背景
本文基于 SPARK 3.3.0
从一个unit test来探究SPARK Codegen的逻辑，
test("SortAggregate should be included in WholeStageCodegen") {
val df = spark.range(10).agg(max(col("id")), avg(col("id")))
withSQLConf("spark.sql.test.forceApplySortAggregate" -> "true") {
val plan = df.queryExecution.executedPlan
assert(plan.exists(p =>
p.isInstanceOf[WholeStageCodegenExec] &&
p.asInstanceOf[WholeStageCodegenExec].child.isInstanceOf[SortAggregateExec]))
assert(df.collect() === Array(Row(9, 4.5)))
}
}
该sql形成的执行计划第二部分的全代码生成部分如下：
WholeStageCodegen
*(2) SortAggregate(key=[], functions=[max(id#0L), avg(id#0L)], output=[max(id)#5L, avg(id)#6])
InputAdapter
+- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#13]
分析
第二阶段wholeStageCodegen
第二阶段的代码生成涉及到SortAggregateExec和ShuffleExchangeExec以及InputAdapter的produce和consume方法，这里一一来分析：
第二阶段wholeStageCodegen数据流如下：
WholeStageCodegenExec SortAggregateExec(Final) InputAdapter ShuffleExchangeExec
====================================================================================
-> execute()
|
doExecute() ---------> inputRDDs() -----------------> inputRDDs() -------> execute()
| |
doCodeGen() doExecute()
| |
+-----------------> produce() ShuffledRowRDD
|
doProduce()
|
doProduceWithoutKeys() -------> produce()
|
doProduce()
|
doConsume() <------------------- consume()
|
doConsumeWithoutKeys()
|并不是doConsumeWithoutKeys调用consume,而是由doProduceWithoutKeys调用
doConsume() <-------- consume()
SortAggregateExec(Final) 的doProduce
这里只列出和SortAggregateExec(Partial)的不同的部分：
val (resultVars, genResult) = if (modes.contains(Final) || modes.contains(Complete)) {
// evaluate aggregate results
ctx.currentVars = flatBufVars
val aggResults = bindReferences(
functions.map(_.evaluateExpression),
aggregateBufferAttributes).map(_.genCode(ctx))
val evaluateAggResults = evaluateVariables(aggResults)
// evaluate result expressions
ctx.currentVars = aggResults
val resultVars = bindReferences(resultExpressions, aggregateAttributes).map(_.genCode(ctx))
(resultVars,
s"""
|$evaluateAggResults
|${evaluateVariables(resultVars)}
""".stripMargin)
InputAdaptor的 doProduce
InputAdaptor的主要作用是承上启下，用来适配不支持Codegen的物理计划，sql如下：
override def doProduce(ctx: CodegenContext): String = {
// Inline mutable state since an InputRDDCodegen is used once in a task for WholeStageCodegen
val input = ctx.addMutableState("scala.collection.Iterator", "input", v => s"$v = inputs[0];",
forceInline = true)
val row = ctx.freshName("row")
val outputVars = if (createUnsafeProjection) {
// creating the vars will make the parent consume add an unsafe projection.
