaStreams streams = new KafkaStreams(topology, props);
+ </pre>
+
+ <p>
+ By calling its <code>start()</code> function we can trigger the execution of this client.
+ The execution won't stop until <code>close()</code> is called on this client.
+ We can, for example, add a shutdown hook with a countdown latch to capture a user interrupt and close the client upon terminating this program:
+ </p>
+
+ <pre class="brush: java;">
+ final CountDownLatch latch = new CountDownLatch(1);
+
+ // attach shutdown handler to catch control-c
+ Runtime.getRuntime().addShutdownHook(new Thread("streams-shutdown-hook") {
+ @Override
+ public void run() {
+ streams.close();
+ latch.countDown();
+ }
+ });
+
+ try {
+ streams.start();
+ latch.await();
+ } catch (Throwable e) {
+ System.exit(1);
+ }
+ System.exit(0);
+ </pre>
+
+ <p>
+ The complete code so far looks like this:
+ </p>
+
+ <pre class="brush: java;">
+ package myapps;
+
+ import org.apache.kafka.common.serialization.Serdes;
+ import org.apache.kafka.streams.KafkaStreams;
+ import org.apache.kafka.streams.StreamsBuilder;
+ import org.apache.kafka.streams.StreamsConfig;
+ import org.apache.kafka.streams.Topology;
+
+ import java.util.Properties;
+ import java.util.concurrent.CountDownLatch;
+
+ public class Pipe {
+
+ public static void main(String[] args) throws Exception {
+ Properties props = new Properties();
+ props.put(StreamsConfig.APPLICATION_ID_CONFIG, "streams-pipe");
+ props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
+ props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
+ props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
+
+ final StreamsBuilder builder = new StreamsBuilder();
+
+ builder.stream("streams-plaintext-input").to("streams-pipe-output");
+
+ final Topology topology = builder.build();
+
+ final KafkaStreams streams = new KafkaStreams(topology, props);
+ final CountDownLatch latch = new CountDownLatch(1);
+
+ // attach shutdown handler to catch control-c
+ Runtime.getRuntime().addShutdownHook(new Thread("streams-shutdown-hook") {
+ @Override
+ public void run() {
+ streams.close();
+ latch.countDown();
+ }
+ });
+
+ try {
+ streams.start();
+ latch.await();
+ } catch (Throwable e) {
+ System.exit(1);
+ }
+ System.exit(0);
+ }
+ }
+ </pre>
+
+ <p>
+ If you already have the Kafka broker up and running at <code>localhost:9092</code>,
+ and the topics <code>streams-plaintext-input</code> and <code>streams-pipe-output</code> created on that broker,
+ you can run this code in your IDE or on the command line, using Maven:
+ </p>
+
+ <pre class="brush: brush;">
+ > mvn clean package
+ > mvn exec:java -Dexec.mainClass=myapps.Pipe
+ </pre>
+
+ <p>
+ For detailed instructions on how to run a Streams application and observe its computing results,
+ please read the <a href="/{{version}}/documentation/streams/quickstart">Play with a Streams Application</a> section.
+ We will not talk about this in the rest of this section.
+ </p>
+
+ <h4><a id="tutorial_code_linesplit" href="#tutorial_code_linesplit">Writing a second Streams application: Line Split</a></h4>
+
+ <p>
+ We have learned how to construct a Streams client with its two key components: the <code>StreamsConfig</code> and <code>Topology</code>.
+ Now let's move on to add some real processing logic by augmenting the current topology.
+ We can first create another program by first copy the existing <code>Pipe.java</code> class:
+ </p>
+
+ <pre class="brush: brush;">
+ > cp src/main/java/myapps/Pipe.java src/main/java/myapps/LineSplit.java
+ </pre>
+
+ <p>
+ And change its class name as well as the application id config to distinguish with the original program:
+ </p>
+
+ <pre class="brush: java;">
+ public class LineSplit {
+
+ public static void main(String[] args) throws Exception {
+ Properties props = new Properties();
+ props.put(StreamsConfig.APPLICATION_ID_CONFIG, "streams-linesplit");
+ // ...
+ }
+ }
+ </pre>
+
+ <p>
+ Since each of the source stream's record is a <code>String</code> typed key-value pair,
+ let's treat the value string as a text line and split it into words with a <code>FlatMapValues</code> operator:
+ </p>
+
+ <pre class="brush: java;">
+ KStream<String, String> source = builder.stream("streams-plaintext-input");
+ KStream<String, String> words = source.flatMapValues(new ValueMapper<String, Iterable<String>>() {
+ @Override
+ public Iterable<String> apply(String value) {
+ return Arrays.asList(value.split("\\W+"));
+ }
+ });
+ </pre>
+
+ <p>
+ The operator will take the <code>source</code> stream as its input, and generate a new stream named <code>words</code>
+ by processing each record from its source stream in order and breaking its value string into a list of words, and producing
+ each word as a new record to the output <code>words</code> stream.
