Add 0.10.2 docs from 0.10.2.0 RC0
Project: http://git-wip-us.apache.org/repos/asf/kafka-site/repo Commit: http://git-wip-us.apache.org/repos/asf/kafka-site/commit/a7c3675d Tree: http://git-wip-us.apache.org/repos/asf/kafka-site/tree/a7c3675d Diff: http://git-wip-us.apache.org/repos/asf/kafka-site/diff/a7c3675d Branch: refs/heads/asf-site Commit: a7c3675d3139f17dd7bbbc9dec3c0fb1236a540a Parents: 9a36603 Author: Ewen Cheslack-Postava <m...@ewencp.org> Authored: Wed Feb 1 15:39:32 2017 -0800 Committer: Ewen Cheslack-Postava <m...@ewencp.org> Committed: Wed Feb 1 15:39:32 2017 -0800 ---------------------------------------------------------------------- 0102/api.html | 98 ++ 0102/configuration.html | 257 ++++ 0102/connect.html | 445 ++++++ 0102/design.html | 587 ++++++++ 0102/documentation.html | 201 +++ 0102/ecosystem.html | 18 + 0102/generated/connect_config.html | 124 ++ 0102/generated/connect_transforms.html | 173 +++ 0102/generated/consumer_config.html | 118 ++ 0102/generated/kafka_config.html | 304 ++++ 0102/generated/producer_config.html | 112 ++ 0102/generated/protocol_api_keys.html | 47 + 0102/generated/protocol_errors.html | 54 + 0102/generated/protocol_messages.html | 2038 +++++++++++++++++++++++++++ 0102/generated/streams_config.html | 74 + 0102/generated/topic_config.html | 59 + 0102/images/consumer-groups.png | Bin 0 -> 26820 bytes 0102/images/kafka-apis.png | Bin 0 -> 86640 bytes 0102/images/kafka_log.png | Bin 0 -> 134321 bytes 0102/images/kafka_multidc.png | Bin 0 -> 33959 bytes 0102/images/kafka_multidc_complex.png | Bin 0 -> 38559 bytes 0102/images/log_anatomy.png | Bin 0 -> 19579 bytes 0102/images/log_cleaner_anatomy.png | Bin 0 -> 18638 bytes 0102/images/log_compaction.png | Bin 0 -> 41414 bytes 0102/images/log_consumer.png | Bin 0 -> 139658 bytes 0102/images/mirror-maker.png | Bin 0 -> 6579 bytes 0102/images/producer_consumer.png | Bin 0 -> 8691 bytes 0102/images/tracking_high_level.png | Bin 0 -> 82759 bytes 0102/implementation.html | 409 ++++++ 0102/introduction.html | 219 +++ 0102/js/templateData.js | 21 + 0102/migration.html | 34 + 0102/ops.html | 1363 ++++++++++++++++++ 0102/protocol.html | 230 +++ 0102/quickstart.html | 403 ++++++ 0102/security.html | 920 ++++++++++++ 0102/streams.html | 424 ++++++ 0102/upgrade.html | 367 +++++ 0102/uses.html | 54 + 39 files changed, 9153 insertions(+) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/kafka-site/blob/a7c3675d/0102/api.html ---------------------------------------------------------------------- diff --git a/0102/api.html b/0102/api.html new file mode 100644 index 0000000..9b9cd96 --- /dev/null +++ b/0102/api.html @@ -0,0 +1,98 @@ +<!-- + Licensed to the Apache Software Foundation (ASF) under one or more + contributor license agreements. See the NOTICE file distributed with + this work for additional information regarding copyright ownership. + The ASF licenses this file to You under the Apache License, Version 2.0 + (the "License"); you may not use this file except in compliance with + the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +--> + +<script id="api-template" type="text/x-handlebars-template"> + Kafka includes four core apis: + <ol> + <li>The <a href="#producerapi">Producer</a> API allows applications to send streams of data to topics in the Kafka cluster. + <li>The <a href="#consumerapi">Consumer</a> API allows applications to read streams of data from topics in the Kafka cluster. + <li>The <a href="#streamsapi">Streams</a> API allows transforming streams of data from input topics to output topics. + <li>The <a href="#connectapi">Connect</a> API allows implementing connectors that continually pull from some source system or application into Kafka or push from Kafka into some sink system or application. + </ol> + + Kafka exposes all its functionality over a language independent protocol which has clients available in many programming languages. However only the Java clients are maintained as part of the main Kafka project, the others are available as independent open source projects. A list of non-Java clients is available <a href="https://cwiki.apache.org/confluence/display/KAFKA/Clients";>here</a>. + + <h3><a id="producerapi" href="#producerapi">2.1 Producer API</a></h3> + + The Producer API allows applications to send streams of data to topics in the Kafka cluster. + <p> + Examples showing how to use the producer are given in the + <a href="/{{version}}/javadoc/index.html?org/apache/kafka/clients/producer/KafkaProducer.html" title="Kafka 0.10.2 Javadoc">javadocs</a>. + <p> + To use the producer, you can use the following maven dependency: + + <pre> + <dependency> + <groupId>org.apache.kafka</groupId> + <artifactId>kafka-clients</artifactId> + <version>0.10.2.0</version> + </dependency> + </pre> + + <h3><a id="consumerapi" href="#consumerapi">2.2 Consumer API</a></h3> + + The Consumer API allows applications to read streams of data from topics in the Kafka cluster. + <p> + Examples showing how to use the consumer are