Apache Spark SQL get_json_object java.lang.String 无法转换为 org.apache.spark.unsafe.types.UTF8String

【问题标题】：Apache Spark SQL get_json_object java.lang.String cannot be cast to org.apache.spark.unsafe.types.UTF8StringApache Spark SQL get_json_object java.lang.String 无法转换为 org.apache.spark.unsafe.types.UTF8String
【发布时间】：2021-02-25 17:00:13
【问题描述】：

我正在尝试使用结构化流从 Apache Spark 中的 MQTT 代理读取 json 流，读取传入 json 的一些属性并将它们输出到控制台。我的代码如下所示：

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()

但是，当 json 到达时，我收到以下错误：

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)

我正在使用 mosquitto 代理发送 JSON 记录，它们看起来像这样：

mosquitto_pub -m '{"eventDate": "2020-11-11T15:17:00.000+0200"}' -t "temp"

【问题讨论】：

    标签： json string scala apache-spark spark-structured-streaming


    【解决方案1】：

    似乎来自 Bahir 流源提供程序的每个字符串都会引发此错误。例如以下代码也会引发此错误：

    spark.readStream
  .format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
  .option("topic", topic).option("persistence", "memory")
  .load(brokerUrl)
  .select("topic")
  .writeStream
  .format("console")
  .start()

    看起来 Spark 无法识别来自 Bahir 的字符串，可能是某种奇怪的字符串类版本问题。我尝试了以下操作来使代码正常工作：

    • 将 java 版本设置为 8
    • 将 spark 版本从 2.4.0 升级到 2.4.7
    • 将 scala 版本设置为 2.11.12
    • 使用decode 函数和所有可能的编码组合而不是.cast(StringType) 将列“有效负载”转换为字符串
    • 在“有效负载”列上使用substring 函数来重新创建兼容的字符串。

    最后，我通过使用构造函数和数据集重新创建字符串得到了工作代码：

    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")

    这个解决方案相当丑陋，但至少它有效。

    我相信问题中提供的代码没有任何问题，并且我怀疑 Bahir 或 Spark 方面的错误阻止 Spark 处理来自 Bahir 源的字符串。

    【讨论】：

    • 非常感谢您所做的所有研究！我想你是对的......这是巴希尔的问题。
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