Filters this scan using runtime predicates.

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

If the scan also implements SupportsReportPartitioning, it must preserve the originally reported partitioning during runtime filtering. While applying runtime predicates, 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.

Returns attributes this scan can be filtered by at runtime.

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

Returns the actual schema of this data source scan, which may be different from the physical schema of the underlying storage, as column pruning or other optimizations may happen.

Subclasses can implement this method to indicate if the support for columnar data should be determined by each partition or is set as a default for the whole scan.

A description string of this scan, which may includes information like: what filters are configured for this scan, what's the value of some important options like path, etc. The description doesn't need to include #readSchema(), as Spark already knows it.

By default this returns the class name of the implementation. Please override it to provide a meaningful description.

Returns an array of custom metrics which are collected with values at the driver side only. Note that these metrics must be included in the supported custom metrics reported by supportedCustomMetrics.

Returns an array of supported custom metrics with name and description. By default it returns empty array.

Returns the physical representation of this scan for batch query. By default this method throws exception, data sources must overwrite this method to provide an implementation, if the Table that creates this scan returns TableCapability#BATCH_READ support in its Table#capabilities().

If the scan supports runtime filtering and implements SupportsRuntimeFiltering, this method may be called multiple times. Therefore, implementations can cache some state to avoid planning the job twice.

Returns the physical representation of this scan for streaming query with continuous mode. By default this method throws exception, data sources must overwrite this method to provide an implementation, if the Table that creates this scan returns TableCapability#CONTINUOUS_READ support in its Table#capabilities().

Returns the physical representation of this scan for streaming query with micro-batch mode. By default this method throws exception, data sources must overwrite this method to provide an implementation, if the Table that creates this scan returns TableCapability#MICRO_BATCH_READ support in its Table#capabilities().