aokolnychyi commented on a change in pull request #26751: [SPARK-30107][SQL]
Expose nested schema pruning to all V2 sources
URL: https://github.com/apache/spark/pull/26751#discussion_r353712082
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/FileScanBuilder.scala
##########
@@ -26,34 +26,40 @@ abstract class FileScanBuilder(
fileIndex: PartitioningAwareFileIndex,
dataSchema: StructType) extends ScanBuilder with
SupportsPushDownRequiredColumns {
private val partitionSchema = fileIndex.partitionSchema
+ private val partitionNameSet = toNameSet(partitionSchema)
private val isCaseSensitive =
sparkSession.sessionState.conf.caseSensitiveAnalysis
protected var requiredSchema = StructType(dataSchema.fields ++
partitionSchema.fields)
override def pruneColumns(requiredSchema: StructType): Unit = {
- this.requiredSchema = requiredSchema
+ // the default implementation prunes only top-level columns
+ // file formats that support nested column pruning must override this
+ val fields = dataSchema.fields ++ partitionSchema.fields
Review comment:
If partition and data columns overlap, `requiredSchema` might contain
duplicates as we match by name. However, `readDataSchema` and
`readPartitionSchema` will still report correct structs.
As an alternative to the current approach, we can keep the previous
implementation of `pruneColumns` and come up with a new `ScanBuilder` that
extends `FileScanBuilder` and overrides `readDataSchema` to iterate through
`requiredSchema` instead of `dataSchema` (or simply have the same logic in
Orc/Parquet scan builders). That approach sounds slightly better, but I am also
open to other suggestions.
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
