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_r357109500
##########
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
}
protected def readDataSchema(): StructType = {
val requiredNameSet = createRequiredNameSet()
- val fields = dataSchema.fields.filter { field =>
+ val schema = if (supportsNestedSchemaPruning) requiredSchema else
dataSchema
Review comment:
The difference between those two is that `requiredSchema` can have pruned
nested columns if nested schema pruning is enabled. Therefore, ORC and Parquet
must always use data types from `requiredSchema`. If nested schema pruning is
disabled, `requiredSchema` will have pruned only top-level columns. In that
case, it is still safe to use data types from `requiredSchema`. CSV/JSON/Avro
will always use `requiredSchema` as a reference but keep data types from
`dataSchema`, so that even if Spark passes a schema with pruned nested columns,
we still use data types defined in `dataSchema` to match the behavior before
this PR.
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