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Posted to commits@kafka.apache.org by ju...@apache.org on 2015/11/10 02:09:43 UTC
[3/3] kafka-site git commit: add 0.9.0 docs
add 0.9.0 docs
Project: http://git-wip-us.apache.org/repos/asf/kafka-site/repo
Commit: http://git-wip-us.apache.org/repos/asf/kafka-site/commit/8c4a140c
Tree: http://git-wip-us.apache.org/repos/asf/kafka-site/tree/8c4a140c
Diff: http://git-wip-us.apache.org/repos/asf/kafka-site/diff/8c4a140c
Branch: refs/heads/asf-site
Commit: 8c4a140cfbb841902ab6ac5c114b43c3092535d5
Parents: e4d9849
Author: Jun Rao <ju...@gmail.com>
Authored: Mon Nov 9 17:08:09 2015 -0800
Committer: Jun Rao <ju...@gmail.com>
Committed: Mon Nov 9 17:08:09 2015 -0800
----------------------------------------------------------------------
090/api.html | 40 +-
090/configuration.html | 642 ++----------------------------
090/connect.html | 328 +++++++++++++++
090/connect_config.html | 112 ++++++
090/consumer_config.html | 102 +++++
090/design.html | 48 ++-
090/documentation.html | 61 ++-
090/ecosystem.html | 17 +
090/images/consumer-groups.png | Bin 0 -> 26820 bytes
090/images/kafka_log.png | Bin 0 -> 134321 bytes
090/images/kafka_multidc.png | Bin 0 -> 33959 bytes
090/images/kafka_multidc_complex.png | Bin 0 -> 38559 bytes
090/images/log_anatomy.png | Bin 0 -> 19579 bytes
090/images/log_cleaner_anatomy.png | Bin 0 -> 18638 bytes
090/images/log_compaction.png | Bin 0 -> 41414 bytes
090/images/mirror-maker.png | Bin 0 -> 6579 bytes
090/images/producer_consumer.png | Bin 0 -> 8691 bytes
090/images/tracking_high_level.png | Bin 0 -> 82759 bytes
090/implementation.html | 103 +++--
090/introduction.html | 23 +-
090/kafka_config.html | 268 +++++++++++++
090/migration.html | 19 +-
090/ops.html | 56 ++-
090/producer_config.html | 106 +++++
090/quickstart.html | 79 ++++
090/security.html | 301 ++++++++++++++
090/upgrade.html | 43 +-
090/uses.html | 17 +
28 files changed, 1686 insertions(+), 679 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/8c4a140c/090/api.html
----------------------------------------------------------------------
diff --git a/090/api.html b/090/api.html
index 63dd8a3..835bdf2 100644
--- a/090/api.html
+++ b/090/api.html
@@ -1,9 +1,25 @@
+<!--
+ 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.
+-->
We are in the process of rewritting the JVM clients for Kafka. As of 0.8.2 Kafka includes a newly rewritten Java producer. The next release will include an equivalent Java consumer. These new clients are meant to supplant the existing Scala clients, but for compatability they will co-exist for some time. These clients are available in a seperate jar with minimal dependencies, while the old Scala clients remain packaged with the server.
<h3><a id="producerapi">2.1 Producer API</a></h3>
-As of the 0.8.2 release we encourage all new development to use the new Java producer. This client is production tested and generally both faster and more fully featured than the previous Scala client. You can use this client by adding a dependency on the client jar using the following maven co-ordinates:
+As of the 0.8.2 release we encourage all new development to use the new Java producer. This client is production tested and generally both faster and more fully featured than the previous Scala client. You can use this client by adding a dependency on the client jar using the following example maven co-ordinates (you can change the version numbers with new releases):
<pre>
<dependency>
<groupId>org.apache.kafka</groupId>
@@ -12,7 +28,7 @@ As of the 0.8.2 release we encourage all new development to use the new Java pro
</dependency>
</pre>
-Examples showing how to use the producer are given in the
+Examples showing how to use the producer are given in the
<a href="http://kafka.apache.org/082/javadoc/index.html?org/apache/kafka/clients/producer/KafkaProducer.html" title="Kafka 0.8.2 Javadoc">javadocs</a>.
<p>
@@ -117,7 +133,7 @@ class kafka.javaapi.consumer.SimpleConsumer {
* @param request a [[kafka.javaapi.OffsetRequest]] object.
* @return a [[kafka.javaapi.OffsetResponse]] object.
*/
- public kafak.javaapi.OffsetResponse getOffsetsBefore(OffsetRequest request);
+ public kafka.javaapi.OffsetResponse getOffsetsBefore(OffsetRequest request);
/**
* Close the SimpleConsumer.
@@ -128,11 +144,15 @@ class kafka.javaapi.consumer.SimpleConsumer {
For most applications, the high level consumer Api is good enough. Some applications want features not exposed to the high level consumer yet (e.g., set initial offset when restarting the consumer). They can instead use our low level SimpleConsumer Api. The logic will be a bit more complicated and you can follow the example in
<a href="https://cwiki.apache.org/confluence/display/KAFKA/0.8.0+SimpleConsumer+Example" title="Kafka 0.8 SimpleConsumer example">here</a>.
-<h3><a id="kafkahadoopconsumerapi">2.4 Kafka Hadoop Consumer API</a></h3>
-<p>
-Providing a horizontally scalable solution for aggregating and loading data into Hadoop was one of our basic use cases. To support this use case, we provide a Hadoop-based consumer which spawns off many map tasks to pull data from the Kafka cluster in parallel. This provides extremely fast pull-based Hadoop data load capabilities (we were able to fully saturate the network with only a handful of Kafka servers).
