Kafka

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Batch process all your records to store structured outputs in Kafka.

The requirements are as follows.

• A Kafka cluster, such as ones offered by the following providers:

  • Confluent Cloud (Create a cluster.)

    The following video shows how to set up a Kafka cluster in Confluent Cloud:

  • Amazon Managed Streaming for Apache Kafka (Amazon MSK) (Create an Amazon MSK Provisioned cluster, or create an Amazon MSK Serverless cluster.)

  • [Apache Kafka in Azure HDInsight] (Create an Apache Kafka cluster in Azure HDInsight.)

  • Google Cloud Managed Service for Apache Kafka (Create a cluster.)

• The hostname and port number of the bootstrap Kafka cluster to connect to.

  • For Confuent Cloud, get the hostname and port number.
  • For Amazon MSK, get the hostname and port number.
  • For Apache Kafka in Azure HDInsight, get the hostname and port number.
  • For Google Cloud Managed Service for Apache Kafka, get the hostname and port number.

• The name of the topic to read messages from and write messages to on the cluster.

  • For Confluent Cloud, create a topic and access available topics.
  • For Amazon MSK, create a topic on an Amazon MSK Provisioned cluster, or create a topic on an Amazon MSK Serverless cluster.
  • For Apache Kafka in Azure HDInsight, create a topic and access available topics.
  • For Google Cloud Managed Service for Apache Kafka, create a topic and access available topics.

• If you use Kafka API keys and secrets for authentication, the key and secret values.

  • For Confluent Cloud, create an API key and secret.
  • For Amazon MSK, create an API key and secret.
  • For Apache Kafka in Azure HDInsight, create an API key and secret.
  • For Google Cloud Managed Service for Apache Kafka, create an API key and secret.

The Kafka connector dependencies:

CLI, Python

pip install "unstructured-ingest[kafka]"

You might also need to install additional dependencies, depending on your needs. Learn more.

The following environment variables:

• KAFKA_BOOTSTRAP_SERVER - The hostname of the bootstrap Kafka cluster to connect to, represented by --bootstrap-server (CLI) or bootstrap_server (Python).
• KAFKA_PORT - The port number of the cluster, represented by --port (CLI) or port (Python).
• KAFKA_TOPIC - The unique name of the topic to read messages from and write messages to on the cluster, represented by --topic (CLI) or topic (Python).

If you use Kafka API keys and secrets for authentication:

• KAFKA_API_KEY - The Kafka API key value, represented by --kafka-api-key (CLI) or kafka_api_key (Python).
• KAFKA_SECRET - The secret value for the Kafka API key, represented by --secret (CLI) or secret (Python).

Additional settings include:

• --confluent (CLI) or confluent (Python): True to indicate that the cluster is running Confluent Kafka.
• --num-messages-to-consume (CLI) or num_messages_to_consume (Python): The maximum number of messages to get from the topic. The default is 1 if not otherwise specified.
• --timeout (CLI) or timeout (Python): The maximum amount of time to wait for the response of a request to the topic, expressed in seconds. The default is 1.0 if not otherwise specified.
• --group-id (CLI) or group_id (Python): The ID of the consumer group, if any, that is associated with the target Kafka cluster. (A consumer group is a way to allow a pool of consumers to divide the consumption of data over topics and partitions.) The default is default_group_id if not otherwise specified.

These environment variables:

• UNSTRUCTURED_API_KEY - Your Unstructured API key value.
• UNSTRUCTURED_API_URL - Your Unstructured API URL.

Now call the Unstructured CLI or Python SDK. The source connector can be any of the ones supported. This example uses the local source connector: