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This guide shows you how to ingest a stream of records from an Apache Kafka topic into a Pinot table.

Apache Kafka

Introduction

In this guide, you'll learn how to import data into Pinot using Apache Kafka for real-time stream ingestion. Pinot has out-of-the-box real-time ingestion support for Kafka.

Let's setup a demo Kafka cluster locally, and create a sample topic transcript-topic

{% tabs %} {% tab title="Docker" %} Start Kafka

docker run \
    --network pinot-demo --name=kafka \
    -e KAFKA_ZOOKEEPER_CONNECT=pinot-quickstart:2123/kafka \
    -e KAFKA_BROKER_ID=0 \
    -e KAFKA_ADVERTISED_HOST_NAME=kafka \
    -d wurstmeister/kafka:latest

Create a Kafka Topic

docker exec \
  -t kafka \
  /opt/kafka/bin/kafka-topics.sh \
  --zookeeper pinot-quickstart:2123/kafka \
  --partitions=1 --replication-factor=1 \
  --create --topic transcript-topic

{% endtab %}

{% tab title="Using launcher scripts" %} Start Kafka

Start Kafka cluster on port 9876 using the same Zookeeper from the quick-start examples.

bin/pinot-admin.sh  StartKafka -zkAddress=localhost:2123/kafka -port 9876

Create a Kafka topic

Download the latest Kafka. Create a topic.

bin/kafka-topics.sh --create --bootstrap-server localhost:9876 --replication-factor 1 --partitions 1 --topic transcript-topic

{% endtab %} {% endtabs %}

Creating Schema Configuration

We will publish the data in the same format as mentioned in the Stream ingestion docs. So you can use the same schema mentioned under Create Schema Configuration.

Creating a table configuration

The real-time table configuration for the transcript table described in the schema from the previous step.

For Kafka, we use streamType as kafka . Currently only JSON format is supported but you can easily write your own decoder by extending the StreamMessageDecoder interface. You can then access your decoder class by putting the jar file in plugins directory

The lowLevel consumer reads data per partition whereas the highLevel consumer utilises Kafka high level consumer to read data from the whole stream. It doesn't have the control over which partition to read at a particular momemt.

For Kafka versions below 2.X, use org.apache.pinot.plugin.stream.kafka09.KafkaConsumerFactory

For Kafka version 2.X and above, use
org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory

You can set the offset to -

  • smallest to start consumer from the earliest offset
  • largest to start consumer from the latest offset
  • timestamp in format yyyy-MM-dd'T'HH:mm:ss.SSSZ to start the consumer from the offset after the timestamp.
  • datetime duration or period to start the consumer from the offset after the period eg., '2d'.

The resulting configuration should look as follows -

{% code title="/tmp/pinot-quick-start/transcript-table-realtime.json" %}

 {
  "tableName": "transcript",
  "tableType": "REALTIME",
  "segmentsConfig": {
    "timeColumnName": "timestamp",
    "timeType": "MILLISECONDS",
    "schemaName": "transcript",
    "replicasPerPartition": "1"
  },
  "tenants": {},
  "tableIndexConfig": {
    "loadMode": "MMAP",
    "streamConfigs": {
      "streamType": "kafka",
      "stream.kafka.consumer.type": "lowlevel",
      "stream.kafka.topic.name": "transcript-topic",
      "stream.kafka.decoder.class.name": "org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder",
      "stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
      "stream.kafka.broker.list": "localhost:9876",
      "realtime.segment.flush.threshold.time": "3600000",
      "realtime.segment.flush.threshold.rows": "50000",
      "stream.kafka.consumer.prop.auto.offset.reset": "smallest"
    }
  },
  "metadata": {
    "customConfigs": {}
  }
}

{% endcode %}

Upgrade from Kafka 0.9 connector to Kafka 2.x connector

  • Update table config for both high level and low level consumer: Update config: stream.kafka.consumer.factory.class.name from org.apache.pinot.core.realtime.impl.kafka.KafkaConsumerFactory to org.apache.pinot.core.realtime.impl.kafka2.KafkaConsumerFactory.
  • If using Stream(High) level consumer: Please also add config stream.kafka.hlc.bootstrap.server into tableIndexConfig.streamConfigs. This config should be the URI of Kafka broker lists, e.g. localhost:9092.

How to consume from higher Kafka version?

This connector is also suitable for Kafka lib version higher than 2.0.0. In Kafka 2.0 connector pom.xml, change the kafka.lib.version from 2.0.0 to 2.1.1 will make this Connector working with Kafka 2.1.1.

