Spark SQL Catalyst源代码分析之Analyzer
/** Spark SQL源代码分析系列文章*/
前面几篇文章解说了Spark SQL的核心运行流程和Spark SQL的Catalyst框架的Sql Parser是如何接受用户输入sql，经过解析生成Unresolved Logical Plan的。
我们记得Spark SQL的运行流程中还有一个核心的组件式Analyzer，本文将会介绍Analyzer在Spark SQL里起到了什么作用。
Analyzer位于Catalyst的analysis package下。主要职责是将Sql Parser 未能Resolved的Logical Plan 给Resolved掉。
一、Analyzer构造
Analyzer会使用Catalog和FunctionRegistry将UnresolvedAttribute和UnresolvedRelation转换为catalyst里全类型的对象。
Analyzer里面有fixedPoint对象，一个Seq[Batch].
class Analyzer(catalog: Catalog, registry: FunctionRegistry, caseSensitive: Boolean)
extends RuleExecutor[LogicalPlan] with HiveTypeCoercion {
// TODO: pass this in as a parameter.
val fixedPoint = FixedPoint(100)
val batches: Seq[Batch] = Seq(
Batch("MultiInstanceRelations", Once,
NewRelationInstances),
Batch("CaseInsensitiveAttributeReferences", Once,
(if (caseSensitive) Nil else LowercaseAttributeReferences :: Nil) : _*),
Batch("Resolution", fixedPoint,
ResolveReferences ::
ResolveRelations ::
NewRelationInstances ::
ImplicitGenerate ::
StarExpansion ::
ResolveFunctions ::
GlobalAggregates ::
typeCoercionRules :_*),
Batch("AnalysisOperators", fixedPoint,
EliminateAnalysisOperators)
) Analyzer里的一些对象解释：FixedPoint：相当于迭代次数的上限。
/** A strategy that runs until fix point or maxIterations times, whichever comes first. */ case class FixedPoint(maxIterations: Int) extends Strategy
Batch: 批次，这个对象是由一系列Rule组成的，採用一个策略（策略事实上是迭代几次的别名吧，eg：Once）
/** A batch of rules. */。 protected case class Batch(name: String, strategy: Strategy, rules: Rule[TreeType]*)Rule：理解为一种规则，这样的规则会应用到Logical Plan 从而将UnResolved 转变为Resolved
abstract class Rule[TreeType <: TreeNode[_]] extends Logging {
/** Name for this rule, automatically inferred based on class name. */
val ruleName: String = {
val className = getClass.getName
if (className endsWith "$") className.dropRight(1) else className
}
def apply(plan: TreeType): TreeType
}Strategy：最大的运行次数，假设运行次数在最大迭代次数之前就达到了fix point，策略就会停止，不再应用了。
/**
* An execution strategy for rules that indicates the maximum number of executions. If the
* execution reaches fix point (i.e. converge) before maxIterations, it will stop.
*/
abstract class Strategy { def maxIterations: Int }Analyzer解析主要是依据这些Batch里面定义的策略和Rule来对Unresolved的逻辑计划进行解析的。
这里Analyzer类本身并未定义运行的方法，而是要从它的父类RuleExecutor[LogicalPlan]寻找，Analyzer也实现了HiveTypeCosercion，这个类是參考Hive的类型自己主动兼容转换的原理。如图：
RuleExecutor：运行Rule的运行环境，它会将包括了一系列的Rule的Batch进行运行，这个过程都是串行的。
详细的运行方法定义在apply里：
能够看到这里是一个while循环，每一个batch下的rules都对当前的plan进行作用，这个过程是迭代的，直到达到Fix Point或者最大迭代次数。
def apply(plan: TreeType): TreeType = {
var curPlan = plan
batches.foreach { batch =>
val batchStartPlan = curPlan
var iteration = 1
var lastPlan = curPlan
var continue = true
// Run until fix point (or the max number of iterations as specified in the strategy.
while (continue) {
curPlan = batch.rules.foldLeft(curPlan) {
case (plan, rule) =>
val result = rule(plan) //这里将调用各个不同Rule的apply方法，将UnResolved Relations，Attrubute和Function进行Resolve
if (!result.fastEquals(plan)) {
logger.trace(
s"""
|=== Applying Rule ${rule.ruleName} ===
|${sideBySide(plan.treeString, result.treeString).mkString("\n")}
""".stripMargin)
}
result //返回作用后的result plan
}
iteration += 1
if (iteration > batch.strategy.maxIterations) { //假设迭代次数已经大于该策略的最大迭代次数，就停止循环
logger.info(s"Max iterations ($iteration) reached for batch ${batch.name}")
continue = false
}
if (curPlan.fastEquals(lastPlan)) { //假设在多次迭代中不再变化，由于plan有个unique id，就停止循环。
logger.trace(s"Fixed point reached for batch ${batch.name} after $iteration iterations.")
continue = false
}
lastPlan = curPlan
}
if (!batchStartPlan.fastEquals(curPlan)) {
logger.debug(
s"""
|=== Result of Batch ${batch.name} ===
|${sideBySide(plan.treeString, curPlan.treeString).mkString("\n")}
""".stripMargin)
} else {
logger.trace(s"Batch ${batch.name} has no effect.")
