Kafka source connector

note

You can download all the Pulsar connectors on download page.

The Kafka source connector pulls messages from Kafka topics and persists the messages to Pulsar topics.

This guide explains how to configure and use the Kafka source connector.

Configuration

The configuration of the Kafka source connector has the following properties.

Property

Name	Type	Required	Default	Description
`bootstrapServers`	String	true	" " (empty string)	A comma-separated list of host and port pairs for establishing the initial connection to the Kafka cluster.
`securityProtocol`	String	false	" " (empty string)	The protocol used to communicate with Kafka brokers.
`saslMechanism`	String	false	" " (empty string)	The SASL mechanism used for Kafka client connections.
`saslJaasConfig`	String	false	" " (empty string)	The JAAS login context parameters for SASL connections in the format used by JAAS configuration files.
`sslEnabledProtocols`	String	false	" " (empty string)	The list of protocols enabled for SSL connections.
`sslEndpointIdentificationAlgorithm`	String	false	" " (empty string)	The endpoint identification algorithm to validate server hostnames using a server certificate.
`sslTruststoreLocation`	String	false	" " (empty string)	The location of the trust store file.
`sslTruststorePassword`	String	false	" " (empty string)	The password of the trust store file.
`groupId`	String	true	" " (empty string)	A unique string that identifies the group of consumer processes to which this consumer belongs.
`fetchMinBytes`	long	false	1	The minimum byte expected for each fetch response.
`autoCommitEnabled`	boolean	false	true	If set to true, the consumer's offset is periodically committed in the background. This committed offset is used when the process fails as the position from which a new consumer begins.
`autoCommitIntervalMs`	long	false	5000	The frequency in milliseconds that the consumer offsets are auto-committed to Kafka if `autoCommitEnabled` is set to true.
`heartbeatIntervalMs`	long	false	3000	The interval between heartbeats to the consumer when using Kafka's group management facilities. Note: `heartbeatIntervalMs` must be smaller than `sessionTimeoutMs`.
`sessionTimeoutMs`	long	false	30000	The timeout used to detect consumer failures when using Kafka's group management facility.
`topic`	String	true	" " (empty string)	The Kafka topic that sends messages to Pulsar.
`consumerConfigProperties`	Map	false	" " (empty string)	The consumer configuration properties to be passed to consumers. Note: other properties specified in the connector configuration file take precedence over this configuration.
`keyDeserializationClass`	String	false	org.apache.kafka.common.serialization.StringDeserializer	The deserializer class for Kafka consumers to deserialize keys. The deserializer is set by a specific implementation of `KafkaAbstractSource`.
`valueDeserializationClass`	String	false	org.apache.kafka.common.serialization.ByteArrayDeserializer	The deserializer class for Kafka consumers to deserialize values.
`autoOffsetReset`	String	false	earliest	The default offset reset policy.

Schema Management

This Kafka source connector applies the schema to the topic depending on the data type that is present on the Kafka topic. You can detect the data type from the keyDeserializationClass and valueDeserializationClass configuration parameters.

If the valueDeserializationClass is org.apache.kafka.common.serialization.StringDeserializer, you can set Schema.STRING() as schema type on the Pulsar topic.

If valueDeserializationClass is io.confluent.kafka.serializers.KafkaAvroDeserializer, Pulsar downloads the AVRO schema from the Confluent Schema Registry® and sets it properly on the Pulsar topic.

In this case, you need to set schema.registry.url inside of the consumerConfigProperties configuration entry of the source.

If keyDeserializationClass is not org.apache.kafka.common.serialization.StringDeserializer, it means that you do not have a string as a key and the Kafka Source uses the KeyValue schema type with the SEPARATED encoding.

Pulsar supports the AVRO format for keys.

In this case, you can have a Pulsar topic with the following properties:

Schema: KeyValue schema with SEPARATED encoding
Key: the key content of the Kafka message (base64-encoded)
Value: the value content of the Kafka message
KeySchema: the schema detected from keyDeserializationClass
ValueSchema: the schema detected from valueDeserializationClass

Topic compaction and partition routing use the Pulsar key, which contains the Kafka key, and so they are driven by the same value that you have on Kafka.

