airflow-provider-kafka 0.1.1

Apache Airflow Kafka provider containing Deferrable Operators & Sensors.

Kafka Airflow Provider

An airflow provider to:

• interact with kafka clusters
• read from topics
• write to topics
• wait for specific messages to arrive to a topic

This package currently contains

3 hooks :

• airflow_provider_kafka.hooks.admin_client.KafkaAdminClientHook - a hook to work against the actual kafka admin client
• airflow_provider_kafka.hooks.consumer.KafkaConsumerHook - a hook that creates a consumer and provides it for interaction
• airflow_provider_kafka.hooks.producer.KafkaProducerHook - a hook that creates a producer and provides it for interaction

3 operators :

• airflow_provider_kafka.operators.await_message.AwaitKafkaMessageOperator - a deferable operator (sensor) that awaits to encounter a message in the log before triggering down stream tasks.
• airflow_provider_kafka.operators.consume_from_topic.ConsumeFromTopicOperator - an operator that reads from a topic and applies a function to each message fetched.
• airflow_provider_kafka.operators.produce_to_topic.ProduceToTopicOperator - an operator that uses a iterable to produce messages as key/value pairs to a kafka topic.

1 trigger :

• airflow_provider_kafka.triggers.await_message.AwaitMessageTrigger

Quick start

pip install airflow-provider-kafka

    # hello_kafka.py 
    
    from airflow_provider_kafka.operators.await_message import AwaitKafkaMessageOperator
    from airflow_provider_kafka.operators.consume_from_topic import ConsumeFromTopicOperator
    from airflow_provider_kafka.operators.produce_to_topic import ProduceToTopicOperator

    def producer_function():
        for i in range(20):
            yield (json.dumps(i), json.dumps(i + 1))



    consumer_logger = logging.getLogger("airflow")
    def consumer_function(message, prefix=None):
        key = json.loads(message.key())
        value = json.loads(message.value())
        consumer_logger.info(f"{prefix} {message.topic()} @ {message.offset()}; {key} : {value}")
        return


    def await_function(message):
        if json.loads(message.value()) % 5 == 0:
            return f" Got the following message: {json.loads(message.value())}"

    t1 = ProduceToTopicOperator(
        task_id="produce_to_topic",
        topic="test_1",
        producer_function="hello_kafka.producer_function",
        kafka_config={"bootstrap.servers": "broker:29092"},
    )

    t2 = ConsumeFromTopicOperator(
        task_id="consume_from_topic",
        topics=["test_1"],
        apply_function="hello_kafka.consumer_function",
        apply_function_kwargs={"prefix": "consumed:::"},
        consumer_config={
            "bootstrap.servers": "broker:29092",
            "group.id": "foo",
            "enable.auto.commit": False,
            "auto.offset.reset": "beginning",
        },
        commit_cadence="end_of_batch",
        max_messages=10,
        max_batch_size=2,
    )

    AwaitKafkaMessageOperator(
        task_id="awaiting_message",
        topics=["test_1"],
        apply_function="hello_kafka.await_function",
        kafka_config={
            "bootstrap.servers": "broker:29092",
            "group.id": "awaiting_message",
            "enable.auto.commit": False,
            "auto.offset.reset": "beginning",
        },
        xcom_push_key="retrieved_message",
    )

FAQs

Why confluent kafka and not (other library) ? A few reasons: the confluent-kafka library is guaranteed to be 1:1 functional with librdkafka, is faster, and is maintained by a company with a commercial stake in ensuring the continued quality and upkeep of it as a product.

Why not release this into airflow directly ? I could probably make the PR and get it through, but the airflow code base is getting huge and I don't want to burden the maintainers with code that they don't own for maintainence. Also there's been multiple attempts to get a Kafka provider in before and this is just faster.

Why is most of the configuration handled in a dict ? Because that's how confluen-kafka does it. I'd rather maintain interfaces that people already using kafka are comfortable with as a starting point - I'm happy to add more options/ interfaces in later but would prefer to be thoughtful about it to ensure that there difference between these operators and the actual client interface are minimal.

Development

Unit Tests

Unit tests are located at tests/unit, a kafka server isn't required to run these tests. execute with pytest

Setup on M1 Mac

Installing on M1 chip means a brew install of the librdkafka library before you can pip install confluent-kafka

brew install librdkafka
export C_INCLUDE_PATH=/opt/homebrew/Cellar/librdkafka/1.8.2/include
export LIBRARY_PATH=/opt/homebrew/Cellar/librdkafka/1.8.2/lib
pip install confluent-kafka