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~ limitations under the License.
~-->
-<h3><a id="connect_overview" href="#connect_overview">8.1 Overview</a></h3>
+<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 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>
+ 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>
+ <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.
+ 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>
+ <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.
+ 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:
+ 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>
+ <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 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 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).
+ 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:
+ 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>
+ <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.
+ 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>
+ 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.
+ 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>
+ <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.
+ 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:
+ 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>
+ <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.
+ 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>
+ 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.
+ For any other options, you should consult the documentation for the
connector.
-<h4><a id="connect_rest" href="#connect_rest">REST API</a></h4>
+ <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:
+ 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>
+ <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:
+ 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>
+ <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>
+ <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.
+ 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>
+ <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>
+ <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).
+ 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.
+ 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>
+ <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.
+ 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.
+ 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>
+ <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>.
+ 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>
+ <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.
+ 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.
+ 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>
+ <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):
+ 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>
+ <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:
+ 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>
+ <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>:
+ 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);
-}
+ <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>
+ @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:
+ 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>
+ <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.
+ 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.
+ 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.
+ 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>
+ <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.
+ 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:
+ 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;
+ <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 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 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);
+ 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 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;
}
- return null;
-}
-</pre>
+ </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.
+ 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>
+ <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 {
- public void initialize(SinkTaskContext context) {
- this.context = context;
- }
+ <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>
+ 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>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.
+ 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>
+ <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);
- 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>
+ <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.
+ 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>
+ <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.
+ 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>
- if (inputsChanged())
- this.context.requestTaskReconfiguration();
-</pre>
+ <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.
+ 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 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.
+ <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>
+ <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.
+ 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.
+ 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");
+ <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>
+ 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.
+ <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.
+ 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>
+ <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.
+ 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>.
+ 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)
- .field("name", Schema.STRING_SCHEMA)
- .field("age", Schema.INT_SCHEMA)
- .field("admin", new SchemaBuilder.boolean().defaultValue(false).build())
- .build();
+ <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>
+ 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.
+ 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.
+ 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>
+ <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>
+ 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>
+ <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": [
+ <pre>
{
- "id": 0,
- "state": "RUNNING",
- "worker_id": "192.168.1.209:8083"
+ "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>
+ </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>
