Skip to main content

Segment purge

Use SegmentPurgeTask to purge records from a Pinot table, for example, to ensure GDPR (General Data Protection Regulation) compliance. SegmentPurgeTask reads input files from a remote location, like AWS S3, to build hash keys used to uniquely identify records to purge in a Pinot table.

StarTree Pinot Version0.13.0-ST.58+

Configure SegmentPurgeTask

Configure SegmentPurgeTask under taskConfig in the table configuration.

Property NameRequiredDescription
input.fs.classNameYesThe class name used to read the files from source location.
inputFormatYesThe input file format.
inputDirURIYesThe input dir containing the purge input files.
input.fs.prop.accessKeyNoRequired if reading from AWS S3.
input.fs.prop.secretKeyNoRequired if reading from AWS S3.
input.fs.prop.regionNoRequired if reading from AWS S3.
recordReader.prop.delimiterNoDefaults to ",". Supported values are "," and ";".
max.num.purge.input.filesNoMaximum number of input files processed at a time. Default value is 10. total size of all input files combined. Default value is 100_000_000 bytes. A single file cannot exceed this value.
table.max.num.tasksNoMaximum number of minion sub tasks generated per task invocation. Default value is 10.

Example Table Configuration

"task": {
"input.fs.className": "org.apache.pinot.plugin.filesystem.S3PinotFS",
"inputFormat": "CSV",
"inputDirURI": "s3://myBucket/myTable/input/",
"input.fs.prop.accessKey": "MY_ACCESS_KEY",
"input.fs.prop.secretKey": "MY_SECRET_KEY",
"input.fs.prop.region": "us-west-2",
"table.max.num.tasks": "100",
"schedule": "0 */5 * * * ?"

Example Input Files


File Name: purgeRecords1.csv


If the above input file was processed against a table named users, all records that match the userId field in the Pinot table would be deleted.


FileName: purgeRecords2.csv


If the above input file was processed against a table named users, all records that match both the firstName and lastName fields in the Pinot table would be deleted.

Guidance on input files

  • An input file must not be appended or overwritten.
  • New data should not be generated for the records which are to be purged. If this does happen, new input files must be dropped.
  • We recommend periodically cleaning up the input directory to purge files that have been processed.


Note the following limits.

Input file formatCSVContents within the input file should conform to CSV format.
Input files sourceAWS S3Only S3 is supported as of now.
Data Typesint, long, boolean, stringThe input files must contain fields that conform to one of these types.
Field ValuesSingle ValuesAll fields in the input file must be of single value dimension. Multi values are not supported.
Comparison Type==Fields from the input record would be matched against the Pinot record using the equals operator.
Null ValueNot SupportedMatching against null values are not supported.
Empty ValueNot SupportedMatching against emtpy values are not supported.
Input file Name ExtensionNot RequiredFile extension is not required.
Input file field delimiter"," and ";"Other delimeters like space, tab are not supported. Default value is ",".


Running other tasks such as SegmentRefresh, MergeRollup

As of now, if a user runs SegmentRefresh and MergeRollup tasks along with the SegmentPurgeTask records won't be purged correctly. The SegmentRefresh and MergeRollup tasks needs to be disabled while the SegmentPurgeTask is running to avoid race conditions. These tasks can be re-enabled after the SegmentPurgeTask completes.

Field Values


The string literal null is not supported as a field value. Records with this value are not purged.

As an example, consider the input file purgeRecords.csv.


This input record would be skipped, and no record in the corresponding Pinot table would be deleted. You can make use of alternate fields in the table to delete these records.


How do I know if a given input file is processed?

Review the minion task metadata to identify the list of files that are successfully processed.

Consider the following minion task metadata for a Pinot table named suspects.

"id": "suspects_OFFLINE",
"simpleFields": {
"": "1682621729000"
"mapFields": {},
"listFields": {
"processed.input.files": [
"processing.input.files": [

The field processed.input.files contains the list of input files that are completely processed.

The field processing.input.files contains the list of input files that are getting processed but have not been completed.

What happens if there are bad records in the input file?

Bad records in the input file would be skipped. For example, consider the following input file.


In this case, the second record would be skipped as no value is specified for the lastName field.

Can I append data to an existing input file?

Appending data to an input file would lead to inconsistencies and data may not be purged as expected.

Would the records be purged if data is backfilled?

If a table is backfilled, then a new input file must be provided to purge the records again.

What happens if new data is generated against records that were purged?

A new input file needs to be dropped to purge the records again.