From e79d9af3cfbb8884e00424f84f3c687114497998 Mon Sep 17 00:00:00 2001 From: Dongjoon Hyun Date: Tue, 12 Apr 2016 13:48:18 -0700 Subject: [PATCH] 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 Reviewers: Ismael Juma Closes #1138 from dongjoon-hyun/KAFKA-3461 --- 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(-) diff --git a/docs/api.html b/docs/api.html index d303244427d..8d5be9b030d 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.

2.1 Producer API

diff --git a/docs/connect.html b/docs/connect.html index dc6ad6e9eb5..88b8c2b5c34 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 Connector for the system they want to pull data from or push data to. Connectors come in two flavors: SourceConnectors import data from another system (e.g. JDBCSourceConnector would import a relational database into Kafka) and SinkConnectors export data (e.g. HDFSSinkConnector would export the contents of a Kafka topic to an HDFS file). -Connectors do not perform any data copying themselves: their configuration describes the data to be copied, and the Connector is responsible for breaking that job into a set of Tasks that can be distributed to workers. These Tasks also come in two corresponding flavors: SourceTaskand SinkTask. +Connectors do not perform any data copying themselves: their configuration describes the data to be copied, and the Connector is responsible for breaking that job into a set of Tasks that can be distributed to workers. These Tasks also come in two corresponding flavors: SourceTask and SinkTask. With an assignment in hand, each Task 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 poll() 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 SourceRecord 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 SourceRecord constructor can also include a specific output partition and a key. -Note that this implementation uses the normal Java InputStreaminterface 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 poll()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 InputStream 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 poll() 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.
Sink Tasks
-The previous section described how to implement a simple SourceTask. Unlike SourceConnectorand SinkConnector, SourceTaskand SinkTaskhave very different interfaces because SourceTaskuses a pull interface and SinkTaskuses a push interface. Both share the common lifecycle methods, but the SinkTaskinterface is quite different: +The previous section described how to implement a simple SourceTask. Unlike SourceConnector and SinkConnector, SourceTask and SinkTask have very different interfaces because SourceTask uses a pull interface and SinkTask uses a push interface. Both share the common lifecycle methods, but the SinkTask interface is quite different: 
 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);
-The SinkTask documentation contains full details, but this interface is nearly as simple as the the SourceTask. The put()method should contain most of the implementation, accepting sets of SinkRecords, 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 SinkRecordscontain essentially the same information as SourceRecords: Kafka topic, partition, offset and the event key and value. +The SinkTask documentation contains full details, but this interface is nearly as simple as the the SourceTask. The put() method should contain most of the implementation, accepting sets of SinkRecords, 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 SinkRecords contain essentially the same information as SourceRecords: Kafka topic, partition, offset and the event key and value. -The flush()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 offsetsparameter 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 flush()operation atomically commits the data and offsets to a final location in HDFS. +The flush() 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 offsets 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 flush() operation atomically commits the data and offsets to a final location in HDFS.
Resuming from Previous Offsets
-The SourceTaskimplementation 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 SourceTask 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 SourceContextpassed into its initialize()method to access the offset data. In initialize(), 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 SourceContext passed into its initialize() method to access the offset data. In initialize(), we would add a bit more code to read the offset (if it exists) and seek to that position: 
     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 ConnectorContextobject that reconfiguration is necessary. For example, in a SourceConnector:
+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 ConnectorContext object that reconfiguration is necessary. For example, in a SourceConnector:
 
 
 
@@ -293,11 +293,11 @@ if (inputsChanged())
     this.context.requestTaskReconfiguration();
 

 
-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 SourceConnectorthis 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 SourceConnector 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 Connectorand 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 Taskencounters the issue before the Connector, which will be common if the Connectorneeds to poll for changes, the Taskwill 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 Connector 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 Task encounters the issue before the Connector, which will be common if the Connector needs to poll for changes, the Task will need to handle the subsequent error. Thankfully, this can usually be handled simply by catching and handling the appropriate exception.
 
-SinkConnectors 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. SinkTasksshould 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 SinkTasksseeing a new input stream for the first time and simultaneoulsy trying to create the new resource. SinkConnectors, on the other hand, will generally require no special code for handling a dynamic set of streams.
+SinkConnectors 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. SinkTasks 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 SinkTasks seeing a new input stream for the first time and simultaneously trying to create the new resource. SinkConnectors, on the other hand, will generally require no special code for handling a dynamic set of streams.
 
 
Working with Schemas

 
@@ -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 data API. Most structured records will need to interact with two classes in addition to primitive types: Schema and Struct.
 
-The API documentation provides a complete reference, but here is a simple example creating a Schemaand Struct:
+The API documentation provides a complete reference, but here is a simple example creating a Schema and Struct:
 
 
 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 ALTER TABLEcommand. 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 ALTER TABLE 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.
 
diff --git a/docs/implementation.html b/docs/implementation.html
index ecd99e708ec..be81227c906 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
 

 
Broker Topic Registry

 
-/brokers/topics/[topic]/[0...N] --> nPartions (ephemeral node)
+/brokers/topics/[topic]/[0...N] --> nPartitions (ephemeral node)
 

 
 

diff --git a/docs/migration.html b/docs/migration.html
index 2da6a7e26ac..5240d866433 100644
--- a/docs/migration.html
+++ b/docs/migration.html
@@ -27,7 +27,7 @@
     
  • Use the 0.7 to 0.8 migration tool to mirror data from the 0.7 cluster into the 0.8 cluster.
     
  • When the 0.8 cluster is fully caught up, redeploy all data consumers running the 0.8 client and reading from the 0.8 cluster.
     
  • Finally migrate all 0.7 producers to 0.8 client publishing data to the 0.8 cluster.
-    
  • Decomission the 0.7 cluster.
+    
  • Decommission the 0.7 cluster.
     
  • Drink.
 
 
diff --git a/docs/ops.html b/docs/ops.html
index b239a0eda55..8b1cc234c64 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 Graceful shutdown
 
-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:
 
      
@@ -138,7 +138,7 @@ Note, however, after 0.9.0, the kafka.tools.ConsumerOffsetChecker tool is deprec
 
 
      Managing Consumer Groups
      
 
-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:
 
 
        > bin/kafka-consumer-groups.sh --zookeeper localhost:2181 --list
@@ -156,7 +156,7 @@ test-consumer-group            test-foo                       0          1
 
      
 
 
-When youre using the new consumer-groups API 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 new consumer-groups API where the broker handles coordination of partition handling and rebalance, you can manage the groups with the "--new-consumer" flags:
 
 
        > bin/kafka-consumer-groups.sh --new-consumer --bootstrap-server broker1:9092 --list