Ingesting JSON files

In this recipe, we'll learn how to ingest JSON documents from a newline-delimited JSON (jsonlines) file.

Mark Needham (opens in a new tab) shows how to ingest JSON files

To automatically ingest JSON data from Amazon S3 to Pinot, check out this video (opens in a new tab).

Pinot Version	1.0.0
Code	startreedata/pinot-recipes/ingest-json-files

Prerequisites

To follow the code examples in this guide, you must install Docker (opens in a new tab) locally and download recipes.

Navigate to recipe

If you haven't already, download recipes.
In terminal, go to the recipe by running the following command:

cd pinot-recipes/recipes/ingest-json-files

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 (opens in a new tab) file on GitHub.

Dataset

We're going to import the following JSON file:

data/import.json

{"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}

Pinot Schema and Table

Now let's create a Pinot Schema and Table.

First, the schema:

config/schema.json

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

We'll also have the following table config:

config/table.json

{
    "tableName": "movies",
    "tableType": "OFFLINE",
    "segmentsConfig": {
      "replication": 1
    },
    "tenants": {
      "broker":"DefaultTenant",
      "server":"DefaultTenant"
    },
    "tableIndexConfig": {
      "loadMode": "MMAP"
    },
    "ingestionConfig": {
      "batchIngestionConfig": {
        "segmentIngestionType": "APPEND",
        "segmentIngestionFrequency": "DAILY"
      }
    },
    "metadata": {}
}

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

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

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.jsonl'
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-json:9000'
pushJobSpec:
  pushParallelism: 2
  pushAttempts: 2

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 json \
   -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 (opens in a new tab) and click on the movies table or copy/paste the following query:

select *
from movies
limit 10

You will see the following output:

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

Query Results

Ingesting JSON files from Kafka Ingest Parquet Files from S3 Using Spark