given in the + <a href="/{{version}}/javadoc/index.html?org/apache/kafka/clients/consumer/KafkaConsumer.html" title="Kafka 0.10.2 Javadoc">javadocs</a>. + <p> + To use the consumer, you can use the following maven dependency: + <pre> + <dependency> + <groupId>org.apache.kafka</groupId> + <artifactId>kafka-clients</artifactId> + <version>0.10.2.0</version> + </dependency> + </pre> + + <h3><a id="streamsapi" href="#streamsapi">2.3 Streams API</a></h3> + + The <a href="#streamsapi">Streams</a> API allows transforming streams of data from input topics to output topics. + <p> + Examples showing how to use this library are given in the + <a href="/{{version}}/javadoc/index.html?org/apache/kafka/streams/KafkaStreams.html" title="Kafka 0.10.2 Javadoc">javadocs</a> + <p> + Additional documentation on using the Streams API is available <a href="/documentation.html#streams">here</a>. + <p> + To use Kafka Streams you can use the following maven dependency: + + <pre> + <dependency> + <groupId>org.apache.kafka</groupId> + <artifactId>kafka-streams</artifactId> + <version>0.10.2.0</version> + </dependency> + </pre> + + <h3><a id="connectapi" href="#connectapi">2.4 Connect API</a></h3> + + The Connect API allows implementing connectors that continually pull from some source data system into Kafka or push from Kafka into some sink data system. + <p> + Many users of Connect won't need to use this API directly, though, they can use pre-built connectors without needing to write any code. Additional information on using Connect is available <a href="/documentation.html#connect">here</a>. + <p> + Those who want to implement custom connectors can see the <a href="/{{version}}/javadoc/index.html?org/apache/kafka/connect" title="Kafka 0.10.2 Javadoc">javadoc</a>. + <p> + + <h3><a id="legacyapis" href="#streamsapi">2.5 Legacy APIs</a></h3> + + <p> + A more limited legacy producer and consumer api is also included in Kafka. These old Scala APIs are deprecated and only still available for compatibility purposes. Information on them can be found here <a href="/081/documentation.html#producerapi" title="Kafka 0.8.1 Docs"> + here</a>. + </p> +</script> + +<div class="p-api"></div> http://git-wip-us.apache.org/repos/asf/kafka-site/blob/a7c3675d/0102/configuration.html ---------------------------------------------------------------------- diff --git a/0102/configuration.html b/0102/configuration.html new file mode 100644 index 0000000..2cad283 --- /dev/null +++ b/0102/configuration.html @@ -0,0 +1,257 @@ +<!-- + Licensed to the Apache Software Foundation (ASF) under one or more + contributor license agreements. See the NOTICE file distributed with + this work for additional information regarding copyright ownership. + The ASF licenses this file to You under the Apache License, Version 2.0 + (the "License"); you may not use this file except in compliance with + the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +--> + +<script id="configuration-template" type="text/x-handlebars-template"> + Kafka uses key-value pairs in the <a href="http://en.wikipedia.org/wiki/.properties";>property file format</a> for configuration. These values can be supplied either from a file or programmatically. + + <h3><a id="brokerconfigs" href="#brokerconfigs">3.1 Broker Configs</a></h3> + + The essential configurations are the following: + <ul> + <li><code>broker.id</code> + <li><code>log.dirs</code> + <li><code>zookeeper.connect</code> + </ul> + + Topic-level configurations and defaults are discussed in more detail <a href="#topic-config">below</a>. + + <!--#include virtual="generated/kafka_config.html" --> + + <p>More details about broker configuration can be found in the scala class <code>kafka.server.KafkaConfig</code>.</p> + + <a id="topic-config" href="#topic-config">Topic-level configuration</a> + + Configurations pertinent to topics have both a server default as well an optional per-topic override. If no per-topic configuration is given the server default is used. The override can be set at topic creation time by giving one or more <code>--config</code> options. This example creates a topic named <i>my-topic</i> with a custom max message size and flush rate: + <pre> + <b> > bin/kafka-topics.sh --zookeeper localhost:2181 --create --topic my-topic --partitions 1 + --replication-factor 1 --config max.message.bytes=64000 --config flush.messages=1</b> + </pre> + Overrides can also be changed or set later using the alter configs command. This example updates the max message size for <i>my-topic</i>: + <pre> + <b> > bin/kafka-configs.sh --zookeeper localhost:2181 --entity-type topics --entity-name my-topic --alter --add-config max.message.bytes=128000</b> + </pre> + + To check overrides set on the topic you can do + <pre> + <b> > bin/kafka-configs.sh --zookeeper localhost:2181 --entity-type topics --entity-name my-topic --describe</b> + </pre> + + To remove an override you can do + <pre> + <b> > bin/kafka-configs.sh --zookeeper localhost:2181 --entity-type topics --entity-name my-topic --alter --delete-config max.message.bytes</b> + </pre> + + The following are the topic-level configurations. The server's default configuration for this property is given under the Server Default Property heading. A given server default config value only applies to a topic if it does not have an explicit topic config override. + + <!