-</p>
+<h3><a id="newconsumerapi">2.4 New Consumer API</a></h3>
+As of the 0.9.0 release we have added a replacement for our existing simple and high-level consumers. This client is considered beta quality. You can use this client by adding a dependency on the client jar using the following example maven co-ordinates (you can change the version numbers with new releases):
+<pre>
+ <dependency>
+ <groupId>org.apache.kafka</groupId>
+ <artifactId>kafka-clients</artifactId>
+ <version>0.9.0.0</version>
+ </dependency>
+</pre>
-<p>
-Usage information on the hadoop consumer can be found <a href="https://github.com/linkedin/camus/">here</a>.
-</p>
+Examples showing how to use the producer are given in the
+<a href="http://kafka.apache.org/090/javadoc/index.html?org/apache/kafka/clients/producer/KafkaConsumer.html" title="Kafka 0.9.0 Javadoc">javadocs</a>.
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/8c4a140c/090/configuration.html
----------------------------------------------------------------------
diff --git a/090/configuration.html b/090/configuration.html
index 9ef621f..abaff63 100644
--- a/090/configuration.html
+++ b/090/configuration.html
@@ -1,3 +1,20 @@
+<!--
+ 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.
+-->
+
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">3.1 Broker Configs</a></h3>
@@ -11,431 +28,11 @@ The essential configurations are the following:
Topic-level configurations and defaults are discussed in more detail <a href="#topic-config">below</a>.
-<table class="data-table">
-<tbody><tr>
- <th>Property</th>
- <th>Default</th>
- <th>Description</th>
- </tr>
- <tr>
- <td>broker.id</td>
- <td></td>
- <td>Each broker is uniquely identified by a non-negative integer id. This id serves as the broker's "name" and allows the broker to be moved to a different host/port without confusing consumers. You can choose any number you like so long as it is unique.
- </td>
- </tr>
- <tr>
- <td>log.dirs</td>
- <td nowrap>/tmp/kafka-logs</td>
- <td>A comma-separated list of one or more directories in which Kafka data is stored. Each new partition that is created will be placed in the directory which currently has the fewest partitions.</td>
- </tr>
- <tr>
- <td>port</td>
- <td>9092</td>
- <td>The port on which the server accepts client connections.</td>
- </tr>
- <tr>
- <td>zookeeper.connect</td>
- <td>null</td>
- <td>Specifies the ZooKeeper connection string in the form <code>hostname:port</code>, where hostname and port are the host and port for a node in your ZooKeeper cluster. To allow connecting through other ZooKeeper nodes when that host is down you can also specify multiple hosts in the form <code>hostname1:port1,hostname2:port2,hostname3:port3</code>.
- <p>
-ZooKeeper also allows you to add a "chroot" path which will make all kafka data for this cluster appear under a particular path. This is a way to setup multiple Kafka clusters or other applications on the same ZooKeeper cluster. To do this give a connection string in the form <code>hostname1:port1,hostname2:port2,hostname3:port3/chroot/path</code> which would put all this cluster's data under the path <code>/chroot/path</code>. Note that consumers must use the same connection string.</td>
- </tr>
- <tr>
- <td>message.max.bytes</td>
- <td>1000000</td>
- <td>The maximum size of a message that the server can receive. It is important that this property be in sync with the maximum fetch size your consumers use or else an unruly producer will be able to publish messages too large for consumers to consume.</td>
- </tr>
- <tr>
- <td>num.network.threads</td>
- <td>3</td>
- <td>The number of network threads that the server uses for handling network requests. You probably don't need to change this.</td>
- </tr>
- <tr>
- <td>num.io.threads</td>
- <td>8</td>
- <td>The number of I/O threads that the server uses for executing requests. You should have at least as many threads as you have disks.</td>
- </tr>
- <tr>
- <td>background.threads</td>
- <td>10</td>
- <td>The number of threads to use for various background processing tasks such as file deletion. You should not need to change this.</td>
- </tr>
- <tr>
- <td>queued.max.requests</td>
- <td>500</td>
- <td>The number of requests that can be queued up for processing by the I/O threads before the network threads stop reading in new requests.</td>
- </tr>
- <tr>
- <td>host.name</td>
- <td>null</td>
- <td>
- <p>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, and publish one to ZK.</p>
- </td>
- </tr>
- <tr>
- <td>advertised.host.name</td>
- <td>null</td>
- <td>
- <p>If this is set this is the hostname that will be given out to producers, consumers, and other brokers to connect to.</p>
- </td>
- </tr>
- <tr>
- <td>advertised.port</td>
- <td>null</td>
- <td>
- <p>The port to give out to producers, consumers, and other brokers to use in establishing connections. This only needs to be set if this port is different from the port the server should bind to.</p>
- </td>
- </tr>
- <tr>
- <td>socket.send.buffer.bytes</td>
- <td>100 * 1024</td>
- <td>The SO_SNDBUFF buffer the server prefers for socket connections.</td>
- </tr>
- <tr>
- <td>socket.receive.buffer.bytes</td>
- <td>100 * 1024</td>
- <td>The SO_RCVBUFF buffer the server prefers for socket connections.</td>
- </tr>
- <tr>
- <td>socket.request.max.bytes</td>
- <td>100 * 1024 * 1024</td>
- <td>The maximum request size the server will allow. This prevents the server from running out of memory and should be smaller than the Java heap size.</td>
- </tr>
- <tr>
- <td>num.partitions</td>
- <td>1</td>
- <td>The default number of partitions per topic if a partition count isn't given at topic creation time.</td>
- </tr>
- <tr>
- <td>log.segment.bytes</td>
- <td nowrap>1024 * 1024 * 1024</td>
- <td>The log for a topic partition is stored as a directory of segment files. This setting controls the size to which a segment file will grow before a new segment is rolled over in the log. This setting can be overridden on a per-topic basis (see <a href="#topic-config">the per-topic configuration section</a>).</td>
- </tr>
- <tr>
- <td>log.roll.{ms,hours}</td>
- <td>24 * 7 hours</td>
