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Posted to commits@kafka.apache.org by gw...@apache.org on 2016/04/12 22:48:23 UTC
kafka git commit: KAFKA-3461: Fix typos in Kafka web documentations.
Repository: kafka
Updated Branches:
refs/heads/trunk 34a594472 -> e79d9af3c
KAFKA-3461: Fix typos in Kafka web documentations.
This PR fixes 8 typos in HTML files of `docs` module. I wrote explicitly here since Github sometimes does not highlight the corrections on long lines correctly.
- docs/api.html: compatability => compatibility
- docs/connect.html: simultaneoulsy => simultaneously
- docs/implementation.html: LATIEST_TIME => LATEST_TIME, nPartions => nPartitions
- docs/migration.html: Decomission => Decommission
- docs/ops.html: stoping => stopping, ConumserGroupCommand => ConsumerGroupCommand, youre => you're
Author: Dongjoon Hyun <do...@apache.org>
Reviewers: Ismael Juma
Closes #1138 from dongjoon-hyun/KAFKA-3461
Project: http://git-wip-us.apache.org/repos/asf/kafka/repo
Commit: http://git-wip-us.apache.org/repos/asf/kafka/commit/e79d9af3
Tree: http://git-wip-us.apache.org/repos/asf/kafka/tree/e79d9af3
Diff: http://git-wip-us.apache.org/repos/asf/kafka/diff/e79d9af3
Branch: refs/heads/trunk
Commit: e79d9af3cfbb8884e00424f84f3c687114497998
Parents: 34a5944
Author: Dongjoon Hyun <do...@apache.org>
Authored: Tue Apr 12 13:48:18 2016 -0700
Committer: Gwen Shapira <cs...@gmail.com>
Committed: Tue Apr 12 13:48:18 2016 -0700
----------------------------------------------------------------------
docs/api.html | 2 +-
docs/connect.html | 28 ++++++++++++++--------------
docs/implementation.html | 4 ++--
docs/migration.html | 2 +-
docs/ops.html | 6 +++---
5 files changed, 21 insertions(+), 21 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/kafka/blob/e79d9af3/docs/api.html
----------------------------------------------------------------------
diff --git a/docs/api.html b/docs/api.html
index d303244..8d5be9b 100644
--- a/docs/api.html
+++ b/docs/api.html
@@ -15,7 +15,7 @@
limitations under the License.
-->
-Apache Kafka includes new java clients (in the org.apache.kafka.clients package). These are meant to supplant the older Scala clients, but for compatability they will co-exist for some time. These clients are available in a separate jar with minimal dependencies, while the old Scala clients remain packaged with the server.
+Apache Kafka includes new java clients (in the org.apache.kafka.clients package). These are meant to supplant the older Scala clients, but for compatibility they will co-exist for some time. These clients are available in a separate jar with minimal dependencies, while the old Scala clients remain packaged with the server.
<h3><a id="producerapi" href="#producerapi">2.1 Producer API</a></h3>
http://git-wip-us.apache.org/repos/asf/kafka/blob/e79d9af3/docs/connect.html
----------------------------------------------------------------------
diff --git a/docs/connect.html b/docs/connect.html
index dc6ad6e..88b8c2b 100644
--- a/docs/connect.html
+++ b/docs/connect.html
@@ -108,7 +108,7 @@ This guide describes how developers can write new connectors for Kafka Connect t
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>.
+<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.
@@ -242,11 +242,11 @@ public List<SourceRecord> poll() throws InterruptedException {
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.
-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.
+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.
<h5><a id="connect_sinktasks" href="#connect_sinktasks">Sink Tasks</a></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:
+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 {
@@ -257,17 +257,17 @@ 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>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.
+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><a id="connect_resuming" href="#connect_resuming">Resuming from Previous Offsets</a></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.
+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:
+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);
@@ -285,7 +285,7 @@ Of course, you might need to read many keys for each of the input streams. The <
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>:
+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>
@@ -293,11 +293,11 @@ 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.
