Apache Spark SQL get_json_object java.lang.String kann nicht in org.apache.spark.unsafe.types.UTF8String umgewandelt werden
Nov 13 2020
Ich versuche, einen JSON-Stream von einem MQTT-Broker in Apache Spark mit strukturiertem Streaming zu lesen, einige Eigenschaften eines eingehenden JSON zu lesen und sie an die Konsole auszugeben. Mein Code sieht so aus:
val spark = SparkSession
.builder()
.appName("BahirStructuredStreaming")
.master("local[*]")
.getOrCreate()
import spark.implicits._
val topic = "temp"
val brokerUrl = "tcp://localhost:1883"
val lines = spark.readStream
.format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
.option("topic", topic).option("persistence", "memory")
.load(brokerUrl)
.toDF().withColumn("payload", $"payload".cast(StringType)) val jsonDF = lines.select(get_json_object($"payload", "$.eventDate").alias("eventDate"))
val query = jsonDF.writeStream
.format("console")
.start()
query.awaitTermination()
Wenn der JSON ankommt, erhalte ich jedoch die folgenden Fehler:
Exception in thread "main" org.apache.spark.sql.streaming.StreamingQueryException: Writing job aborted.
=== Streaming Query ===
Identifier: [id = 14d28475-d435-49be-a303-8e47e2f907e3, runId = b5bd28bb-b247-48a9-8a58-cb990edaf139]
Current Committed Offsets: {MQTTStreamSource[brokerUrl: tcp://localhost:1883, topic: temp clientId: paho7247541031496]: -1}
Current Available Offsets: {MQTTStreamSource[brokerUrl: tcp://localhost:1883, topic: temp clientId: paho7247541031496]: 0}
Current State: ACTIVE
Thread State: RUNNABLE
Logical Plan:
Project [get_json_object(payload#22, $.id) AS eventDate#27]
+- Project [id#10, topic#11, cast(payload#12 as string) AS payload#22, timestamp#13]
+- StreamingExecutionRelation MQTTStreamSource[brokerUrl: tcp://localhost:1883, topic: temp clientId: paho7247541031496], [id#10, topic#11, payload#12, timestamp#13]
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:300) at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189) Caused by: org.apache.spark.SparkException: Writing job aborted. at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.doExecute(WriteToDataSourceV2Exec.scala:92) at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:131)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.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.executeCollect(SparkPlan.scala:296)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3384)
at org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:2783) at org.apache.spark.sql.Dataset.$anonfun$withAction$2(Dataset.scala:3365)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:78)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3365)
at org.apache.spark.sql.Dataset.collect(Dataset.scala:2783)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$15(MicroBatchExecution.scala:537) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:78) at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73) at org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$14(MicroBatchExecution.scala:533)
at org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken(ProgressReporter.scala:351)
at org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken$(ProgressReporter.scala:349) at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58) at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runBatch(MicroBatchExecution.scala:532) at org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runActivatedStream$2(MicroBatchExecution.scala:198)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken(ProgressReporter.scala:351)
at org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken$(ProgressReporter.scala:349) at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58) at org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runActivatedStream$1(MicroBatchExecution.scala:166)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:160)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279) ... 1 more Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 8, localhost, executor driver): java.lang.ClassCastException: java.lang.String cannot be cast to org.apache.spark.unsafe.types.UTF8String at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getUTF8String(rows.scala:46) at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getUTF8String$(rows.scala:46)
at org.apache.spark.sql.catalyst.expressions.GenericInternalRow.getUTF8String(rows.scala:195)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:619) at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460) at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.$anonfun$run$2(WriteToDataSourceV2Exec.scala:117) at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1394)
at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2Exec.scala:116) at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.$anonfun$doExecute$2(WriteToDataSourceV2Exec.scala:67)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:405)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:1887) at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:1875)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:1874)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1874) at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:926)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2108)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2057)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2046)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.doExecute(WriteToDataSourceV2Exec.scala:64)
... 34 more
Caused by: java.lang.ClassCastException: java.lang.String cannot be cast to org.apache.spark.unsafe.types.UTF8String
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getUTF8String(rows.scala:46)
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getUTF8String$(rows.scala:46) at org.apache.spark.sql.catalyst.expressions.GenericInternalRow.getUTF8String(rows.scala:195) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.$anonfun$run$2(WriteToDataSourceV2Exec.scala:117)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1394) at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2Exec.scala:116)
at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.$anonfun$doExecute$2(WriteToDataSourceV2Exec.scala:67) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:121) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:405) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Ich sende die JSON-Datensätze mit dem Moskito-Broker und sie sehen folgendermaßen aus:
mosquitto_pub -m '{"eventDate": "2020-11-11T15:17:00.000+0200"}' -t "temp"
Antworten
1 VincentDoba Nov 16 2020 at 00:06
Es scheint, dass alle Zeichenfolgen, die vom Bahir-Stream-Quellanbieter stammen, diesen Fehler auslösen. Der folgende Code löst beispielsweise auch diesen Fehler aus:
spark.readStream
.format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
.option("topic", topic).option("persistence", "memory")
.load(brokerUrl)
.select("topic")
.writeStream
.format("console")
.start()
Es sieht so aus, als ob Spark Zeichenfolgen von Bahir nicht erkennt, möglicherweise ein seltsames Problem mit der Version der Zeichenfolgenklasse. Ich habe die folgenden Aktionen versucht, damit der Code funktioniert:
- Richten Sie die Java-Version auf 8 ein
- Aktualisieren Sie die Spark-Version von 2.4.0 auf 2.4.7
- Scala-Version auf 2.11.12 einrichten
- Verwenden Sie die Dekodierungsfunktion mit allen möglichen Codierungskombinationen, anstatt
.cast(StringType)die Spalte "payload" in String umzuwandeln - Verwenden Sie die Teilzeichenfolgenfunktion in der Spalte "payload", um einen kompatiblen String neu zu erstellen.
Schließlich erhielt ich Arbeitscode, indem ich die Zeichenfolge mit Konstruktor und Datensatz neu erstellte:
val lines = spark.readStream
.format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
.option("topic", topic).option("persistence", "memory")
.load(brokerUrl)
.select("payload")
.as[Array[Byte]]
.map(payload => new String(payload))
.toDF("payload")
Diese Lösung ist ziemlich hässlich, aber zumindest funktioniert sie.
Ich glaube, dass an dem in der Frage angegebenen Code nichts falsch ist, und ich vermute einen Fehler auf der Bahir- oder Spark-Seite, der Spark daran hindert, String aus der Bahir-Quelle zu verarbeiten.