When you consume data from Pulsar topics, you can use the KeyValue schema. In this way, you can decode the data properly. If you want to access the raw key, you can use the Message#getKeyBytes() API.

Example

Before using the Kafka source connector, you need to create a configuration file through one of the following methods.

JSON

  {
    "bootstrapServers": "pulsar-kafka:9092",
    "groupId": "test-pulsar-io",
    "topic": "my-topic",
    "sessionTimeoutMs": "10000",
    "autoCommitEnabled": false
  }

YAML

configs:
   bootstrapServers: "pulsar-kafka:9092"
   groupId: "test-pulsar-io"
   topic: "my-topic"
   sessionTimeoutMs: "10000"
   autoCommitEnabled: false

Usage

You can make the Kafka source connector as a Pulsar built-in connector and use it on a standalone cluster or an on-premises cluster.

Standalone cluster

This example describes how to use the Kafka source connector to feed data from Kafka and write data to Pulsar topics in the standalone mode.

Prerequisites

Install Docker(Community Edition).

Steps

Download and start the Confluent Platform. For details, see the documentation to install the Kafka service locally.

Pull a Pulsar image and start Pulsar in standalone mode.

docker pull apachepulsar/pulsar:latest

docker run -d -it -p 6650:6650 -p 8080:8080 -v $PWD/data:/pulsar/data --name pulsar-kafka-standalone apachepulsar/pulsar:latest bin/pulsar standalone

Create a producer file kafka-producer.py.

from kafka import KafkaProducer
producer = KafkaProducer(bootstrap_servers='localhost:9092')
future = producer.send('my-topic', b'hello world')
future.get()

Create a consumer file pulsar-client.py.

import pulsar

client = pulsar.Client('pulsar://localhost:6650')
consumer = client.subscribe('my-topic', subscription_name='my-aa')

while True:
    msg = consumer.receive()
    print msg
    print dir(msg)
    print("Received message: '%s'" % msg.data())
    consumer.acknowledge(msg)

client.close()

Copy the following files to Pulsar.

docker cp pulsar-io-kafka.nar pulsar-kafka-standalone:/pulsar
docker cp kafkaSourceConfig.yaml pulsar-kafka-standalone:/pulsar/conf

Open a new terminal window and start the Kafka source connector in local run mode.

docker exec -it pulsar-kafka-standalone /bin/bash

./bin/pulsar-admin source localrun \
   --archive $PWD/pulsar-io-kafka.nar \
   --tenant public \
   --namespace default \
   --name kafka \
   --destination-topic-name my-topic \
   --source-config-file $PWD/conf/kafkaSourceConfig.yaml \
   --parallelism 1

Open a new terminal window and run the Kafka producer locally.
```
python3 kafka-producer.py
```
Open a new terminal window and run the Pulsar consumer locally.
```
python3 pulsar-client.py
```

The following information appears on the consumer terminal window.

Received message: 'hello world'

On-premises cluster

This example explains how to create a Kafka source connector in an on-premises cluster.

Copy the NAR package of the Kafka connector to the Pulsar connectors directory.

cp pulsar-io-kafka-{{connector:version}}.nar $PULSAR_HOME/connectors/pulsar-io-kafka-{{connector:version}}.nar

Reload all built-in connectors.

PULSAR_HOME/bin/pulsar-admin sources reload

Check whether the Kafka source connector is available on the list or not.
```
PULSAR_HOME/bin/pulsar-admin sources available-sources
```

Create a Kafka source connector on a Pulsar cluster using the pulsar-admin sources create command.

PULSAR_HOME/bin/pulsar-admin sources create \
--source-config-file <absolute path to kafka-source-config.yaml>

Configuration​

Property​

Schema Management​

Example​

Usage​

Standalone cluster​

Prerequisites​

Steps​

On-premises cluster​