+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.
+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.
+<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.
<h4><a id="connect_schemas" href="#connect_schemas">Working with Schemas</a></h4>
@@ -305,7 +305,7 @@ The FileStream connectors are good examples because they are simple, but they al
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>:
+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)
@@ -322,7 +322,7 @@ Struct struct = new Struct(schema)
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.
+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.
http://git-wip-us.apache.org/repos/asf/kafka/blob/e79d9af3/docs/implementation.html
----------------------------------------------------------------------
diff --git a/docs/implementation.html b/docs/implementation.html
index ecd99e7..be81227 100644
--- a/docs/implementation.html
+++ b/docs/implementation.html
@@ -90,7 +90,7 @@ class SimpleConsumer {
* Get a list of valid offsets (up to maxSize) before the given time.
* The result is a list of offsets, in descending order.
* @param time: time in millisecs,
- * if set to OffsetRequest$.MODULE$.LATIEST_TIME(), get from the latest offset available.
+ * if set to OffsetRequest$.MODULE$.LATEST_TIME(), get from the latest offset available.
* if set to OffsetRequest$.MODULE$.EARLIEST_TIME(), get from the earliest offset available.
*/
public long[] getOffsetsBefore(String topic, int partition, long time, int maxNumOffsets);
@@ -292,7 +292,7 @@ Since the broker registers itself in ZooKeeper using ephemeral znodes, this regi
</p>
<h4><a id="impl_zktopic" href="#impl_zktopic">Broker Topic Registry</a></h4>
<pre>
-/brokers/topics/[topic]/[0...N] --> nPartions (ephemeral node)
+/brokers/topics/[topic]/[0...N] --> nPartitions (ephemeral node)
</pre>
<p>
http://git-wip-us.apache.org/repos/asf/kafka/blob/e79d9af3/docs/migration.html
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diff --git a/docs/migration.html b/docs/migration.html
index 2da6a7e..5240d86 100644
--- a/docs/migration.html
+++ b/docs/migration.html
@@ -27,7 +27,7 @@
<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>Decomission the 0.7 cluster.
+ <li>Decommission the 0.7 cluster.
<li>Drink.
</ol>
http://git-wip-us.apache.org/repos/asf/kafka/blob/e79d9af3/docs/ops.html
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diff --git a/docs/ops.html b/docs/ops.html
index b239a0e..8b1cc23 100644
--- a/docs/ops.html
+++ b/docs/ops.html
@@ -70,7 +70,7 @@ Instructions for changing the replication factor of a topic can be found <a href
<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 stoping a server than just killing it.
+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>
@@ -138,7 +138,7 @@ Note, however, after 0.9.0, the kafka.tools.ConsumerOffsetChecker tool is deprec
<h4><a id="basic_ops_consumer_group" href="#basic_ops_consumer_group">Managing Consumer Groups</a></h4>
-With the ConumserGroupCommand tool, we can list, delete, or describe consumer groups. For example, to list all consumer groups across all topics:
+With the ConsumerGroupCommand tool, we can list, delete, or describe consumer groups. For example, to list all consumer groups across all topics:
<pre>
> bin/kafka-consumer-groups.sh --zookeeper localhost:2181 --list
@@ -156,7 +156,7 @@ test-consumer-group test-foo 0 1
</pre>
-When youre using the <a href="https://cwiki.apache.org/confluence/display/KAFKA/Consumer+Client+Re-Design">new consumer-groups API</a> where the broker handles coordination of partition handling and rebalance, you can manage the groups with the "--new-consumer" flags:
+When you're using the <a href="https://cwiki.apache.org/confluence/display/KAFKA/Consumer+Client+Re-Design">new consumer-groups API</a> where the broker handles coordination of partition handling and rebalance, you can manage the groups with the "--new-consumer" flags:
<pre>
> bin/kafka-consumer-groups.sh --new-consumer --bootstrap-server broker1:9092 --list