我收到一个异常,它指示我需要启动一个流才能使用它。但是,正在启动流。这个装置怎么了?
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
例外情况如下:
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和矢量汇编程序的问题,但是我使用的是spark2.3.1。
