Details
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Bug
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Status: Resolved
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Major
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Resolution: Incomplete
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2.4.0
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None
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None
Description
I found some strange error when I'm coding Pyspark UDAF. After I call groupBy function and agg function, I want to filter some data from remaining dataframe, but it seems not work. My sample code is below.
>>> from pyspark.sql.functions import pandas_udf, PandasUDFType, col >>> df = spark.createDataFrame( ... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)], ... ("id", "v")) >>> @pandas_udf("double", PandasUDFType.GROUPED_AGG) ... def mean_udf(v): ... return v.mean() >>> df.groupby("id").agg(mean_udf(df['v']).alias("mean")).filter(col("mean") > 5).show()
The code above will cause exception printed below
Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/opt/spark/python/pyspark/sql/dataframe.py", line 378, in show print(self._jdf.showString(n, 20, vertical)) File "/opt/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__ File "/opt/spark/python/pyspark/sql/utils.py", line 63, in deco return f(*a, **kw) File "/opt/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value py4j.protocol.Py4JJavaError: An error occurred while calling o3717.showString. : org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree: Exchange hashpartitioning(id#1726L, 200) +- *(1) Filter (mean_udf(v#1727) > 5.0) +- Scan ExistingRDD[id#1726L,v#1727] at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56) at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.doExecute(ShuffleExchangeExec.scala:119) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) at org.apache.spark.sql.execution.InputAdapter.inputRDDs(WholeStageCodegenExec.scala:391) at org.apache.spark.sql.execution.SortExec.inputRDDs(SortExec.scala:121) at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:627) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) at org.apache.spark.sql.execution.python.AggregateInPandasExec.doExecute(AggregateInPandasExec.scala:80) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) at org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:247) at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:339) at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38) at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3383) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2544) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2544) at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3364) at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78) at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3363) at org.apache.spark.sql.Dataset.head(Dataset.scala:2544) at org.apache.spark.sql.Dataset.take(Dataset.scala:2758) at org.apache.spark.sql.Dataset.getRows(Dataset.scala:254) at org.apache.spark.sql.Dataset.showString(Dataset.scala:291) at sun.reflect.GeneratedMethodAccessor139.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:282) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:238) at java.lang.Thread.run(Thread.java:748) Caused by: java.lang.UnsupportedOperationException: Cannot evaluate expression: mean_udf(input[1, double, true]) at org.apache.spark.sql.catalyst.expressions.Unevaluable$class.doGenCode(Expression.scala:261) at org.apache.spark.sql.catalyst.expressions.PythonUDF.doGenCode(PythonUDF.scala:50) at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:108) at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:105) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:105) at org.apache.spark.sql.catalyst.expressions.BinaryExpression.nullSafeCodeGen(Expression.scala:525) at org.apache.spark.sql.catalyst.expressions.BinaryExpression.defineCodeGen(Expression.scala:508) at org.apache.spark.sql.catalyst.expressions.BinaryComparison.doGenCode(predicates.scala:563) at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:108) at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:105) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:105) at org.apache.spark.sql.execution.FilterExec.org$apache$spark$sql$execution$FilterExec$$genPredicate$1(basicPhysicalOperators.scala:139) at org.apache.spark.sql.execution.FilterExec$$anonfun$13.apply(basicPhysicalOperators.scala:179) at org.apache.spark.sql.execution.FilterExec$$anonfun$13.apply(basicPhysicalOperators.scala:163) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.immutable.List.foreach(List.scala:392) at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) at scala.collection.immutable.List.map(List.scala:296) at org.apache.spark.sql.execution.FilterExec.doConsume(basicPhysicalOperators.scala:163) at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:189) at org.apache.spark.sql.execution.InputAdapter.consume(WholeStageCodegenExec.scala:374) at org.apache.spark.sql.execution.InputAdapter.doProduce(WholeStageCodegenExec.scala:403) at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:90) at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:85) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:85) at org.apache.spark.sql.execution.InputAdapter.produce(WholeStageCodegenExec.scala:374) at org.apache.spark.sql.execution.FilterExec.doProduce(basicPhysicalOperators.scala:125) at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:90) at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:85) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:85) at org.apache.spark.sql.execution.FilterExec.produce(basicPhysicalOperators.scala:85) at org.apache.spark.sql.execution.WholeStageCodegenExec.doCodeGen(WholeStageCodegenExec.scala:544) at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:598) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.prepareShuffleDependency(ShuffleExchangeExec.scala:92) at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec$$anonfun$doExecute$1.apply(ShuffleExchangeExec.scala:128) at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec$$anonfun$doExecute$1.apply(ShuffleExchangeExec.scala:119) at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52) ... 48 more
Optimized Logical Plan here, I found Optimizer had already push down the Filter through PushDownPredicates rule.
