scala – 如何在Spark中强制DataFrame评估

有时(例如,测试和bechmarking)我想强制执行在DataFrame上定义的转换.调用像count这样的动作的AFAIK并不能确保实际计算所有列,show只能计算所有行的子集(参见下面的示例)

我的解决方案是使用df.write.saveAsTable将DataFrame写入HDFS,但这会使我的系统“混乱”我不希望继续使用的表.

那么触发DataFrame评估的最佳方法是什么？

编辑：

请注意,最近还有关于spark开发人员列表的讨论：http://apache-spark-developers-list.1001551.n3.nabble.com/Will-count-always-trigger-an-evaluation-of-each-row-td21018.html

我做了一个小例子,显示DataFrame上的计数不会评估所有内容(使用Spark 1.6.3和spark-master = local [2]进行测试)：

val df = sc.parallelize(Seq(1)).toDF("id")
val myUDF = udf((i:Int) => {throw new RuntimeException;i})

df.withColumn("test",myUDF($"id")).count // runs fine
df.withColumn("test",myUDF($"id")).show() // gives Exception

使用相同的逻辑,这里显示的示例不评估所有行：

val df = sc.parallelize(1 to 10).toDF("id")
val myUDF = udf((i:Int) => {if(i==10) throw new RuntimeException;i})

df.withColumn("test",myUDF($"id")).show(5) // runs fine
df.withColumn("test",myUDF($"id")).show(10) // gives Exception

编辑2：对于Eliasah：例外情况说：

org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 6.0 Failed 1 times,most recent failure: Lost task 0.0 in stage 6.0 (TID 6,localhost): java.lang.RuntimeException
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply$mcII$sp(<console>:68)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:68)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:68)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(UnkNown Source)
    at org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$1.apply(basicoperators.scala:51)
    at org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$1.apply(basicoperators.scala:49)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
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Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndindependentStages(DAGScheduler.scala:1431)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:212)
    at org.apache.spark.sql.execution.Limit.executeCollect(basicoperators.scala:165)
    at org.apache.spark.sql.execution.SparkPlan.executeCollectPublic(SparkPlan.scala:174)
    at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1500)
    at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1500)
    at org.apache.spark.sql.execution.sqlExecution$.withNewExecutionId(sqlExecution.scala:56)
    at org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:2087)
    at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$execute$1(DataFrame.scala:1499)
    at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$collect(DataFrame.scala:1506)
    at org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1376)
    at org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1375)
    at org.apache.spark.sql.DataFrame.withCallback(DataFrame.scala:2100)
    at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1375)
    at org.apache.spark.sql.DataFrame.take(DataFrame.scala:1457)
    at org.apache.spark.sql.DataFrame.showString(DataFrame.scala:170)
    at org.apache.spark.sql.DataFrame.show(DataFrame.scala:350)
    at org.apache.spark.sql.DataFrame.show(DataFrame.scala:311)
    at org.apache.spark.sql.DataFrame.show(DataFrame.scala:319)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:74)
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解决方法

我想简单地从DataFrame获取一个底层的rdd并在其上触发一个动作应该可以实现你正在寻找的东西.

df.withColumn("test",myUDF($"id")).rdd.count // this gives proper exceptions