--#include virtual="generated/topic_config.html" --> + + <h3><a id="producerconfigs" href="#producerconfigs">3.2 Producer Configs</a></h3> + + Below is the configuration of the Java producer: + <!--#include virtual="generated/producer_config.html" --> + + <p> + For those interested in the legacy Scala producer configs, information can be found <a href="http://kafka.apache.org/082/documentation.html#producerconfigs";> + here</a>. + </p> + + <h3><a id="consumerconfigs" href="#consumerconfigs">3.3 Consumer Configs</a></h3> + + In 0.9.0.0 we introduced the new Java consumer as a replacement for the older Scala-based simple and high-level consumers. + The configs for both new and old consumers are described below. + + <h4><a id="newconsumerconfigs" href="#newconsumerconfigs">3.3.1 New Consumer Configs</a></h4> + Below is the configuration for the new consumer: + <!--#include virtual="generated/consumer_config.html" --> + + <h4><a id="oldconsumerconfigs" href="#oldconsumerconfigs">3.3.2 Old Consumer Configs</a></h4> + + The essential old consumer configurations are the following: + <ul> + <li><code>group.id</code> + <li><code>zookeeper.connect</code> + </ul> + + <table class="data-table"> + <tbody><tr> + <th>Property</th> + <th>Default</th> + <th>Description</th> + </tr> + <tr> + <td>group.id</td> + <td colspan="1"></td> + <td>A string that uniquely identifies the group of consumer processes to which this consumer belongs. By setting the same group id multiple processes indicate that they are all part of the same consumer group.</td> + </tr> + <tr> + <td>zookeeper.connect</td> + <td colspan="1"></td> + <td>Specifies the ZooKeeper connection string in the form <code>hostname:port</code> where host and port are the host and port of a ZooKeeper server. To allow connecting through other ZooKeeper nodes when that ZooKeeper machine is down you can also specify multiple hosts in the form <code>hostname1:port1,hostname2:port2,hostname3:port3</code>. + <p> + The server may also have a ZooKeeper chroot path as part of its ZooKeeper connection string which puts its data under some path in the global ZooKeeper namespace. If so the consumer should use the same chroot path in its connection string. For example to give a chroot path of <code>/chroot/path</code> you would give the connection string as <code>hostname1:port1,hostname2:port2,hostname3:port3/chroot/path</code>.</td> + </tr> + <tr> + <td>consumer.id</td> + <td colspan="1">null</td> + <td> + <p>Generated automatically if not set.</p> + </td> + </tr> + <tr> + <td>socket.timeout.ms</td> + <td colspan="1">30 * 1000</td> + <td>The socket timeout for network requests. The actual timeout set will be max.fetch.wait + socket.timeout.ms.</td> + </tr> + <tr> + <td>socket.receive.buffer.bytes</td> + <td colspan="1">64 * 1024</td> + <td>The socket receive buffer for network requests</td> + </tr> + <tr> + <td>fetch.message.max.bytes</td> + <td nowrap>1024 * 1024</td> + <td>The number of bytes of messages to attempt to fetch for each topic-partition in each fetch request. These bytes will be read into memory for each partition, so this helps control the memory used by the consumer. The fetch request size must be at least as large as the maximum message size the server allows or else it is possible for the producer to send messages larger than the consumer can fetch.</td> + </tr> + <tr> + <td>num.consumer.fetchers</td> + <td colspan="1">1</td> + <td>The number fetcher threads used to fetch data.</td> + </tr> + <tr> + <td>auto.commit.enable</td> + <td colspan="1">true</td> + <td>If true, periodically commit to ZooKeeper the offset of messages already fetched by the consumer. This committed offset will be used when the process fails as the position from which the new consumer will begin.</td> + </tr> + <tr> + <td>auto.commit.interval.ms</td> + <td colspan="1">60 * 1000</td> + <td>The frequency in ms that the consumer offsets are committed to zookeeper.</td> + </tr> + <tr> + <td>queued.max.message.chunks</td> + <td colspan="1">2</td> + <td>Max number of message chunks buffered for consumption. Each chunk can be up to fetch.message.max.bytes.</td> + </tr> + <tr> + <td>rebalance.max.retries</td> + <td colspan="1">4</td> + <td>When a new consumer joins a consumer group the set of consumers attempt to "rebalance" the load to assign partitions to each consumer. If the set of consumers changes while this assignment is taking place the rebalance will fail and retry. This setting controls the maximum number of attempts before giving up.