<@U06TM7M40RK> integrating kafka and msk platform instances with kafka-connect
To integrate Kafka and AWS Managed Streaming for Apache Kafka (MSK) with Kafka Connect, you need to configure Kafka Connect to use the MSK cluster as its Kafka broker. Here are the steps to achieve this:
-
Set Up AWS MSK Cluster: Ensure you have an AWS MSK cluster set up and running. Note down the bootstrap servers and other configuration details.
-
Configure Kafka Connect: Update the Kafka Connect configuration to use the AWS MSK cluster.
-
Deploy Kafka Connect: Deploy Kafka Connect with the updated configuration.
Detailed Steps
Step 1: Set Up AWS MSK Cluster
Ensure your AWS MSK cluster is set up and running. You should have the bootstrap server endpoints available. For example:
b-1.msk-cluster.amazonaws.com:9092,b-2.msk-cluster.amazonaws.com:9092,b-3.msk-cluster.amazonaws.com:9092
Step 2: Configure Kafka Connect
Update the Kafka Connect configuration to use the AWS MSK cluster. This involves setting the appropriate environment variables and configuration settings.
Example Configuration for Kafka Connect
- Create a Kafka Connect Properties File: Create a properties file (e.g.,
connect-distributed.properties
) with the following content:
bootstrap.servers=b-1.msk-cluster.amazonaws.com:9092,b-2.msk-cluster.amazonaws.com:9092,b-3.msk-cluster.amazonaws.com:9092
key.converter=org.apache.kafka.connect.json.JsonConverter
value.converter=org.apache.kafka.connect.json.JsonConverter
key.converter.schemas.enable=false
value.converter.schemas.enable=false
config.storage.topic=connect-configs
offset.storage.topic=connect-offsets
status.storage.topic=connect-status
config.storage.replication.factor=3
offset.storage.replication.factor=3
status.storage.replication.factor=3
security.protocol=SASL_SSL
sasl.mechanism=AWS_MSK_IAM
sasl.jaas.config=software.amazon.msk.auth.iam.IAMLoginModule required;
sasl.client.callback.handler.class=software.amazon.msk.auth.iam.IAMClientCallbackHandler
- Set Environment Variables: Ensure the necessary environment variables are set for Kafka Connect to use AWS MSK IAM authentication.
export KAFKA_BOOTSTRAP_SERVERS=b-1.msk-cluster.amazonaws.com:9092,b-2.msk-cluster.amazonaws.com:9092,b-3.msk-cluster.amazonaws.com:9092
export KAFKA_SECURITY_PROTOCOL=SASL_SSL
export KAFKA_SASL_MECHANISM=AWS_MSK_IAM
export KAFKA_SASL_JAAS_CONFIG="software.amazon.msk.auth.iam.IAMLoginModule required;"
export KAFKA_SASL_CLIENT_CALLBACK_HANDLER_CLASS=software.amazon.msk.auth.iam.IAMClientCallbackHandler
Step 3: Deploy Kafka Connect
Deploy Kafka Connect with the updated configuration. This can be done using Docker, Kubernetes, or any other deployment method you prefer.
