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.. _howto/connection:spark:

Apache Spark Connection
=======================

The Apache Spark connection type enables connection to Apache Spark.

Default Connection IDs
----------------------

Spark Submit and Spark JDBC hooks and operators use ``spark_default`` by default. Spark SQL hooks and operators point to ``spark_sql_default`` by default.

Configuring the Connection
--------------------------
Host (required)
    The host to connect to, it can be ``local``, ``yarn`` or an URL.

Port (optional)
    Specify the port in case of host be an URL.

Extra (optional)
    Specify the extra parameters (as json dictionary) that can be used in spark connection. The following parameters out of the standard python parameters are supported:

    * ``queue`` - The name of the YARN queue to which the application is submitted.
    * ``deploy-mode`` - Whether to deploy your driver on the worker nodes (cluster) or locally as an external client (client).
    * ``spark-home`` - If passed then build the ``spark-binary`` executable path using it (``spark-home``/bin/``spark-binary``); otherwise assume that ``spark-binary`` is present in the PATH of the executing user.
    * ``spark-binary`` - The command to use for Spark submit. Some distros may use ``spark2-submit``. Default ``spark-submit``.
    * ``namespace`` - Kubernetes namespace (``spark.kubernetes.namespace``) to divide cluster resources between multiple users (via resource quota).

When specifying the connection in environment variable you should specify
it using URI syntax.

Note that all components of the URI should be URL-encoded. The URI and and the mongo
connection string are not the same.

For example:

.. code-block:: bash

   export AIRFLOW_CONN_SPARK_DEFAULT='spark://mysparkcluster.com:80?deploy-mode=cluster&spark_binary=command&namespace=kube+namespace'