</td> + </tr> + <tr> + <td>fetch.min.bytes</td> + <td colspan="1">1</td> + <td>The minimum amount of data the server should return for a fetch request. If insufficient data is available the request will wait for that much data to accumulate before answering the request.</td> + </tr> + <tr> + <td>fetch.wait.max.ms</td> + <td colspan="1">100</td> + <td>The maximum amount of time the server will block before answering the fetch request if there isn't sufficient data to immediately satisfy fetch.min.bytes</td> + </tr> + <tr> + <td>rebalance.backoff.ms</td> + <td>2000</td> + <td>Backoff time between retries during rebalance. If not set explicitly, the value in zookeeper.sync.time.ms is used. + </td> + </tr> + <tr> + <td>refresh.leader.backoff.ms</td> + <td colspan="1">200</td> + <td>Backoff time to wait before trying to determine the leader of a partition that has just lost its leader.</td> + </tr> + <tr> + <td>auto.offset.reset</td> + <td colspan="1">largest</td> + <td> + <p>What to do when there is no initial offset in ZooKeeper or if an offset is out of range:<br/>* smallest : automatically reset the offset to the smallest offset<br/>* largest : automatically reset the offset to the largest offset<br/>* anything else: throw exception to the consumer</p> + </td> + </tr> + <tr> + <td>consumer.timeout.ms</td> + <td colspan="1">-1</td> + <td>Throw a timeout exception to the consumer if no message is available for consumption after the specified interval</td> + </tr> + <tr> + <td>exclude.internal.topics</td> + <td colspan="1">true</td> + <td>Whether messages from internal topics (such as offsets) should be exposed to the consumer.</td> + </tr> + <tr> + <td>client.id</td> + <td colspan="1">group id value</td> + <td>The client id is a user-specified string sent in each request to help trace calls. It should logically identify the application making the request.</td> + </tr> + <tr> + <td>zookeeper.session.timeout.ms </td> + <td colspan="1">6000</td> + <td>ZooKeeper session timeout. If the consumer fails to heartbeat to ZooKeeper for this period of time it is considered dead and a rebalance will occur.</td> + </tr> + <tr> + <td>zookeeper.connection.timeout.ms</td> + <td colspan="1">6000</td> + <td>The max time that the client waits while establishing a connection to zookeeper.</td> + </tr> + <tr> + <td>zookeeper.sync.time.ms </td> + <td colspan="1">2000</td> + <td>How far a ZK follower can be behind a ZK leader</td> + </tr> + <tr> + <td>offsets.storage</td> + <td colspan="1">zookeeper</td> + <td>Select where offsets should be stored (zookeeper or kafka).</td> + </tr> + <tr> + <td>offsets.channel.backoff.ms</td> + <td colspan="1">1000</td> + <td>The backoff period when reconnecting the offsets channel or retrying failed offset fetch/commit requests.</td> + </tr> + <tr> + <td>offsets.channel.socket.timeout.ms</td> + <td colspan="1">10000</td> + <td>Socket timeout when reading responses for offset fetch/commit requests. This timeout is also used for ConsumerMetadata requests that are used to query for the offset manager.</td> + </tr> + <tr> + <td>offsets.commit.max.retries</td> + <td colspan="1">5</td> + <td>Retry the offset commit up to this many times on failure. This retry count only applies to offset commits during shut-down. It does not apply to commits originating from the auto-commit thread. It also does not apply to attempts to query for the offset coordinator before committing offsets. i.e., if a consumer metadata request fails for any reason, it will be retried and that retry does not count toward this limit.</td> + </tr> + <tr> + <td>dual.commit.enabled</td> + <td colspan="1">true</td> + <td>If you are using "kafka" as offsets.storage, you can dual commit offsets to ZooKeeper (in addition to Kafka). This is required during migration from zookeeper-based offset storage to kafka-based offset storage. With respect to any given consumer group, it is safe to turn this off after all instances within that group have been migrated to the new version that commits offsets to the broker (instead of directly to ZooKeeper).</td> + </tr> + <tr> + <td>partition.assignment.strategy</td> + <td colspan="1">range</td> + <td><p>Select between the "range" or "roundrobin" strategy for assigning partitions to consumer streams.<p>The round-robin partition assignor lays out all the available partitions and all the available consumer threads. It then proceeds to do a round-robin assignment from partition to consumer thread. If the subscriptions of all consumer instances are identical, then the partitions will be uniformly distributed. (i.e., the partition ownership counts will be within a delta of exactly one across all consumer threads.) Round-robin assignment is permitted only if: (a) Every topic has the same number of streams within a consumer instance (b) The set of subscribed topics is identical for every consumer instance within the group.