Example Docker Compose Configuration
If you are using Docker Compose, you can create a docker-compose.yml
file with the following content:
version: '2'
services:
kafka-connect:
image: confluentinc/cp-kafka-connect:latest
environment:
CONNECT_BOOTSTRAP_SERVERS: ${KAFKA_BOOTSTRAP_SERVERS}
CONNECT_REST_PORT: 8083
CONNECT_GROUP_ID: "connect-cluster"
CONNECT_CONFIG_STORAGE_TOPIC: "connect-configs"
CONNECT_OFFSET_STORAGE_TOPIC: "connect-offsets"
CONNECT_STATUS_STORAGE_TOPIC: "connect-status"
CONNECT_CONFIG_STORAGE_REPLICATION_FACTOR: 3
CONNECT_OFFSET_STORAGE_REPLICATION_FACTOR: 3
CONNECT_STATUS_STORAGE_REPLICATION_FACTOR: 3
CONNECT_KEY_CONVERTER: "org.apache.kafka.connect.json.JsonConverter"
CONNECT_VALUE_CONVERTER: "org.apache.kafka.connect.json.JsonConverter"
CONNECT_KEY_CONVERTER_SCHEMAS_ENABLE: "false"
CONNECT_VALUE_CONVERTER_SCHEMAS_ENABLE: "false"
CONNECT_SECURITY_PROTOCOL: ${KAFKA_SECURITY_PROTOCOL}
CONNECT_SASL_MECHANISM: ${KAFKA_SASL_MECHANISM}
CONNECT_SASL_JAAS_CONFIG: ${KAFKA_SASL_JAAS_CONFIG}
CONNECT_SASL_CLIENT_CALLBACK_HANDLER_CLASS: ${KAFKA_SASL_CLIENT_CALLBACK_HANDLER_CLASS}
ports:
- 8083:8083
Run the Docker Compose setup:
docker-compose up -d
Additional Resources
For more detailed information, you can refer to the following resources:
- <https://datahubproject.io/docs/0.14.0/how/kafka-config|Configuring Kafka in DataHub>
- <https://docs.aws.amazon.com/msk/latest/developerguide/iam-access-control.html|AWS MSK IAM Authentication>
Would you like more detailed steps or further assistance on a specific part of the process?
Sources:
- <https://datahubproject.io/docs/0.14.0/how/kafka-config|Configuring Kafka in DataHub> 0 button 0 button Hint: Mention @runllm in the thread for followups.
multiple platform instance mapping <@U06TM7M40RK>
To handle multiple platform instances in DataHub, especially when using Kafka Connect, you need to configure the platform_instance_map
and connect_to_platform_map
in your Kafka Connect recipe. This allows you to specify different platform instances for different connectors or platforms.
Configuration Steps
- Define Platform Instances: Ensure that each platform instance is defined in your ingestion recipes.
- Configure Kafka Connect Recipe: Use
platform_instance_map
andconnect_to_platform_map
to map platform instances to connectors.
Example Configuration
Step 1: Define Platform Instances
In your ingestion recipes, define the platform instances for each source or sink system. For example, for MySQL and Snowflake:
MySQL Ingestion Recipe:
source:
type: mysql
config:
host_port: localhost:3306
platform_instance: mysql_instance1
database: dbname
username: root
password: example
Snowflake Ingestion Recipe:
source:
type: snowflake
config:
host_port: your_snowflake_host
database: your_database
username: your_username
password: your_password
platform_instance: snowflake_instance1
env: PROD
Step 2: Configure Kafka Connect Recipe
In your Kafka Connect recipe, use platform_instance_map
and connect_to_platform_map
to specify the platform instances for each connector.
Kafka Connect Recipe:
source:
type: kafka-connect
config:
bootstrap_servers: "b-1.msk-cluster.amazonaws.com:9092,b-2.msk-cluster.amazonaws.com:9092,b-3.msk-cluster.amazonaws.com:9092"
platform_instance_map:
snowflake: snowflake_instance1
mysql: mysql_instance1
connect_to_platform_map:
mysql_connector1:
mysql: mysql_instance1
mysql_connector2:
mysql: mysql_instance2
snowflake_connector:
snowflake: snowflake_instance1
Explanation
- platform_instance_map: This is used to specify a default platform instance for each platform. If a single instance of a platform is used across all connectors, you can use this map.
- connect_to_platform_map: This is used to specify platform instances for individual connectors. This takes precedence over
platform_instance_map
if both are specified.