>>> df.groupby("id").agg(mean_udf(df['v']).alias("mean")).filter(col("mean") > 5).explain(True) == Parsed Logical Plan == 'Filter ('mean > 5) +- Aggregate [id#0L], [id#0L, mean_udf(v#1) AS mean#79] +- LogicalRDD [id#0L, v#1], false== Analyzed Logical Plan == id: bigint, mean: double Filter (mean#79 > cast(5 as double)) +- Aggregate [id#0L], [id#0L, mean_udf(v#1) AS mean#79] +- LogicalRDD [id#0L, v#1], false== Optimized Logical Plan == Aggregate [id#0L], [id#0L, mean_udf(v#1) AS mean#79] +- Filter (mean_udf(v#1) > 5.0) +- LogicalRDD [id#0L, v#1], false== Physical Plan == !AggregateInPandas [id#0L], [mean_udf(v#1)], [id#0L, mean_udf(v)#78 AS mean#79] +- *(2) Sort [id#0L ASC NULLS FIRST], false, 0 +- Exchange hashpartitioning(id#0L, 200) +- *(1) Filter (mean_udf(v#1) > 5.0) +- Scan ExistingRDD[id#0L,v#1]
Compare with the official mean function, it will not push down Filter node throuph PushDownPredicates rule.
>>> from pyspark.sql import functions as F >>> df.groupby("id").agg(F.mean(df['v']).alias("mean")).filter(col("mean") > 5).explain(True) == Parsed Logical Plan == 'Filter ('mean > 5) +- Aggregate [id#0L], [id#0L, avg(v#1) AS mean#7] +- LogicalRDD [id#0L, v#1], false== Analyzed Logical Plan == id: bigint, mean: double Filter (mean#7 > cast(5 as double)) +- Aggregate [id#0L], [id#0L, avg(v#1) AS mean#7] +- LogicalRDD [id#0L, v#1], false== Optimized Logical Plan == Filter (isnotnull(mean#7) && (mean#7 > 5.0)) +- Aggregate [id#0L], [id#0L, avg(v#1) AS mean#7] +- LogicalRDD [id#0L, v#1], false== Physical Plan == *(2) Filter (isnotnull(mean#7) && (mean#7 > 5.0)) +- *(2) HashAggregate(keys=[id#0L], functions=[avg(v#1)], output=[id#0L, mean#7]) +- Exchange hashpartitioning(id#0L, 200) +- *(1) HashAggregate(keys=[id#0L], functions=[partial_avg(v#1)], output=[id#0L, sum#15, count#16L]) +- Scan ExistingRDD[id#0L,v#1]
Update:
Delete useless confusing guess. I think I found the reason.
In branch 2.4, I think wrong aliasMap has been created without filtering the pandas aggregate function. See codes here
case filter @ Filter(condition, aggregate: Aggregate)
if aggregate.aggregateExpressions.forall(_.deterministic)
&& aggregate.groupingExpressions.nonEmpty =>
// Find all the aliased expressions in the aggregate list that don't include any actual
// AggregateExpression, and create a map from the alias to the expression
val aliasMap = AttributeMap(aggregate.aggregateExpressions.collect {
case a: Alias if a.child.find(_.isInstanceOf[AggregateExpression]).isEmpty =>
(a.toAttribute, a.child)
})
......
But in master branch, it has been corrected by using getAliasMap function in AliasHelper.scala.
protected def getAliasMap(plan: Aggregate): AttributeMap[Alias] = { // Find all the aliased expressions in the aggregate list that don't include any actual // AggregateExpression or PythonUDF, and create a map from the alias to the expression val aliasMap = plan.aggregateExpressions.collect { case a: Alias if a.child.find(e => e.isInstanceOf[AggregateExpression] || PythonUDF.isGroupedAggPandasUDF(e)).isEmpty => (a.toAttribute, a) } AttributeMap(aliasMap) }
So in branch 2.4, it has not filtered all the aggregate functions.