How to flatten objects in JSON documents

In this recipe we’ll learn how to flatten nested objects when ingesting JSON documents into Apache Pinot.

Pinot Version	0.10.0
Code	startreedata/pinot-recipes/json-flatten

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.
In terminal, go to the recipe by running the following command:

cd pinot-recipes/recipes/json-flatten

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.

Dataset

We’re going to import the following JSON file:

{"id":1,"name":"Leanne Graham","username":"Bret","email":"Sincere@april.biz","address":{"street":"Kulas Light","suite":"Apt. 556","city":"Gwenborough","zipcode":"92998-3874","geo":{"lat":"-37.3159","lng":"81.1496"}},"phone":"1-770-736-8031 x56442","website":"hildegard.org","company":{"name":"Romaguera-Crona","catchPhrase":"Multi-layered client-server neural-net","bs":"harness real-time e-markets"}}
{"id":2,"name":"Ervin Howell","username":"Antonette","email":"Shanna@melissa.tv","address":{"street":"Victor Plains","suite":"Suite 879","city":"Wisokyburgh","zipcode":"90566-7771","geo":{"lat":"-43.9509","lng":"-34.4618"}},"phone":"010-692-6593 x09125","website":"anastasia.net","company":{"name":"Deckow-Crist","catchPhrase":"Proactive didactic contingency","bs":"synergize scalable supply-chains"}}
{"id":3,"name":"Clementine Bauch","username":"Samantha","email":"Nathan@yesenia.net","address":{"street":"Douglas Extension","suite":"Suite 847","city":"McKenziehaven","zipcode":"59590-4157","geo":{"lat":"-68.6102","lng":"-47.0653"}},"phone":"1-463-123-4447","website":"ramiro.info","company":{"name":"Romaguera-Jacobson","catchPhrase":"Face to face bifurcated interface","bs":"e-enable strategic applications"}}
{"id":4,"name":"Patricia Lebsack","username":"Karianne","email":"Julianne.OConner@kory.org","address":{"street":"Hoeger Mall","suite":"Apt. 692","city":"South Elvis","zipcode":"53919-4257","geo":{"lat":"29.4572","lng":"-164.2990"}},"phone":"493-170-9623 x156","website":"kale.biz","company":{"name":"Robel-Corkery","catchPhrase":"Multi-tiered zero tolerance productivity","bs":"transition cutting-edge web services"}}
{"id":5,"name":"Chelsey Dietrich","username":"Kamren","email":"Lucio_Hettinger@annie.ca","address":{"street":"Skiles Walks","suite":"Suite 351","city":"Roscoeview","zipcode":"33263","geo":{"lat":"-31.8129","lng":"62.5342"}},"phone":"(254)954-1289","website":"demarco.info","company":{"name":"Keebler LLC","catchPhrase":"User-centric fault-tolerant solution","bs":"revolutionize end-to-end systems"}}
{"id":6,"name":"Mrs. Dennis Schulist","username":"Leopoldo_Corkery","email":"Karley_Dach@jasper.info","address":{"street":"Norberto Crossing","suite":"Apt. 950","city":"South Christy","zipcode":"23505-1337","geo":{"lat":"-71.4197","lng":"71.7478"}},"phone":"1-477-935-8478 x6430","website":"ola.org","company":{"name":"Considine-Lockman","catchPhrase":"Synchronised bottom-line interface","bs":"e-enable innovative applications"}}
{"id":7,"name":"Kurtis Weissnat","username":"Elwyn.Skiles","email":"Telly.Hoeger@billy.biz","address":{"street":"Rex Trail","suite":"Suite 280","city":"Howemouth","zipcode":"58804-1099","geo":{"lat":"24.8918","lng":"21.8984"}},"phone":"210.067.6132","website":"elvis.io","company":{"name":"Johns Group","catchPhrase":"Configurable multimedia task-force","bs":"generate enterprise e-tailers"}}
{"id":8,"name":"Nicholas Runolfsdottir V","username":"Maxime_Nienow","email":"Sherwood@rosamond.me","address":{"street":"Ellsworth Summit","suite":"Suite 729","city":"Aliyaview","zipcode":"45169","geo":{"lat":"-14.3990","lng":"-120.7677"}},"phone":"586.493.6943 x140","website":"jacynthe.com","company":{"name":"Abernathy Group","catchPhrase":"Implemented secondary concept","bs":"e-enable extensible e-tailers"}}
{"id":9,"name":"Glenna Reichert","username":"Delphine","email":"Chaim_McDermott@dana.io","address":{"street":"Dayna Park","suite":"Suite 449","city":"Bartholomebury","zipcode":"76495-3109","geo":{"lat":"24.6463","lng":"-168.8889"}},"phone":"(775)976-6794 x41206","website":"conrad.com","company":{"name":"Yost and Sons","catchPhrase":"Switchable contextually-based project","bs":"aggregate real-time technologies"}}
{"id":10,"name":"Clementina DuBuque","username":"Moriah.Stanton","email":"Rey.Padberg@karina.biz","address":{"street":"Kattie Turnpike","suite":"Suite 198","city":"Lebsackbury","zipcode":"31428-2261","geo":{"lat":"-38.2386","lng":"57.2232"}},"phone":"024-648-3804","website":"ambrose.net","company":{"name":"Hoeger LLC","catchPhrase":"Centralized empowering task-force","bs":"target end-to-end models"}}

data/users.json

Each document has a nested address and company that we’re going to flatten so that we can import all the fields into Pinot.

