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Stream ingestion

Apache Pinot allows user to consume data from streams and push it directly to pinot database. This process is known as Stream Ingestion. Stream Ingestion allows user to query data within seconds of publishing.

Stream Ingestion provides support for checkpoints out of the box for preventing data loss.

Stream ingestion requires the following steps -

Create schema configuration
Create table configuration
Upload table and schema spec

Let's take a look at each of the following steps in a bit more detail. Let us assume the data to be ingested is in the following format -

Create Schema Configuration

Schema defines the fields along with their data types which are available in the datasource. Schema also defines the fields which serve as dimensions , metrics and timestamp respectively.

Follow for more details on schema configuration. For our sample data, the schema configuration should look as follows

Create Table Configuration

The next step is to create a table where all the ingested data will flow and can be queried. Unlike batch ingestion, table configuration for realtime ingestion also triggers the data ingestion job.For a more detailed overview about tables, check out the reference.

The realtime table configuration consists of the the following fields -

tableName - The name of the table where the data should flow
tableType - The internal type for the table. Should always be set to REALTIME for realtime ingestion
segmentsConfig -

You can also specify additional configs for the consumer by prefixing the key with stream.[streamType] where streamType is the name of the streaming platform. For our sample data and schema, the table config will look like -

Upload schema and table config

Now that we have our table and schema configurations, let's upload them to the Pinot cluster. As soon as the configs are uploaded, pinot will start ingesting available records from the topic.

Custom Ingestion Support

We are working on adding more integrations such as Kinesis out of the box. You can easily write your on ingestion plugin in case it is not supported out of the box. Follow for a walkthrough.

Apache Kafka

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

Start Kafka

Create a Kafka Topic

Start Kafka

Start Kafka cluster on port 9876 using the same Zookeeper from the .

Create a Kafka topic

Download the latest . Create a topic.

Creating Schema Configuration

We will publish the data in the same format as mentioned in the docs. So you can use the same schema mentioned under .

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 milliseconds

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.

How to consume from higher Kafka version?

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.

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.

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.

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.

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 to checkout the real-time data.

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.

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,

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

Amazon Kinesis

This is not tested in production. You may hit some snags while trying to use this.

To ingest events from an Amazon Kinesis stream into Pinot, set the following configs into the table config

where the Kinesis specific properties are:

Property

Kinesis supports authentication using the . The credential provider looks for the credentials in the following order -

Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY (RECOMMENDED since they are recognized by all the AWS SDKs and CLI except for .NET), or AWS_ACCESS_KEY and AWS_SECRET_KEY (only recognized by Java SDK)
Java System Properties - aws.accessKeyId and aws.secretKey

You can also specify the accessKey and secretKey using the properties. However, this method is not secure and should be used only for POC setups. You can also specify other aws fields such as AWS_SESSION_TOKEN as environment variables and config and it will work.

Limitations

ShardID is of the format "shardId-000000000001". We use the numeric part as partitionId. Our partitionId variable is integer. If shardIds grow beyond Integer.MAX_VALUE, we will overflow
Segment size based thresholds for segment completion will not work. It assumes that partition "0" always exists. However, once the shard 0 is split/merged, we will no longer have partition 0.

Apache Kafka

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

Start Kafka

Create a Kafka Topic

Start Kafka

Start Kafka cluster on port 9876 using the same Zookeeper from the .

Create a Kafka topic

Download the latest . Create a topic.

Creating Schema Configuration

We will publish the data in the same format as mentioned in the docs. So you can use the same schema mentioned under .

Creating a table configuration

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

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

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.

How to consume from higher Kafka version?

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.

Upload schema and table

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.

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.

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 to checkout the real-time data.

Some More kafka ingestion configs

Use Kafka Partition(Low) Level Consumer with SSL

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,

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

Amazon Kinesis

This is not tested in production. You may hit some snags while trying to use this.

To ingest events from an Amazon Kinesis stream into Pinot, set the following configs into the table config

{
  "tableName": "kinesisTable",
  "tableType": "REALTIME",
  "segmentsConfig": {
    "timeColumnName": "timestamp",
    "replicasPerPartition": "1"
  },
  "tenants": {},
  "tableIndexConfig": {
    "loadMode": "MMAP",
    "streamConfigs": {
      "streamType": "kinesis",
      "stream.kinesis.topic.name": "<your kinesis stream name>",
      "region": "<your region>",
      "accessKey": "<your access key>",
      "secretKey": "<your secret key>",
      "shardIteratorType": "AFTER_SEQUENCE_NUMBER",
      "stream.kinesis.consumer.type": "lowlevel",
      "stream.kinesis.fetch.timeout.millis": "30000",
      "stream.kinesis.decoder.class.name": "org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder",
      "stream.kinesis.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kinesis.KinesisConsumerFactory",
      "realtime.segment.flush.threshold.size": "1000000",
      "realtime.segment.flush.threshold.time": "6h"
    }
  },
  "metadata": {
    "customConfigs": {}
  }
}

where the Kinesis specific properties are:

Property

Kinesis supports authentication using the . The credential provider looks for the credentials in the following order -

Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY (RECOMMENDED since they are recognized by all the AWS SDKs and CLI except for .NET), or AWS_ACCESS_KEY and AWS_SECRET_KEY (only recognized by Java SDK)
Java System Properties - aws.accessKeyId and aws.secretKey

Limitations

ShardID is of the format "shardId-000000000001". We use the numeric part as partitionId. Our partitionId variable is integer. If shardIds grow beyond Integer.MAX_VALUE, we will overflow
Segment size based thresholds for segment completion will not work. It assumes that partition "0" always exists. However, once the shard 0 is split/merged, we will no longer have partition 0.

Stream ingestion

Stream Ingestion provides support for checkpoints out of the box for preventing data loss.

Stream ingestion requires the following steps -

Create schema configuration
Create table configuration
Upload table and schema spec

Let's take a look at each of the following steps in a bit more detail. Let us assume the data to be ingested is in the following format -

Create Schema Configuration

Schema defines the fields along with their data types which are available in the datasource. Schema also defines the fields which serve as dimensions , metrics and timestamp respectively.

Follow for more details on schema configuration. For our sample data, the schema configuration should look as follows

Create Table Configuration

The realtime table configuration consists of the the following fields -

tableName - The name of the table where the data should flow
tableType - The internal type for the table. Should always be set to REALTIME for realtime ingestion
segmentsConfig -

Upload schema and table config

Now that we have our table and schema configurations, let's upload them to the Pinot cluster. As soon as the configs are uploaded, pinot will start ingesting available records from the topic.

Custom Ingestion Support

We are working on adding more integrations such as Kinesis out of the box. You can easily write your on ingestion plugin in case it is not supported out of the box. Follow for a walkthrough.

Stream ingestion

hashtagCreate Schema Configuration

hashtagCreate Table Configuration

hashtagUpload schema and table config

hashtagCustom Ingestion Support

Apache Kafka

hashtagIntroduction

hashtagCreating Schema Configuration

hashtagCreating a table configuration

hashtagUpgrade from Kafka 0.9 connector to Kafka 2.x connector

hashtagHow to consume from higher Kafka version?

hashtagUpload schema and table

hashtagAdd sample data to the Kafka topic

hashtagIngesting streaming data

hashtagSome More kafka ingestion configs

hashtagUse Kafka Partition(Low) Level Consumer with SSL

hashtagIngest transactionally committed messages only from Kafka

Amazon Kinesis

hashtagLimitations

Apache Kafka

hashtagIntroduction

hashtagCreating Schema Configuration

hashtagCreating a table configuration

hashtagUpgrade from Kafka 0.9 connector to Kafka 2.x connector

hashtagHow to consume from higher Kafka version?

hashtagUpload schema and table

hashtagAdd sample data to the Kafka topic

hashtagIngesting streaming data

hashtagSome More kafka ingestion configs

hashtagUse Kafka Partition(Low) Level Consumer with SSL

hashtagIngest transactionally committed messages only from Kafka

Amazon Kinesis

hashtagLimitations

Stream ingestion

hashtagCreate Schema Configuration

hashtagCreate Table Configuration

hashtagUpload schema and table config

hashtagCustom Ingestion Support

Create Schema Configuration

Create Table Configuration

Upload schema and table config

Custom Ingestion Support

Introduction

Creating Schema Configuration

Creating a table configuration

Upgrade from Kafka 0.9 connector to Kafka 2.x connector

How to consume from higher Kafka version?

Upload schema and table

Add sample data to the Kafka topic

Ingesting streaming data

Some More kafka ingestion configs

Use Kafka Partition(Low) Level Consumer with SSL

Ingest transactionally committed messages only from Kafka

Limitations

Introduction

Creating Schema Configuration

Creating a table configuration

Upgrade from Kafka 0.9 connector to Kafka 2.x connector

How to consume from higher Kafka version?

Upload schema and table

Add sample data to the Kafka topic

Ingesting streaming data

Some More kafka ingestion configs

Use Kafka Partition(Low) Level Consumer with SSL

Ingest transactionally committed messages only from Kafka

Limitations

Create Schema Configuration

Create Table Configuration

Upload schema and table config

Custom Ingestion Support