Spark无法评估表达式：窗口表达式的滞后答案

【问题标题】：Spark Cannot evaluate expression: lag of a window expressionSpark无法评估表达式：窗口表达式的滞后
【发布时间】：2019-03-06 16:27:16
【问题描述】：

我正在尝试对 cassandra 表中的数据帧执行大量操作，然后将其保存在另一个表中。这些操作之一如下：

val leadWindow = Window.partitionBy(col("id")).orderBy(col("timestamp").asc).rowsBetween(Window.currentRow, 2)
df.withColumn("lead1", lag(sum(col("temp1")).over(leadWindow), 2, 0))

当我运行我的工作时，我收到一个异常，指出无法评估 lag 操作..

2018-10-08 12:02:22 INFO  Cluster:1543 - New Cassandra host /127.0.0.1:9042 added
    2018-10-08 12:02:22 INFO  CassandraConnector:35 - Connected to Cassandra cluster: Test Cluster
    2018-10-08 12:02:23 INFO  CassandraSourceRelation:35 - Input Predicates: [IsNotNull(ts)]
    2018-10-08 12:02:23 INFO  CassandraSourceRelation:35 - Input Predicates: [IsNotNull(ts)]
    Exception in thread "main" java.lang.UnsupportedOperationException: Cannot evaluate expression: lag(input[43, bigint, true], 2, 0)
            at org.apache.spark.sql.catalyst.expressions.Unevaluable$class.doGenCode(Expression.scala:258)
            at org.apache.spark.sql.catalyst.expressions.OffsetWindowFunction.doGenCode(windowExpressions.scala:326)
            at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:107)
            at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:104)
            at scala.Option.getOrElse(Option.scala:121)
            at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:104)
            at org.apache.spark.sql.catalyst.expressions.BinaryExpression.nullSafeCodeGen(Expression.scala:496)
            at org.apache.spark.sql.catalyst.expressions.BinaryExpression.defineCodeGen(Expression.scala:479)
            at org.apache.spark.sql.catalyst.expressions.Add.doGenCode(arithmetic.scala:174)
            at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:107)
            at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:104)
            at scala.Option.getOrElse(Option.scala:121)
            at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:104)
            at org.apache.spark.sql.catalyst.expressions.BinaryExpression.nullSafeCodeGen(Expression.scala:496)
            at org.apache.spark.sql.catalyst.expressions.BinaryExpression.defineCodeGen(Expression.scala:479)
            at org.apache.spark.sql.catalyst.expressions.BinaryComparison.doGenCode(predicates.scala:513)
            at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:107)
            at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:104)
            at scala.Option.getOrElse(Option.scala:121)
            at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:104)
            at org.apache.spark.sql.catalyst.expressions.And.doGenCode(predicates.scala:397)
            at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:107)
            at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:104)
            at scala.Option.getOrElse(Option.scala:121)
            at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:104)
            at org.apache.spark.sql.catalyst.expressions.CaseWhen$$anonfun$8.apply(conditionalExpressions.scala:202)
            at org.apache.spark.sql.catalyst.expressions.CaseWhen$$anonfun$8.apply(conditionalExpressions.scala:201)
            at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
            at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
            at scala.collection.immutable.List.foreach(List.scala:381)
            at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
            at scala.collection.immutable.List.map(List.scala:285)
            at org.apache.spark.sql.catalyst.expressions.CaseWhen.doGenCode(conditionalExpressions.scala:201)
            at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:107)
            at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:104)
            at scala.Option.getOrElse(Option.scala:121)
            at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:104)
            at org.apache.spark.sql.catalyst.expressions.Alias.genCode(namedExpressions.scala:142)
            at org.apache.spark.sql.execution.ProjectExec$$anonfun$6.apply(basicPhysicalOperators.scala:60)
            at org.apache.spark.sql.execution.ProjectExec$$anonfun$6.apply(basicPhysicalOperators.scala:60)
            at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
            at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
            at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
            at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
            at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
            at scala.collection.AbstractTraversable.map(Traversable.scala:104)
            at org.apache.spark.sql.execution.ProjectExec.doConsume(basicPhysicalOperators.scala:60)
            at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:181)
            at org.apache.spark.sql.execution.InputAdapter.consume(WholeStageCodegenExec.scala:354)
            at org.apache.spark.sql.execution.InputAdapter.doProduce(WholeStageCodegenExec.scala:383)
            at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:88)
            at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:83)
            at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
            at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
            at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
            at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:83)
            at org.apache.spark.sql.execution.InputAdapter.produce(WholeStageCodegenExec.scala:354)
            at org.apache.spark.sql.execution.ProjectExec.doProduce(basicPhysicalOperators.scala:45)
            at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:88)
            at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:83)
            at org.apache.spark.sql.execution.SparkPlan$$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:83)
            at org.apache.spark.sql.execution.ProjectExec.produce(basicPhysicalOperators.scala:35)
            at org.apache.spark.sql.execution.WholeStageCodegenExec.doCodeGen(WholeStageCodegenExec.scala:524)
            at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:576)
            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$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
            at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
            at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
            at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
            at org.apache.spark.sql.execution.DeserializeToObjectExec.doExecute(objects.scala:89)
            at org.apache.spark.sql.execution.SparkPlan$$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.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
            at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
            at org.apache.spark.sql.Dataset.rdd$lzycompute(Dataset.scala:2975)
            at org.apache.spark.sql.Dataset.rdd(Dataset.scala:2973)
            at org.apache.spark.sql.cassandra.CassandraSourceRelation.insert(CassandraSourceRelation.scala:76)
            at org.apache.spark.sql.cassandra.DefaultSource.createRelation(DefaultSource.scala:86)
            at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:45)
            at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
            at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
            at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:86)
            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$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
            at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
            at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
            at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
            at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
            at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
            at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:654)
            at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:654)
            at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
            at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:654)
            at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:273)
            at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:267)
            at com.test.functions.package$ChecksFunctions.appendToTable(package.scala:66)
            at com.test.TestFromCassandra$.main(TestFromCassandra.scala:66)
            at com.test.TestFromCassandra.main(TestFromCassandra.scala)
            at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
            at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
            at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
            at java.lang.reflect.Method.invoke(Method.java:498)
            at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
            at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:894)
            at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:198)
            at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:228)
            at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:137)
            at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
    2018-10-08 12:02:31 INFO  CassandraConnector:35 - Disconnected from Cassandra cluster: Test Cluster

