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REST Proxy

In this tutorial, you will use Confluent REST Proxy to produce messages to and consumes messages from an Apache Kafka® cluster.

After you run the tutorial, view the provided source code and use it as a reference to develop your own Kafka client application.

Prerequisites

Client

  • Docker version 17.06.1-ce
  • Docker Compose version 1.25.4
  • wget

Kafka Cluster

  • You can use this tutorial with a Kafka cluster in any environment:
  • If you are running on Confluent Cloud, you must have access to a Confluent Cloud cluster with an API key and secret.

Setup

  1. Clone the confluentinc/examples GitHub repository and check out the 6.0.0-post branch.

    git clone https://github.com/confluentinc/examples
cd examples
git checkout 6.0.0-post

  2. Change directory to the example for REST Proxy.

    cd clients/cloud/rest-proxy/

  3. Create a local file (for example, at $HOME/.confluent/java.config) with configuration parameters to connect to your Kafka cluster. Starting with one of the templates below, customize the file with connection information to your cluster. Substitute your values for {{ BROKER_ENDPOINT }}, {{CLUSTER_API_KEY }}, and {{ CLUSTER_API_SECRET }} (see Configure Confluent Cloud Clients for instructions on how to manually find these values, or use the ccloud-stack Utility for Confluent Cloud to automatically create them).

    • Template configuration file for Confluent Cloud

      # Required connection configs for Kafka producer, consumer, and admin
bootstrap.servers={{ BROKER_ENDPOINT }}
security.protocol=SASL_SSL
sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required username='{{ CLUSTER_API_KEY }}' password='{{ CLUSTER_API_SECRET }}';
sasl.mechanism=PLAIN
# Required for correctness in Apache Kafka clients prior to 2.6
client.dns.lookup=use_all_dns_ips

# Best practice for Kafka producer to prevent data loss 
acks=all

    • Template configuration file for local host

      # Kafka
bootstrap.servers=localhost:9092

  4. Generate a file of ENV variables used by Docker to set the bootstrap servers and security configuration.

    ../../../ccloud/ccloud-generate-cp-configs.sh $HOME/.confluent/java.config

  5. Source the generated file of ENV variables.

    source ./delta_configs/env.delta

  6. Get the cp-all-in-one-cloud docker-compose.yml file, which runs Confluent Platform in containers in your local host, and automatically configures them to connect to Confluent Cloud.

    wget -O docker-compose.yml https://raw.githubusercontent.com/confluentinc/cp-all-in-one/6.0.0/cp-all-in-one-cloud/docker-compose.yml

  7. For the full REST Proxy configuration, view the REST Proxy section in the docker-compose.yml file which you just downloaded in the previous step.

    cat docker-compose.yml

  8. Start the REST Proxy Docker container by running the following command:

    docker-compose up -d rest-proxy

  9. View the REST Proxy logs in Docker and wait till you see the log message Server started, listening for requests to confirm REST Proxy has started.

    docker-compose logs -f rest-proxy

Basic Producer and Consumer

In this example, the producer application writes Kafka data to a topic in your Kafka cluster. If the topic does not already exist in your Kafka cluster, the producer application will use the Kafka Admin Client API to create the topic. Each record written to Kafka has a key representing a username (for example, alice) and a value of a count, formatted as json (for example, {"count": 0}). The consumer application reads the same Kafka topic and keeps a rolling sum of the count as it processes each record.

Produce Records

  1. Get the Kafka cluster ID that the REST Proxy is connected to.

    KAFKA_CLUSTER_ID=$(docker-compose exec rest-proxy curl -X GET \
     "http://localhost:8082/v3/clusters/" | jq -r ".data[0].cluster_id")

    Verify the parameter KAFKA_CLUSTER_ID has a valid value. For the example in this tutorial, it is shown as lkc-56ngz, but it will differ in your output.

    echo $KAFKA_CLUSTER_ID

  2. Create the Kafka topic test1 using the AdminClient functionality of the REST Proxy API v3. If REST Proxy is backed to Confluent Cloud, configure the replication factor to 3.

    docker-compose exec rest-proxy curl -X POST \
     -H "Content-Type: application/json" \
     -d "{\"topic_name\":\"test1\",\"partitions_count\":6,\"replication_factor\":3,\"configs\":[]}" \
     "http://localhost:8082/v3/clusters/${KAFKA_CLUSTER_ID}/topics" | jq .