- <td>This setting will force Kafka to roll a new log segment even if the log.segment.bytes size has not been reached. This setting can be overridden on a per-topic basis (see <a href="#topic-config">the per-topic configuration section</a>).</td>
- </tr>
- <tr>
- <td>log.cleanup.policy</td>
- <td>delete</td>
- <td>This can take either the value <i>delete</i> or <i>compact</i>. If <i>delete</i> is set, log segments will be deleted when they reach the size or time limits set. If <i>compact</i> is set <a href="#compaction">log compaction</a> will be used to clean out obsolete records. This setting can be overridden on a per-topic basis (see <a href="#topic-config">the per-topic configuration section</a>).</td>
- </tr>
- <tr>
- <td>log.retention.{ms,minutes,hours}</td>
- <td>7 days</td>
- <td>The amount of time to keep a log segment before it is deleted, i.e. the default data retention window for all topics. Note that if both log.retention.minutes and log.retention.bytes are both set we delete a segment when either limit is exceeded. This setting can be overridden on a per-topic basis (see <a href="#topic-config">the per-topic configuration section</a>).</td>
- </tr>
- <tr>
- <td>log.retention.bytes</td>
- <td>-1</td>
- <td>The amount of data to retain in the log for each topic-partitions. Note that this is the limit per-partition so multiply by the number of partitions to get the total data retained for the topic. Also note that if both log.retention.hours and log.retention.bytes are both set we delete a segment when either limit is exceeded. This setting can be overridden on a per-topic basis (see <a href="#topic-config">the per-topic configuration section</a>).</td>
- </tr>
- <tr>
- <td>log.retention.check.interval.ms</td>
- <td>5 minutes</td>
- <td>The period with which we check whether any log segment is eligible for deletion to meet the retention policies.</td>
- </tr>
- <tr>
- <td>log.cleaner.enable</td>
- <td>false</td>
- <td>This configuration must be set to true for log compaction to run.</td>
- </tr>
- <tr>
- <td>log.cleaner.threads</td>
- <td>1</td>
- <td>The number of threads to use for cleaning logs in log compaction.</td>
- </tr>
- <tr>
- <td>log.cleaner.io.max.bytes.per.second</td>
- <td>Double.MaxValue</td>
- <td>The maximum amount of I/O the log cleaner can do while performing log compaction. This setting allows setting a limit for the cleaner to avoid impacting live request serving.</td>
- </tr>
- <tr>
- <td>log.cleaner.dedupe.buffer.size</td>
- <td>500*1024*1024</td>
- <td>The size of the buffer the log cleaner uses for indexing and deduplicating logs during cleaning. Larger is better provided you have sufficient memory.</td>
- </tr>
- <tr>
- <td>log.cleaner.io.buffer.size</td>
- <td>512*1024</td>
- <td>The size of the I/O chunk used during log cleaning. You probably don't need to change this.</td>
- </tr>
- <tr>
- <td>log.cleaner.io.buffer.load.factor</td>
- <td>0.9</td>
- <td>The load factor of the hash table used in log cleaning. You probably don't need to change this.</td>
- </tr>
- <tr>
- <td>log.cleaner.backoff.ms</td>
- <td>15000</td>
- <td>The interval between checks to see if any logs need cleaning.</td>
- </tr>
- <tr>
- <td>log.cleaner.min.cleanable.ratio</td>
- <td>0.5</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. This setting can be overridden on a per-topic basis (see <a href="#topic-config">the per-topic configuration section</a>).</td>
- </tr>
- <tr>
- <td>log.cleaner.delete.retention.ms</td>
- <td>1 day</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). This setting can be overridden on a per-topic basis (see <a href="#topic-config">the per-topic configuration section</a>).</td>
- </tr>
- <tr>
- <td>log.index.size.max.bytes</td>
- <td>10 * 1024 * 1024</td>
- <td>The maximum size in bytes we allow for the offset index for each log segment. Note that we will always pre-allocate a sparse file with this much space and shrink it down when the log rolls. If the index fills up we will roll a new log segment even if we haven't reached the log.segment.bytes limit. This setting can be overridden on a per-topic basis (see <a href="#topic-config">the per-topic configuration section</a>).</td>
- </tr>
- <tr>
- <td>log.index.interval.bytes</td>
- <td>4096</td>
- <td>The byte interval at which we add an entry to the offset index. When executing a fetch request the server must do a linear scan for up to this many bytes to find the correct position in the log to begin and end the fetch. So setting this value to be larger will mean larger index files (and a bit more memory usage) but less scanning. However the server will never add more than one index entry per log append (even if more than log.index.interval worth of messages are appended). In general you probably don't need to mess with this value.</td>
- </tr>
- <tr>
- <td>log.flush.interval.messages</td>
- <td>Long.MaxValue</td>
- <td>The number of messages written to a log partition before we force an fsync on the log. Setting this lower will sync data to disk more often but will have a major impact on performance. We generally recommend that people make use of replication for durability rather than depending on single-server fsync, however this setting can be used to be extra certain.</td>
- </tr>
- <tr>
- <td>log.flush.scheduler.interval.ms</td>
- <td>Long.MaxValue</td>
- <td>The frequency in ms that the log flusher checks whether any log is eligible to be flushed to disk.</td>
- </tr>
- <tr>
- <td>log.flush.interval.ms</td>
- <td>Long.MaxValue</td>
- <td>The maximum time between fsync calls on the log. If used in conjuction with log.flush.interval.messages the log will be flushed when either criteria is met.</td>
- </tr>
- <tr>
- <td>log.delete.delay.ms</td>
- <td>60000</td>
- <td>The period of time we hold log files around after they are removed from the in-memory segment index. This period of time allows any in-progress reads to complete uninterrupted without locking. You generally don't need to change this.</td>
- </tr>
- <tr>
- <td>log.flush.offset.checkpoint.interval.ms</td>
- <td>60000</td>
- <td>The frequency with which we checkpoint the last flush point for logs for recovery. You should not need to change this.</td>