}
}
curPlan //返回Resolved的Logical Plan
}
二、Rules介绍
眼下Spark SQL 1.0.0的Rule都定义在了Analyzer.scala的内部类。
在batches里面定义了4个Batch。
MultiInstanceRelations、CaseInsensitiveAttributeReferences、Resolution、AnalysisOperators 四个。
这4个Batch是将不同的Rule进行归类。每种类别採用不同的策略来进行Resolve。
2.1、MultiInstanceRelation
假设一个实例在Logical Plan里出现了多次，则会应用NewRelationInstances这儿Rule
Batch("MultiInstanceRelations", Once,
NewRelationInstances)trait MultiInstanceRelation {
def newInstance: this.type
}
object NewRelationInstances extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = {
val localRelations = plan collect { case l: MultiInstanceRelation => l} //将logical plan应用partial function得到全部MultiInstanceRelation的plan的集合
val multiAppearance = localRelations
.groupBy(identity[MultiInstanceRelation]) //group by操作
.filter { case (_, ls) => ls.size > 1 } //假设仅仅取size大于1的进行兴许操作
.map(_._1)
.toSet
//更新plan，使得每一个实例的expId是唯一的。
plan transform {
case l: MultiInstanceRelation if multiAppearance contains l => l.newInstance
}
}
}
2.2、LowercaseAttributeReferences
相同是partital function，对当前plan应用，将全部匹配的如UnresolvedRelation的别名alise转换为小写，将Subquery的别名也转换为小写。
总结：这是一个使属性名大写和小写不敏感的Rule，由于它将全部属性都to lower case了。
object LowercaseAttributeReferences extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan transform {
case UnresolvedRelation(databaseName, name, alias) =>
UnresolvedRelation(databaseName, name, alias.map(_.toLowerCase))
case Subquery(alias, child) => Subquery(alias.toLowerCase, child)
case q: LogicalPlan => q transformExpressions {
case s: Star => s.copy(table = s.table.map(_.toLowerCase))
case UnresolvedAttribute(name) => UnresolvedAttribute(name.toLowerCase)
case Alias(c, name) => Alias(c, name.toLowerCase)()
case GetField(c, name) => GetField(c, name.toLowerCase)
}
}
}2.3、ResolveReferences
将Sql parser解析出来的UnresolvedAttribute全部都转为相应的实际的catalyst.expressions.AttributeReference AttributeReferences
这里调用了logical plan 的resolve方法。将属性转为NamedExepression。
object ResolveReferences extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
case q: LogicalPlan if q.childrenResolved =>
logger.trace(s"Attempting to resolve ${q.simpleString}")
q transformExpressions {
case u @ UnresolvedAttribute(name) =>
// Leave unchanged if resolution fails. Hopefully will be resolved next round.
val result = q.resolve(name).getOrElse(u)//转化为NamedExpression
logger.debug(s"Resolving $u to $result")
result
}
}
}2.4、 ResolveRelations
这个比較好理解，还记得前面Sql parser吗。比方select * from src，这个src表parse后就是一个UnresolvedRelation节点。
这一步ResolveRelations调用了catalog这个对象。
Catalog对象里面维护了一个tableName,Logical Plan的HashMap结果。
通过这个Catalog文件夹来寻找当前表的结构。从而从中解析出这个表的字段，如UnResolvedRelations 会得到一个tableWithQualifiers。（即表和字段）
这也解释了为什么流程图那，我会画一个catalog在上面。由于它是Analyzer工作时须要的meta data。
object ResolveRelations extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan transform {
case UnresolvedRelation(databaseName, name, alias) =>
catalog.lookupRelation(databaseName, name, alias)
}
}2.5、ImplicitGenerate
假设在select语句里仅仅有一个表达式，并且这个表达式是一个Generator（Generator是一个1条记录生成到N条记录的映射）
当在解析逻辑计划时，遇到Project节点的时候，就能够将它转换为Generate类（Generate类是将输入流应用一个函数。从而生成一个新的流）。
object ImplicitGenerate extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan transform {
case Project(Seq(Alias(g: Generator, _)), child) =>
Generate(g, join = false, outer = false, None, child)
}
}
2.6 StarExpansion
在Project操作符里。假设是*符号，即select * 语句。能够将全部的references都展开，即将select * 中的*展开成实际的字段。
object StarExpansion extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan transform {
// Wait until children are resolved
case p: LogicalPlan if !p.childrenResolved => p
// If the projection list contains Stars, expand it.