    Verify your output resembles:

    {
  "kind": "KafkaTopic",
  "metadata": {
    "self": "http://rest-proxy:8082/v3/clusters/lkc-56ngz/topics/test1",
    "resource_name": "crn:///kafka=lkc-56ngz/topic=test1"
  },
  "cluster_id": "lkc-56ngz",
  "topic_name": "test2",
  "is_internal": false,
  "replication_factor": 3,
  "partitions": {
    "related": "http://rest-proxy:8082/v3/clusters/lkc-56ngz/topics/test2/partitions"
  },
  "configs": {
    "related": "http://rest-proxy:8082/v3/clusters/lkc-56ngz/topics/test2/configs"
  },
  "partition_reassignments": {
    "related": "http://rest-proxy:8082/v3/clusters/lkc-56ngz/topics/test1/partitions/-/reassignment"
  }
}

  3. Produce three JSON messages to the topic, with key alice, and values {"count":0}, {"count":1}, and {"count":2}.

    docker-compose exec rest-proxy curl -X POST \
     -H "Content-Type: application/vnd.kafka.json.v2+json" \
     -H "Accept: application/vnd.kafka.v2+json" \
     --data '{"records":[{"key":"alice","value":{"count":0}},{"key":"alice","value":{"count":1}},{"key":"alice","value":{"count":2}}]}' \
     "http://localhost:8082/topics/test1" | jq .

    Verify your output resembles:

    {
  "offsets": [
    {
      "partition": 0,
      "offset": 0,
      "error_code": null,
      "error": null
    },
    {
      "partition": 0,
      "offset": 1,
      "error_code": null,
      "error": null
    },
    {
      "partition": 0,
      "offset": 2,
      "error_code": null,
      "error": null
    }
  ],
  "key_schema_id": null,
  "value_schema_id": null
}

  4. View the producer code.

Consume Records

  1. Create a consumer ci1 belonging to consumer group cg1. Specify auto.offset.reset to be earliest so it starts at the beginning of the topic.

    docker-compose exec rest-proxy curl -X POST \
     -H "Content-Type: application/vnd.kafka.v2+json" \
     --data '{"name": "ci1", "format": "json", "auto.offset.reset": "earliest"}' \
     http://localhost:8082/consumers/cg1 | jq .

    Verify your output resembles:

    {
  "instance_id": "ci1",
  "base_uri": "http://rest-proxy:8082/consumers/cg1/instances/ci1"
}

  2. Subscribe the consumer to topic test1.

    docker-compose exec rest-proxy curl -X POST \
     -H "Content-Type: application/vnd.kafka.v2+json" \
     --data '{"topics":["test1"]}' \
     http://localhost:8082/consumers/cg1/instances/ci1/subscription | jq .

  3. Consume data using the base URL in the first response. Issue the curl command twice, sleeping 10 seconds in between—this is intentional due to https://github.com/confluentinc/kafka-rest/issues/432.

    docker-compose exec rest-proxy curl -X GET \
     -H "Accept: application/vnd.kafka.json.v2+json" \
     http://localhost:8082/consumers/cg1/instances/ci1/records | jq .

sleep 10

docker-compose exec rest-proxy curl -X GET \
     -H "Accept: application/vnd.kafka.json.v2+json" \
     http://localhost:8082/consumers/cg1/instances/ci1/records | jq .

    Verify your output resembles:

    []
[
  {
    "topic": "test1",
    "key": "alice",
    "value": {
      "count": 0
    },
    "partition": 0,
    "offset": 0
  },
  {
    "topic": "test1",
    "key": "alice",
    "value": {
      "count": 1
    },
    "partition": 0,
    "offset": 1
  },
  {
    "topic": "test1",
    "key": "alice",
    "value": {
      "count": 2
    },
    "partition": 0,
    "offset": 2
  }
]

  4. Delete the consumer instance to clean up its resources

    docker-compose exec rest-proxy curl -X DELETE \
     -H "Content-Type: application/vnd.kafka.v2+json" \
     http://localhost:8082/consumers/cg1/instances/ci1 | jq .

  5. View the consumer code.

Avro and Confluent Cloud Schema Registry

This example is similar to the previous example, except the value is formatted as Avro and integrates with the Confluent Cloud Schema Registry. Before using Confluent Cloud Schema Registry, check its availability and limits.

  1. As described in the Quick Start for Schema Management on Confluent Cloud in the Confluent Cloud GUI, enable Confluent Cloud Schema Registry and create an API key and secret to connect to it.

