OpenMetadata/openmetadata-docs/content/v1.0.0/connectors/messaging/kafka/cli.md

title	slug
Run Kafka Connector using the CLI	/connectors/messaging/kafka/cli

Run Kafka using the metadata CLI

In this section, we provide guides and references to use the Kafka connector.

Configure and schedule Kafka metadata and profiler workflows from the OpenMetadata UI:

• Requirements
• Metadata Ingestion

Requirements

{%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%} To deploy OpenMetadata, check the Deployment guides. {%/inlineCallout%}

To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment.

Python Requirements

To run the Kafka ingestion, you will need to install:

pip3 install "openmetadata-ingestion[kafka]"

Metadata Ingestion

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Kafka.

In order to create and run a Metadata Ingestion workflow, we will follow the steps to create a YAML configuration able to connect to the source, process the Entities if needed, and reach the OpenMetadata server.

The workflow is modeled around the following JSON Schema

1. Define the YAML Config

This is a sample config for Kafka:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

bootstrapServers: List of brokers as comma separated values of broker host or host:port.

Example: host1:9092,host2:9092

{% /codeInfo %}

{% codeInfo srNumber=2 %}

schemaRegistryURL: URL of the Schema Registry used to ingest the schemas of the topics.

NOTE: For now, the schema will be the last version found for the schema name {topic-name}-value. An issue to improve how it currently works has been opened.

{% /codeInfo %}

{% codeInfo srNumber=3 %}

saslUsername: SASL username for use with the PLAIN and SASL-SCRAM mechanisms.

{% /codeInfo %}

{% codeInfo srNumber=4 %}

saslPassword: SASL password for use with the PLAIN and SASL-SCRAM mechanisms.

{% /codeInfo %}

{% codeInfo srNumber=5 %}

saslMechanism: SASL mechanism to use for authentication.

Supported: GSSAPI, PLAIN, SCRAM-SHA-256, SCRAM-SHA-512, OAUTHBEARER.

NOTE: Despite the name only one mechanism must be configured.

{% /codeInfo %}

{% codeInfo srNumber=6 %}

basicAuthUserInfo: Schema Registry Client HTTP credentials in the form of username:password.

By default, user info is extracted from the URL if present.

{% /codeInfo %}

{% codeInfo srNumber=7 %}

consumerConfig: The accepted additional values for the consumer configuration can be found in the following link.

{% /codeInfo %}

{% codeInfo srNumber=8 %}

schemaRegistryConfig: The accepted additional values for the Schema Registry configuration can be found in the following link.

Note: To ingest the topic schema, schemaRegistryURL must be passed.

{% /codeInfo %}

Source Configuration - Source Config

{% codeInfo srNumber=9 %}

The sourceConfig is defined here:

generateSampleData: Option to turn on/off generating sample data during metadata extraction.

topicFilterPattern: Note that the topicFilterPattern supports regex as include or exclude.

{% /codeInfo %}

Sink Configuration

{% codeInfo srNumber=10 %}

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.

{% /codeInfo %}

Workflow Configuration

{% codeInfo srNumber=11 %}

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

For a simple, local installation using our docker containers, this looks like:

{% /codeInfo %}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}

source:
  type: kafka
  serviceName: local_kafka
  serviceConnection:
    config:
      type: Kafka

      bootstrapServers: localhost:9092

      schemaRegistryURL: http://localhost:8081  # Needs to be a URI

      saslUsername: username

      saslPassword: password

      saslMechanism: PLAIN

      basicAuthUserInfo: username:password

      consumerConfig: {}

      schemaRegistryConfig: {}

  sourceConfig:
    config:
      type: MessagingMetadata
      topicFilterPattern:
        excludes:
          - _confluent.*
      # includes:
      #   - topic1
      # generateSampleData: true

sink:
  type: metadata-rest
  config: {}

workflowConfig:
  openMetadataServerConfig:
    hostPort: "http://localhost:8585/api"
    authProvider: openmetadata
    securityConfig:
      jwtToken: "{bot_jwt_token}"

{% /codeBlock %}

{% /codePreview %}

Workflow Configs for Security Provider

We support different security providers. You can find their definitions here.

Openmetadata JWT Auth

• JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.

workflowConfig:
  openMetadataServerConfig:
    hostPort: "http://localhost:8585/api"
    authProvider: openmetadata
    securityConfig:
      jwtToken: "{bot_jwt_token}"

• You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc link.

2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

metadata ingest -c <path-to-yaml>

Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources.