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Rename fields in unnested or flattened JSON documents

How to rename fields when unnesting arrays or flattening objects in JSON documents

In this recipe we'll learn how to rename fields when unnesting/exploding values or flattening objects while ingesting JSON documents into Apache Pinot.


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

Navigate to recipe

  1. If you haven't already, download recipes.
  2. In terminal, go to the recipe by running the following command:
cd pinot-recipes/recipes/json-unnest-rename-fields

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.


We're going to import the following JSON file:

{"Title":"The Matrix","Year":"1999","Rated":"R","Meta": {"Released":"31 Mar 1999","Runtime":"136 min"},"Genre":"Action, Sci-Fi","Director":"Lana Wachowski, Lilly Wachowski","Writer":"Lilly Wachowski, Lana Wachowski","Actors":"Keanu Reeves, Laurence Fishburne, Carrie-Anne Moss","Plot":"When a beautiful stranger leads computer hacker Neo to a forbidding underworld, he discovers the shocking truth--the life he knows is the elaborate deception of an evil cyber-intelligence.","Language":"English","Country":"United States, Australia","Awards":"Won 4 Oscars. 42 wins & 51 nominations total","Ratings":[{"Source":"Internet Movie Database","Value":"8.7/10"},{"Source":"Rotten Tomatoes","Value":"88%"},{"Source":"Metacritic","Value":"73/100"}],"Metascore":"73","imdbRating":"8.7","imdbVotes":"1,851,767","imdbID":"tt0133093","Type":"movie","DVD":"15 May 2007","BoxOffice":"$172,076,928","Production":"N/A","Website":"N/A","Response":"True"}
{"Title":"Avatar","Year":"2009","Rated":"PG-13","Meta": {"Released":"18 Dec 2009","Runtime":"162 min"},"Genre":"Action, Adventure, Fantasy","Director":"James Cameron","Writer":"James Cameron","Actors":"Sam Worthington, Zoe Saldana, Sigourney Weaver","Plot":"A paraplegic Marine dispatched to the moon Pandora on a unique mission becomes torn between following his orders and protecting the world he feels is his home.","Language":"English, Spanish","Country":"United States","Awards":"Won 3 Oscars. 89 wins & 131 nominations total","Ratings":[{"Source":"Internet Movie Database","Value":"7.8/10"},{"Source":"Rotten Tomatoes","Value":"82%"},{"Source":"Metacritic","Value":"83/100"}],"Metascore":"83","imdbRating":"7.8","imdbVotes":"1,189,326","imdbID":"tt0499549","Type":"movie","DVD":"22 Apr 2010","BoxOffice":"$760,507,625","Production":"N/A","Website":"N/A","Response":"True"}
{"Title":"The Boss Baby","Year":"2017","Rated":"PG","Meta": {"Released":"31 Mar 2017","Runtime":"97 min"},"Genre":"Animation, Adventure, Comedy","Director":"Tom McGrath","Writer":"Michael McCullers, Marla Frazee","Actors":"Alec Baldwin, Steve Buscemi, Jimmy Kimmel","Plot":"A suit-wearing, briefcase-carrying baby pairs up with his 7-year old brother to stop the dastardly plot of the CEO of Puppy Co.","Language":"English, Spanish","Country":"United States","Awards":"Nominated for 1 Oscar. 4 wins & 21 nominations total","Ratings":[{"Source":"Internet Movie Database","Value":"6.3/10"},{"Source":"Rotten Tomatoes","Value":"53%"},{"Source":"Metacritic","Value":"50/100"}],"Metascore":"50","imdbRating":"6.3","imdbVotes":"122,684","imdbID":"tt3874544","Type":"movie","DVD":"25 Jul 2017","BoxOffice":"$175,003,033","Production":"N/A","Website":"N/A","Response":"True"}
{"Title":"Tall Girl","Year":"2019","Rated":"TV-PG","Meta": {"Released":"13 Sep 2019","Runtime":"101 min"},"Genre":"Comedy, Drama, Family","Director":"Nzingha Stewart","Writer":"Sam Wolfson","Actors":"Ava Michelle, Griffin Gluck, Sabrina Carpenter","Plot":"Jodi, the tallest girl in her high school, has always felt uncomfortable in her own skin. But after years of slouching, being made fun of, and avoiding attention at all costs, Jodi finally decides to find the confidence to stand t...","Language":"English","Country":"United States","Awards":"2 wins & 1 nomination","Ratings":[{"Source":"Internet Movie Database","Value":"5.2/10"},{"Source":"Rotten Tomatoes","Value":"38%"}],"Metascore":"N/A","imdbRating":"5.2","imdbVotes":"22,990","imdbID":"tt9252508","Type":"movie","DVD":"13 Sep 2019","BoxOffice":"N/A","Production":"N/A","Website":"N/A","Response":"True"}
{"Title":"Love Hard","Year":"2021","Rated":"TV-MA","Meta": {"Released":"05 Nov 2021","Runtime":"104 min"},"Genre":"Comedy, Romance","Director":"Hernan Jimenez","Writer":"Daniel Mackey, Rebecca Ewing","Actors":"Nina Dobrev, Jimmy O. Yang, Darren Barnet","Plot":"An LA girl, unlucky in love, falls for an East Coast guy on a dating app and decides to surprise him for the holidays, only to discover that she's been catfished. This lighthearted romantic comedy chronicles her attempt to reel in lo","Language":"English","Country":"United States","Awards":"1 win & 1 nomination","Ratings":[{"Source":"Internet Movie Database","Value":"6.3/10"},{"Source":"Rotten Tomatoes","Value":"57%"},{"Source":"Metacritic","Value":"42/100"}],"Metascore":"42","imdbRating":"6.3","imdbVotes":"48,458","imdbID":"tt10752004","Type":"movie","DVD":"05 Nov 2021","BoxOffice":"N/A","Production":"N/A","Website":"N/A","Response":"True"}