case p @ Project(projectList, child) if containsStar(projectList) =>
Project(
projectList.flatMap {
case s: Star => s.expand(child.output) //展开，将输入的Attributeexpand(input: Seq[Attribute]) 转化为Seq[NamedExpression]
case o => o :: Nil
},
child)
case t: ScriptTransformation if containsStar(t.input) =>
t.copy(
input = t.input.flatMap {
case s: Star => s.expand(t.child.output)
case o => o :: Nil
}
)
// If the aggregate function argument contains Stars, expand it.
case a: Aggregate if containsStar(a.aggregateExpressions) =>
a.copy(
aggregateExpressions = a.aggregateExpressions.flatMap {
case s: Star => s.expand(a.child.output)
case o => o :: Nil
}
)
}
/**
* Returns true if `exprs` contains a [[Star]].
*/
protected def containsStar(exprs: Seq[Expression]): Boolean =
exprs.collect { case _: Star => true }.nonEmpty
}
}2.7 ResolveFunctions
这个和ResolveReferences几乎相同，这里主要是对udf进行resolve。
将这些UDF都在FunctionRegistry里进行查找。
object ResolveFunctions extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan transform {
case q: LogicalPlan =>
q transformExpressions {
case u @ UnresolvedFunction(name, children) if u.childrenResolved =>
registry.lookupFunction(name, children) //看是否注冊了当前udf
}
}
}2.8 GlobalAggregates
全局的聚合，假设遇到了Project就返回一个Aggregate.
object GlobalAggregates extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan transform {
case Project(projectList, child) if containsAggregates(projectList) =>
Aggregate(Nil, projectList, child)
}
def containsAggregates(exprs: Seq[Expression]): Boolean = {
exprs.foreach(_.foreach {
case agg: AggregateExpression => return true
case _ =>
})
false
}
}2.9 typeCoercionRules
这个是Hive里的兼容SQL语法，比方将String和Int互相转换，不须要显示的调用cast xxx as yyy了。如StringToIntegerCasts。
val typeCoercionRules =
PropagateTypes ::
ConvertNaNs ::
WidenTypes ::
PromoteStrings ::
BooleanComparisons ::
BooleanCasts ::
StringToIntegralCasts ::
FunctionArgumentConversion ::
CastNulls ::
Nil2.10 EliminateAnalysisOperators
将分析的操作符移除。这里仅支持2种，一种是Subquery须要移除，一种是LowerCaseSchema。这些节点都会从Logical Plan里移除。
object EliminateAnalysisOperators extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan transform {
case Subquery(_, child) => child //遇到Subquery,不反悔本身，返回它的Child，即删除了该元素
case LowerCaseSchema(child) => child
}
}三、实践
补充昨天DEBUG的一个样例。这个样例证实了如何将上面的规则应用到Unresolved Logical Plan：
当传递sql语句的时候，的确调用了ResolveReferences将mobile解析成NamedExpression。
能够对比这看运行流程，左边是Unresolved Logical Plan，右边是Resoveld Logical Plan。
先是运行了Batch Resolution，eg: 调用ResovelRalation这个RUle来使 Unresovled Relation 转化为 SparkLogicalPlan并通过Catalog找到了其对于的字段属性。
然后运行了Batch Analysis Operator。eg：调用EliminateAnalysisOperators来将SubQuery给remove掉了。
可能格式显示的不太好，能够向右边拖动下滚动轴看下结果。 ：）
val exec = sqlContext.sql("select mobile as mb, sid as id, mobile*2 multi2mobile, count(1) times from (select * from temp_shengli_mobile)a where pfrom_id=0.0 group by mobile, sid, mobile*2")
14/07/21 18:23:32 DEBUG SparkILoop$SparkILoopInterpreter: Invoking: public static java.lang.String $line47.$eval.$print()
14/07/21 18:23:33 INFO Analyzer: Max iterations (2) reached for batch MultiInstanceRelations
14/07/21 18:23:33 INFO Analyzer: Max iterations (2) reached for batch CaseInsensitiveAttributeReferences