+ This is a stateless operator that does not need to keep track of any previously received records or processed results.
+ Note if you are using JDK 8 you can use lambda expression and simplify the above code as:
+ </p>
+
+ <pre class="brush: java;">
+ KStream<String, String> source = builder.stream("streams-plaintext-input");
+ KStream<String, String> words = source.flatMapValues(value -> Arrays.asList(value.split("\\W+")));
+ </pre>
+
+ <p>
+ And finally we can write the word stream back into another Kafka topic, say <code>streams-linesplit-output</code>.
+ Again, these two steps can be concatenated as the following (assuming lambda expression is used):
+ </p>
+
+ <pre class="brush: java;">
+ KStream<String, String> source = builder.stream("streams-plaintext-input");
+ source.flatMapValues(value -> Arrays.asList(value.split("\\W+")))
+ .to("streams-linesplit-output");
+ </pre>
+
+ <p>
+ If we now describe this augmented topology as <code>System.out.println(topology.describe())</code>, we will get the following:
+ </p>
+
+ <pre class="brush: bash;">
+ > mvn clean package
+ > mvn exec:java -Dexec.mainClass=myapps.LineSplit
+ Sub-topologies:
+ Sub-topology: 0
+ Source: KSTREAM-SOURCE-0000000000(topics: streams-plaintext-input) --> KSTREAM-FLATMAPVALUES-0000000001
+ Processor: KSTREAM-FLATMAPVALUES-0000000001(stores: []) --> KSTREAM-SINK-0000000002 <-- KSTREAM-SOURCE-0000000000
+ Sink: KSTREAM-SINK-0000000002(topic: streams-linesplit-output) <-- KSTREAM-FLATMAPVALUES-0000000001
+ Global Stores:
+ none
+ </pre>
+
+ <p>
+ As we can see above, a new processor node <code>KSTREAM-FLATMAPVALUES-0000000001</code> is injected into the topology between the original source and sink nodes.
+ It takes the source node as its parent and the sink node as its child.
+ In other words, each record fetched by the source node will first traverse to the newly added <code>KSTREAM-FLATMAPVALUES-0000000001</code> node to be processed,
+ and one or more new records will be generated as a result. They will continue traverse down to the sink node to be written back to Kafka.
+ Note this processor node is "stateless" as it is not associated with any stores (i.e. <code>(stores: [])</code>).
+ </p>
+
+ <p>
+ The complete code looks like this (assuming lambda expression is used):
+ </p>
+
+ <pre class="brush: java;">
+ package myapps;
+
+ import org.apache.kafka.common.serialization.Serdes;
+ import org.apache.kafka.streams.KafkaStreams;
+ import org.apache.kafka.streams.StreamsBuilder;
+ import org.apache.kafka.streams.StreamsConfig;
+ import org.apache.kafka.streams.Topology;
+ import org.apache.kafka.streams.kstream.KStream;
+
+ import java.util.Arrays;
+ import java.util.Properties;
+ import java.util.concurrent.CountDownLatch;
+
+ public class LineSplit {
+
+ public static void main(String[] args) throws Exception {
+ Properties props = new Properties();
+ props.put(StreamsConfig.APPLICATION_ID_CONFIG, "streams-linesplit");
+ props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
+ props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
+ props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
+
+ final StreamsBuilder builder = new StreamsBuilder();
+
+ KStream<String, String> source = builder.stream("streams-plaintext-input");
+ source.flatMapValues(value -> Arrays.asList(value.split("\\W+")))
+ .to("streams-linesplit-output");
+
+ final Topology topology = builder.build();
+ final KafkaStreams streams = new KafkaStreams(topology, props);
+ final CountDownLatch latch = new CountDownLatch(1);
+
+ // ... same as Pipe.java above
+ }
+ }
+ </pre>
+
+ <h4><a id="tutorial_code_wordcount" href="#tutorial_code_wordcount">Writing a third Streams application: Wordcount</a></h4>
+
+ <p>
+ Let's now take a step further to add some "stateful" computations to the topology by counting the occurrence of the words split from the source text stream.
+ Following similar steps let's create another program based on the <code>LineSplit.java</code> class:
+ </p>
+
+ <pre class="brush: java;">
+ public class WordCount {
+
+ public static void main(String[] args) throws Exception {
+ Properties props = new Properties();
+ props.put(StreamsConfig.APPLICATION_ID_CONFIG, "streams-wordcount");
+ // ...