<p> Range partitioning works on a per-topic basis. For each topic, we lay out the available partitions in numeric order and the consumer threads in lexicographic order. We then divide the number of partitions by the total number of consumer streams (threads ) to determine the number of partitions to assign to each consumer. If it does not evenly divide, then the first few consumers will have one extra partition.</td> + </tr> + </tbody> + </table> + + + <p>More details about consumer configuration can be found in the scala class <code>kafka.consumer.ConsumerConfig</code>.</p> + + <h3><a id="connectconfigs" href="#connectconfigs">3.4 Kafka Connect Configs</a></h3> + Below is the configuration of the Kafka Connect framework. + <!--#include virtual="generated/connect_config.html" --> + + <h3><a id="streamsconfigs" href="#streamsconfigs">3.5 Kafka Streams Configs</a></h3> + Below is the configuration of the Kafka Streams client library. + <!--#include virtual="generated/streams_config.html" --> +</script> + +<div class="p-configuration"></div> http://git-wip-us.apache.org/repos/asf/kafka-site/blob/a7c3675d/0102/connect.html ---------------------------------------------------------------------- diff --git a/0102/connect.html b/0102/connect.html new file mode 100644 index 0000000..1af5ed9 --- /dev/null +++ b/0102/connect.html @@ -0,0 +1,445 @@ +<!--~ + ~ Licensed to the Apache Software Foundation (ASF) under one or more + ~ contributor license agreements. See the NOTICE file distributed with + ~ this work for additional information regarding copyright ownership. + ~ The ASF licenses this file to You under the Apache License, Version 2.0 + ~ (the "License"); you may not use this file except in compliance with + ~ the License. You may obtain a copy of the License at + ~ + ~ http://www.apache.org/licenses/LICENSE-2.0 + ~ + ~ Unless required by applicable law or agreed to in writing, software + ~ distributed under the License is distributed on an "AS IS" BASIS, + ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + ~ See the License for the specific language governing permissions and + ~ limitations under the License. + ~--> + +<script id="connect-template" type="text/x-handlebars-template"> + <h3><a id="connect_overview" href="#connect_overview">8.1 Overview</a></h3> + + 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 group management protocol. More workers can be added to scale up a Kafka Connect cluster.</li> + <li><b>Streaming/batch integration</b> - leveraging Kafka's existing capabilities, Kafka Connect is an ideal solution for bridging streaming and batch data systems</li> + </ul> + + <h3><a id="connect_user" href="#connect_user">8.2 User Guide</a></h3> + + 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><a id="connect_running" href="#connect_running">Running Kafka Connect</a></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. All workers (both standalone and distributed) require a few configs: + <ul> + <li><code>bootstrap.servers</code> - List of Kafka servers used to bootstrap connections to Kafka</li> + <li><code>key.converter</code> - Converter class used to convert between Kafka Connect format and the serialized form that is written to Kafka. This controls the format of the keys in messages written to or read from Kafka, and since this is independent of connectors it allows any connector to work with any serialization format. Examples of common formats include JSON and Avro.</li> + <li><code>value.converter</code> - Converter class used to convert between Kafka Connect format and the serialized form that is written to Kafka. This controls the format of the values in messages written to or read from Kafka, and since this is independent of connectors it allows any connector to work with any serialization format. Examples of common formats include JSON and Avro.</li> + </ul> + + The important configuration options specific to standalone mode are: + <ul> + <li><code>offset.storage.file.filename</code> - File to store offset data in</li> + </ul> + + 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 and task statues. In the distributed mode, Kafka Connect stores the offsets, configs and task statuses in Kafka topics. It is recommended to manually create the topics for offset, configs and statuses in order to achieve the desired the number of partitions and replication factors. If the topics are not yet created when starting Kafka Connect, the topics will be auto created with default number of partitions and replication factor, which may not be best suited for its usage. + + In particular, the following configuration parameters, in addition to the common settings mentioned above, 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, compacted topic. You may need to manually create the topic to ensure the correct configuration as auto created topics may have multiple partitions or be automatically configured for deletion rather than compaction</li> + <li><code>offset.storage.topic</code> (default <code>connect-offsets</code>) - topic to use for storing offsets; this topic should have many partitions, be replicated, and be configured for compaction</li> + <li><code>status.storage.topic</code> (default <code>connect-status</code>) - topic to use for storing statuses; this topic can have multiple partitions, and should be replicated and configured for compaction</li> + </ul> + + 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><a id="connect_configuring" href="#connect_configuring">Configuring Connectors</a></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> + <li><code>key.converter</code> - (optional) Override the default key converter set by the worker.