14/07/21 18:23:33 DEBUG Analyzer$ResolveReferences$: Resolving 'pfrom_id to pfrom_id#5
14/07/21 18:23:33 DEBUG Analyzer$ResolveReferences$: Resolving 'mobile to mobile#2
14/07/21 18:23:33 DEBUG Analyzer$ResolveReferences$: Resolving 'sid to sid#1
14/07/21 18:23:33 DEBUG Analyzer$ResolveReferences$: Resolving 'mobile to mobile#2
14/07/21 18:23:33 DEBUG Analyzer$ResolveReferences$: Resolving 'mobile to mobile#2
14/07/21 18:23:33 DEBUG Analyzer$ResolveReferences$: Resolving 'sid to sid#1
14/07/21 18:23:33 DEBUG Analyzer$ResolveReferences$: Resolving 'mobile to mobile#2
14/07/21 18:23:33 DEBUG Analyzer:
=== Result of Batch Resolution ===
!Aggregate ['mobile,'sid,('mobile * 2) AS c2#27], ['mobile AS mb#23,'sid AS id#24,('mobile * 2) AS multi2mobile#25,COUNT(1) AS times#26L] Aggregate [mobile#2,sid#1,(CAST(mobile#2, DoubleType) * CAST(2, DoubleType)) AS c2#27], [mobile#2 AS mb#23,sid#1 AS id#24,(CAST(mobile#2, DoubleType) * CAST(2, DoubleType)) AS multi2mobile#25,COUNT(1) AS times#26L]
! Filter ('pfrom_id = 0.0) Filter (CAST(pfrom_id#5, DoubleType) = 0.0)
Subquery a Subquery a
! Project [*] Project [data_date#0,sid#1,mobile#2,pverify_type#3,create_time#4,pfrom_id#5,p_status#6,pvalidate_time#7,feffect_time#8,plastupdate_ip#9,update_time#10,status#11,preserve_int#12]
! UnresolvedRelation None, temp_shengli_mobile, None Subquery temp_shengli_mobile
! SparkLogicalPlan (ExistingRdd [data_date#0,sid#1,mobile#2,pverify_type#3,create_time#4,pfrom_id#5,p_status#6,pvalidate_time#7,feffect_time#8,plastupdate_ip#9,update_time#10,status#11,preserve_int#12], MapPartitionsRDD[4] at mapPartitions at basicOperators.scala:174)
14/07/21 18:23:33 DEBUG Analyzer:
=== Result of Batch AnalysisOperators ===
!Aggregate ['mobile,'sid,('mobile * 2) AS c2#27], ['mobile AS mb#23,'sid AS id#24,('mobile * 2) AS multi2mobile#25,COUNT(1) AS times#26L] Aggregate [mobile#2,sid#1,(CAST(mobile#2, DoubleType) * CAST(2, DoubleType)) AS c2#27], [mobile#2 AS mb#23,sid#1 AS id#24,(CAST(mobile#2, DoubleType) * CAST(2, DoubleType)) AS multi2mobile#25,COUNT(1) AS times#26L]
! Filter ('pfrom_id = 0.0) Filter (CAST(pfrom_id#5, DoubleType) = 0.0)
! Subquery a Project [data_date#0,sid#1,mobile#2,pverify_type#3,create_time#4,pfrom_id#5,p_status#6,pvalidate_time#7,feffect_time#8,plastupdate_ip#9,update_time#10,status#11,preserve_int#12]
! Project [*] SparkLogicalPlan (ExistingRdd [data_date#0,sid#1,mobile#2,pverify_type#3,create_time#4,pfrom_id#5,p_status#6,pvalidate_time#7,feffect_time#8,plastupdate_ip#9,update_time#10,status#11,preserve_int#12], MapPartitionsRDD[4] at mapPartitions at basicOperators.scala:174)
! UnresolvedRelation None, temp_shengli_mobile, None
四、总结
本文从源代码角度分析了Analyzer在对Sql Parser解析出的UnResolve Logical Plan 进行analyze的过程中，所运行的流程。
流程是实例化一个SimpleAnalyzer，定义一些Batch，然后遍历这些Batch在RuleExecutor的环境下。运行Batch里面的Rules。每一个Rule会对Unresolved Logical Plan进行Resolve，有些可能不能一次解析出。须要多次迭代，直到达到max迭代次数或者达到fix point。这里Rule里比較经常使用的就是ResolveReferences、ResolveRelations、StarExpansion、GlobalAggregates、typeCoercionRules和EliminateAnalysisOperators。
——EOF——
原创文章，转载请注明：
转载自：OopsOutOfMemory盛利的Blog，作者： OopsOutOfMemory
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