Ingest streaming data from Apache Kafka
This guide shows you how to ingest a stream of records from an Apache Kafka topic into a Pinot table.
Learn how to ingest data from Kafka, a stream processing platform. You should have a local cluster up and running, following the instructions in Set up a cluster.
Install and Launch Kafka
Let's start by downloading Kafka to our local machine.
To pull down the latest Docker image, run the following command:
docker pull wurstmeister/kafka:latest
Next we'll spin up a Kafka broker:
docker run --network pinot-demo --name=kafka -e KAFKA_ZOOKEEPER_CONNECT=zookeeper:2181/kafka -e KAFKA_BROKER_ID=0 -e KAFKA_ADVERTISED_HOST_NAME=kafka wurstmeister/kafka:latest
Note: The --network pinot-demo flag is optional and assumes that you have a Docker network named pinot-demo that you want to connect the Kafka container to.
Data Source
We're going to generate some JSON messages from the terminal using the following script:
import datetime
import uuid
import random
import json
while True:
ts = int(datetime.datetime.now().timestamp()* 1000)
id = str(uuid.uuid4())
count = random.randint(0, 1000)
print(
json.dumps({"ts": ts, "uuid": id, "count": count})
)
datagen.py
If you run this script (python datagen.py
), you'll see the following output:
{"ts": 1644586485807, "uuid": "93633f7c01d54453a144", "count": 807}
{"ts": 1644586485836, "uuid": "87ebf97feead4e848a2e", "count": 41}
{"ts": 1644586485866, "uuid": "960d4ffa201a4425bb18", "count": 146}
Ingesting Data into Kafka
Let's now pipe that stream of messages into Kafka, by running the following command:
python datagen.py | docker exec -i kafka /opt/kafka/bin/kafka-console-producer.sh --bootstrap-server localhost:9092 --topic events;
We can check how many messages have been ingested by running the following command:
docker exec -i kafka kafka-run-class.sh kafka.tools.GetOffsetShell --broker-list localhost:9092 --topic events
Output
events:0:11940
And we can print out the messages themselves by running the following command
docker exec -i kafka /opt/kafka/bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic events
Output
...
{"ts": 1644586485807, "uuid": "93633f7c01d54453a144", "count": 807}
{"ts": 1644586485836, "uuid": "87ebf97feead4e848a2e", "count": 41}
{"ts": 1644586485866, "uuid": "960d4ffa201a4425bb18", "count": 146}
...
Schema
A schema defines what fields are present in the table along with their data types in JSON format.
Create a file called /tmp/pinot/schema-stream.json
and add the following content to it.
{
"schemaName": "events",
"dimensionFieldSpecs": [
{
"name": "uuid",
"dataType": "STRING"
}
],
"metricFieldSpecs": [
{
"name": "count",
"dataType": "INT"
}
],
"dateTimeFieldSpecs": [{
"name": "ts",
"dataType": "TIMESTAMP",
"format" : "1:MILLISECONDS:EPOCH",
"granularity": "1:MILLISECONDS"
}]
}
Table Config
A table is a logical abstraction that represents a collection of related data. It is composed of columns and rows (known as documents in Pinot). The table config defines the table's properties in JSON format.
Create a file called /tmp/pinot/table-config-stream.json
and add the following content to it.
{
"tableName": "events",
"tableType": "REALTIME",
"segmentsConfig": {
"timeColumnName": "ts",
"schemaName": "events",
"replicasPerPartition": "1"
},
"tenants": {},
"tableIndexConfig": {
"loadMode": "MMAP",
"streamConfigs": {
"streamType": "kafka",
"stream.kafka.topic.name": "events",
"stream.kafka.decoder.class.name": "org.apache.pinot.plugin.inputformat.json.JSONMessageDecoder",
"stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
"stream.kafka.broker.list": "kafka:9092",
"realtime.segment.flush.threshold.rows": "0",
"realtime.segment.flush.threshold.time": "24h",
"realtime.segment.flush.threshold.segment.size": "50M",
"stream.kafka.consumer.prop.auto.offset.reset": "smallest"
}
},
"metadata": {
"customConfigs": {}
}
}
Create schema and table
Create the table and schema by running the appropriate command below:
docker run --rm -ti --network=pinot-demo -v /tmp/pinot:/tmp/pinot apachepinot/pinot:1.0.0 AddTable -schemaFile /tmp/pinot/schema-stream.json -tableConfigFile /tmp/pinot/table-config-stream.json -controllerHost pinot-controller -controllerPort 9000 -exec
Querying
Navigate to localhost:9000/#/query and click on the events
table to run a query that shows the first 10 rows in this table.