- </tr>
- <tr>
- <td>log.segment.delete.delay.ms</td>
- <td>60000</td>
- <td>the amount of time to wait before deleting a file from the filesystem.</td>
- </tr>
- <tr>
- <td>auto.create.topics.enable</td>
- <td>true</td>
- <td>Enable auto creation of topic on the server. If this is set to true then attempts to produce data or fetch metadata for a non-existent topic will automatically create it with the default replication factor and number of partitions.</td>
- </tr>
- <tr>
- <td>controller.socket.timeout.ms</td>
- <td>30000</td>
- <td>The socket timeout for commands from the partition management controller to the replicas.</td>
- </tr>
- <tr>
- <td>controller.message.queue.size</td>
- <td>Int.MaxValue</td>
- <td>The buffer size for controller-to-broker-channels</td>
- </tr>
- <tr>
- <td>default.replication.factor</td>
- <td>1</td>
- <td>The default replication factor for automatically created topics.</td>
- </tr>
- <tr>
- <td>replica.lag.time.max.ms</td>
- <td>10000</td>
- <td>If a follower hasn't sent any fetch requests for this window of time, the leader will remove the follower from ISR (in-sync replicas) and treat it as dead.</td>
- </tr>
- <tr>
- <td>replica.socket.timeout.ms</td>
- <td>30 * 1000</td>
- <td>The socket timeout for network requests to the leader for replicating data.</td>
- </tr>
- <tr>
- <td>replica.socket.receive.buffer.bytes</td>
- <td>64 * 1024</td>
- <td>The socket receive buffer for network requests to the leader for replicating data.</td>
- </tr>
- <tr>
- <td>replica.fetch.max.bytes</td>
- <td nowrap>1024 * 1024</td>
- <td>The number of byes of messages to attempt to fetch for each partition in the fetch requests the replicas send to the leader.</td>
- </tr>
- <tr>
- <td>replica.fetch.wait.max.ms</td>
- <td>500</td>
- <td>The maximum amount of time to wait time for data to arrive on the leader in the fetch requests sent by the replicas to the leader.</td>
- </tr>
- <tr>
- <td>replica.fetch.min.bytes</td>
- <td>1</td>
- <td>Minimum bytes expected for each fetch response for the fetch requests from the replica to the leader. If not enough bytes, wait up to replica.fetch.wait.max.ms for this many bytes to arrive.</td>
- </tr>
- <tr>
- <td>num.replica.fetchers</td>
- <td>1</td>
- <td>
- <p>Number of threads used to replicate messages from leaders. Increasing this value can increase the degree of I/O parallelism in the follower broker.</p>
- </td>
- </tr>
- <tr>
- <td>replica.high.watermark.checkpoint.interval.ms</td>
- <td>5000</td>
- <td>The frequency with which each replica saves its high watermark to disk to handle recovery.</td>
- </tr>
- <tr>
- <td>fetch.purgatory.purge.interval.requests</td>
- <td>1000</td>
- <td>The purge interval (in number of requests) of the fetch request purgatory.</td>
- </tr>
- <tr>
- <td>producer.purgatory.purge.interval.requests</td>
- <td>1000</td>
- <td>The purge interval (in number of requests) of the producer request purgatory.</td>
- </tr>
- <tr>
- <td>zookeeper.session.timeout.ms</td>
- <td>6000</td>
- <td>ZooKeeper session timeout. If the server fails to heartbeat to ZooKeeper within this period of time it is considered dead. If you set this too low the server may be falsely considered dead; if you set it too high it may take too long to recognize a truly dead server.</td>
- </tr>
- <tr>
- <td>zookeeper.connection.timeout.ms</td>
- <td>6000</td>
- <td>The maximum amount of time that the client waits to establish a connection to zookeeper.</td>
- </tr>
- <tr>
- <td>zookeeper.sync.time.ms</td>
- <td>2000</td>
- <td>How far a ZK follower can be behind a ZK leader.</td>
- </tr>
- <tr>
- <td>controlled.shutdown.enable</td>
- <td>true</td>
- <td>Enable controlled shutdown of the broker. If enabled, the broker will move all leaders on it to some other brokers before shutting itself down. This reduces the unavailability window during shutdown.</td>
- </tr>
- <tr>
- <td>controlled.shutdown.max.retries</td>
- <td>3</td>
- <td>Number of retries to complete the controlled shutdown successfully before executing an unclean shutdown.</td>
- </tr>
- <tr>
- <td>controlled.shutdown.retry.backoff.ms</td>
- <td>5000</td>
- <td>Backoff time between shutdown retries.</td>
- </tr>
- <tr>
- <td>auto.leader.rebalance.enable</td>
- <td>true</td>
- <td>If this is enabled the controller will automatically try to balance leadership for partitions among the brokers by periodically returning leadership to the "preferred" replica for each partition if it is available.</td>
- </tr>
- <tr>
- <td>leader.imbalance.per.broker.percentage</td>
- <td>10</td>
- <td>The percentage of leader imbalance allowed per broker. The controller will rebalance leadership if this ratio goes above
- the configured value per broker.</td>
- </tr>
- <tr>
- <td>leader.imbalance.check.interval.seconds</td>
- <td>300</td>
- <td>The frequency with which to check for leader imbalance.</td>
- </tr>
- <tr>
- <td>offset.metadata.max.bytes</td>
- <td>4096</td>
- <td>The maximum amount of metadata to allow clients to save with their offsets.</td>
- </tr>
- <tr>
- <td>max.connections.per.ip</td>
- <td>Int.MaxValue</td>
- <td>The maximum number of connections that a broker allows from each ip address.</td>
- </tr>
- <tr>
- <td>max.connections.per.ip.overrides</td>
- <td></td>
- <td>Per-ip or hostname overrides to the default maximum number of connections.</td>
- </tr>
- <tr>
- <td>connections.max.idle.ms</td>
- <td>600000</td>
- <td>Idle connections timeout: the server socket processor threads close the connections that idle more than this.</td>
- </tr>
- <tr>
- <td>log.roll.jitter.{ms,hours}</td>
- <td>0</td>
- <td>The maximum jitter to subtract from logRollTimeMillis.</td>
- </tr>
- <tr>
- <td>num.recovery.threads.per.data.dir</td>
- <td>1</td>
- <td>The number of threads per data directory to be used for log recovery at startup and flushing at shutdown.</td>
- </tr>
- <tr>
- <td>unclean.leader.election.enable</td>
- <td>true</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>
- </tr>
- <tr>
- <td>delete.topic.enable</td>
- <td>false</td>
- <td>Enable delete topic.</td>
- </tr>
- <tr>