Example with Multiple MySQL Connectors
If you have multiple MySQL connectors, each reading from different MySQL instances, you can configure them as follows:
source:
type: kafka-connect
config:
bootstrap_servers: "b-1.msk-cluster.amazonaws.com:9092,b-2.msk-cluster.amazonaws.com:9092,b-3.msk-cluster.amazonaws.com:9092"
connect_to_platform_map:
mysql_connector1:
mysql: mysql_instance1
mysql_connector2:
mysql: mysql_instance2
kafka_connector1:
kafka: kafka_instance1
kafka_connector2:
kafka: kafka_instance2
Additional Resources
For more detailed information, you can refer to the following resources:
- <https://datahubproject.io/docs/0.14.0/platform-instances|Working with Platform Instances>
- <Kafka Connect | DataHub Connect Advanced Configurations>
Would you like more detailed steps or further assistance on a specific part of the process?
Sources:
- <https://datahubproject.io/docs/0.14.0/platform-instances|Working with Platform Instances>
- <Kafka Connect | DataHub Connect Advanced Configurations> 0 button 0 button Hint: Mention @runllm in the thread for followups.
<@U06TM7M40RK> differentiate platform_instance, platform_instance_map, connect_to_platforn_map in kafka connect ingestion
To differentiate between platform_instance
, platform_instance_map
, and connect_to_platform_map
in Kafka Connect ingestion, let’s break down each term and its usage:
1. platform_instance
- Definition: This specifies the instance of the platform that all assets produced by this recipe belong to.
- Usage: It is used when you have a single instance of a platform and want to apply it globally across all connectors in your Kafka Connect ingestion recipe.
- Example:
source: type: kafka-connect config: platform_instance: my_kafka_instance
2. platform_instance_map
- Definition: This is a mapping of platform names to platform instances. It is used when you have multiple platforms and want to specify a default instance for each platform.
- Usage: It is useful when you have a single instance of each platform but multiple platforms in your Kafka Connect setup.
- Example:
source: type: kafka-connect config: platform_instance_map: snowflake: snowflake_instance1 mysql: mysql_instance1
3. connect_to_platform_map
- Definition: This is a mapping of connector names to platform instances. It is used when you have multiple instances of a platform and need to specify which instance to use for each connector.
- Usage: It is useful when you have multiple instances of the same platform and need to differentiate between them for different connectors.
- Example:
source: type: kafka-connect config: connect_to_platform_map: mysql_connector1: mysql: mysql_instance1 mysql_connector2: mysql: mysql_instance2 kafka_connector1: kafka: kafka_instance1 kafka_connector2: kafka: kafka_instance2
Detailed Explanation
platform_instance
- Scope: Global for the entire recipe.
- Precedence: Lowest precedence. If
platform_instance_map
orconnect_to_platform_map
is specified, they will override this setting. - Use Case: When you have a single instance of a platform and want to apply it globally.
platform_instance_map
- Scope: Platform-specific.
- Precedence: Medium precedence. It overrides
platform_instance
but is overridden byconnect_to_platform_map
. - Use Case: When you have multiple platforms and want to specify a default instance for each platform.
connect_to_platform_map
- Scope: Connector-specific.
- Precedence: Highest precedence. It overrides both
platform_instance
andplatform_instance_map
. - Use Case: When you have multiple instances of the same platform and need to specify which instance to use for each connector.
Example Configuration Combining All Three
Here is an example configuration that combines all three settings:
source:
type: kafka-connect
config:
platform_instance: default_instance
platform_instance_map:
snowflake: snowflake_instance1
mysql: mysql_instance1
connect_to_platform_map:
mysql_connector1:
mysql: mysql_instance1
mysql_connector2:
mysql: mysql_instance2
kafka_connector1:
kafka: kafka_instance1
kafka_connector2:
kafka: kafka_instance2
In this example:
default_instance
is the global default.snowflake_instance1
andmysql_instance1
are the default instances for Snowflake and MySQL platforms, respectively.mysql_connector1
andmysql_connector2
use different MySQL instances, overriding the default specified inplatform_instance_map
.kafka_connector1
andkafka_connector2
use different Kafka instances.