</li> + <li><code>value.converter</code> - (optional) Override the default value converter set by the worker.</li> + </ul> + + The <code>connector.class</code> config supports several formats: the full name or alias of the class for this connector. If the connector is org.apache.kafka.connect.file.FileStreamSinkConnector, you can either specify this full name or use FileStreamSink or FileStreamSinkConnector to make the configuration a bit shorter. + + 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><a id="connect_transforms" href="#connect_transforms">Transformations</a></h4> + + Connectors can be configured with transformations to make lightweight message-at-a-time modifications. They can be convenient for minor data massaging and routing changes. + + A transformation chain can be specified in the connector configuration. + + <ul> + <li><code>transforms</code> - List of aliases for the transformation, specifying the order in which the transformations will be applied.</li> + <li><code>transforms.$alias.type</code> - Fully qualified class name for the transformation.</li> + <li><code>transforms.$alias.$transformationSpecificConfig</code> Configuration properties for the transformation</li> + </ul> + + Several widely-applicable data and routing transformations are included with Kafka Connect: + + <!--#include virtual="generated/connect_transforms.html" --> + + <h4><a id="connect_rest" href="#connect_rest">REST API</a></h4> + + Since Kafka Connect is intended to be run as a service, it also provides a REST API for managing connectors. By default, this service runs on port 8083. The following are the currently supported endpoints: + + <ul> + <li><code>GET /connectors</code> - return a list of active connectors</li> + <li><code>POST /connectors</code> - create a new connector; the request body should be a JSON object containing a string <code>name</code> field and an object <code>config</code> field with the connector configuration parameters</li> + <li><code>GET /connectors/{name}</code> - get information about a specific connector</li> + <li><code>GET /connectors/{name}/config</code> - get the configuration parameters for a specific connector</li> + <li><code>PUT /connectors/{name}/config</code> - update the configuration parameters for a specific connector</li> + <li><code>GET /connectors/{name}/status</code> - get current status of the connector, including if it is running, failed, paused, etc., which worker it is assigned to, error information if it has failed, and the state of all its tasks</li> + <li><code>GET /connectors/{name}/tasks</code> - get a list of tasks currently running for a connector</li> + <li><code>GET /connectors/{name}/tasks/{taskid}/status</code> - get current status of the task, including if it is running, failed, paused, etc., which worker it is assigned to, and error information if it has failed</li> + <li><code>PUT /connectors/{name}/pause</code> - pause the connector and its tasks, which stops message processing until the connector is resumed</li> + <li><code>PUT /connectors/{name}/resume</code> - resume a paused connector (or do nothing if the connector is not paused)</li> + <li><code>POST /connectors/{name}/restart</code> - restart a connector (typically because it has failed)</li> + <li><code>POST /connectors/{name}/tasks/{taskId}/restart</code> - restart an individual task (typically because it has failed)</li> + <li><code>DELETE /connectors/{name}</code> - delete a connector, halting all tasks and deleting its configuration</li> + </ul> + + Kafka Connect also provides a REST API for getting information about connector plugins: + + <ul> + <li><code>GET /connector-plugins</code>- return a list of connector plugins installed in the Kafka Connect cluster. Note that the API only checks for connectors on the worker that handles the request, which means you may see inconsistent results, especially during a rolling upgrade if you add new connector jars</li> + <li><code>PUT /connector-plugins/{connector-type}/config/validate</code> - validate the provided configuration values against the configuration definition. This API performs per config validation, returns suggested values and error messages during validation.