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.
0.13.0-ST.58+
Pinot Version
0.13.0-ST.58+
Yes
The class name used to read the files from source location.
input.fs.className
Yes
The class name used to read the files from source location.
inputFormat
Yes
The input file format.
inputDirURI
Yes
The input dir containing the purge input files.
input.fs.prop.accessKey
No
Required if reading from AWS S3.
input.fs.prop.secretKey
No
Required if reading from AWS S3.
input.fs.prop.region
No
Required if reading from AWS S3.
recordReader.prop.delimiter
No
Defaults to “,”. Supported values are “,” and “;”.
max.num.purge.input.files
No
Maximum number of input files processed at a time. Default value is 10.
max.total.purge.input.file.size
No
Maximum total size of all input files combined. Default value is 100_000_000 bytes. A single file cannot exceed this value.
table.max.num.tasks
No
Maximum number of minion sub tasks generated per task invocation. Default value is 10.
Example Table Configuration
"task": {
"taskTypeConfigsMap":
{
"SegmentPurgeTask":
{
"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#1
File Name: purgeRecords1.csv
userId
1000
1001
1002
1003
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.
File#2
FileName: purgeRecords2.csv
firstName,lastName
john,doe
jane,doe
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.
Limits
Note the following limits.
CSV
Contents within the input file should conform to CSV format.
Input file format
CSV
Contents within the input file should conform to CSV format.
Input files source
AWS S3
Only S3 is supported as of now.
Data Types
int, long, boolean, string
The input files must contain fields that conform to one of these types.
Field Values
Single Values
All 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 Value
Not Supported
Matching against null values are not supported.
Empty Value
Not Supported
Matching against emtpy values are not supported.
Input file Name Extension
Not Required
File extension is not required.
Input file field delimiter
“,” and “;”
Other delimeters like space, tab are not supported. Default value is “,”.
Limitations
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
.
firstName,lastName
john,null
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.
FAQ
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": {
"watermark.start.time.ms": "1682621729000"
},
"mapFields": {},
"listFields": {
"processed.input.files": [
"s3://startree-pinot-purge/suspects/input/suspectsInput1.csv"
],
"processing.input.files": [
"s3://startree-pinot-purge/suspects/input/suspectsInput2.csv"
]
}
}
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.
firstName,lastName
john,doe
jane,
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.