Yaohua628 commented on a change in pull request #35147:
URL: https://github.com/apache/spark/pull/35147#discussion_r784427771
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/SchemaPruning.scala
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@@ -31,58 +31,69 @@ import org.apache.spark.sql.util.SchemaUtils._
* By "physical column", we mean a column as defined in the data source format
like Parquet format
* or ORC format. For example, in Spark SQL, a root-level Parquet column
corresponds to a SQL
* column, and a nested Parquet column corresponds to a [[StructField]].
+ *
+ * Also prunes the unnecessary metadata columns if any for all file formats.
*/
object SchemaPruning extends Rule[LogicalPlan] {
import org.apache.spark.sql.catalyst.expressions.SchemaPruning._
- override def apply(plan: LogicalPlan): LogicalPlan =
- if (conf.nestedSchemaPruningEnabled) {
- apply0(plan)
- } else {
- plan
- }
+ override def apply(plan: LogicalPlan): LogicalPlan = apply0(plan)
private def apply0(plan: LogicalPlan): LogicalPlan =
plan transformDown {
case op @ PhysicalOperation(projects, filters,
- l @ LogicalRelation(hadoopFsRelation: HadoopFsRelation, _, _, _))
- if canPruneRelation(hadoopFsRelation) =>
-
- prunePhysicalColumns(l.output, projects, filters,
hadoopFsRelation.dataSchema,
- prunedDataSchema => {
+ l @ LogicalRelation(hadoopFsRelation: HadoopFsRelation, _, _, _)) =>
+ prunePhysicalColumns(l, projects, filters, hadoopFsRelation,
+ (prunedDataSchema, prunedMetadataSchema) => {
val prunedHadoopRelation =
hadoopFsRelation.copy(dataSchema =
prunedDataSchema)(hadoopFsRelation.sparkSession)
- buildPrunedRelation(l, prunedHadoopRelation)
+ buildPrunedRelation(l, prunedHadoopRelation, prunedMetadataSchema)
}).getOrElse(op)
}
/**
* This method returns optional logical plan. `None` is returned if no
nested field is required or
* all nested fields are required.
+ *
+ * This method will prune both the data schema and the metadata schema
*/
private def prunePhysicalColumns(
- output: Seq[AttributeReference],
+ relation: LogicalRelation,
projects: Seq[NamedExpression],
filters: Seq[Expression],
- dataSchema: StructType,
- leafNodeBuilder: StructType => LeafNode): Option[LogicalPlan] = {
+ hadoopFsRelation: HadoopFsRelation,
+ leafNodeBuilder: (StructType, StructType) => LeafNode):
Option[LogicalPlan] = {
+
val (normalizedProjects, normalizedFilters) =
- normalizeAttributeRefNames(output, projects, filters)
+ normalizeAttributeRefNames(relation.output, projects, filters)
val requestedRootFields = identifyRootFields(normalizedProjects,
normalizedFilters)
// If requestedRootFields includes a nested field, continue. Otherwise,
// return op
if (requestedRootFields.exists { root: RootField => !root.derivedFromAtt
}) {
- val prunedDataSchema = pruneDataSchema(dataSchema, requestedRootFields)
- // If the data schema is different from the pruned data schema,
continue. Otherwise,
- // return op. We effect this comparison by counting the number of "leaf"
fields in
- // each schemata, assuming the fields in prunedDataSchema are a subset
of the fields
- // in dataSchema.
- if (countLeaves(dataSchema) > countLeaves(prunedDataSchema)) {
- val prunedRelation = leafNodeBuilder(prunedDataSchema)
- val projectionOverSchema = ProjectionOverSchema(prunedDataSchema)
+ val prunedDataSchema = if (canPruneDataSchema(hadoopFsRelation)) {
+ pruneDataSchema(hadoopFsRelation.dataSchema, requestedRootFields)
+ } else {
+ hadoopFsRelation.dataSchema
+ }
+
+ val metadataSchema =
+ relation.output.collect { case MetadataAttribute(attr) => attr
}.toStructType
+ val prunedMetadataSchema = if (canPruneMetadataSchema(relation)) {
+ pruneDataSchema(metadataSchema, requestedRootFields)
Review comment:
good idea, sure!
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