How to consume transactional-committed Kafka messages

The connector with Kafka lib 2.0+ supports Kafka transactions. The transaction support is controlled by config kafka.isolation.level in Kafka stream config, which can be read_committed or read_uncommitted (default). Setting it to read_committed will ingest transactionally committed messages in Kafka stream only.

Upload schema and table

Now that we have our table and schema configurations, let's upload them to the Pinot cluster. As soon as the real-time table is created, it will begin ingesting available records from the Kafka topic.

{% tabs %} {% tab title="Docker" %}

docker run \
    --network=pinot-demo \
    -v /tmp/pinot-quick-start:/tmp/pinot-quick-start \
    --name pinot-streaming-table-creation \
    apachepinot/pinot:latest AddTable \
    -schemaFile /tmp/pinot-quick-start/transcript-schema.json \
    -tableConfigFile /tmp/pinot-quick-start/transcript-table-realtime.json \
    -controllerHost pinot-quickstart \
    -controllerPort 9000 \
    -exec

{% endtab %}

{% tab title="Launcher Script" %}

bin/pinot-admin.sh AddTable \
    -schemaFile /tmp/pinot-quick-start/transcript-schema.json \
    -tableConfigFile /tmp/pinot-quick-start/transcript-table-realtime.json \
    -exec

{% endtab %} {% endtabs %}

Add sample data to the Kafka topic

We will publish data in the following format to Kafka. Let us save the data in a file named as transcript.json.

{% code title="transcript.json" %}

{"studentID":205,"firstName":"Natalie","lastName":"Jones","gender":"Female","subject":"Maths","score":3.8,"timestamp":1571900400000}
{"studentID":205,"firstName":"Natalie","lastName":"Jones","gender":"Female","subject":"History","score":3.5,"timestamp":1571900400000}
{"studentID":207,"firstName":"Bob","lastName":"Lewis","gender":"Male","subject":"Maths","score":3.2,"timestamp":1571900400000}
{"studentID":207,"firstName":"Bob","lastName":"Lewis","gender":"Male","subject":"Chemistry","score":3.6,"timestamp":1572418800000}
{"studentID":209,"firstName":"Jane","lastName":"Doe","gender":"Female","subject":"Geography","score":3.8,"timestamp":1572505200000}
{"studentID":209,"firstName":"Jane","lastName":"Doe","gender":"Female","subject":"English","score":3.5,"timestamp":1572505200000}
{"studentID":209,"firstName":"Jane","lastName":"Doe","gender":"Female","subject":"Maths","score":3.2,"timestamp":1572678000000}
{"studentID":209,"firstName":"Jane","lastName":"Doe","gender":"Female","subject":"Physics","score":3.6,"timestamp":1572678000000}
{"studentID":211,"firstName":"John","lastName":"Doe","gender":"Male","subject":"Maths","score":3.8,"timestamp":1572678000000}
{"studentID":211,"firstName":"John","lastName":"Doe","gender":"Male","subject":"English","score":3.5,"timestamp":1572678000000}
{"studentID":211,"firstName":"John","lastName":"Doe","gender":"Male","subject":"History","score":3.2,"timestamp":1572854400000}
{"studentID":212,"firstName":"Nick","lastName":"Young","gender":"Male","subject":"History","score":3.6,"timestamp":1572854400000}

{% endcode %}

Push sample JSON into the transcript-topic Kafka topic, using the Kafka console producer. This will add 12 records to the topic described in the transcript.json file.

bin/kafka-console-producer.sh \
    --broker-list localhost:9876 \
    --topic transcript-topic < transcript.json

Ingesting streaming data

As soon as data flows into the stream, the Pinot table will consume it and it will be ready for querying. Head over to the Query Console to checkout the real-time data.

SELECT * FROM transcript

Some More kafka ingestion configs

Use Kafka Partition(Low) Level Consumer with SSL

Here is an example config which uses SSL based authentication to talk with kafka and schema-registry. Notice there are two sets of SSL options, ones starting with ssl. are for kafka consumer and ones with stream.kafka.decoder.prop.schema.registry. are for SchemaRegistryClient used by KafkaConfluentSchemaRegistryAvroMessageDecoder.

