dbtsai 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_r356359449
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/FileScanBuilder.scala
##########
@@ -27,15 +27,20 @@ abstract class FileScanBuilder(
dataSchema: StructType) extends ScanBuilder with
SupportsPushDownRequiredColumns {
private val partitionSchema = fileIndex.partitionSchema
private val isCaseSensitive =
sparkSession.sessionState.conf.caseSensitiveAnalysis
+ protected val supportsNestedSchemaPruning: Boolean = false
protected var requiredSchema = StructType(dataSchema.fields ++
partitionSchema.fields)
override def pruneColumns(requiredSchema: StructType): Unit = {
+ // [SPARK-30107] While the passed `requiredSchema` always have pruned
nested columns, the actual
+ // data schema of this scan is determined in `readDataSchema`. File
formats that don't support
+ // nested schema pruning, use `requiredSchema` as a reference and perform
the pruning partially.
this.requiredSchema = requiredSchema
Review comment:
could we write "use `requiredSchema` as a reference and perform the pruning
on top level."?
BTW, for data source such as csv or json, even the top level pruning is not
supported. In this case, does it mean the `readDaraSchema` will be always the
full schema?
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