We're particularly interested in the Ratings property of each movie. We're going to unnest or explode the ratings so that we have one row per rating in our database after the data is ingested.

We'll also pull out the Meta.Released property.

Pinot Schema and Table

Now let's create a Pinot Schema and Table.

First, the schema:

  "schemaName": "movie_ratings",
  "dimensionFieldSpecs": [
      "name": "Title",
      "dataType": "STRING"
      "name": "Released",
      "dataType": "STRING"
      "name": "Rated",
      "dataType": "STRING"
      "name": "Source",
      "dataType": "STRING"
      "name": "Rating",
      "dataType": "STRING"


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

docker exec -it pinot-controller-json bin/ AddSchema \
  -schemaFile /config/schema.json \

We'll also have the following table config:

  "tableName": "movie_ratings",
  "tableType": "OFFLINE",
  "segmentsConfig": {
    "replication": 1,
    "schemaName": "movie_ratings"
  "ingestionConfig": {
    "complexTypeConfig": {
      "fieldsToUnnest": ["Ratings"]
    "transformConfigs": [
        "columnName": "Released",
        "transformFunction": "Meta.Released"
        "columnName": "Source",
        "transformFunction": "Ratings.Source"
        "columnName": "Rating",
        "transformFunction": "Ratings.Value"
    "batchIngestionConfig": {
      "segmentIngestionType": "APPEND",
      "segmentIngestionFrequency": "DAILY"
  "tenants": {
    "broker": "DefaultTenant",
    "server": "DefaultTenant"
  "tableIndexConfig": {
    "loadMode": "MMAP"
  "metadata": {}


The existence of the complexTypeConfig section means that nested JSON objects will be flattened into individual fields. It also which field in the data source should be unnested. The Ratings field contains an array of JSON objects and that array will be exploded to create one row for each item in the array.

The transformConfigs section maps the following values:

  • Ratings.Source to the Source column
  • Ratings.Value to the Rating column
  • Meta.Released to the Released column.

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

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

Ingestion Job

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

  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:**/movies.json'
outputDirURI: '/opt/pinot/data/movies/'
overwriteOutput: true
  - scheme: file
    className: org.apache.pinot.spi.filesystem.LocalPinotFS
  dataFormat: 'json'
  className: 'org.apache.pinot.plugin.inputformat.json.JSONRecordReader'
  tableName: 'movie_ratings'
  - controllerURI: 'http://pinot-controller-json:9000'
  pushAttempts: 2
  pushRetryIntervalMillis: 1000


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

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


Once that's completed, navigate to localhost:9000/#/query (opens in a new tab) and click on the movie_ratings table or copy/paste the following query:

select * 
from movie_ratings 
limit 10

You will see the following output:

R8.7/1031 Mar 1999Internet Movie DatabaseThe Matrix
R88%31 Mar 1999Rotten TomatoesThe Matrix
R73/10031 Mar 1999MetacriticThe Matrix
PG-137.8/1018 Dec 2009Internet Movie DatabaseAvatar
PG-1382%18 Dec 2009Rotten TomatoesAvatar
PG-1383/10018 Dec 2009MetacriticAvatar
PG6.3/1031 Mar 2017Internet Movie DatabaseThe Boss Baby
PG53%31 Mar 2017Rotten TomatoesThe Boss Baby
PG50/10031 Mar 2017MetacriticThe Boss Baby
TV-PG5.2/1013 Sep 2019Internet Movie DatabaseTall Girl

Query Results

We can see that each movie has multiple rows, one for each rating given to that movie.