[GitHub] [spark] viirya commented on a change in pull request #26751: [SPARK-30107][SQL] Expose nested schema pruning to all V2 sources
viirya 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_r356911410
##
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/PushDownUtils.scala
##
@@ -76,28 +78,46 @@ object PushDownUtils extends PredicateHelper {
* @return the created `ScanConfig`(since column pruning is the last step of
operator pushdown),
* and new output attributes after column pruning.
*/
- // TODO: nested column pruning.
def pruneColumns(
scanBuilder: ScanBuilder,
relation: DataSourceV2Relation,
- exprs: Seq[Expression]): (Scan, Seq[AttributeReference]) = {
+ projects: Seq[NamedExpression],
+ filters: Seq[Expression]): (Scan, Seq[AttributeReference]) = {
scanBuilder match {
+ case r: SupportsPushDownRequiredColumns if
SQLConf.get.nestedSchemaPruningEnabled =>
Review comment:
If we know scanBuilder does not support nested schema pruning, do we still
need go this path?
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[GitHub] [spark] viirya commented on a change in pull request #26751: [SPARK-30107][SQL] Expose nested schema pruning to all V2 sources
viirya 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_r356910815
##
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:
hmm, so what is the difference using `requiredSchema` or `dataSchema`? When
using the old path without nested schema pruning, the passed `requiredSchema`
is always the source of truth. When `supportsNestedSchemaPruning` is false,
don't we also go the old path, but we turn to `dataSchema`??
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[GitHub] [spark] viirya commented on a change in pull request #26751: [SPARK-30107][SQL] Expose nested schema pruning to all V2 sources
viirya 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_r356910815
##
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:
hmm, so what is the difference using `requiredSchema` or `dataSchema`? When
using the old path without nested schema pruning, the passed `requiredSchema`
is always the source of truth. When `supportsNestedSchemaPruning` is false, we
turn to `dataSchema`??
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[GitHub] [spark] viirya commented on a change in pull request #26751: [SPARK-30107][SQL] Expose nested schema pruning to all V2 sources
viirya 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_r356364276
##
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:
If supportsNestedSchemaPruning is true but
SQLConf.get.nestedSchemaPruningEnabled is disabled? We should use `dataSchema`?
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[GitHub] [spark] viirya commented on a change in pull request #26751: [SPARK-30107][SQL] Expose nested schema pruning to all V2 sources
viirya 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_r354114582
##
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/parquet/ParquetScanBuilder.scala
##
@@ -68,6 +68,10 @@ case class ParquetScanBuilder(
// All filters that can be converted to Parquet are pushed down.
override def pushedFilters(): Array[Filter] = pushedParquetFilters
+ override def pruneColumns(requiredSchema: StructType): Unit = {
+this.requiredSchema = requiredSchema
+ }
Review comment:
for a datasource, how does it know passed in requiredSchema is for normal
column pruning or nested column pruning?
I have this question because for a datasource that does not support nested
column pruning, looks like when SQLConf.get.nestedSchemaPruningEnabled is true,
a nested pruning required schema is still passed in. How does such datasource
know to act?
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[GitHub] [spark] viirya commented on a change in pull request #26751: [SPARK-30107][SQL] Expose nested schema pruning to all V2 sources
viirya 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_r354112143
##
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/PushDownUtils.scala
##
@@ -76,28 +78,48 @@ object PushDownUtils extends PredicateHelper {
* @return the created `ScanConfig`(since column pruning is the last step of
operator pushdown),
* and new output attributes after column pruning.
*/
- // TODO: nested column pruning.
def pruneColumns(
scanBuilder: ScanBuilder,
relation: DataSourceV2Relation,
- exprs: Seq[Expression]): (Scan, Seq[AttributeReference]) = {
+ projects: Seq[NamedExpression],
+ filters: Seq[Expression]): (Scan, Seq[AttributeReference]) = {
scanBuilder match {
+ case r: SupportsPushDownRequiredColumns if
SQLConf.get.nestedSchemaPruningEnabled =>
+val rootFields = SchemaPruning.identifyRootFields(projects, filters)
+val prunedSchema = if (rootFields.nonEmpty) {
Review comment:
there was a check in `prunePhysicalColumns`:
```
if (requestedRootFields.exists { root: RootField => !root.derivedFromAtt }) {
...
}
```
Is it removed from this move?
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