This guide shows you how to ingest a stream of records from an Apache Kafka topic into a Pinot table.
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
The real-time table configuration for the transcript table described in the schema from the previous step.
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 -
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.
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
Kafka Ingestion Guidelines
Kafka Versions in Pinot
Pinot supports 2 major generations of Kafka library - kafka-0.9 and kafka-2.x for both high and low level consumers.
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.
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 a Kafka version > 2.0.0?
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.
Kafka Configurations in Pinot
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.
The connector with Kafka library 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.
Note that the default value of this config read_uncommitted to read all messages. Also, this config supports low-level consumer only.
Use Kafka Partition(Low) 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.
Pinot's Kafka connector now supports automatically extracting record headers and metadata into the Pinot table columns. The following table shows the mapping for record header/metadata to Pinot table column names:
In order to enable the metadata extraction in a Kafka table, you can set the stream config metadata.populate to true.
In addition to this, if you want to actually use any of these columns in your table, you have to list them explicitly in your table's schema.
For example, if you want to add only the offset and key as dimension columns in your Pinot table, it can listed in the schema as follows:
For Kafka, we use streamType as kafka . See for available decoder class options. You can also write your own decoder by extending the StreamMessageDecoder interface and putting the jar file in plugins directory.