Kafka

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Connect Kafka to your preprocessing pipeline, and use the Unstructured Ingest CLI or the Unstructured Ingest Python library to batch process all your documents and store structured outputs locally on your filesystem.

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 Ingest CLI or the Unstructured Ingest Python library. The destination connector can be any of the ones supported. This example uses the local destination connector: