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[SPARK-26450][SQL] Avoid rebuilding map of schema for every column in projection #23392

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[SPARK-26450][SQL] Avoid rebuilding map of schema for every column in projection #23392

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6b66711
Initial commit
bersprockets Dec 12, 2018
a9dbbec
Initial try at helper function
bersprockets Dec 29, 2018
67a943a
Use helper function
bersprockets Dec 31, 2018
3362744
More uses of helper function
bersprockets Dec 31, 2018
306e0a5
Even more uses of helper function
bersprockets Dec 31, 2018
a25b59c
Some clean up
bersprockets Jan 2, 2019
b977d3e
Review feedback
bersprockets Jan 2, 2019
1497d3a
Fix style issue
bersprockets Jan 2, 2019
777c5b4
Move helper function to BindReferences
bersprockets Jan 4, 2019
fbd6787
Handle cases missed before
bersprockets Jan 4, 2019
686e7b5
Review feedback
bersprockets Jan 5, 2019
b1afb3a
Close gap in ExpandExec
bersprockets Jan 13, 2019
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2 changes: 1 addition & 1 deletion sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
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Expand Up @@ -30,7 +30,7 @@ import org.apache.spark.sql.types.{DataType, StructType}
*/
class InterpretedProjection(expressions: Seq[Expression]) extends Projection {
def this(expressions: Seq[Expression], inputSchema: Seq[Attribute]) =
this(expressions.map(BindReferences.bindReference(_, inputSchema)))
this(toBoundExprs(expressions, inputSchema))

override def initialize(partitionIndex: Int): Unit = {
expressions.foreach(_.foreach {
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2 changes: 1 addition & 1 deletion ...n/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
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Expand Up @@ -35,7 +35,7 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], MutableP
in.map(ExpressionCanonicalizer.execute)

protected def bind(in: Seq[Expression], inputSchema: Seq[Attribute]): Seq[Expression] =
in.map(BindReferences.bindReference(_, inputSchema))
toBoundExprs(in, inputSchema)

def generate(
expressions: Seq[Expression],
Expand Down
4 changes: 2 additions & 2 deletions ...t/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala
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Expand Up @@ -46,7 +46,7 @@ object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[InternalR
in.map(ExpressionCanonicalizer.execute(_).asInstanceOf[SortOrder])

protected def bind(in: Seq[SortOrder], inputSchema: Seq[Attribute]): Seq[SortOrder] =
in.map(BindReferences.bindReference(_, inputSchema))
toBoundExprs(in, inputSchema)

/**
* Creates a code gen ordering for sorting this schema, in ascending order.
Expand Down Expand Up @@ -188,7 +188,7 @@ class LazilyGeneratedOrdering(val ordering: Seq[SortOrder])
extends Ordering[InternalRow] with KryoSerializable {

def this(ordering: Seq[SortOrder], inputSchema: Seq[Attribute]) =
this(ordering.map(BindReferences.bindReference(_, inputSchema)))
this(toBoundExprs(ordering, inputSchema))

@transient
private[this] var generatedOrdering = GenerateOrdering.generate(ordering)
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2 changes: 1 addition & 1 deletion ...main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
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Expand Up @@ -41,7 +41,7 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
in.map(ExpressionCanonicalizer.execute)

protected def bind(in: Seq[Expression], inputSchema: Seq[Attribute]): Seq[Expression] =
in.map(BindReferences.bindReference(_, inputSchema))
toBoundExprs(in, inputSchema)

private def createCodeForStruct(
ctx: CodegenContext,
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2 changes: 1 addition & 1 deletion ...in/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
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Expand Up @@ -317,7 +317,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
in.map(ExpressionCanonicalizer.execute)

protected def bind(in: Seq[Expression], inputSchema: Seq[Attribute]): Seq[Expression] =
in.map(BindReferences.bindReference(_, inputSchema))
toBoundExprs(in, inputSchema)

def generate(
expressions: Seq[Expression],
Expand Down
2 changes: 1 addition & 1 deletion sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ordering.scala
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Expand Up @@ -27,7 +27,7 @@ import org.apache.spark.sql.types._
class InterpretedOrdering(ordering: Seq[SortOrder]) extends Ordering[InternalRow] {

def this(ordering: Seq[SortOrder], inputSchema: Seq[Attribute]) =
this(ordering.map(BindReferences.bindReference(_, inputSchema)))
this(toBoundExprs(ordering, inputSchema))

def compare(a: InternalRow, b: InternalRow): Int = {
var i = 0
Expand Down
7 changes: 5 additions & 2 deletions sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala
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Expand Up @@ -88,8 +88,11 @@ package object expressions {
/**
* A helper function to bind given expressions to an input schema.
*/
def toBoundExprs(exprs: Seq[Expression], inputSchema: Seq[Attribute]): Seq[Expression] = {
exprs.map(BindReferences.bindReference(_, inputSchema))
def toBoundExprs[A <: Expression](

@hvanhovell hvanhovell Jan 3, 2019

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I would like to minimize the chance that future changes suffer from the same issue. In order to do that we should provide API in a logical place, it does not make a whole lot of sense to me that I need to look in package.scala to find a more performant version of BindReferences.bindReference(..) for a seq. Can we move this function to BindReference and name it bindReferences?

exprs: Seq[A],

@mgaido91 mgaido91 Jan 2, 2019

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nit: indent

inputSchema: Seq[Attribute]): Seq[A] = {

@mgaido91 mgaido91 Jan 2, 2019

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why not just changing this to AttributeSeq?