</li> + </ul> + + <h3><a id="connect_development" href="#connect_development">8.3 Connector Development Guide</a></h3> + + 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><a id="connect_concepts" href="#connect_concepts">Core Concepts and APIs</a></h4> + + <h5><a id="connect_connectorsandtasks" href="#connect_connectorsandtasks">Connectors and Tasks</a></h5> + + 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><a id="connect_streamsandrecords" href="#connect_streamsandrecords">Streams and Records</a></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><a id="connect_dynamicconnectors" href="#connect_dynamicconnectors">Dynamic Connectors</a></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 any corresponding <code>Tasks</code>. + + + <h4><a id="connect_developing" href="#connect_developing">Developing a Simple Connector</a></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><a id="connect_connectorexample" href="#connect_connectorexample">Connector Example</a></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>taskConfigs()</code>. In this case we are only + handling a single file, so even though we may be permitted to generate more tasks as per the + <code>maxTasks</code> argument, we return a list with only one entry: + + <pre> + @Override + public List<Map<String, String>> taskConfigs(int maxTasks) { + ArrayList<Map<String, String>> configs = new ArrayList<>(); + // Only one input stream makes sense. + Map<String, String> config = new HashMap<>(); + if (filename != null) + config.put(FILE_CONFIG, filename); + config.put(TOPIC_CONFIG, topic); + configs.add(config); + return configs; + } + </pre> + + Although not used in the example, <code>SourceTask</code> also provides two APIs to commit offsets in the source system: <code>commit</code> and <code>commitRecord</code>. The APIs are provided for source systems which have an acknowledgement mechanism for messages. Overriding these methods allows the source connector to acknowledge messages in the source system, either in bulk or individually, once they have been written to Kafka. + The <code>commit</code> API stores the offsets in the source system, up to the offsets that have been returned by <code>poll</code>. The implementation of this API should block until the commit is complete. The <code>commitRecord</code> API saves the offset in the source system for each <code>SourceRecord</code> after it is written to Kafka. As Kafka Connect will record offsets automatically, <code>SourceTask</code>s are not required to implement them. In cases where a connector does need to acknowledge messages in the source system, only one of the APIs is typically required. + + 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><a id="connect_taskexample" href="#connect_taskexample">Task Example - Source Task</a></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 { + String filename; + InputStream stream; + String topic; + + @Override + public void start(Map<String, String> props) { + filename = props.get(FileStreamSourceConnector.FILE_CONFIG); + stream = openOrThrowError(filename); + topic = props.get(FileStreamSourceConnector.TOPIC_CONFIG); + } + + @Override + public synchronized void stop() { + stream.close(); + } + </pre> + + 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 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. + + <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: + + <pre> + public abstract class SinkTask implements Task { + public void initialize(SinkTaskContext context) { + this.context = 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 <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><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. + + 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><a id="connect_dynamicio" href="#connect_dynamicio">Dynamic Input/Output Streams</a></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 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_configs" href="#connect_configs">Connect Configuration Validation</a></h4> + + Kafka Connect allows you to validate connector configurations before submitting a connector to be executed and can provide feedback about errors and recommended values. To take advantage of this, connector developers need to provide an implementation of <code>config()</code> to expose the configuration definition to the framework. + + The following code in <code>FileStreamSourceConnector</code> defines the configuration and exposes it to the framework. + + <pre> + private static final ConfigDef CONFIG_DEF = new ConfigDef() + .define(FILE_CONFIG, Type.STRING, Importance.HIGH, "Source filename.") + .define(TOPIC_CONFIG, Type.STRING, Importance.HIGH, "The topic to publish data to"); + + public ConfigDef config() { + return CONFIG_DEF; + } + </pre> + + <code>ConfigDef</code> class is used for specifying the set of expected configurations. For each configuration, you can specify the name, the type, the default value, the documentation, the group information, the order in the group, the width of the configuration value and the name suitable for display in the UI. Plus, you can provide special validation logic used for single configuration validation by overriding the <code>Validator</code> class. Moreover, as there may be