To learn how to filter records when ingesting data into Pinot, watch the following video, or complete the tutorial below.

Pinot Version1.0.0
Codestartreedata/pinot-recipes/filtering

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Prerequisites

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

  1. If you haven’t already, download recipes.
  2. In terminal, go to the recipe by running the following command:
cd pinot-recipes/recipes/filtering

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Launch Pinot Cluster

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

docker-compose up

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

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Dataset

We’re going to import the following JSON file:

{"title": "Valentine's Day", "genre": "Comedy", "year": 2010, "id": 361248901147483647}
{"title": "The Ugly Truth", "genre": "Comedy", "year": 2009, "id": 332567813147483648}
{"title": "P.S. I Love You", "genre": "Romance", "year": 2007, "id": 346905752147483649}
{"title": "Dear John", "genre": "Drama", "year": 2010, "id": 300441473147483650}
{"title": "The Curious Case of Benjamin Button", "genre": "Fantasy", "year": 2008, "id": 394030854147483651}

data/import.json

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Pinot Schema and Table

Now let’s create a Pinot Schema and Table.

First, the schema:

{
    "schemaName": "movies",
    "dimensionFieldSpecs": [
      {
        "name": "id",
        "dataType": "LONG"
      },
      {
        "name": "title",
        "dataType": "STRING"
      },
      {
        "name": "genre",
        "dataType": "STRING"
      },
      {
        "name": "year",
        "dataType": "INT"
      }
    ]
}

config/schema.json

We’ll also have the following table config:

{
    "tableName": "movies",
    "tableType": "OFFLINE",
    "segmentsConfig": {
      "replication": 1,
      "schemaName": "movies"
    },
    "tenants": {
      "broker":"DefaultTenant",
      "server":"DefaultTenant"
    },
    "tableIndexConfig": {
      "loadMode": "MMAP"
    },
    "ingestionConfig": {
      "filterConfig": {
        "filterFunction": "year >= 2010"
      },
      "batchIngestionConfig": {
        "segmentIngestionType": "APPEND",
        "segmentIngestionFrequency": "DAILY"
      }
    },
    "metadata": {}
}

config/table.json

Our filtering function ensures that any records with a year property with a value of 2010 or more are not imported.

You can create the table and schema by running the following command:`

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

You should see a message similar to the following if everything is working correctly:

2022/02/25 09:39:00.839 INFO [AddTableCommand] [main] Executing command: AddTable -tableConfigFile /config/table.json -schemaFile /config/schema.json -controllerProtocol http -controllerHost 172.29.0.3 -controllerPort 9000 -user null -password [hidden] -exec
2022/02/25 09:39:01.308 INFO [AddTableCommand] [main] {"status":"Table movies_OFFLINE succesfully added"}

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Ingestion Job

Now we’re going to import the JSON file into Pinot. We’ll do this with the following ingestion spec:

executionFrameworkSpec:
  name: 'standalone'
  segmentGenerationJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner'
  segmentTarPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner'
jobType: SegmentCreationAndTarPush
inputDirURI: '/data'
includeFileNamePattern: 'glob:**/import.json'
outputDirURI: '/opt/pinot/data/movies/'
overwriteOutput: true
pinotFSSpecs:
  - scheme: file
    className: org.apache.pinot.spi.filesystem.LocalPinotFS
recordReaderSpec:
  dataFormat: 'json'
  className: 'org.apache.pinot.plugin.inputformat.json.JSONRecordReader'
tableSpec:
  tableName: 'movies'
pinotClusterSpecs:
  - controllerURI: 'http://pinot-controller-filtering:9000'
pushJobSpec:
  pushAttempts: 2
  pushRetryIntervalMillis: 1000

The import job will map fields in each JSON document to a corresponding column in the movies schema. If one of the fields doesn’t exist in the schema it will be skipped.

You can also apply transformation functions to JSON documents during the ingestion process. For more details, see the JSON Transformation Functions guide.

You can run the following command to run the import:

docker run \
   --network filtering \
   -v $PWD/config:/config \
   -v $PWD/data:/data \
   apachepinot/pinot:1.0.0 LaunchDataIngestionJob \
     -jobSpecFile /config/job-spec.yml

​
Querying

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

select * 
from movies 
limit 10

You will see the following output:

genreidtitleyear
Comedy332567813147483648The Ugly Truth2009
Romance346905752147483649P.S. I Love You2007
Fantasy394030854147483651The Curious Case of Benjamin Button2008

Query Results