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_r356361601
##########
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:
Okay, I figure. For those data sources that don't support top level pruning,
we will only return the required top level columns in readDataSchema. I guess
in this case, the reader implementations still read the full data, and handle
it internally, but pass less data into Spark. Wondering why we can not do
similar thing in readers for nested data structure?
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