Additional Resources
For more detailed information, you can refer to the following resources:
- <https://datahubproject.io/docs/0.14.0/platform-instances|Working with Platform Instances>
- <Kafka Connect | DataHub Connect Advanced Configurations>
Would you like more detailed steps or further assistance on a specific part of the process?
Sources:
- <Kafka Connect | DataHub Connect Advanced Configurations> 0 button 0 button Hint: Mention @runllm in the thread for followups.
<@U06TM7M40RK> I have one instance of dynamodb, documentdb, postgres, mongodb, s3 and two instances of kafka (msk and kafka). What will my config look like?
To configure your Kafka Connect ingestion with one instance of DynamoDB, DocumentDB, PostgreSQL, MongoDB, S3, and two instances of Kafka (MSK and Kafka), you will need to use platform_instance_map
and connect_to_platform_map
to specify the platform instances appropriately.
Configuration Steps
- Define Platform Instances: Specify the platform instances for each source and sink system.
- Configure Kafka Connect Recipe: Use
platform_instance_map
for single-instance platforms andconnect_to_platform_map
for multiple-instance platforms.
Example Configuration
Step 1: Define Platform Instances
In your ingestion recipes, define the platform instances for each source or sink system. For example:
DynamoDB Ingestion Recipe:
source:
type: dynamodb
config:
platform_instance: dynamodb_instance1
table_name: your_table_name
region: your_region
access_key_id: your_access_key_id
secret_access_key: your_secret_access_key
DocumentDB Ingestion Recipe:
source:
type: documentdb
config:
platform_instance: documentdb_instance1
host_port: your_documentdb_host:27017
database: your_database
username: your_username
password: your_password
PostgreSQL Ingestion Recipe:
source:
type: postgres
config:
platform_instance: postgres_instance1
host_port: your_postgres_host:5432
database: your_database
username: your_username
password: your_password
MongoDB Ingestion Recipe:
source:
type: mongodb
config:
platform_instance: mongodb_instance1
host_port: your_mongodb_host:27017
database: your_database
username: your_username
password: your_password
S3 Ingestion Recipe:
source:
type: s3
config:
platform_instance: s3_instance1
bucket_name: your_bucket_name
region: your_region
access_key_id: your_access_key_id
secret_access_key: your_secret_access_key
Step 2: Configure Kafka Connect Recipe
In your Kafka Connect recipe, use platform_instance_map
for single-instance platforms and connect_to_platform_map
for multiple-instance platforms.
Kafka Connect Recipe:
source:
type: kafka-connect
config:
bootstrap_servers: "b-1.msk-cluster.amazonaws.com:9092,b-2.msk-cluster.amazonaws.com:9092,b-3.msk-cluster.amazonaws.com:9092"
platform_instance_map:
dynamodb: dynamodb_instance1
documentdb: documentdb_instance1
postgres: postgres_instance1
mongodb: mongodb_instance1
s3: s3_instance1
connect_to_platform_map:
kafka_connector1:
kafka: kafka_instance1
kafka_connector2:
kafka: kafka_instance2
Explanation
- platform_instance_map: This map specifies the default platform instance for each platform. Since you have only one instance for DynamoDB, DocumentDB, PostgreSQL, MongoDB, and S3, you can use this map to set their instances.
- connect_to_platform_map: This map specifies the platform instance for each connector. Since you have two instances of Kafka (MSK and Kafka), you need to specify which instance to use for each Kafka connector.
Additional Resources
For more detailed information, you can refer to the following resources:
- <https://datahubproject.io/docs/0.14.0/platform-instances|Working with Platform Instances>
- <Kafka Connect | DataHub Connect Advanced Configurations>
Would you like more detailed steps or further assistance on a specific part of the process?
Sources:
- <Kafka Connect | DataHub Connect Advanced Configurations> 0 button 0 button Hint: Mention @runllm in the thread for followups.
