Importing CSV files with columns containing spaces

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 Version	1.0.0
Code	startreedata/pinot-recipes/csv-files-spaces-column-names

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 the terminal, navigate to this recipe’s directory:

cd pinot-recipes/recipes/csv-files-spaces-column-names

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.

Dataset

We’re going to import the following CSV file, in which the Case Number column heading contains a space:

ID	Case Number
10224738	HY411648
10224739	HY411615
11646166	JC213529
10224740	HY411595

data/import.csv

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"}

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

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:

CaseNumber	ID
HY411648	10224738
HY411615	10224739
JC213529	11646166
HY411595	10224740

Query Results

​Prerequisites

​Navigate to recipe

​Launch Pinot Cluster

​Dataset

​Pinot Schema and Table

​Ingestion Job

​Querying

Prerequisites

Navigate to recipe

Launch Pinot Cluster

Dataset

Pinot Schema and Table

Ingestion Job

Querying