org.apache.spark.sql.connector.read

Interface SupportsRuntimeFiltering

All Superinterfaces:

Scan

@Experimental
public interface SupportsRuntimeFiltering
extends Scan

A mix-in interface for Scan. Data sources can implement this interface if they can filter initially planned InputPartitions using predicates Spark infers at runtime.

Note that Spark will push runtime filters only if they are beneficial.

Since:

3.2.0

Method Summary

Modifier and Type	Method and Description
void	filter(Filter[] filters) Filters this scan using runtime filters.
NamedReference[]	filterAttributes() Returns attributes this scan can be filtered by at runtime.

Methods inherited from interface org.apache.spark.sql.connector.read.Scan

description, readSchema, supportedCustomMetrics, toBatch, toContinuousStream, toMicroBatchStream

Method Detail

filterAttributes

NamedReference[] filterAttributes()

Returns attributes this scan can be filtered by at runtime.

Spark will call filter(Filter[]) if it can derive a runtime predicate for any of the filter attributes.

filter

void filter(Filter[] filters)

Filters this scan using runtime filters.

The provided expressions must be interpreted as a set of filters that are ANDed together. Implementations may use the filters to prune initially planned InputPartitions.

If the scan also implements SupportsReportPartitioning, it must preserve the originally reported partitioning during runtime filtering. While applying runtime filters, the scan may detect that some InputPartitions have no matching data. It can omit such partitions entirely only if it does not report a specific partitioning. Otherwise, the scan can replace the initially planned InputPartitions that have no matching data with empty InputPartitions but must preserve the overall number of partitions.

Note that Spark will call Scan.toBatch() again after filtering the scan at runtime.

Parameters:

filters - data source filters used to filter the scan at runtime