+ }
+ }
+ </pre>
+
+ <p>
+ In order to count the words we can first modify the <code>flatMapValues</code> operator to treat all of them as lower case (assuming lambda expression is used):
+ </p>
+
+ <pre class="brush: java;">
+ source.flatMapValues(new ValueMapper<String, Iterable<String>>() {
+ @Override
+ public Iterable<String> apply(String value) {
+ return Arrays.asList(value.toLowerCase(Locale.getDefault()).split("\\W+"));
+ }
+ });
+ </pre>
+
+ <p>
+ In order to do the counting aggregation we have to first specify that we want to key the stream on the value string, i.e. the lower cased word, with a <code>groupBy</code> operator.
+ This operator generate a new grouped stream, which can then be aggregated by a <code>count</code> operator, which generates a running count on each of the grouped keys:
+ </p>
+
+ <pre class="brush: java;">
+ KTable<String, Long> counts =
+ source.flatMapValues(new ValueMapper<String, Iterable<String>>() {
+ @Override
+ public Iterable<String> apply(String value) {
+ return Arrays.asList(value.toLowerCase(Locale.getDefault()).split("\\W+"));
+ }
+ })
+ .groupBy(new KeyValueMapper<String, String, String>() {
+ @Override
+ public String apply(String key, String value) {
+ return value;
+ }
+ })
+ // Materialize the result into a KeyValueStore named "counts-store".
+ // The Materialized store is always of type <Bytes, byte[]> as this is the format of the inner most store.
+ .count(Materialized.<String, Long, KeyValueStore<Bytes, byte[]>> as("counts-store"));
+ </pre>
+
+ <p>
+ Note that the <code>count</code> operator has a <code>Materialized</code> parameter that specifies that the
+ running count should be stored in a state store named <code>counts-store</code>.
+ This <code>Counts</code> store can be queried in real-time, with details described in the <a href="/{{version}}/documentation/streams/developer-guide#streams_interactive_queries">Developer Manual</a>.
+ </p>
+
+ <p>
+ We can also write the <code>counts</code> KTable's changelog stream back into another Kafka topic, say <code>streams-wordcount-output</code>.
+ Because the result is a changelog stream, the output topic <code>streams-wordcount-output</code> should be configured with log compaction enabled.
+ Note that this time the value type is no longer <code>String</code> but <code>Long</code>, so the default serialization classes are not viable for writing it to Kafka anymore.
+ We need to provide overridden serialization methods for <code>Long</code> types, otherwise a runtime exception will be thrown:
+ </p>
+
+ <pre class="brush: java;">
+ counts.toStream().to("streams-wordcount-output", Produced.with(Serdes.String(), Serdes.Long());
+ </pre>
+
+ <p>
+ Note that in order to read the changelog stream from topic <code>streams-wordcount-output</code>,
+ one needs to set the value deserialization as <code>org.apache.kafka.common.serialization.LongDeserializer</code>.
+ Details of this can be found in the <a href="/{{version}}/documentation/streams/quickstart">Play with a Streams Application</a> section.
+ Assuming lambda expression from JDK 8 can be used, the above code can be simplified as:
+ </p>
+
+ <pre class="brush: java;">
+ KStream<String, String> source = builder.stream("streams-plaintext-input");
+ source.flatMapValues(value -> Arrays.asList(value.toLowerCase(Locale.getDefault()).split("\\W+")))
+ .groupBy((key, value) -> value)
+ .count(Materialized.<String, Long, KeyValueStore<Bytes, byte[]>>as("counts-store"))
+ .toStream()
+ .to("streams-wordcount-output", Produced.with(Serdes.String(), Serdes.Long());
+ </pre>
+
+ <p>
+ If we again describe this augmented topology as <code>System.out.println(topology.describe())</code>, we will get the following:
+ </p>
+
+ <pre class="brush: bash;">
+ > mvn clean package
+ > mvn exec:java -Dexec.mainClass=myapps.WordCount
+ Sub-topologies:
+ Sub-topology: 0
+ Source: KSTREAM-SOURCE-0000000000(topics: streams-plaintext-input) --> KSTREAM-FLATMAPVALUES-0000000001
+ Processor: KSTREAM-FLATMAPVALUES-0000000001(stores: []) --> KSTREAM-KEY-SELECT-0000000002 <-- KSTREAM-SOURCE-0000000000
+ Processor: KSTREAM-KEY-SELECT-0000000002(stores: []) --> KSTREAM-FILTER-0000000005 <-- KSTREAM-FLATMAPVALUES-0000000001
+ Processor: KSTREAM-FILTER-0000000005(stores: []) --> KSTREAM-SINK-0000000004 <-- KSTREAM-KEY-SELECT-0000000002
+ Sink: KSTREAM-SINK-0000000004(topic: Counts-repartition) <-- KSTREAM-FILTER-0000000005
+ Sub-topology: 1
+ Source: KSTREAM-SOURCE-0000000006(topics: Counts-repartition) --> KSTREAM-AGGREGATE-0000000003
+ Processor: KSTREAM-AGGREGATE-0000000003(stores: [Counts]) --> KTABLE-TOSTREAM-0000000007 <-- KSTREAM-SOURCE-0000000006
+ Processor: KTABLE-TOSTREAM-0000000007(stores: []) --> KSTREAM-SINK-0000000008 <-- KSTREAM-AGGREGATE-0000000003
+ Sink: KSTREAM-SINK-0000000008(topic: streams-wordcount-output) <-- KTABLE-TOSTREAM-0000000007
+ Global Stores:
+ none
+ </pre>
+
+ <p>
+ As we can see above, the topology now contains two disconnected sub-topologies.
