spark from_avro()dataframe.show()errors java.lang.arrayindexoutofboundsexception

00jrzges  于 2021-05-29  发布在  Spark
关注(0)|答案(0)|浏览(358)

我将Dataframe字段转换为avro字段结构，使用to_avro，然后使用from_avro，如下所示。最终，我希望将avro负载流式传输到kafka write/read。当我试图通过执行df.show（）来打印最终重新转换的Dataframe时，java.lang.arrayindexoutofboundsexception出错。printschema显示正确的模式。
我使用的是spark 2.4.0和scala 2.11
我做错了什么。字段中的空值是否导致此问题？如果是这样的话，解决方法是什么？
这是密码

import org.apache.spark.sql.{ Row, SparkSession, Column, functions }
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
import org.apache.spark.sql.avro._
import java.nio.file.{Files, Paths}

    val spark = SparkSession
      .builder()
      .appName("Spark SQL basic example")
      .master("local[*]")
      .getOrCreate()
    spark.sparkContext.setLogLevel("WARN")

    import spark.implicits._

    val jsonDS = spark.createDataset( 
                  """{  "name": "Alyssa", "favorite_color": null,  "favorite_numbers": [3, 9, 15, 20] } """ ::
                  """{  "name": "Ben",    "favorite_color": "red", "favorite_numbers": []             } """ :: Nil)
    val usersDF = spark.read.json(jsonDS)
    usersDF.show()
    usersDF.printSchema()

    val structDF = usersDF.select(
                     struct(usersDF.columns.map(colname => {col(colname)}):_* ).alias("structCol")
                   )
    structDF.printSchema()
    structDF.show()

    val avroDF = usersDF.select(
                    to_avro(  
                      struct(usersDF.columns.map(colname => {col(colname)}):_* )
                    ).alias("structAvroCol")  
                  )
    avroDF.printSchema()
    avroDF.show()

    val jsonFormatSchema = """
      {
        "type":"record",
        "namespace":"example.avro",
        "name":"User",
        "fields":[
          {
            "name":"name",
            "type":"string"
          },
          {
            "name":"favorite_color",
            "type":[
              "string",
              "null"
            ]
          },
          {
            "name":"favorite_numbers",
          "default": [],
            "type":{
              "type":"array",
              "items":[
                "int",
                "null"
              ]
            }
          }
        ]
      }
      """
    val user2DF = avroDF.select(from_avro(col("structAvroCol"), jsonFormatSchema).as("user"))
        .select("user.*")
    user2DF.printSchema()
    user2DF.show()

    spark.close()

这是输出

+--------------+----------------+------+
|favorite_color|favorite_numbers|  name|
+--------------+----------------+------+
|          blue|  [3, 9, 15, 20]|Alyssa|
|           red|    [1, 2, 3, 4]|   Ben|
+--------------+----------------+------+

root
 |-- favorite_color: string (nullable = true)
 |-- favorite_numbers: array (nullable = true)
 |    |-- element: long (containsNull = true)
 |-- name: string (nullable = true)

root
 |-- structCol: struct (nullable = false)
 |    |-- favorite_color: string (nullable = true)
 |    |-- favorite_numbers: array (nullable = true)
 |    |    |-- element: long (containsNull = true)
 |    |-- name: string (nullable = true)

+--------------------+
|           structCol|
+--------------------+
|[blue, [3, 9, 15,...|
|[red, [1, 2, 3, 4...|
+--------------------+

root
 |-- structAvroCol: binary (nullable = false)

+--------------------+
|       structAvroCol|
+--------------------+
|[00 08 62 6C 75 6...|
|[00 06 72 65 64 0...|
+--------------------+

      {
        "type":"record",
        "namespace":"example.avro",
        "name":"User",
        "fields":[
          {
            "name":"name",
            "type":"string"
          },
          {
            "name":"favorite_color",
            "type":[
              "string",
              "null"
            ]
          },
          {
            "name":"favorite_numbers",
          "default": [],
            "type":{
              "type":"array",
              "items":[
                "int",
                "null"
              ]
            }
          }
        ]
      }

root
 |-- name: string (nullable = true)
 |-- favorite_color: string (nullable = true)
 |-- favorite_numbers: array (nullable = true)
 |    |-- element: integer (containsNull = true)

