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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.

note
Pinot Version0.10.0
Codestartreedata/pinot-recipes/json-flatten

Pre-requisites​

You will need to install Docker locally to follow the code examples in this guide.

Download Recipe​

First, clone the GitHub repository to your local machine and navigate to this recipe:

git clone git@github.com:startreedata/pinot-recipes.git
cd pinot-recipes/recipes/json-flatten

If you don't have a Git client, you can also download a zip file that contains the code and then navigate to the recipe.

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:

data/users.json
{"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"}}

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:

config/schema.json
{
  "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"
  } ]
}

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

info

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.

You can create the schema by running the following command:

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

Pinot Table​

Now let's create a Pinot table that uses the above schema. 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:

config/table-no-flatten.json
{
  "tableName": "users_no_flatten",
  "tableType": "OFFLINE",
  "segmentsConfig": {
    "replication": 1,
    "schemaName": "users"
  },
  "ingestionConfig": {
    "batchIngestionConfig": {
      "segmentIngestionType": "APPEND",
      "segmentIngestionFrequency": "DAILY"
    }
  },
  "tenants": {
    "broker": "DefaultTenant",
    "server": "DefaultTenant"
  },
  "tableIndexConfig": {
    "loadMode": "MMAP"
  },
  "metadata": {}
}

And now one that has the flattening config:

config/table-flatten.json
{
  "tableName": "users_flatten",
  "tableType": "OFFLINE",
  "segmentsConfig": {
    "replication": 1,
    "schemaName": "users"
  },
  "ingestionConfig": {
    "batchIngestionConfig": {
      "segmentIngestionType": "APPEND",
      "segmentIngestionFrequency": "DAILY"
    },
    "complexTypeConfig": {
      "delimeter": "_"
    }
  },
  "tenants": {
    "broker": "DefaultTenant",
    "server": "DefaultTenant"
  },
  "tableIndexConfig": {
    "loadMode": "MMAP"
  },
  "metadata": {}
}

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 command:`

docker exec -it pinot-controller-json bin/pinot-admin.sh AddTable \
  -tableConfigFile /config/table-no-flatten.json \
  -exec
docker exec -it pinot-controller-json bin/pinot-admin.sh AddTable \
  -tableConfigFile /config/table-flatten.json \
  -exec

Ingestion Job​

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

config/job-spec.yml
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://localhost:9000'

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 exec -it pinot-controller-json bin/pinot-admin.sh LaunchDataIngestionJob \
  -jobSpecFile /config/job-spec.yml \
  -values tableName='users_no_flatten'
docker exec -it pinot-controller-json bin/pinot-admin.sh 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:

idnameusernamecompany_namecompany_bsaddress_streetaddress_geo_lataddress_geo_lng
1Leanne GrahamBretnullnullnullnullnull
2Ervin HowellAntonettenullnullnullnullnull
3Clementine BauchSamanthanullnullnullnullnull
4Patricia LebsackKariannenullnullnullnullnull
5Chelsey DietrichKamrennullnullnullnullnull

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:

idnameusernamecompany_namecompany_bsaddress_streetaddress_geo_lataddress_geo_lng
1Leanne GrahamBretRomaguera-Cronaharness real-time e-marketsKulas Light-37.315981.1496
2Ervin HowellAntonetteDeckow-Cristsynergize scalable supply-chainsVictor Plains-43.9509-34.4618
3Clementine BauchSamanthaRomaguera-Jacobsone-enable strategic applicationsDouglas Extension-68.6102-47.0653
4Patricia LebsackKarianneRobel-Corkerytransition cutting-edge web servicesHoeger Mall29.4572-164.2990
5Chelsey DietrichKamrenKeebler LLCrevolutionize end-to-end systemsSkiles Walks-31.812962.5342

Query Results

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