Pinot can transform data at ingestion. In this recipe, we’ll learn how to use a transformation to change the name of a column. We will ingest a CSV file with a column containing spaces in its name. We will then use a transformation function to remove the space while the data is being ingested into Pinot.
Pinot Version1.0.0
Codestartreedata/pinot-recipes/csv-files-spaces-column-names

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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 the terminal, navigate to this recipe’s directory:
cd pinot-recipes/recipes/csv-files-spaces-column-names

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Launch Pinot Cluster

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

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Dataset

We’re going to import the following CSV file, in which the Case Number column heading contains a space:
IDCase Number
10224738HY411648
10224739HY411615
11646166JC213529
10224740HY411595
data/import.csv

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Pinot Schema and Table

Next we create a Pinot Schema and Table. A common pattern when creating a schema is to create columns that map directly to the names of the fields in our data source. We can’t do that in this case since column names can’t contain spaces, so instead we’ll use the following: 
{
    "schemaName": "crimes",
    "dimensionFieldSpecs": [
      {
        "name": "ID",
        "dataType": "INT"
      },
      {
        "name": "CaseNumber",
        "dataType": "STRING"
      }
    ]
}
config/schema.json We’ll also have the following table config: 
{
    "tableName": "crimes",
    "tableType": "OFFLINE",
    "segmentsConfig": {
      "replication": 1
    },
    "tenants": {
      "broker":"DefaultTenant",
      "server":"DefaultTenant"
    },
    "tableIndexConfig": {
      "loadMode": "MMAP"
    },
    "ingestionConfig": {
      "batchIngestionConfig": {
        "segmentIngestionType": "APPEND",
        "segmentIngestionFrequency": "DAILY"
      },
      "transformConfigs": [
        {"columnName": "CaseNumber", "transformFunction": "\"Case Number\"" }
      ]
    },
    "metadata": {}
}
config/table.json
The entry under ingestionConfig.transformConfigs makes sure that data in the Case Number field in the data source is ingested into the CaseNumber column of the table. To learn more about writing these functions, see the ingestion transformation documentation.
Create the table and schema by running the following command: 
docker run \
   --network csv \
   -v $PWD/config:/config \
   apachepinot/pinot:1.0.0 AddTable \
     -schemaFile /config/schema.json \
     -tableConfigFile /config/table.json \
     -controllerHost "pinot-controller-csv" \
    -exec
You should see a message similar to the following if everything is working correctly: 
2021/11/25 12:02:04.606 INFO [AddTableCommand] [main] Executing command: AddTable -tableConfigFile /config/table.json -schemaFile /config/schema.json -controllerProtocol http -controllerHost 192.168.144.3 -controllerPort 9000 -user null -password [hidden] -exec
2021/11/25 12:02:05.084 INFO [AddTableCommand] [main] {"status":"Table crimes_OFFLINE succesfully added"}

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Ingestion Job

Next, we import the CSV 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'
  segmentUriPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentUriPushJobRunner'
jobType: SegmentCreationAndTarPush
inputDirURI: '/data'
includeFileNamePattern: 'glob:**/import.csv'
outputDirURI: '/opt/pinot/data/crimes/'
overwriteOutput: true
pinotFSSpecs:
  - scheme: file
    className: org.apache.pinot.spi.filesystem.LocalPinotFS
recordReaderSpec:
  dataFormat: 'csv'
  className: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReader'
  configClassName: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReaderConfig'
tableSpec:
  tableName: 'crimes'
pinotClusterSpecs:
  - controllerURI: 'http://pinot-controller-csv:9000'
pushJobSpec:
  pushAttempts: 2
  pushRetryIntervalMillis: 1000
config/job-spec.yml Run the following command to run the import: 
docker run \
   --network csv \
   -v $PWD/config:/config \
   -v $PWD/data:/data \
   apachepinot/pinot:1.0.0 LaunchDataIngestionJob \
  -jobSpecFile /config/job-spec.yml

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Querying

Once that’s completed, navigate to localhost:9000/#/query and click on the crimes table or copy/paste the following query: 
select * 
from crimes 
limit 10
You will see the following output:
CaseNumberID
HY41164810224738
HY41161510224739
JC21352911646166
HY41159510224740
Query Results