20/06/16 19:25:22 ERROR Executor: Exception in task 0.0 in stage 7.0 (TID 8)
java.lang.ArrayIndexOutOfBoundsException: 4
    at org.apache.avro.io.parsing.Symbol$Alternative.getSymbol(Symbol.java:424)
    at org.apache.avro.io.ResolvingDecoder.doAction(ResolvingDecoder.java:290)
    at org.apache.avro.io.parsing.Parser.advance(Parser.java:88)
    at org.apache.avro.io.ResolvingDecoder.readIndex(ResolvingDecoder.java:267)
    at org.apache.avro.generic.GenericDatumReader.readWithoutConversion(GenericDatumReader.java:179)
    at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:153)
    at org.apache.avro.generic.GenericDatumReader.readField(GenericDatumReader.java:232)
    at org.apache.avro.generic.GenericDatumReader.readRecord(GenericDatumReader.java:222)
    at org.apache.avro.generic.GenericDatumReader.readWithoutConversion(GenericDatumReader.java:175)
    at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:153)
    at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:145)
    at org.apache.spark.sql.avro.AvroDataToCatalyst.nullSafeEval(AvroDataToCatalyst.scala:50)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.project_doConsume_0$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:121)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
    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)
20/06/16 19:25:22 WARN TaskSetManager: Lost task 0.0 in stage 7.0 (TID 8, localhost, executor driver): java.lang.ArrayIndexOutOfBoundsException: 4
    at org.apache.avro.io.parsing.Symbol$Alternative.getSymbol(Symbol.java:424)
    at org.apache.avro.io.ResolvingDecoder.doAction(ResolvingDecoder.java:290)
    at org.apache.avro.io.parsing.Parser.advance(Parser.java:88)
    at org.apache.avro.io.ResolvingDecoder.readIndex(ResolvingDecoder.java:267)
    at org.apache.avro.generic.GenericDatumReader.readWithoutConversion(GenericDatumReader.java:179)
    at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:153)
    at org.apache.avro.generic.GenericDatumReader.readField(GenericDatumReader.java:232)
    at org.apache.avro.generic.GenericDatumReader.readRecord(GenericDatumReader.java:222)
    at org.apache.avro.generic.GenericDatumReader.readWithoutConversion(GenericDatumReader.java:175)
    at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:153)
    at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:145)
    at org.apache.spark.sql.avro.AvroDataToCatalyst.nullSafeEval(AvroDataToCatalyst.scala:50)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.project_doConsume_0$(Unknown Source)
    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$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:121)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
    at org.apache.spark.util.Utils$.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)

20/06/16 19:25:22 ERROR TaskSetManager: Task 0 in stage 7.0 failed 1 times; aborting job
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 7.0 failed 1 times, most recent failure: Lost task 0.0 in stage 7.0 (TID 8, localhost, executor driver): java.lang.ArrayIndexOutOfBoundsException: 4
    at org.apache.avro.io.parsing.Symbol$Alternative.getSymbol(Symbol.java:424)
    at org.apache.avro.io.ResolvingDecoder.doAction(ResolvingDecoder.java:290)
    at org.apache.avro.io.parsing.Parser.advance(Parser.java:88)
    at org.apache.avro.io.ResolvingDecoder.readIndex(ResolvingDecoder.java:267)
    at org.apache.avro.generic.GenericDatumReader.readWithoutConversion(GenericDatumReader.java:179)
    at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:153)
    at org.apache.avro.generic.GenericDatumReader.readField(GenericDatumReader.java:232)
    at org.apache.avro.generic.GenericDatumReader.readRecord(GenericDatumReader.java:222)
    at org.apache.avro.generic.GenericDatumReader.readWithoutConversion(GenericDatumReader.java:175)
    at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:153)
    at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:145)
    at org.apache.spark.sql.avro.AvroDataToCatalyst.nullSafeEval(AvroDataToCatalyst.scala:50)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.project_doConsume_0$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:121)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
    at org.apache.spark.util.Utils$.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.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1887)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1875)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1874)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1874)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2108)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2057)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2046)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
    at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
    at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3384)
    at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2545)
    at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2545)
    at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3365)
    at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(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:3364)
    at org.apache.spark.sql.Dataset.head(Dataset.scala:2545)
    at org.apache.spark.sql.Dataset.take(Dataset.scala:2759)
    at org.apache.spark.sql.Dataset.getRows(Dataset.scala:255)
    at org.apache.spark.sql.Dataset.showString(Dataset.scala:292)
    at org.apache.spark.sql.Dataset.show(Dataset.scala:746)
    at org.apache.spark.sql.Dataset.show(Dataset.scala:705)
    at org.apache.spark.sql.Dataset.show(Dataset.scala:714)
    at sparkAvroTest$.main(sparkAvroTest.scala:101)
    at sparkAvroTest.main(sparkAvroTest.scala)
Caused by: java.lang.ArrayIndexOutOfBoundsException: 4
    at org.apache.avro.io.parsing.Symbol$Alternative.getSymbol(Symbol.java:424)
    at org.apache.avro.io.ResolvingDecoder.doAction(ResolvingDecoder.java:290)
    at org.apache.avro.io.parsing.Parser.advance(Parser.java:88)
    at org.apache.avro.io.ResolvingDecoder.readIndex(ResolvingDecoder.java:267)
    at org.apache.avro.generic.GenericDatumReader.readWithoutConversion(GenericDatumReader.java:179)
    at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:153)
    at org.apache.avro.generic.GenericDatumReader.readField(GenericDatumReader.java:232)
    at org.apache.avro.generic.GenericDatumReader.readRecord(GenericDatumReader.java:222)
    at org.apache.avro.generic.GenericDatumReader.readWithoutConversion(GenericDatumReader.java:175)
    at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:153)
    at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:145)
    at org.apache.spark.sql.avro.AvroDataToCatalyst.nullSafeEval(AvroDataToCatalyst.scala:50)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.project_doConsume_0$(Unknown Source)
    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$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:121)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
    at org.apache.spark.util.Utils$.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)
avroscalaapache-kafkaapache-sparkspark-avro

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