- <td>offsets.topic.num.partitions</td>
- <td>50</td>
- <td>The number of partitions for the offset commit topic. Since changing this after deployment is currently unsupported, we recommend using a higher setting for production (e.g., 100-200).</td>
- </tr>
- <tr>
- <td>offsets.topic.retention.minutes</td>
- <td>1440</td>
- <td>Offsets that are older than this age will be marked for deletion. The actual purge will occur when the log cleaner compacts the offsets topic.</td>
- </tr>
- <tr>
- <td>offsets.retention.check.interval.ms</td>
- <td>600000</td>
- <td>The frequency at which the offset manager checks for stale offsets.</td>
- </tr>
- <tr>
- <td>offsets.topic.replication.factor</td>
- <td>3</td>
- <td>The replication factor for the offset commit topic. A higher setting (e.g., three or four) is recommended in order to ensure higher availability. If the offsets topic is created when fewer brokers than the replication factor then the offsets topic will be created with fewer replicas.</td>
- </tr>
- <tr>
- <td>offsets.topic.segment.bytes</td>
- <td>104857600</td>
- <td>Segment size for the offsets topic. Since it uses a compacted topic, this should be kept relatively low in order to facilitate faster log compaction and loads.</td>
- </tr>
- <tr>
- <td>offsets.load.buffer.size</td>
- <td>5242880</td>
- <td>An offset load occurs when a broker becomes the offset manager for a set of consumer groups (i.e., when it becomes a leader for an offsets topic partition). This setting corresponds to the batch size (in bytes) to use when reading from the offsets segments when loading offsets into the offset manager's cache.</td>
- </tr>
-<!--
- <tr>
- <td>offsets.topic.compression.codec</td>
- <td>none</td>
- <td>(Should not be used until KAFKA-1374 is implemented.) Compression codec for the offsets topic. Compression should be enabled in order to achieve "atomic" commits.</td>
- </tr>
--->
- <tr>
- <td>offsets.commit.required.acks</td>
- <td>-1</td>
- <td>The number of acknowledgements that are required before the offset commit can be accepted. This is similar to the producer's acknowledgement setting. In general, the default should not be overridden.</td>
- </tr>
- <tr>
- <td>offsets.commit.timeout.ms</td>
- <td>5000</td>
- <td>The offset commit will be delayed until this timeout or the required number of replicas have received the offset commit. This is similar to the producer request timeout.</td>
- </tr>
- <tr>
- <td>inter.broker.protocol.version</td>
- <td>0.8.3</td>
- <td>Version of the protocol brokers will use to communicate with each other. This will default for the current version of the broker, but may need to be set to older versions during a rolling upgrade process. In that scenario, upgraded brokers will use the older version of the protocol and therefore will be able to communicate with brokers that were not yet upgraded. See <a href="#upgrade">upgrade section</a> for more details.</td>
- </tr>
-</tbody></table>
+<!--#include virtual="kafka_config.html" -->
<p>More details about broker configuration can be found in the scala class <code>kafka.server.KafkaConfig</code>.</p>
-<h4><a id="topic-config">Topic-level configuration</a></h3>
+<a id="topic-config">Topic-level configuration</a>
Configurations pertinent to topics have both a global default as well an optional per-topic override. If no per-topic configuration is given the global 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>
@@ -509,7 +106,7 @@ The following are the topic-level configurations. The server's default configura
<td>min.insync.replicas</td>
<td>1</td>
<td>min.insync.replicas</td>
- <td>When a producer sets request.required.acks to -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>
+ <td>When a producer sets request.required.acks to -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).
When used together, min.insync.replicas and request.required.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 request.required.acks of -1. This will ensure that the producer raises an exception if a majority of replicas do not receive a write.</td>
</tr>
<tr>
@@ -550,7 +147,17 @@ The following are the topic-level configurations. The server's default configura
</tr>
</table>
-<h3><a id="consumerconfigs">3.2 Consumer Configs</a></h3>
+<h3><a id="producerconfigs">3.2 Producer Configs</a></h3>
+
+Below is the configuration of the Java producer:
+<!--#include virtual="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">3.3 Consumer Configs</a></h3>
The essential consumer configurations are the following:
<ul>
<li><code>group.id</code>
@@ -719,187 +326,10 @@ The essential consumer configurations are the following:
<p>More details about consumer configuration can be found in the scala class <code>kafka.consumer.ConsumerConfig</code>.</p>
-<h3><a id="producerconfigs">3.3 Producer Configs</a></h3>
-Essential configuration properties for the producer include:
-<ul>
- <li><code>metadata.broker.list</code>
- <li><code>request.required.acks</code>
- <li><code>producer.type</code>
- <li><code>serializer.class</code>
-</ul>
-<table class="data-table">
-<tbody><tr>
- <th>Property</th>
- <th>Default</th>
- <th>Description</th>
- </tr>
- <tr>
- <td>metadata.broker.list</td>
- <td colspan="1"></td>
- <td>
- <p>This is for bootstrapping and the producer will only use it for getting metadata (topics, partitions and replicas). The socket connections for sending the actual data will be established based on the broker information returned in the metadata. The format is host1:port1,host2:port2, and the list can be a subset of brokers or a VIP pointing to a subset of brokers.</p>
- </td>
- </tr>
- <tr>
- <td>request.required.acks</td>
- <td colspan="1">0</td>
- <td>
- <p>This value controls when a produce request is considered completed. Specifically, how many other brokers must have committed the data to their log and acknowledged this to the leader? Typical values are
- <ul>
- <li>0, which means that the producer never waits for an acknowledgement from the broker (the same behavior as 0.7). This option provides the lowest latency but the weakest durability guarantees (some data will be lost when a server fails).
