import Header from './_source-info-header.md';

Kafka#

Kafka is an open-source distributed event streaming platform, organized
in the form of a log with message publishers and subscribers.
The Kafka dlt verified source loads data using the Confluent Kafka API to the destination of your choice.
See a pipeline example.

The resource that can be loaded:

Setup guide#

Grab Kafka cluster credentials#

1. Follow the Kafka Setup to tweak a
   project.
2. Follow the Configuration to
   get the project credentials.

Initialize the verified source#

To get started with your data pipeline, follow these steps:

1. Enter the following command:

   ⧉

   dlt init kafka duckdb
   

   This command will initialize
   the pipeline example
   with Kafka as the source and duckdb
   as the destination.

2. If you'd like to use a different destination, simply replace duckdb with the name of your
   preferred destination.

3. After running this command, a new directory will be created with the necessary files and
   configuration settings to get started.

Add credentials#

1. In the .dlt folder, there's a file called secrets.toml. It's where you store sensitive
   information securely, like access tokens. Keep this file safe.

   Use the following format for service account authentication:

[sources.kafka.credentials]
bootstrap_servers="web.address.gcp.confluent.cloud:9092"
group_id="test_group"
security_protocol="SASL_SSL"
sasl_mechanisms="PLAIN"
sasl_username="example_username"
sasl_password="example_secret"

2. Enter credentials for your chosen destination as per the docs.

Run the pipeline#

1. Before running the pipeline, ensure that you have installed all the necessary dependencies by
   running the command:

   ⧉

   pip install -r requirements.txt
   

2. You're now ready to run the pipeline! To get started, run the following command:

   ⧉

   python kafka_pipeline.py
   

3. Once the pipeline has finished running, you can verify that everything loaded correctly by using
   the following command:

   ⧉

   dlt pipeline <pipeline_name> show
   

For more information, read the Walkthrough: Run a pipeline.

Sources and resources#

dlt works on the principle of sources and
resources.

Source kafka_consumer#

This function retrieves messages from the given Kafka topics.

@dlt.resource(name="kafka_messages", table_name=lambda msg: msg["_kafka"]["topic"])
def kafka_consumer(
    topics: Union[str, List[str]],
    credentials: Union[KafkaCredentials, Consumer] = dlt.secrets.value,
    msg_processor: Optional[Callable[[Message], Dict[str, Any]]] = default_msg_processor,
    batch_size: Optional[int] = 3000,
    batch_timeout: Optional[int] = 3,
    start_from: Optional[TAnyDateTime] = None,
) -> Iterable[TDataItem]:
   ...

topics: A list of Kafka topics to be extracted.

credentials: By default, it is initialized with the data from
the secrets.toml. It may be used explicitly to pass an initialized
Kafka Consumer object.

msg_processor: A function that will be used to process every message
read from the given topics before saving them in the destination.
It can be used explicitly to pass a custom processor. See the
default processor
as an example of how to implement processors.

batch_size: The number of messages to extract from the cluster
at once. It can be set to tweak performance.

batch_timeout: The maximum timeout (in seconds) for a single batch reading
operation. It can be set to tweak performance.

start_from: A timestamp, starting from which the messages must
be read. When passed, dlt asks the Kafka cluster for an offset,
which is actual for the given timestamp, and starts to read messages from
this offset.

Customization#

Create your own pipeline#

1. Configure the pipeline by specifying the pipeline name, destination, and dataset as follows:

   ⧉

   pipeline = dlt.pipeline(
        pipeline_name="kafka", # Use a custom name if desired
        destination="duckdb", # Choose the appropriate destination (e.g., duckdb, redshift, post)
        dataset_name="kafka_data" # Use a custom name if desired
   )
   

2. To extract several topics:

   ⧉

   topics = ["topic1", "topic2", "topic3"]
   
   resource = kafka_consumer(topics)
   pipeline.run(resource, write_disposition="replace")
   

3. To extract messages and process them in a custom way:

   ⧉

    def custom_msg_processor(msg: confluent_kafka.Message) -> Dict[str, Any]:
        return {
            "_kafka": {
                "topic": msg.topic(), # required field
                "key": msg.key().decode("utf-8"),
                "partition": msg.partition(),
            },
            "data": msg.value().decode("utf-8"),
        }
   
    resource = kafka_consumer("topic", msg_processor=custom_msg_processor)
    pipeline.run(resource)
   

4. To extract messages, starting from a timestamp:

   ⧉

    resource = kafka_consumer("topic", start_from=pendulum.DateTime(2023, 12, 15))
    pipeline.run(resource)