AnalysisException:必须使用writeStream.start()执行带有流源的查询

Question

我收到一个异常,表示我需要启动一个流才能使用它.但是,正在启动流.这个设置有什么问题？

spark.readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", kafkaBootstrapServers)
  .option("subscribe", "inputTopic")
  .option("startingOffsets", "earliest")
  .load
  .selectExpr(deserializeKeyExpression, deserializeValueExpression)
  .select("value.*")
  .withColumn("date", to_timestamp(from_unixtime(col("date"))))
  .transform(model.transform)
  .select(col("id") as "key", func(col(config.probabilityCol)) as "value.prediction")
  .selectExpr(serializeKeyExpression, serializeValueExpression)
  .writeStream
  .outputMode("update")
  .format("kafka")
  .option("kafka.bootstrap.servers", kafkaBootstrapServers)
  .option("checkpointLocation", "checkpoint")
  .option("topic", "outputTopic")
  .start

这是例外:

Caused by: org.apache.spark.sql.AnalysisException: Queries with streaming sources must be executed with writeStream.start();;
kafka
    at org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker$.org$apache$spark$sql$catalyst$analysis$UnsupportedOperationChecker$$throwError(UnsupportedOperationChecker.scala:374)
    at org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker$$anonfun$checkForBatch$1.apply(UnsupportedOperationChecker.scala:37)
    at org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker$$anonfun$checkForBatch$1.apply(UnsupportedOperationChecker.scala:35)
    at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
    at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:126)
    at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:126)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:126)
    ...
    at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:126)
    at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:126)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:126)
    at org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker$.checkForBatch(UnsupportedOperationChecker.scala:35)
    at org.apache.spark.sql.execution.QueryExecution.assertSupported(QueryExecution.scala:51)
    at org.apache.spark.sql.execution.QueryExecution.withCachedData$lzycompute(QueryExecution.scala:62)
    at org.apache.spark.sql.execution.QueryExecution.withCachedData(QueryExecution.scala:60)
    at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:66)
    at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:66)
    at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:72)
    at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:68)
    at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:77)
    at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:77)
    at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3249)
    at org.apache.spark.sql.Dataset.head(Dataset.scala:2484)
    at org.apache.spark.sql.Dataset.head(Dataset.scala:2491)
    at org.apache.spark.sql.Dataset.first(Dataset.scala:2498)
    at org.apache.spark.ml.feature.VectorAssembler.first$lzycompute$1(VectorAssembler.scala:57)
    at org.apache.spark.ml.feature.VectorAssembler.org$apache$spark$ml$feature$VectorAssembler$$first$1(VectorAssembler.scala:57)
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$2$$anonfun$1.apply$mcI$sp(VectorAssembler.scala:88)
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$2$$anonfun$1.apply(VectorAssembler.scala:88)
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$2$$anonfun$1.apply(VectorAssembler.scala:88)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$2.apply(VectorAssembler.scala:88)
    at org.apache.spark.ml.feature.VectorAssembler$$anonfun$2.apply(VectorAssembler.scala:58)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
    at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
    at scala.collection.mutable.ArrayOps$ofRef.flatMap(ArrayOps.scala:186)
    at org.apache.spark.ml.feature.VectorAssembler.transform(VectorAssembler.scala:58)
    at org.apache.spark.ml.PipelineModel$$anonfun$transform$1.apply(Pipeline.scala:306)
    at org.apache.spark.ml.PipelineModel$$anonfun$transform$1.apply(Pipeline.scala:306)
    at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57)
    at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66)
    at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:186)
    at org.apache.spark.ml.PipelineModel.transform(Pipeline.scala:306)
    at com.company.project.Stream$$anonfun$transform$1.apply(NewsRateJob.scala:65)
    at com.company.project.Stream$$anonfun$transform$1.apply(NewsRateJob.scala:65)
    at org.apache.spark.sql.Dataset.transform(Dataset.scala:2513)
    at com.company.project.Stream.transform(NewsRateJob.scala:65)
    at com.company.project.Stream.setupStream(NewsRateJob.scala:47)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
    at java.lang.reflect.Method.invoke(Unknown Source)
    at org.springframework.beans.factory.annotation.InitDestroyAnnotationBeanPostProcessor$LifecycleElement.invoke(InitDestroyAnnotationBeanPostProcessor.java:366)
    at org.springframework.beans.factory.annotation.InitDestroyAnnotationBeanPostProcessor$LifecycleMetadata.invokeInitMethods(InitDestroyAnnotationBeanPostProcessor.java:311)
    at org.springframework.beans.factory.annotation.InitDestroyAnnotationBeanPostProcessor.postProcessBeforeInitialization(InitDestroyAnnotationBeanPostProcessor.java:134)
    ... 18 common frames omitted

我熟悉spark 2.2和VectorAssembler的问题,但我使用的是spark 2.3.1.

Answer 1

发生此异常的原因是模型在流启动之前尝试访问流中的数据。在本例中，VectorAssembler 调用first数据集以确定向量的宽度。

2.3 不会自动修复 VectorAssembler 和结构化流的问题，它只是提供一个类（特别是 VectorSizeHint 类），可以与具有结构化流的 VectorAssembler 结合使用。将其添加到管道的各个阶段解决了问题。

stages += new VectorSizeHint()
  .setInputCol(column)
  .setSize(size)

以下是一些说明如何使用它的文档：https://docs.databricks.com/spark/latest/mllib/mllib-pipelines-and-stuctured-streaming.html

注意：这对于 OneHotEncoderEstimator 功能来说不是必需的。

由于其他几个原因，我们也经历了类似的堆栈跟踪。一个是因为我们在模型中使用 OneHotEstimator（需要更新为 OneHotEncoderEstimator）而引起的，另一个是因为我们缓存管道（我们删除了缓存步骤）而引起的。