- <li> 1, which means that the producer gets an acknowledgement after the leader replica has received the data. This option provides better durability as the client waits until the server acknowledges the request as successful (only messages that were written to the now-dead leader but not yet replicated will be lost).
- <li> -1, The producer gets an acknowledgement after all in-sync replicas have received the data. This option provides the greatest level of durability. However, it does not completely eliminate the risk of message loss because the number of in sync replicas may, in rare cases, shrink to 1. If you want to ensure that some minimum number of replicas (typically a majority) receive a write, then you must set the topic-level min.insync.replicas setting. Please read the Replication section of the design documentation for a more in-depth discussion.
- </ul>
- </p>
- </td>
- </tr>
- <tr>
- <td>request.timeout.ms</td>
- <td colspan="1">10000</td>
- <td>The amount of time the broker will wait trying to meet the request.required.acks requirement before sending back an error to the client.</td>
- </tr>
- <tr>
- <td>producer.type</td>
- <td colspan="1">sync</td>
- <td>
- <p>This parameter specifies whether the messages are sent asynchronously in a background thread. Valid values are (1) async for asynchronous send and (2) sync for synchronous send. By setting the producer to async we allow batching together of requests (which is great for throughput) but open the possibility of a failure of the client machine dropping unsent data.</p>
- </td>
- <tr>
- <td>serializer.class</td>
- <td colspan="1">kafka.serializer.DefaultEncoder</td>
- <td>The serializer class for messages. The default encoder takes a byte[] and returns the same byte[].</td>
- </tr>
- <tr>
- <td>key.serializer.class</td>
- <td colspan="1"></td>
- <td>The serializer class for keys (defaults to the same as for messages if nothing is given).</td>
- </tr>
- <tr>
- <td>partitioner.class</td>
- <td colspan="1">kafka.producer.DefaultPartitioner</td>
- <td>The partitioner class for partitioning messages amongst sub-topics. The default partitioner is based on the hash of the key.</td>
- </tr>
- <tr>
- <td>compression.codec</td>
- <td colspan="1">none</td>
- <td>
- <p>This parameter allows you to specify the compression codec for all data generated by this producer. Valid values are "none", "gzip" and "snappy".</p>
- </td>
- </tr>
- <tr>
- <td>compressed.topics</td>
- <td colspan="1">null</td>
- <td>
- <p>This parameter allows you to set whether compression should be turned on for particular topics. If the compression codec is anything other than NoCompressionCodec, enable compression only for specified topics if any. If the list of compressed topics is empty, then enable the specified compression codec for all topics. If the compression codec is NoCompressionCodec, compression is disabled for all topics</p>
- </td>
- </tr>
- <tr>
- <td>message.send.max.retries</td>
- <td colspan="1">3</td>
- <td>
- <p>This property will cause the producer to automatically retry a failed send request. This property specifies the number of retries when such failures occur. Note that setting a non-zero value here can lead to duplicates in the case of network errors that cause a message to be sent but the acknowledgement to be lost.</p>
- </td>
- </tr>
- <tr>
- <td>retry.backoff.ms</td>
- <td colspan="1">100</td>
- <td>
- <p>Before each retry, the producer refreshes the metadata of relevant topics to see if a new leader has been elected. Since leader election takes a bit of time, this property specifies the amount of time that the producer waits before refreshing the metadata.</p>
- </td>
- </tr>
- <tr>
- <td>topic.metadata.refresh.interval.ms</td>
- <td colspan="1">600 * 1000</td>
- <td>
- <p>The producer generally refreshes the topic metadata from brokers when there is a failure (partition missing, leader not available...). It will also poll regularly (default: every 10min so 600000ms). If you set this to a negative value, metadata will only get refreshed on failure. If you set this to zero, the metadata will get refreshed after each message sent (not recommended). Important note: the refresh happen only AFTER the message is sent, so if the producer never sends a message the metadata is never refreshed</p>
- </td>
- </tr>
- <tr>
- <td>queue.buffering.max.ms</td>
- <td colspan="1">5000</td>
- <td>Maximum time to buffer data when using async mode. For example a setting of 100 will try to batch together 100ms of messages to send at once. This will improve throughput but adds message delivery latency due to the buffering.</td>
- </tr>
- <tr>
- <td>queue.buffering.max.messages</td>
- <td colspan="1">10000</td>
- <td>The maximum number of unsent messages that can be queued up the producer when using async mode before either the producer must be blocked or data must be dropped.</td>
- </tr>
- <tr>
- <td>queue.enqueue.timeout.ms</td>
- <td colspan="1">-1</td>
- <td>
- <p>The amount of time to block before dropping messages when running in async mode and the buffer has reached queue.buffering.max.messages. If set to 0 events will be enqueued immediately or dropped if the queue is full (the producer send call will never block). If set to -1 the producer will block indefinitely and never willingly drop a send.</p>
- </td>
- </tr>
- <tr>
- <td>batch.num.messages</td>
- <td colspan="1">200</td>
- <td>The number of messages to send in one batch when using async mode. The producer will wait until either this number of messages are ready to send or queue.buffer.max.ms is reached.</td>
- </tr>
- <tr>
- <td>send.buffer.bytes</td>
- <td colspan="1">100 * 1024</td>
- <td>Socket write buffer size</td>
- </tr>
- <tr>
- <td>client.id</td>
- <td colspan="1">""</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>
-</tbody></table>
-<p>More details about producer configuration can be found in the scala class <code>kafka.producer.ProducerConfig</code>.</p>
-
-<h3><a id="newproducerconfigs">3.4 New Producer Configs</a></h3>
+<h3><a id="newconsumerconfigs">3.4 New Consumer Configs</a></h3>
+Since 0.9.0.0 we have been working on a replacement for our existing simple and high-level consumers. The code can be considered beta quality. Below is the configuration for the new consumer:
+<!--#include virtual="consumer_config.html" -->
-We are working on a replacement for our existing producer. The code is available in trunk now and can be considered beta quality. Below is the configuration for the new producer.
