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
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 milliseconds to start the consumer from the offset after the timestamp.
The resulting configuration should look as follows -
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?
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.
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.
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.
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.
We will publish the data in the same format as mentioned in the docs. So you can use the same schema mentioned under .
This connector is also suitable for Kafka lib version higher than 2.0.0. In , change the kafka.lib.version from 2.0.0 to 2.1.1 will make this Connector working with Kafka 2.1.1.
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 to checkout the real-time data.