  {
    "tableName": "transcript",
    "tableType": "REALTIME",
    "segmentsConfig": {
    "timeColumnName": "timestamp",
    "timeType": "MILLISECONDS",
    "schemaName": "transcript",
    "replicasPerPartition": "1"
    },
    "tenants": {},
    "tableIndexConfig": {
      "loadMode": "MMAP",
      "streamConfigs": {
        "streamType": "kafka",
        "stream.kafka.consumer.type": "LowLevel",
        "stream.kafka.topic.name": "transcript-topic",
        "stream.kafka.decoder.class.name": "org.apache.pinot.plugin.inputformat.avro.confluent.KafkaConfluentSchemaRegistryAvroMessageDecoder",
        "stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
        "stream.kafka.zk.broker.url": "localhost:2191/kafka",
        "stream.kafka.broker.list": "localhost:9876",
        "schema.registry.url": "",
        "security.protocol": "SSL",
        "ssl.truststore.location": "",
        "ssl.keystore.location": "",
        "ssl.truststore.password": "",
        "ssl.keystore.password": "",
        "ssl.key.password": "",
        "stream.kafka.decoder.prop.schema.registry.rest.url": "",
        "stream.kafka.decoder.prop.schema.registry.ssl.truststore.location": "",
        "stream.kafka.decoder.prop.schema.registry.ssl.keystore.location": "",
        "stream.kafka.decoder.prop.schema.registry.ssl.truststore.password": "",
        "stream.kafka.decoder.prop.schema.registry.ssl.keystore.password": "",
        "stream.kafka.decoder.prop.schema.registry.ssl.keystore.type": "",
        "stream.kafka.decoder.prop.schema.registry.ssl.truststore.type": "",
        "stream.kafka.decoder.prop.schema.registry.ssl.key.password": "",
        "stream.kafka.decoder.prop.schema.registry.ssl.protocol": "",
      }
    },
    "metadata": {
      "customConfigs": {}
    }
  }

Ingest transactionally committed messages only from Kafka

With Kafka consumer 2.0, you can ingest transactionally committed messages only by configuring kafka.isolation.level to read_committed. For example,

  {
    "tableName": "transcript",
    "tableType": "REALTIME",
    "segmentsConfig": {
    "timeColumnName": "timestamp",
    "timeType": "MILLISECONDS",
    "schemaName": "transcript",
    "replicasPerPartition": "1"
    },
    "tenants": {},
    "tableIndexConfig": {
      "loadMode": "MMAP",
      "streamConfigs": {
        "streamType": "kafka",
        "stream.kafka.consumer.type": "LowLevel",
        "stream.kafka.topic.name": "transcript-topic",
        "stream.kafka.decoder.class.name": "org.apache.pinot.plugin.inputformat.avro.confluent.KafkaConfluentSchemaRegistryAvroMessageDecoder",
        "stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
        "stream.kafka.zk.broker.url": "localhost:2191/kafka",
        "stream.kafka.broker.list": "localhost:9876",
        "stream.kafka.isolation.level": "read_committed"
      }
    },
    "metadata": {
      "customConfigs": {}
    }
  }

Note that the default value of this config read_uncommitted to read all messages. Also, this config supports low-level consumer only.

Use Kafka Level Consumer with SASL_SSL

Here is an example config which uses SASL_SSL based authentication to talk with kafka and schema-registry. Notice there are two sets of SSL options, some for kafka consumer and ones with stream.kafka.decoder.prop.schema.registry. are for SchemaRegistryClient used by KafkaConfluentSchemaRegistryAvroMessageDecoder.

"streamConfigs": {
        "streamType": "kafka",
        "stream.kafka.consumer.type": "lowlevel",
        "stream.kafka.topic.name": "mytopic",
        "stream.kafka.consumer.prop.auto.offset.reset": "largest",
        "stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
        "stream.kafka.broker.list": "kafka-broker-host:9092",
        "stream.kafka.schema.registry.url": "https://xxx",
        "stream.kafka.decoder.class.name": "org.apache.pinot.plugin.inputformat.avro.confluent.KafkaConfluentSchemaRegistryAvroMessageDecoder",
        "stream.kafka.decoder.prop.schema.registry.rest.url": "https://xxx",
        "stream.kafka.decoder.prop.basic.auth.credentials.source": "USER_INFO",
        "stream.kafka.decoder.prop.schema.registry.basic.auth.user.info": "schema_registry_username:schema_registry_password",
        "sasl.mechanism": "PLAIN" ,
        "security.protocol": "SASL_SSL" ,
        "sasl.jaas.config":"org.apache.kafka.common.security.scram.ScramLoginModule required username=\"kafkausername\" password=\"kafkapassword\";",
        "realtime.segment.flush.threshold.rows": "0",
        "realtime.segment.flush.threshold.time": "24h",
        "realtime.segment.flush.autotune.initialRows": "3000000",
        "realtime.segment.flush.threshold.segment.size": "500M"
      },

{% hint style="info" %} Post release 0.10.0, we have started shading kafka packages inside Pinot. If you are using our latest tagged docker images or master build, you should replace org.apache.kafka with shaded.org.apache.kafka in your table config. {% endhint %}