-
-<table class="data-table">
- <tr>
- <th>Name</th>
- <th>Type</th>
- <th>Default</th>
- <th>Importance</th>
- <th>Description</th>
- </tr>
- <tr>
- <td>bootstrap.servers</td><td>list</td><td></td><td>high</td><td>A list of host/port pairs to use for establishing the initial connection to the Kafka cluster. Data will be load balanced over 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). If no server in this list is available sending data will fail until on becomes available.</td></tr>
- <tr>
- <td>acks</td><td>string</td><td>1</td><td>high</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 common: <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 will 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. <li>Other settings such as <code>acks=2</code> are also possible, and will require the given number of acknowledgements but this is generally less useful.</td></tr>
- <tr>
- <td>buffer.memory</td><td>long</td><td>33554432</td><td>high</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 either block or throw an exception based on the preference specified by <code>block.on.buffer.full</code>. <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></tr>
- <tr>
- <td>compression.type</td><td>string</td><td>none</td><td>high</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>, or <code>snappy</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></tr>
- <tr>
- <td>retries</td><td>int</td><td>0</td><td>high</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 will potentially change the ordering of records because if two records are sent to a single partition, and the first fails and is retried but the second succeeds, then the second record may appear first.</td></tr>
- <tr>
- <td>batch.size</td><td>int</td><td>16384</td><td>medium</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></tr>
- <tr>
- <td>client.id</td><td>string</td><td></td><td>medium</td><td>The 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 with the request. The application can set any string it wants as this has no functional purpose other than in logging and metrics.</td></tr>
- <tr>
- <td>linger.ms</td><td>long</td><td>0</td><td>medium</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 spe
cified 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></tr>
- <tr>
- <td>max.request.size</td><td>int</td><td>1048576</td><td>medium</td><td>The maximum size of a request. This is also effectively a cap on the maximum record size. Note that the server has its own cap on record size which may be different from this. This setting will limit the number of record batches the producer will send in a single request to avoid sending huge requests.</td></tr>
- <tr>
- <td>receive.buffer.bytes</td><td>int</td><td>32768</td><td>medium</td><td>The size of the TCP receive buffer to use when reading data</td></tr>
- <tr>
- <td>send.buffer.bytes</td><td>int</td><td>131072</td><td>medium</td><td>The size of the TCP send buffer to use when sending data</td></tr>
- <tr>
- <td>timeout.ms</td><td>int</td><td>30000</td><td>medium</td><td>The configuration controls the maximum amount of time the server will wait for acknowledgments from followers to meet the acknowledgment requirements the producer has specified with the <code>acks</code> configuration. If the requested number of acknowledgments are not met when the timeout elapses an error will be returned. This timeout is measured on the server side and does not include the network latency of the request.</td></tr>
- <tr>
- <td>block.on.buffer.full</td><td>boolean</td><td>true</td><td>low</td><td>When our memory buffer is exhausted we must either stop accepting new records (block) or throw errors. By default this setting is true and we block, however in some scenarios blocking is not desirable and it is better to immediately give an error. Setting this to <code>false</code> will accomplish that: the producer will throw a BufferExhaustedException if a recrord is sent and the buffer space is full.</td></tr>
- <tr>
- <td>metadata.fetch.timeout.ms</td><td>long</td><td>60000</td><td>low</td><td>The first time data is sent to a topic we must fetch metadata about that topic to know which servers host the topic's partitions. This configuration controls the maximum amount of time we will block waiting for the metadata fetch to succeed before throwing an exception back to the client.</td></tr>
- <tr>
- <td>metadata.max.age.ms</td><td>long</td><td>300000</td><td>low</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></tr>
- <tr>
- <td>metric.reporters</td><td>list</td><td>[]</td><td>low</td><td>A list of classes to use as metrics reporters. Implementing the <code>MetricReporter</code> interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.</td></tr>
- <tr>
- <td>metrics.num.samples</td><td>int</td><td>2</td><td>low</td><td>The number of samples maintained to compute metrics.</td></tr>
- <tr>
- <td>metrics.sample.window.ms</td><td>long</td><td>30000</td><td>low</td><td>The metrics system maintains a configurable number of samples over a fixed window size. This configuration controls the size of the window. For example we might maintain two samples each measured over a 30 second period. When a window expires we erase and overwrite the oldest window.</td></tr>
- <tr>
- <td>reconnect.backoff.ms</td><td>long</td><td>10</td><td>low</td><td>The amount of time to wait before attempting to reconnect to a given host when a connection fails. This avoids a scenario where the client repeatedly attempts to connect to a host in a tight loop.</td></tr>
- <tr>
- <td>retry.backoff.ms</td><td>long</td><td>100</td><td>low</td><td>The amount of time to wait before attempting to retry a failed produce request to a given topic partition. This avoids repeated sending-and-failing in a tight loop.</td></tr>
- </table>
+<h3><a id="connectconfigs">3.5 Kafka Connect Configs</a></h3>
+<!--#include virtual="connect_config.html" -->
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/8c4a140c/090/connect.html
----------------------------------------------------------------------
diff --git a/090/connect.html b/090/connect.html
new file mode 100644
index 0000000..8791ab0
--- /dev/null
+++ b/090/connect.html
@@ -0,0 +1,328 @@
+<!--~
+ ~ 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.
