.. _connect_concepts:

|kconnect-long| Concepts
========================

|kconnect-long| is a framework to stream data into and out of |ak-tm|.  The |cp| ships with several
`built-in connectors <https://www.confluent.io/product/connectors/>`_ that can be used to stream data to or
from commonly used systems such as relational databases or HDFS.  In order to efficiently discuss the inner workings 
of |kconnect-long|, it is helpful to establish a few major concepts.

* :ref:`Connectors <connect_connectors>` -- the high level abstraction that coordinates data streaming by managing tasks
* :ref:`Tasks <connect_tasks>` -- the implementation of how data is copied to or from |ak|
* :ref:`Workers <connect_workers>` -- the running processes that execute connectors and tasks
* :ref:`Converters <connect_converters>` -- the code used to translate data between |kconnect| and the system sending or receiving data
* :ref:`Transforms <connect_transforms>` -- simple logic to alter each message produced by or sent to a connector

.. _connect_connectors:

Connectors
----------

Connectors in |kconnect-long| define where data should be copied to and from. A **connector instance** is a
logical job that is responsible for managing the copying of data between |ak| and another system.
All of the classes that implement or are used by a connector are defined in a **connector plugin**.
Both connector instances and connector plugins may be referred to as "connectors", but it should always be clear from the context
which is being referred to (e.g., :ref:`"install a connector" <connect_installing_plugins>` refers to the plugin,
and "check the status of a connector" refers to a connector instance).

We encourage users to leverage `existing connectors <https://www.confluent.io/product/connectors/>`_.  However, it is possible to write
a new connector plugin from scratch.  At a high level, a developer who wishes to write a new connector plugin follows the workflow below.
Further information is available in the :ref:`developer guide <connect_devguide>`.

.. figure:: images/connector-model-simple.png
   :align: center
   
.. _connect_tasks:

Tasks
-----

Tasks are the main actor in the data model for |kconnect|. Each connector instance coordinates a set of **tasks** that actually copy
the data. By allowing the connector to break a single job into
many tasks, |kconnect-long| provides built-in support for parallelism and scalable data copying with very little
configuration. These tasks have no state stored within them. Task state is stored in |ak| in special topics ``config.storage.topic`` and ``status.storage.topic``
and managed by the associated connector. As such, tasks may be started, stopped, or restarted at any time in order 
to provide a resilient, scalable data pipeline. 

.. figure:: images/data-model-simple.png
   :align: center

   High level representation of data passing through a |kconnect| source task into |ak|.  Note that internal offsets are stored either in |ak| or on disk rather than within the task itself.

Task Rebalancing 
~~~~~~~~~~~~~~~~

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. When a worker fails, tasks are rebalanced across the active workers. 
When a task fails, no rebalance is triggered as a task failure is considered an exceptional case. As such, failed tasks are 
not automatically restarted by the framework and should be restarted via the :ref:`REST API <connect_managing_rest_examples>`. 

.. figure:: images/task-failover.png
   :align: center
   
   Task failover example showing how tasks rebalance in the event of a worker failure.

.. _connect_workers:

Workers
-------

Connectors and tasks are logical units of work and must be scheduled to execute in a process. |kconnect-long| calls these
processes **workers** and has two types of workers: standalone and distributed.

.. _standalone-workers:

Standalone Workers
~~~~~~~~~~~~~~~~~~

Standalone mode is the simplest mode, where a single process is responsible for executing all connectors and tasks.

Since it is a single process, it requires minimal configuration. Standalone mode is 
convenient for getting started, during development, and in certain situations where only one
process makes sense, such as collecting logs from a host. However, because there is only a single process, it also
has more limited functionality: scalability is limited to the single process and there is no fault tolerance beyond
any monitoring you add to the single process.

.. _distributed-workers:

Distributed Workers
~~~~~~~~~~~~~~~~~~~

Distributed mode provides scalability and automatic fault tolerance for |kconnect-long|. In distributed mode,
you start many worker processes using the same ``group.id`` and they automatically coordinate to schedule execution of
connectors and tasks across all available workers. If you add a worker, shut down a worker, or a worker fails
unexpectedly, the rest of the workers detect this and automatically coordinate to redistribute connectors and tasks
across the updated set of available workers. Note the similarity to consumer group rebalance.  Under the covers, 
connect workers are using consumer groups to coordinate and rebalance.

.. important:: All workers with the same ``group.id`` will be in the same connect cluster. For example, if worker-a has
               ``group.id=connect-cluster-a`` and worker-b has the same ``group.id``, worker-a and worker-b will form a
               cluster called ``connect-cluster-a``.



.. figure:: images/worker-model-basics.png
   :align: center

   A three-node |kconnect-long| distributed mode cluster. Connectors (monitoring the source or sink system for changes that
   require reconfiguring tasks) and tasks (copying a subset of a connector's data) are automatically balanced across the
   active workers. The division of work between tasks is shown by the partitions that each task is assigned.

.. _connect_converters:

Converters
----------

Converters are necessary to have a |kconnect-long| deployment support a particular data format when writing to or reading from |ak|.
Tasks use converters to change the format of data from bytes to |kconnect| internal data format and vice versa.

By default, |cp| provides the following converters:

* ``AvroConverter`` (recommended): use with |sr-long|
* ``JsonConverter``: great for structured data
* ``StringConverter``: simple string format
* ``ByteArrayConverter``: provides a "pass-through" option that does no conversion

Converters are decoupled from connectors themselves to allow for reuse of converters between connectors naturally.
For example, using the same Avro converter, the JDBC Source Connector
can write Avro data to |ak| and the HDFS Sink Connector can read Avro data from |ak|. This means the same converter can be used even though, for example,
the JDBC source returns a ResultSet that is eventually written to HDFS as a parquet file.

.. figure:: images/converter-basics.png
   :align: center

   Example showing how converters are used when reading from a database using a JDBC Source Connector, writing to |ak|,
   and writing to HDFS with an HDFS Sink Connector.


.. _connect_transforms:

Transforms
----------

Connectors can be configured with transformations to make simple and lightweight modifications to individual messages.
This can be convenient for minor data adjustments and event routing, and multiple transformations can be chained together
in the connector configuration. However, more complex transformations and operations that apply to multiple messages
are best implemented with :ref:`ksql_home` and :ref:`kafka_streams`.

A transform is a simple function that accepts one record as input and outputs a modified record. A number of
transforms are provided by |kconnect-long|, and these all perform simple but commonly useful modifications.
However, you can implement the `Transformation <javadocs/index.html?org/apache/kafka/connect/transforms/Transformation.html>`_
interface with your own custom logic, package them as a :ref:`Kafka Connect plugin <connect_installing_plugins>`,
and use them with any connectors.

When transforms are used with a source connector, |kconnect-long| passes each source record produced by the connector through
the first transformation, which makes its modifications and outputs a new source record. This updated source record
is then passed to the next transform in the chain, which generates a new modified source record. This continues for
the remaining transforms, and the final updated source record is then :ref:`converted to the binary form <connect_converters>`
and written to |ak|.

Transforms can also be used with sink connectors. |kconnect-long| reads message from |ak| and
:ref:`converts the binary representation to a sink record <connect_converters>`. If there is a transform, 
|kconnect-long| and passes the record through the first transformation, which makes its modifications and outputs a new, updated sink record.
The updated sink record is then passed through the next transform in the chain, which generates a new sink record. This continues for
the remaining transforms, and the final updated sink record is then passed to the sink connector for processing.

For more information, see :ref:`connect_transforms_supported`.

.. include:: includes/transforms-list.rst