dependencies between configurations, for example, the valid values and visibility of a configuration may change according to the values of other configurations. To handle this, <code>ConfigDef</code> allows you to specify the dependents of a configuration and to provide an implementation of <code>Recommender</code> to get valid values and set visibility of a configuration given the current configuration values. + + Also, the <code>validate()</code> method in <code>Connector</code> provides a default validation implementation which returns a list of allowed configurations together with configuration errors and recommended values for each configuration. However, it does not use the recommended values for configuration validation. You may provide an override of the default implementation for customized configuration validation, which may use the recommended values. + + <h4><a id="connect_schemas" href="#connect_schemas">Working with Schemas</a></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); + </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. + + <h4><a id="connect_administration" href="#connect_administration">Kafka Connect Administration</a></h4> + + <p> + Kafka Connect's <a href="#connect_rest">REST layer</a> provides a set of APIs to enable administration of the cluster. This includes APIs to view the configuration of connectors and the status of their tasks, as well as to alter their current behavior (e.g. changing configuration and restarting tasks). + </p> + + <p> + When a connector is first submitted to the cluster, the workers rebalance the full set of connectors in the cluster and their tasks so that each worker has approximately the same amount of work. This same rebalancing procedure is also used when connectors increase or decrease the number of tasks they require, or when a connector's configuration is changed. You can use the REST API to view the current status of a connector and its tasks, including the id of the worker to which each was assigned. For example, querying the status of a file source (using <code>GET /connectors/file-source/status</code>) might produce output like the following: + </p> + + <pre> + { + "name": "file-source", + "connector": { + "state": "RUNNING", + "worker_id": "192.168.1.208:8083" + }, + "tasks": [ + { + "id": 0, + "state": "RUNNING", + "worker_id": "192.168.1.209:8083" + } + ] + } + </pre> + + <p> + Connectors and their tasks publish status updates to a shared topic (configured with <code>status.storage.topic</code>) which all workers in the cluster monitor. Because the workers consume this topic asynchronously, there is typically a (short) delay before a state change is visible through the status API. The following states are possible for a connector or one of its tasks: + </p> + + <ul> + <li><b>UNASSIGNED:</b> The connector/task has not yet been assigned to a worker.</li> + <li><b>RUNNING:</b> The connector/task is running.</li> + <li><b>PAUSED:</b> The connector/task has been administratively paused.</li> + <li><b>FAILED:</b> The connector/task has failed (usually by raising an exception, which is reported in the status output).</li> + </ul> + + <p> + In most cases, connector and task states will match, though they may be different for short periods of time when changes are occurring or if tasks have failed. For example, when a connector is first started, there may be a noticeable delay before the connector and its tasks have all transitioned to the RUNNING state. States will also diverge when tasks fail since Connect does not automatically restart failed tasks. To restart a connector/task manually, you can use the restart APIs listed above. Note that if you try to restart a task while a rebalance is taking place, Connect will return a 409 (Conflict) status code. You can retry after the rebalance completes, but it might not be necessary since rebalances effectively restart all the connectors and tasks in the cluster. + </p> + + <p> + It's sometimes useful to temporarily stop the message processing of a connector. For example, if the remote system is undergoing maintenance, it would be preferable for source connectors to stop polling it for new data instead of filling logs with exception spam. For this use case, Connect offers a pause/resume API. While a source connector is paused, Connect will stop polling it for additional records. While a sink connector is paused, Connect will stop pushing new messages to it. The pause state is persistent, so even if you restart the cluster, the connector will not begin message processing again until the task has been resumed. Note that there may be a delay before all of a connector's tasks have transitioned to the PAUSED state since it may take time for them to finish whatever processing they were in the middle of when being paused. Additionally, failed tasks will not transition to the PAUSED state until they have been restarted. + </p> +</script> + +<div class="p-connect"></div> \ No newline at end of file