  2. Verify that your VPC can connect to the Confluent Cloud Schema Registry public internet endpoint.

  3. Update your local configuration file (for example, at $HOME/.confluent/java.config) with parameters to connect to Schema Registry.

    • Template configuration file for Confluent Cloud

      # Required connection configs for Kafka producer, consumer, and admin
bootstrap.servers={{ BROKER_ENDPOINT }}
security.protocol=SASL_SSL
sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required username='{{ CLUSTER_API_KEY }}' password='{{ CLUSTER_API_SECRET }}';
sasl.mechanism=PLAIN
# Required for correctness in Apache Kafka clients prior to 2.6
client.dns.lookup=use_all_dns_ips

# Best practice for Kafka producer to prevent data loss 
acks=all

# Required connection configs for Confluent Cloud Schema Registry
schema.registry.url=https://{{ SR_ENDPOINT }}
basic.auth.credentials.source=USER_INFO
schema.registry.basic.auth.user.info={{ SR_API_KEY }}:{{ SR_API_SECRET }}

    • Template configuration file for local host

      # Kafka
bootstrap.servers=localhost:9092

# Confluent Schema Registry
schema.registry.url=http://localhost:8081

  4. Verify your Confluent Cloud Schema Registry credentials work from your host. In the following example, substitute your values for {{ SR_API_KEY}}, {{SR_API_SECRET }}, and {{ SR_ENDPOINT }}.

    # View the list of registered subjects
$ curl -u {{ SR_API_KEY }}:{{ SR_API_SECRET }} https://{{ SR_ENDPOINT }}/subjects

# Same as above, as a single bash command to parse the values out of  $HOME/.confluent/java.config
$ curl -u $(grep "^schema.registry.basic.auth.user.info"  $HOME/.confluent/java.config | cut -d'=' -f2) $(grep "^schema.registry.url"  $HOME/.confluent/java.config | cut -d'=' -f2)/subjects

Produce Avro Records

  1. Get the Kafka cluster ID that the REST Proxy is connected to.

    KAFKA_CLUSTER_ID=$(docker-compose exec rest-proxy curl -X GET \
     "http://localhost:8082/v3/clusters/" | jq -r ".data[0].cluster_id")

    Verify the parameter KAFKA_CLUSTER_ID has a valid value. For the example in this tutorial, it is shown as lkc-56ngz, but it will differ in your output.

  2. Create the Kafka topic test2 using the AdminClient functionality of the REST Proxy API v3. If REST Proxy is backed to Confluent Cloud, configure the replication factor to 3.

    docker-compose exec rest-proxy curl -X POST \
     -H "Content-Type: application/json" \
     -d "{\"topic_name\":\"test2\",\"partitions_count\":6,\"replication_factor\":3,\"configs\":[]}" \
     "http://localhost:8082/v3/clusters/${KAFKA_CLUSTER_ID}/topics" | jq .

    Verify your output resembles:

    {
  "kind": "KafkaTopic",
  "metadata": {
    "self": "http://rest-proxy:8082/v3/clusters/lkc-56ngz/topics/test2",
    "resource_name": "crn:///kafka=lkc-56ngz/topic=test2"
  },
  "cluster_id": "lkc-56ngz",
  "topic_name": "test2",
  "is_internal": false,
  "replication_factor": 3,
  "partitions": {
    "related": "http://rest-proxy:8082/v3/clusters/lkc-56ngz/topics/test2/partitions"
  },
  "configs": {
    "related": "http://rest-proxy:8082/v3/clusters/lkc-56ngz/topics/test2/configs"
  },
  "partition_reassignments": {
    "related": "http://rest-proxy:8082/v3/clusters/lkc-56ngz/topics/test2/partitions/-/reassignment"
  }
}

  3. Register a new Avro schema for topic test2 with the Confluent Cloud Schema Registry.

    docker-compose exec rest-proxy curl -X POST -H "Content-Type: application/vnd.schemaregistry.v1+json" --data '{ "schema": "[ { \"type\":\"record\", \"name\":\"countInfo\", \"fields\": [ {\"name\":\"count\",\"type\":\"long\"}]} ]" }' -u "$SCHEMA_REGISTRY_BASIC_AUTH_USER_INFO" "$SCHEMA_REGISTRY_URL/subjects/test2-value/versions"

    Verify the output shows the new schema id:

    {"id":100001}

  4. Set the variable schemaid to the value of the schema ID.

    schemaid=$(docker-compose exec rest-proxy curl -X GET -u "$SCHEMA_REGISTRY_BASIC_AUTH_USER_INFO" "$SCHEMA_REGISTRY_URL/subjects/test2-value/versions/latest" | jq '.id')

