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This integration instruments the ``confluent-kafka<https://github.com/confluentinc/confluent-kafka-python>``
library to trace event streaming.

Enabling
~~~~~~~~

The kafka integration is enabled automatically when using
:ref:`ddtrace-run<ddtracerun>` or :ref:`import ddtrace.auto<ddtraceauto>`.

Or use :func:`patch() <ddtrace.patch>` to manually enable the integration::

    from ddtrace import patch
    patch(kafka=True)
    import confluent_kafka
    ...

Configuration
~~~~~~~~~~~~~

.. py:data:: ddtrace.config.kafka["service"]

   The service name reported by default for your kafka spans.

   This option can also be set with the ``DD_KAFKA_SERVICE`` environment
   variable.

   Default: ``"kafka"``

.. py:data:: ddtrace.config.kafka["distributed_tracing_enabled"]

   Whether to enable distributed tracing between Kafka messages.

   This option can also be set with the ``DD_KAFKA_PROPAGATION_ENABLED`` environment
   variable.

   Default: ``"False"``

**Note**: `Data Streams Monitoring <https://docs.datadoghq.com/data_streams/>`_ (``DD_DATA_STREAMS_ENABLED=true``) or
distributed tracing (``DD_KAFKA_PROPAGATION_ENABLED=true``) will only work if Kafka message headers are supported.
If `log.message.format.version` is set in the Kafka broker configuration, it must be set to `0.11.0.0` or higher.
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