Querying the events table
Kafka ingestion guidelines
Kafka versions in Pinot
Pinot supports two versions of the Kafka library: kafka-0.9
and kafka-2.x
for low level consumers.
Upgrade from Kafka 0.9 connector to Kafka 2.x connector
Update table config for low level consumer:
stream.kafka.consumer.factory.class.name
fromorg.apache.pinot.core.realtime.impl.kafka.KafkaConsumerFactory
toorg.apache.pinot.core.realtime.impl.kafka2.KafkaConsumerFactory
.
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
.
{
"tableName": "transcript",
"tableType": "REALTIME",
"segmentsConfig": {
"timeColumnName": "timestamp",
"timeType": "MILLISECONDS",
"schemaName": "transcript",
"replicasPerPartition": "1"
},
"tenants": {},
"tableIndexConfig": {
"loadMode": "MMAP",
"streamConfigs": {
"streamType": "kafka",
"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": "pinot-zookeeper:2191/kafka",
"stream.kafka.broker.list": "localhost:9092",
"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": {}
}
}
Consume transactionally-committed messages
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.
For example,
{
"tableName": "transcript",
"tableType": "REALTIME",
"segmentsConfig": {
"timeColumnName": "timestamp",
"timeType": "MILLISECONDS",
"schemaName": "transcript",
"replicasPerPartition": "1"
},
"tenants": {},
"tableIndexConfig": {
"loadMode": "MMAP",
"streamConfigs": {
"streamType": "kafka",
"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": "pinot-zookeeper:2191/kafka",
"stream.kafka.broker.list": "kafka:9092",
"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 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
.
"streamConfigs": {
"streamType": "kafka",
"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: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"
},
Extract record headers as Pinot table columns
Pinot's Kafka connector 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:
Record key: any type <K>
__key
: String
For simplicity of design, we assume that the record key is always a UTF-8 encoded String
Record Headers: Map<String, String>
Each header key is listed as a separate column:
__header$HeaderKeyName
: String
For simplicity of design, we directly map the string headers from kafka record to pinot table column
Record metadata - offset : long
__metadata$offset
: String
Record metadata - partition : int
__metadata$partition
: String
Record metadata - recordTimestamp : long
__metadata$recordTimestamp
: String
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 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:
"dimensionFieldSpecs": [
{
"name": "__key",
"dataType": "STRING"
},
{
"name": "__metadata$offset",
"dataType": "STRING"
},
{
"name": "__metadata$partition",
"dataType": "STRING"
},
...
],
Once the schema is updated, these columns are similar to any other pinot column. You can apply ingestion transforms and / or define indexes on them.
Tell Pinot where to find an Avro schema
There is a standalone utility to generate the schema from an Avro file. See [infer the pinot schema from the avro schema and JSON data](https://docs.pinot.apache.org/basics/data-import/complex-type#infer-the-pinot-schema-from-the-avro-schema-and-json-data) for details.
To avoid errors like The Avro schema must be provided
, designate the location of the schema in your streamConfigs
section. For example, if your current section contains the following:
...
"streamConfigs": {
"streamType": "kafka",
"stream.kafka.topic.name": "",
"stream.kafka.decoder.class.name": "org.apache.pinot.plugin.inputformat.avro.SimpleAvroMessageDecoder",
"stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
"stream.kafka.broker.list": "",
"stream.kafka.consumer.prop.auto.offset.reset": "largest"
...
}
Then add this key: "stream.kafka.decoder.prop.schema"
followed by a value that denotes the location of your schema.
Last updated
Was this helpful?