  5. Produce three Avro messages to the topic, with values {"count":0}, {"count":1}, and {"count":2}. Notice that the request body includes the schema ID.

    docker-compose exec rest-proxy curl -X POST \
     -H "Content-Type: application/vnd.kafka.avro.v2+json" \
     -H "Accept: application/vnd.kafka.v2+json" \
     --data '{"value_schema_id": '"$schemaid"', "records": [{"value": {"countInfo":{"count": 0}}},{"value": {"countInfo":{"count": 1}}},{"value": {"countInfo":{"count": 2}}}]}' \
     "http://localhost:8082/topics/test2" | jq .

    Verify your output resembles:

    {
  "offsets": [
    {
      "partition": 4,
      "offset": 0,
      "error_code": null,
      "error": null
    },
    {
      "partition": 4,
      "offset": 1,
      "error_code": null,
      "error": null
    },
    {
      "partition": 4,
      "offset": 2,
      "error_code": null,
      "error": null
    }
  ],
  "key_schema_id": null,
  "value_schema_id": 100001
}

  6. View the producer Avro code.

Consume Avro Records

  1. Create a consumer ci2 belonging to consumer group cg2. Specify auto.offset.reset to be earliest so it starts at the beginning of the topic.

    docker-compose exec rest-proxy curl -X POST \
     -H "Content-Type: application/vnd.kafka.v2+json" \
     --data '{"name": "ci2", "format": "avro", "auto.offset.reset": "earliest"}' \
     http://localhost:8082/consumers/cg2 | jq .

    Verify your output resembles:

    {
  "instance_id": "ci2",
  "base_uri": "http://rest-proxy:8082/consumers/cg2/instances/ci2"
}

  2. Subscribe the consumer to topic test2.

    docker-compose exec rest-proxy curl -X POST \
     -H "Content-Type: application/vnd.kafka.v2+json" \
     --data '{"topics":["test2"]}' \
     http://localhost:8082/consumers/cg2/instances/ci2/subscription | jq .

  3. Consume data using the base URL in the first response. Issue the curl command twice, sleeping 10 seconds in between—this is intentional due to https://github.com/confluentinc/kafka-rest/issues/432.

    docker-compose exec rest-proxy curl -X GET \
     -H "Accept: application/vnd.kafka.avro.v2+json" \
     http://localhost:8082/consumers/cg2/instances/ci2/records | jq .

sleep 10

docker-compose exec rest-proxy curl -X GET \
     -H "Accept: application/vnd.kafka.avro.v2+json" \
     http://localhost:8082/consumers/cg2/instances/ci2/records | jq .

    Verify your output resembles:

    []
[
  {
    "topic": "test2",
    "key": null,
    "value": {
      "count": 0
    },
    "partition": 0,
    "offset": 0
  },
  {
    "topic": "test2",
    "key": null,
    "value": {
      "count": 1
    },
    "partition": 0,
    "offset": 1
  },
  {
    "topic": "test2",
    "key": null,
    "value": {
      "count": 2
    },
    "partition": 0,
    "offset": 2
  }
]

  4. Delete the consumer instance to clean up its resources

    docker-compose exec rest-proxy curl -X DELETE \
     -H "Content-Type: application/vnd.kafka.v2+json" \
     http://localhost:8082/consumers/cg2/instances/ci2 | jq .

  5. View the consumer Avro code.

Confluent Cloud Schema Registry

  1. View the schema subjects registered in Confluent Cloud Schema Registry. In the following output, substitute values for <SR API KEY>, <SR API SECRET>, and <SR ENDPOINT>.

    curl -u <SR API KEY>:<SR API SECRET> https://<SR ENDPOINT>/subjects

  2. Verify that the subject test2-value exists.

    ["test2-value"]

  3. View the schema information for subject test2-value. In the following output, substitute values for <SR API KEY>, <SR API SECRET>, and <SR ENDPOINT>.

    curl -u <SR API KEY>:<SR API SECRET> https://<SR ENDPOINT>/subjects/test2-value/versions/1

  4. Verify the schema information for subject test2-value.

    {"subject":"test2-value","version":1,"id":100001,"schema":"[{\"type\":\"record\",\"name\":\"countInfo\",\"fields\":[{\"name\":\"count\",\"type\":\"long\"}]}]"}

Stop

  1. Stop Docker by running the following command:

    docker-compose down