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Prerequisites

Before deploying a kafka connect to StreamNative Cloud, make sure the following prerequisites have been met:

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Create a built-in kafka connect

Before creating a kafka connect, it’s highly recommended to do the following:
  1. Check kafka connect availability to ensure the version number of the kafka connect you want to create is supported on StreamNative Cloud.
  2. Go to StreamNative Hub and find the connect-specific docs of your version for configuration reference.
You may see below error logs for the first time you create a connector:
org.apache.kafka.common.config.ConfigException: Topic '__kafka_connect_offset_storage' supplied via the 'offset.storage.topic' property is required to have 'cleanup.policy=compact' to guarantee consistency and durability of source connector offsets, but found the topic currently has 'cleanup.policy=delete'. Continuing would likely result in eventually losing source connector offsets and problems restarting this Connect cluster in the future. Change the 'offset.storage.topic' property in the Connect worker configurations to use a topic with 'cleanup.policy=compact'.
You should set the cleanup.policy of the __kafka_connect_offset_storage topic to compact to avoid the above error with below command:
./bin/kafka-configs.sh --bootstrap-server xxxx:9093 --command-config ~/kafka/kafka-token.properties --alter --topic __kafka_connect_offset_storage --add-config cleanup.policy=compact
The following example shows how to create a data generator source connect named test on Streamnative Cloud using different tools.
  • snctl
  • kcctl
  • Rest API
  • Console
To create a data generator source connect named test, run the following command.
> cat datagen.json
{
  "name": "test",
  "config": {
    "connector.class": "io.confluent.kafka.connect.datagen.DatagenConnector",
    "kafka.topic": "testusers",
    "quickstart": "users",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter.schemas.enable": "false",
    "max.interval": 1000,
    "iterations": 10000000,
    "tasks.max": "1"
  }
}
> snctl kafka admin connect apply -f datagen.json --use-service-account
You should see the following output:
Created connector test
If you want to verify whether the data generator source connect has been created successfully, run the following command:
snctl kafka admin connect get connectors
You should see the following output:

 NAME                  TYPE     STATE        TASKS
 test                  source   RUNNING      0: RUNNING

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Create kafka connect with SMT

StreamNative Cloud supports Single Message Transformations (SMTs) for Kafka Connect. You can use SMTs to transform messages before they are written to the target system. The following example shows how to create a Datagen source connector named test on StreamNative Cloud using different tools. Please refer to the Kafka Connect SMTs to check the supported SMTs in StreamNative cloud.
  • snctl
  • kcctl
  • Rest API
  • Console
To create a data generator source connect named test with SMT, run the following command.
> cat datagen.json
{
  "name": "test",
  "config": {
    "connector.class": "io.confluent.kafka.connect.datagen.DatagenConnector",
    "kafka.topic": "testusers",
    "quickstart": "users",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter.schemas.enable": "false",
    "max.interval": 1000,
    "iterations": 10000000,
    "tasks.max": "1",
    "transforms": "InsertSource",
    "transforms.InsertSource.type": "org.apache.kafka.connect.transforms.InsertField$Value",
    "transforms.InsertSource.static.field": "data_source",
    "transforms.InsertSource.static.value": "test-file-source"
  }
}
> snctl kafka admin connect apply -f datagen.json --use-service-account
You should see the following output:
Created connector test
If you want to verify whether the data generator source connect has been created successfully, run the following command:
snctl kafka admin connect get connectors
You should see the following output:

 NAME                  TYPE     STATE        TASKS
 test                  source   RUNNING      0: RUNNING

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Create kafka connect with secret