<@U06TM7M40RK> explain generic connectors
Generic connectors in DataHub are used to integrate with data sources or sinks that are not natively supported by DataHub. These connectors allow you to define custom ingestion logic to fetch metadata from various systems and ingest it into DataHub. This flexibility is crucial for integrating with proprietary systems, custom databases, or any other data sources that do not have out-of-the-box support.
Key Concepts
- Custom Source Class: You need to create a custom source class that extends DataHub’s
Source
class. This class will contain the logic to fetch metadata from your data source. - Configuration Class: Define a configuration class that includes all the necessary parameters required to connect to your data source.
- Ingestion Recipe: Create an ingestion recipe that specifies the custom source and its configuration.
- Registration: Register your custom source with DataHub so that it can be used in ingestion recipes.
Steps to Create a Generic Connector
Step 1: Define the Configuration Class
Create a configuration class that includes fields for all the necessary parameters required to connect to your data source.
from pydantic import BaseModel
class CustomSourceConfig(BaseModel):
host: str
port: int
username: str
password: str
database: str
# Add other configuration fields as needed
Step 2: Create the Custom Source Class
Create a custom source class that extends DataHub’s Source
class. Implement the logic to fetch metadata from your data source.
from datahub.ingestion.api.source import Source
from datahub.ingestion.api.common import PipelineContext
from typing import Dict, Any, Iterable
from datahub.metadata.schema_classes import MetadataWorkUnit
class CustomSource(Source):
def __init__(self, config: CustomSourceConfig, ctx: PipelineContext):
super().__init__(ctx)
self.config = config
def get_workunits(self) -> Iterable[MetadataWorkUnit]:
# Implement your custom logic to fetch metadata
# Example: Fetch tables from a custom database
tables = self.fetch_tables()
for table in tables:
workunit = self.create_workunit(table)
yield workunit
def fetch_tables(self):
# Custom logic to fetch tables from the database
pass
def create_workunit(self, table):
# Custom logic to create a MetadataWorkUnit from a table
pass
@classmethod
def create(cls, config_dict: Dict[str, Any], ctx: PipelineContext) -> "CustomSource":
config = CustomSourceConfig.parse_obj(config_dict)
return cls(config, ctx)
Step 3: Create an Ingestion Recipe
Create a YAML file (e.g., custom_source_recipe.yaml
) that specifies the custom source and its configuration.
source:
type: custom-source
config:
host: "localhost"
port: 5432
username: "user"
password: "password"
database: "my_database"
sink:
type: datahub-rest
config:
server: "<http://localhost:8080>"
token: "your-auth-token"
Step 4: Register the Custom Source
Register your custom source with DataHub so that it can be used in ingestion recipes.
from datahub.ingestion.source.source_registry import source_registry
# Register the custom source
source_registry.register("custom-source", CustomSource)
Example Usage
Once you have defined and registered your custom source, you can run the ingestion using the DataHub CLI:
datahub ingest -c custom_source_recipe.yaml
Additional Resources
For more detailed information, you can refer to the following resources:
- <https://datahubproject.io/docs/metadata-ingestion/adding-source|Using a Custom Ingestion Source>
- <https://github.com/datahub-project/datahub/blob/master/docs/cli.md|DataHub CLI Documentation>
Would you like more detailed steps or further assistance on a specific part of the process?
Sources:
- https://datahubspace.slack.com/archives/CUMUWQU66/p1725614059.327059|https://datahubspace.slack.com/archives/CUMUWQU66/p1725614059.327059
- https://datahubspace.slack.com/archives/C029A3M079U/p1683627985.599799|https://datahubspace.slack.com/archives/C029A3M079U/p1683627985.599799 0 button 0 button Hint: Mention @runllm in the thread for followups.
Hi ! Did this interaction resolve your issue or do you still require assistance ?
Please tag @ RunLLM if you still need further assistance !
Or tag @Datahub Community Support if you require a person to take a look
Hope this helps ! ><