Pinot Schema

Working out all the column names and their types is tedious with complex JSON documents, but luckily Pinot provides a tool that infers the schema for us. We’ll call it by running the following command:

docker exec -it pinot-controller-json \
  bin/pinot-admin.sh JsonToPinotSchema \
  -dimensions "" \
  -jsonFile /data/users.json \
  -pinotSchemaName users \
  -outputDir /data/schema \ 
  -delimeter '_'

The generated schema file, which gets written to /data/schema/users.json, looks like this:

{
  "schemaName" : "users",
  "dimensionFieldSpecs" : [ {
    "name" : "id",
    "dataType" : "INT"
  }, {
    "name" : "name",
    "dataType" : "STRING"
  }, {
    "name" : "username",
    "dataType" : "STRING"
  }, {
    "name" : "email",
    "dataType" : "STRING"
  }, {
    "name" : "address_street",
    "dataType" : "STRING"
  }, {
    "name" : "address_suite",
    "dataType" : "STRING"
  }, {
    "name" : "address_city",
    "dataType" : "STRING"
  }, {
    "name" : "address_zipcode",
    "dataType" : "STRING"
  }, {
    "name" : "address_geo_lat",
    "dataType" : "STRING"
  }, {
    "name" : "address_geo_lng",
    "dataType" : "STRING"
  }, {
    "name" : "phone",
    "dataType" : "STRING"
  }, {
    "name" : "website",
    "dataType" : "STRING"
  }, {
    "name" : "company_name",
    "dataType" : "STRING"
  }, {
    "name" : "company_catchPhrase",
    "dataType" : "STRING"
  }, {
    "name" : "company_bs",
    "dataType" : "STRING"
  } ]
}

config/schema.json

We can see that those nested properties have been converted into individual columns e.g. address_street, address_geo_lat, company_name.

The . character is used as the default delimiter, but we have overriden that by passing in the -delimiter parameter. We’ll need to make sure that we pass in this delimiter to our table config later on in this guide.

We’ll need to create two copies of the schema file as Pinot requires each table to have its own schema. We also need to update the schemaName to be users_flatten in one and users_no_flatten in the other.

Pinot Table

Now let’s create Pinot tables that uses the above schemas. We’re going to create two different tables so that we can see what happens if we don’t specify the config that flattens JSON documents.

First, the table config that doesn’t do any flattening:

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

config/table-no-flatten.json

And now one that has the flattening config:

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

config/table-flatten.json

The highlighted config flattens the nested JSON fields. We need to specify delimiter if our schema doesn’t use the . character to separate nested field names.

You can create the tables by running the following commands:

docker run \
   --network json \
   -v $PWD/config:/config \
   apachepinot/pinot:1.0.0 AddTable \
     -schemaFile /config/schema-no-flatten.json \
     -tableConfigFile /config/table-no-flatten.json \
     -controllerHost "pinot-controller-json" \
    -exec

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

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:users.json'
outputDirURI: '/opt/pinot/data/${tableName}/'
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: ${tableName}
pinotClusterSpecs:
  - controllerURI: 'http://pinot-controller-json:9000'
pushJobSpec:
  pushAttempts: 2
  pushRetryIntervalMillis: 1000

config/job-spec.yml

The import job will map fields in each JSON document to a corresponding column in the users schema.

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 \
  -values tableName='users_no_flatten'

docker run \
   --network json \
   -v $PWD/config:/config \
      -v $PWD/data:/data \
   apachepinot/pinot:1.0.0 LaunchDataIngestionJob \
  -jobSpecFile /config/job-spec.yml \
  -values tableName='users_flatten'

Querying

Once that’s completed, navigate to localhost:9000/#/query and we’ll run some queries see how the JSON flattening works.

First, let’s query users_no_flatten:

select id, name, username, 
       company_name, company_bs,
	   address_street, address_geo_lat, address_geo_lng
from users_no_flatten 
limit 5

You will see the following output:

id	name	username	company_name	company_bs	address_street	address_geo_lat	address_geo_lng
1	Leanne Graham	Bret	null	null	null	null	null
2	Ervin Howell	Antonette	null	null	null	null	null
3	Clementine Bauch	Samantha	null	null	null	null	null
4	Patricia Lebsack	Karianne	null	null	null	null	null
5	Chelsey Dietrich	Kamren	null	null	null	null	null

Query Results

We can see that the nested fields all have null values.

Now let’s try the same query on the users_flatten table:

select id, name, username, 
       company_name, company_bs,
	   address_street, address_geo_lat, address_geo_lng
from users_flatten 
limit 5

You will see the following output:

id	name	username	company_name	company_bs	address_street	address_geo_lat	address_geo_lng
1	Leanne Graham	Bret	Romaguera-Crona	harness real-time e-markets	Kulas Light	-37.3159	81.1496
2	Ervin Howell	Antonette	Deckow-Crist	synergize scalable supply-chains	Victor Plains	-43.9509	-34.4618
3	Clementine Bauch	Samantha	Romaguera-Jacobson	e-enable strategic applications	Douglas Extension	-68.6102	-47.0653
4	Patricia Lebsack	Karianne	Robel-Corkery	transition cutting-edge web services	Hoeger Mall	29.4572	-164.2990
5	Chelsey Dietrich	Kamren	Keebler LLC	revolutionize end-to-end systems	Skiles Walks	-31.8129	62.5342

Query Results

On this table the flattened fields are all hydrated with values from the source data.

Recipes

​Prerequisites

​Navigate to recipe

​Launch Pinot Cluster

​Dataset

​Pinot Schema

​Pinot Table

​Ingestion Job

​Querying

Prerequisites

Navigate to recipe

Launch Pinot Cluster

Dataset

Pinot Schema

Pinot Table

Ingestion Job

Querying