TestFromCassandra 文件的第 130 行是对save() 函数的调用。我在 Stackoverflow 上没有发现任何类似的问题..

有人知道我为什么遇到这个异常吗？ lag 函数是否对滚动 sum 函数有任何限制？

编辑： 我在 Spark 的 Jira 上找到了 a similar issue。在引用 window 函数之后，filter 函数似乎存在错误，并且由于 cassandra 连接器在保存之前过滤主键成员上的数据帧（使用 isnotnull 函数），这就是可能会导致异常。有没有办法通过避免这个错误来执行这个操作，但不使用聚合函数？或者有人知道如何修复这个错误吗？

编辑 2： 我还尝试使用foreach 编写器和连接器withSessionDo 函数来存储我的数据帧，但我仍然遇到同样的异常。没有人遇到过这个问题？

编辑 3： 我找到了另一种方法来实现我想要的操作：

val leadWindow = Window.partitionBy(col("id")).orderBy(col("timestamp").desc).rowsBetween(Window.currentRow, 2)
df.withColumn("lead1", sum(col("temp1")).over(leadWindow))

问题不是由于过滤器造成的。似乎无法在窗口表达式上使用 lag 函数。

【问题讨论】：

标签： scala apache-spark cassandra spark-cassandra-connector

【解决方案1】：

我看到了同样的错误。尽管有解决此问题的方法，但 spark 应该可以解决此问题。我相信你会用任何窗口函数来解决这个问题，而不仅仅是 LAG。我相信的原因是 spark 尝试在过滤器上进行代码生成，但窗口函数不是代码生成的。一种解决方法是使用此窗口表达式创建一个列并在过滤器中使用该列。

【讨论】：

【解决方案2】：

我遇到了同样的问题，然后我注意到你在 lag 中使用了 over 函数（和我一样）。我改成这样：

df.withColumn("lag1", lag(sum(col("temp1")), 2, 0).over(lagWindow))

【讨论】：

感谢您的建议，但我收到了一个新的 AnalysisException：Window Frame specifiedwindowframe(RowFrame, currentrow$(), 2) must match the required frame specifiedwindowframe(RowFrame, -2, -2)。另外，我想要实现的是将 2 行滚动总和的结果移动 2 行。
我会说这与您对 rowsBetween 函数的使用有关。您正在使用 lag 对最后两行求和（从当前行开始计算），然后使用 rowsBetween 来计算接下来的两行（也从当前行开始计算）。它不会匹配。这是一个猜测，因为我对此不是很有经验。