+ The first sub-topology's sink node <code>KSTREAM-SINK-0000000004</code> will write to a repartition topic <code>Counts-repartition</code>,
+ which will be read by the second sub-topology's source node <code>KSTREAM-SOURCE-0000000006</code>.
+ The repartition topic is used to "shuffle" the source stream by its aggregation key, which is in this case the value string.
+ In addition, inside the first sub-topology a stateless <code>KSTREAM-FILTER-0000000005</code> node is injected between the grouping <code>KSTREAM-KEY-SELECT-0000000002</code> node and the sink node to filter out any intermediate record whose aggregate key is empty.
+ </p>
+ <p>
+ In the second sub-topology, the aggregation node <code>KSTREAM-AGGREGATE-0000000003</code> is associated with a state store named <code>Counts</code> (the name is specified by the user in the <code>count</code> operator).
+ Upon receiving each record from its upcoming stream source node, the aggregation processor will first query its associated <code>Counts</code> store to get the current count for that key, augment by one, and then write the new count back to the store.
+ Each updated count for the key will also be piped downstream to the <code>KTABLE-TOSTREAM-0000000007</code> node, which interpret this update stream as a record stream before further piping to the sink node <code>KSTREAM-SINK-0000000008</code> for writing back to Kafka.
+ </p>
+
+ <p>
+ The complete code looks like this (assuming lambda expression is used):
+ </p>
+
+ <pre class="brush: java;">
+ package myapps;
+
+ import org.apache.kafka.common.serialization.Serdes;
+ import org.apache.kafka.streams.KafkaStreams;
+ import org.apache.kafka.streams.StreamsBuilder;
+ import org.apache.kafka.streams.StreamsConfig;
+ import org.apache.kafka.streams.Topology;
+ import org.apache.kafka.streams.kstream.KStream;
+
+ import java.util.Arrays;
+ import java.util.Locale;
+ import java.util.Properties;
+ import java.util.concurrent.CountDownLatch;
+
+ public class WordCount {
+
+ public static void main(String[] args) throws Exception {
+ Properties props = new Properties();
+ props.put(StreamsConfig.APPLICATION_ID_CONFIG, "streams-wordcount");
+ props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
+ props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
+ props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
+
+ final StreamsBuilder builder = new StreamsBuilder();
+
+ KStream<String, String> source = builder.stream("streams-plaintext-input");
+ source.flatMapValues(value -> Arrays.asList(value.toLowerCase(Locale.getDefault()).split("\\W+")))
+ .groupBy((key, value) -> value)
+ .count(Materialized.<String, Long, KeyValueStore<Bytes, byte[]>>as("counts-store"))
+ .toStream()
+ .to("streams-wordcount-output", Produced.with(Serdes.String(), Serdes.Long());
+
+ final Topology topology = builder.build();
+ final KafkaStreams streams = new KafkaStreams(topology, props);
+ final CountDownLatch latch = new CountDownLatch(1);
+
+ // ... same as Pipe.java above
+ }
+ }
+ </pre>
+
+ <div class="pagination">
+ <a href="/{{version}}/documentation/streams/quickstart" class="pagination__btn pagination__btn__prev">Previous</a>
+ <a href="/{{version}}/documentation/streams/developer-guide" class="pagination__btn pagination__btn__next">Next</a>
+ </div>
+</script>
+
+<div class="p-quickstart-streams"></div>
+
+<!--#include virtual="../../includes/_header.htm" -->
+<!--#include virtual="../../includes/_top.htm" -->
+<div class="content documentation documentation--current">
+ <!--#include virtual="../../includes/_nav.htm" -->
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+ <!--#include virtual="../../includes/_docs_banner.htm" -->
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+ </ul>
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+</div>
+<!--#include virtual="../../includes/_footer.htm" -->
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+$(function() {
+ // Show selected style on nav item
+ $('.b-nav__streams').addClass('selected');
+
+ // Display docs subnav items
+ $('.b-nav__docs').parent().toggleClass('nav__item__with__subs--expanded');
+});
+</script>