Groovy Transformation Functions

In this recipe we’ll learn how to use Groovy transformation functions to ingest a CSV file with column names contain spaces.

Pinot Version	1.0.0
Code	startreedata/pinot-recipes/groovy-transformation-functions

Prerequisites

To follow the code examples in this guide, you must install Docker locally and download recipes.

Navigate to recipe

If you haven’t already, download recipes.
Navigate to the recipe by running the following command:

cd pinot-recipes/recipes/groovy-transformation-functions

Launch Pinot Cluster

You can spin up a Pinot Cluster and Kafka Broker by running the following command:

docker compose up

This command will run a single instance of the Pinot Controller, Pinot Server, Pinot Broker, Kafka Broker, and Zookeeper. You can find the docker-compose.yml file on GitHub.

Controller configuration

We need to provide configuration parameters to the Pinot Controller to enable Groovy in transformation functions. This is done in the following section of the Docker Compose file:

pinot-controller:
  image: apachepinot/pinot:1.0.0
  command: "StartController -zkAddress zookeeper-grooy:2181 -config /config/controller-conf.conf"

The configuration is specified in /config/controller-conf.conf, the contents of which are shown below:

controller.access.protocols.http.port=9000
controller.zk.str=zookeeper-groovy:2181
controller.helix.cluster.name=PinotCluster
controller.host=pinot-controller-groovy
controller.port=9000
controller.data.dir=/tmp/data/PinotController

controller.disable.ingestion.groovy=false

/config/controller-conf.conf

Dataset

We’re going to import a couple of JSON documents into Kafka and then from there into Pinot.

{"timestamp": "2019-10-09 21:25:25", "payload": {"firstName": "James", "lastName": "Smith", "before": {"id": 2}, "after": { "id": 3}}}
{"timestamp": "2019-10-10 21:33:25", "payload": {"firstName": "John", "lastName": "Gates", "before": {"id": 2}}}

Pinot Schema and Table

Now let’s create a Pinot Schema and Table. Only the timestamp field from our data source maps to a schema column name - we’ll be using transformation functions to populate the id and name columns.

{
    "schemaName": "events",
    "dimensionFieldSpecs": [
      {
        "name": "id",
        "dataType": "INT"
      },
      {
        "name": "name",
        "dataType": "STRING"
      }
    ],
    "dateTimeFieldSpecs": [{
      "name": "timestamp",
      "dataType": "TIMESTAMP",
      "format" : "1:MILLISECONDS:EPOCH",
      "granularity": "1:MILLISECONDS"
    }]
}

config/schema.json The table config indicates that data will be ingested from the Kafka events topic:

{
  "tableName": "events",
  "tableType": "REALTIME",
  "segmentsConfig": {
    "timeColumnName": "timestamp",
    "schemaName": "events",
    "replication": "1",
    "replicasPerPartition": "1"
  },
  "query" : {
    "disableGroovy": false
  },
  "tenants": {},
  "tableIndexConfig": {
    "loadMode": "MMAP",
    "streamConfigs": {
      "streamType": "kafka",
      "stream.kafka.topic.name": "events",
      "stream.kafka.broker.list": "kafka-groovy:9093",
      "stream.kafka.consumer.type": "lowlevel",
      "stream.kafka.consumer.prop.auto.offset.reset": "smallest",
      "stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
      "stream.kafka.decoder.class.name": "org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder"
    }
  },
  "ingestionConfig": {
    "batchIngestionConfig": {
      "segmentIngestionType": "APPEND",
      "segmentIngestionFrequency": "DAILY"
    },
    "transformConfigs": [
      {
        "columnName": "id",
        "transformFunction": "Groovy({def jsonSlurper = new groovy.json.JsonSlurper(); def object = jsonSlurper.parseText(new groovy.json.JsonBuilder(payload).toPrettyString()); def result = object.after == null ? Long.valueOf(object.before.id) : Long.valueOf(object.after.id); return result}, payload)"
      },
      {
        "columnName": "name",
        "transformFunction": "Groovy({def jsonSlurper = new groovy.json.JsonSlurper(); def object = jsonSlurper.parseText(new groovy.json.JsonBuilder(payload).toPrettyString()); return object.firstName + ' ' + object.lastName}, payload)"
      }
    ]
  },
  "metadata": {}
}

config/table.json Let’s dive into the transformation functions defined under ingestionConfig.transformConfigs:

The id one extracts payload.after.id if the after property exists, otherwise it uses payload.before.id
The name one concatenates payload.firstName and payload.lastName

They both use Groovy’s JSON parser to create an object from the payload, before using logic from the programming language to return the desired out.

If you only need to do simple data transformation, you can use the built-in transformation functions.

We can add the table and schema by running the following command:

docker run \
   --network groovy \
   -v $PWD/config:/config \
   apachepinot/pinot:1.0.0 AddTable \
     -schemaFile /config/schema.json \
     -tableConfigFile /config/table.json \
     -controllerHost "pinot-controller-groovy" \
    -exec

Ingest Data into Kafka

We can run the following command to import a couple of documents into Kafka:

printf '{"timestamp": "2019-10-09 21:25:25", "payload": {"firstName": "James", "lastName": "Smith", "before": {"id": 2}, "after": { "id": 3}}}
{"timestamp": "2019-10-10 21:33:25", "payload": {"firstName": "John", "lastName": "Gates", "before": {"id": 2}}}\n' |
kcat -P -b localhost:9092 -t events

Let’s check those documents have been imported by running the following command:

kcat -C -b localhost:9092 -t events -c2

{"timestamp": "2019-10-09 21:25:25", "payload": {"firstName": "James", "lastName": "Smith", "before": {"id": 2}, "after": { "id": 3}}}
{"timestamp": "2019-10-09 21:25:25", "payload": {"firstName": "James", "lastName": "Smith", "before": {"id": 2}}}

Output Looks good so far.

Querying

Once that’s completed, navigate to localhost:9000/#/query and click on the events table or copy/paste the following query:

select * 
from events 
limit 10

You will see the following output:

id	name	timestamp
3	James Smith	2019-10-09 21:25:25.0
2	John Gates	2019-10-10 21:33:25.0

Query Results

​Prerequisites

​Navigate to recipe

​Launch Pinot Cluster

​Controller configuration

​Dataset

​Pinot Schema and Table

​Ingest Data into Kafka

​Querying

Prerequisites

Navigate to recipe

Launch Pinot Cluster

Controller configuration

Dataset

Pinot Schema and Table

Ingest Data into Kafka

Querying