ctx.INPUT_ROW = row
ctx.currentVars = null
output.zipWithIndex.map { case (a, i) =>
BoundReference(i, a.dataType, a.nullable).genCode(ctx)
}
} else {
null
}
val updateNumOutputRowsMetrics = if (metrics.contains("numOutputRows")) {
val numOutputRows = metricTerm(ctx, "numOutputRows")
s"$numOutputRows.add(1);"
} else {
""
}
s"""
| while ($limitNotReachedCond $input.hasNext()) {
| InternalRow $row = (InternalRow) $input.next();
| ${updateNumOutputRowsMetrics}
| ${consume(ctx, outputVars, if (createUnsafeProjection) null else row).trim}
| ${shouldStopCheckCode}
| }
""".stripMargin
}
- val input = ctx.addMutableState(“scala.collection.Iterator”, “input”, v => s"$v = inputs[0];"
定义一个input变量用来接受sortaggregate(partial)的输出的InteralRow（unsafeRow）,对应的初始化方法会在init方法中调用 - val row = ctx.freshName(“row”)
定义一个临时变量用来接受input中的unsafe类型的InteralRow,便于进行迭代操作 - val outputVars = if (createUnsafeProjection)
对于InputAdaptor来说createUnsafeProjection是 false, 所以这块返回的是null - val updateNumOutputRowsMetrics =
因为metrics不满足条件，所以这里也是返回空字符串 - 代码组装
s"""
| while ($limitNotReachedCond $input.hasNext()) {
| InternalRow $row = (InternalRow) $input.next();
| ${updateNumOutputRowsMetrics}
| ${consume(ctx, outputVars, if (createUnsafeProjection) null else row).trim}
| ${shouldStopCheckCode}
| }
""".stripMargin
对输入的每一行数据进行迭代操作， 之后再调用consume方法,
注意: 这里的consume传入的是row，是InteralRow类型，而不是在RangeExec中的Long类型的变量
InputAdaptor的 consume
我们这里只说明和之前不一样的部分，对应的sql如下：
final def consume(ctx: CodegenContext, outputVars: Seq[ExprCode], row: String = null): String =
注意这里的参数 outputVars为null
row 为InteralRow类型的变量
val inputVarsCandidate =
if (outputVars != null) {
assert(outputVars.length == output.length)
// outputVars will be used to generate the code for UnsafeRow, so we should copy them
outputVars.map(_.copy())
} else {
assert(row != null, "outputVars and row cannot both be null.")
ctx.currentVars = null
ctx.INPUT_ROW = row
output.zipWithIndex.map { case (attr, i) =>
BoundReference(i, attr.dataType, attr.nullable).genCode(ctx)
}
}
这里的数据流向了 else :
- ctx.INPUT_ROW = row
设置当前的INPUT_ROW为row
BoundReference的doGenCode方法也是走向了另一个分支:
assert(ctx.INPUT_ROW != null, "INPUT_ROW and currentVars cannot both be null.")
val javaType = JavaCode.javaType(dataType)
val value = CodeGenerator.getValue(ctx.INPUT_ROW, dataType, ordinal.toString)
if (nullable) {
ev.copy(code =
code"""
|boolean ${ev.isNull} = ${ctx.INPUT_ROW}.isNullAt($ordinal);
|$javaType ${ev.value} = ${ev.isNull} ?
| ${CodeGenerator.defaultValue(dataType)} : ($value);
""".stripMargin)
} else {
ev.copy(code = code"$javaType ${ev.value} = $value;", isNull = FalseLiteral)
}
- 分析
-
val value = CodeGenerator.getValue(ctx.INPUT_ROW, dataType,ordinal.toString)
根据数据类型的不同，调用UnsafeRow的不同方法
-
if (nullable)
因为AttributeReference("sum", sumDataType)()和AttributeReference("count", LongType)()表达式 nullable 为 TRUE，所以生成的代码为：
boolean inputadapter_isNull_0 = inputadapter_row_0.isNullAt(0);
long inputadapter_value_0 = inputadapter_isNull_0 ?
-1L : (inputadapter_row_0.getLong(0));
boolean inputadapter_isNull_1 = inputadapter_row_0.isNullAt(1);
double inputadapter_value_1 = inputadapter_isNull_1 ?
-1.0 : (inputadapter_row_0.getDouble(1));
boolean inputadapter_isNull_2 = inputadapter_row_0.isNullAt(2);
long inputadapter_value_2 = inputadapter_isNull_2 ?
-1L : (inputadapter_row_0.getLong(2));
- constructDoConsumeFunction方法中inputVarsInFunc
这里会多一个名为inputadapter_row_0的InternalRow类型的实参