Some connects require sensitive information, such as passwords, token, to be passed to the connector. And you may not want to expose these sensitive information in the connector configuration. To solve this problem, you can use the following methods to pass sensitive information to the connector:
  • Create a secret For example, the Milvus sink connector requires a token to be passed to the connector. You can create a secret in the console UI and pass the secret name to the connector configuration. screenshot of creating secret
The location should be the same as the region of your Pulsar cluster.The awsAccessKey and awsSecretKey is the field name, and the lambda-sink-secret can be any unique name you want to give to the secret.For a Milvus sink, we should create a secret with a token field.
  • Pass secrets to the connector configuration The following example shows how to create a Milvus sink connector named test on Streamnative Cloud using different tools.
  • snctl
  • kcctl
  • Rest API
  • Console
To create a Milvus sink connector named test, run the following command.
> cat milvus.json
{
  "name": "test",
  "config": {
    "connector.class": "com.milvus.io.kafka.MilvusSinkConnector",
    "public.endpoint": "http://dockerhost.default.svc.cluster.local:19530",
    "collection.name": "demo"
    "token": "${snsecret:miluvs-sec:token}",
    "topics": "kafka-milvus-input",
    "key.converter": "org.apache.kafka.connect.json.JsonConverter",
    "key.converter.schemas.enable": "false",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter.schemas.enable": "false",
    "tasks.max": "1"
  }
}
> snctl kafka admin connect apply -f milvus.json --use-service-account
The miluvs-sec is the name of the secret you created
You should see the following output:
Created connector test
If you want to verify whether the data generator source connect has been created successfully, run the following command:
snctl kafka admin connect get connectors
You should see the following output:

NAME                  TYPE     STATE        TASKS
test                  sink     RUNNING      0: RUNNING

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Create a custom kafka connect

Before creating a kafka connect, it’s highly recommended to do the following:
  1. Check kafka connect availability to ensure the version number of the kafka connect you want to create is supported on StreamNative Cloud.
  2. Go to StreamNative Hub and find the connect-specific docs of your version for configuration reference.
To create a custom kafka connect, you need to upload the connector jar/zip file to the StreamNative Cloud Package service first. Below are the steps:

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Upload your connector file to Pulsar

  • snctl
  • Pulsarctl
  • Pulsar-admin
Upload packages
snctl pulsar admin packages upload function://public/default/mongo-connect-zip@v1 \
--path /tmp/mongodb-kafka-connect-mongodb-1.12.0.zip \
--description "mongodb kafka connect in zip format" \
--properties fileName=mongo-kafka-connect.zip \
--properties libDir=mongodb-kafka-connect-mongodb-1.12.0/lib
You should see the following output:
The package 'function://public/default/mongo-connect-zip@v1' uploaded from path '/tmp/mongodb-kafka-connect-mongodb-1.12.0.zip' successfully
the property libDir specifies the directory where the third-party libraries are located in the zip file.
The following example shows how to create a custom mongodb source connect named mongo-source on Streamnative Cloud using different tools.
  • snctl
  • kcctl
  • Rest API
To create a custom mongodb source connect named mongo-source, run the following command.
> cat mongo.json
{
  "name": "mongo-source",
  "config": {
    "connector.class": "com.mongodb.kafka.connect.MongoSourceConnector",
    "connection.uri": "mongodb://mongo.default.svc.cluster.local:27017/?authSource=admin",
    "database": "kafka-mongo",
    "collection": "source",
    "key.converter.schemas.enable": false,
    "value.converter.schemas.enable": false,
    "sn.pulsar.package.url": "function://public/default/mongo-connect-zip@v1",
    "tasks.max": "1"
  }
}
> snctl kafka admin connect apply -f mongo.json --use-service-account
The sn.pulsar.package.url is the package url you uploaded to the StreamNative Cloud Package service.
You should see the following output:
Created connector mongo-source
If you want to verify whether the data generator source connect has been created successfully, run the following command:
snctl kafka admin connect get connectors
You should see the following output:

 NAME                  TYPE     STATE        TASKS
 mongo-source          source   RUNNING      0: RUNNING

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Set resources for kafka connect

You can use below two configs to set the resources for the kafka connect to control the CPU and memory usage of the connector:
  • sn.cpu: The number of CPU cores to allocate to the connector, default to 0.5.
  • sn.memory: The bytes of memory to allocate to the connector, default to 2147483648 (2G).
You need to upgrade your Pulsar cluster to v3.0.8.4+, v3.3.3.4+ or v4.0.1.3+ to use the sn.cpu and sn.memory configs.

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Schema Registry Support

Kafka Connect supports using schema registry to save Avro/Protobuf/Json schema for the value and key. And StreamNative has an internal schema registry which can be used without complex configurations. To use it, you just need to set the following configuration in the connector configuration:
  • value.converter.schema.registry.internal: true: if you want to use the internal schema registry for the value converter.
  • key.converter.schema.registry.internal: true: if you want to use the internal schema registry for the key converter.