@bersprockets bersprockets Jan 2, 2019

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Sometimes this function is called with a zero-length exprs (df.count, for example). I am attempting to avoid constructing the AttributeSeq in that case, because AttributeSeq's constructor eagerly builds a data structure based on the attributes (private val qualified3Part).

@hvanhovell hvanhovell Jan 2, 2019

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How expensive is it to build those structures on empty data? We could consider cachin an empty attribute seq and use that when the there are no attributes.

@bersprockets bersprockets Jan 2, 2019

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I was thinking that just because exprs is empty, that would not necessarily mean inputSchema is empty. But experiments seem to indicate that inputSchema is also empty. So building the AttributeSeq would be extremely low-cost. I guess there is no reason to lazily build it.

lazy val inputSchemaAttrSeq: AttributeSeq = inputSchema
exprs.map(BindReferences.bindReference(_, inputSchemaAttrSeq))
}

/**
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6 changes: 3 additions & 3 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala
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Expand Up @@ -264,7 +264,7 @@ case class HashAggregateExec(
}
}
ctx.currentVars = bufVars ++ input
val boundUpdateExpr = updateExpr.map(BindReferences.bindReference(_, inputAttrs))
val boundUpdateExpr = toBoundExprs(updateExpr, inputAttrs)
val subExprs = ctx.subexpressionEliminationForWholeStageCodegen(boundUpdateExpr)
val effectiveCodes = subExprs.codes.mkString("\n")
val aggVals = ctx.withSubExprEliminationExprs(subExprs.states) {
Expand Down Expand Up @@ -825,7 +825,7 @@ case class HashAggregateExec(

val updateRowInRegularHashMap: String = {
ctx.INPUT_ROW = unsafeRowBuffer
val boundUpdateExpr = updateExpr.map(BindReferences.bindReference(_, inputAttr))
val boundUpdateExpr = toBoundExprs(updateExpr, inputAttr)
val subExprs = ctx.subexpressionEliminationForWholeStageCodegen(boundUpdateExpr)
val effectiveCodes = subExprs.codes.mkString("\n")
val unsafeRowBufferEvals = ctx.withSubExprEliminationExprs(subExprs.states) {
Expand All @@ -849,7 +849,7 @@ case class HashAggregateExec(
if (isFastHashMapEnabled) {
if (isVectorizedHashMapEnabled) {
ctx.INPUT_ROW = fastRowBuffer
val boundUpdateExpr = updateExpr.map(BindReferences.bindReference(_, inputAttr))
val boundUpdateExpr = toBoundExprs(updateExpr, inputAttr)
val subExprs = ctx.subexpressionEliminationForWholeStageCodegen(boundUpdateExpr)
val effectiveCodes = subExprs.codes.mkString("\n")
val fastRowEvals = ctx.withSubExprEliminationExprs(subExprs.states) {
Expand Down
5 changes: 2 additions & 3 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
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Expand Up @@ -63,9 +63,8 @@ trait HashJoin {
protected lazy val (buildKeys, streamedKeys) = {
require(leftKeys.map(_.dataType) == rightKeys.map(_.dataType),
"Join keys from two sides should have same types")
val lkeys = HashJoin.rewriteKeyExpr(leftKeys).map(BindReferences.bindReference(_, left.output))
val rkeys = HashJoin.rewriteKeyExpr(rightKeys)
.map(BindReferences.bindReference(_, right.output))
val lkeys = toBoundExprs(HashJoin.rewriteKeyExpr(leftKeys), left.output)
val rkeys = toBoundExprs(HashJoin.rewriteKeyExpr(rightKeys), right.output)
buildSide match {
case BuildLeft => (lkeys, rkeys)
case BuildRight => (rkeys, lkeys)
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3 changes: 2 additions & 1 deletion sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoinExec.scala
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Expand Up @@ -393,7 +393,8 @@ case class SortMergeJoinExec(
input: Seq[Attribute]): Seq[ExprCode] = {
ctx.INPUT_ROW = row
ctx.currentVars = null
keys.map(BindReferences.bindReference(_, input).genCode(ctx))
val inputAttributeSeq: AttributeSeq = input
keys.map(BindReferences.bindReference(_, inputAttributeSeq).genCode(ctx))

@mgaido91 mgaido91 Jan 2, 2019 •
edited
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why not toBoundExprs(keys, input).map(_.genCode(ctx))?

@bersprockets bersprockets Jan 2, 2019

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It should be...

}

private def copyKeys(ctx: CodegenContext, vars: Seq[ExprCode]): Seq[ExprCode] = {
Expand Down