+ ~-->
+
+<h3><a id="connect_overview">8.1 Overview</a></h3>
+
+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.
+
+Kafka Connect features include:
+<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 </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">8.2 User Guide</a></h3>
+
+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.
+
+<h4>Running Kafka Connect</h4>
+
+Kafka Connect currently supports two modes of execution: standalone (single process) and distributed.
+
+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:
+
+<pre>
+> bin/connect-standalone.sh config/connect-standalone.properties connector1.properties [connector2.properties ...]
+</pre>
+
+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.
+
+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).
+
+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:
+
+<pre>
+> bin/connect-distributed.sh config/connect-distributed.properties
+</pre>
+
+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. In particular, the following configuration parameters are critical to set before starting your cluster:
+
+<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 topic</li>
+ <li><code>offset.storage.topic</code> (default <code>connect-offsets</code>) - topic to use for ; this topic should have many partitions and be replicated</li>
+</ul>
+
+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.
+
+
+<h4>Configuring Connectors</h4>
+
+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.
+
+Most configurations are connector dependent, so they can't be outlined here. However, there are a few common options:
+
+<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>
+</ul>
+
+Sink connectors also have one additional option to control their input:
+<ul>
+ <li><code>topics</code> - A list of topics to use as input for this connector</li>
+</ul>
+
+For any other options, you should consult the documentation for the connector.
+
+
+<h4>REST API</h4>
+
+Since Kafka Connect is intended to be run as a service, it also supports a REST API for managing connectors. By default this service runs on port 8083. The following are the currently supported endpoints:
+
+<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 a 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}/tasks</code> - get a list of tasks currently running for a connector</li>
+ <li><code>DELETE /connectors/{name}</code> - delete a connector, halting all tasks and deleting its configuration</li>
+</ul>
+
+<h3><a id="connect_development">8.3 Connector Development Guide</a></h3>
+
+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.
+
+<h4>Core Concepts and APIs</h4>
+
+<h5>Connectors and Tasks</h5>
+
+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).
+
+<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>.
+
+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.
+
+
+<h5>Streams and Records</h5>
+
+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.
+
+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.
+
+<h5>Dynamic Connectors</h5>
+
+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 anycorresponding <code>Tasks</code>.
+
+
+<h4>Developing a Simple Connector</h4>
+
+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.
+
+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.
+
+<h5>Connector Example</h5>
+
+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):
+
+<pre>
+public class FileStreamSourceConnector extends SourceConnector {
+ private String filename;
+ private String topic;
+</pre>
+
+The easiest method to fill in is <code>getTaskClass()</code>, which defines the class that should be instantiated in worker processes to actually read the data:
+
+<pre>
+@Override
+public Class<? extends Task> getTaskClass() {
+ return FileStreamSourceTask.class;
+}
+</pre>
+
+We will define the <code>FileStreamSourceTask</code> class below. Next, we add some standard lifecycle methods, <code>start()</code> and <code>stop()</code>:
+
+<pre>
+@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>
+
+Finally, the real core of the implementation is in <code>getTaskConfigs()</code>. In this case we're 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:
+
+<pre>
+@Override
+public List<Map<String, String>> getTaskConfigs(int maxTasks) {
+ ArrayList>Map<String, String>> configs = new ArrayList<>();
+ // Only one input stream makes sense.
+ Map<String, String> config = new Map<>();
+ if (filename != null)
+ config.put(FILE_CONFIG, filename);
+ config.put(TOPIC_CONFIG, topic);
+ configs.add(config);
+ return configs;
+}
+</pre>
+
+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.
+
+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.
+
+<h5>Task Example - Source Task</h5>
+
+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.
+
+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:
+
+
+<pre>
+public class FileStreamSourceTask extends SourceTask<Object, Object> {
+ String filename;
+ InputStream stream;
+ String topic;
+
+ 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>
+
+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.
+
+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>:
+
+<pre>
+@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>
+
+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 inclue a specific output partition and a key.
+
+Note that this implementation uses the normal Java <code>InputStream</code>interface and may sleep if data is not avaiable. 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.
+
+<h5>Sink Tasks</h5>
+
+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:
+
+<pre>
+public abstract class SinkTask implements Task {
+public void initialize(SinkTaskContext context) { ... }
+
+public abstract void put(Collection<SinkRecord> records);
+
+public abstract void flush(Map<TopicPartition, Long> offsets);
+</pre>
+
+The <code>SinkTask</code> documentation contains full details, but this interface is nearly as simple as the 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.
+
+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.
+
+
+<h5>Resuming from Previous Offsets</h5>
+
+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.
+
+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:
+
+<pre>
+ 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>
+
+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.
+
+<h4>Dynamic Input/Output Streams</h4>
+
+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.
+
+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>:
+
+
+<pre>
+if (inputsChanged())
+ this.context.requestTaskReconfiguration();
+</pre>
+
+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.
+
+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.
+
+<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 simultaneoulsy 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.
+
+<h4>Working with Schemas</h4>
+
+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.
+
+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>.
+
+The API documentation provides a complete reference, but here is a simple example creating a <code>Schema</code>and <code>Struct</code>:
+
+<pre>
+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)
+ .build();
+</pre>
+
+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.
+
+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.
+
+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.
+