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Apache Spark Scala - Hive插入抛出“太大的数据帧错误”

社区小助手 2018-12-06 13:50:44 659

我试图使用下面的代码插入Hive但由于某种原因它总是失败。我试过调整内存但没有帮助。

错误堆栈跟踪:

[Stage 4:=====================================================>(999 + 1) / 1000]18/11/27 09:59:44 WARN TaskSetManager: Lost task 364.0 in stage 4.0 (TID 1367, spark-node, executor 1): org.apache.spark.SparkException: Task failed while writing rows.
at org.apache.spark.sql.hive.SparkHiveDynamicPartitionWriterContainer.writeToFile(hiveWriterContainers.scala:328)
at org.apache.spark.sql.hive.execution.InsertIntoHiveTable$$anonfun$saveAsHiveFile$3.apply(InsertIntoHiveTable.scala:159)
at org.apache.spark.sql.hive.execution.InsertIntoHiveTable$$anonfun$saveAsHiveFile$3.apply(InsertIntoHiveTable.scala:159)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.shuffle.FetchFailedException: Too large frame: 5587345928
at org.apache.spark.storage.ShuffleBlockFetcherIterator.throwFetchFailedException(ShuffleBlockFetcherIterator.scala:357)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:332)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:54)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:377)
at org.apache.spark.sql.hive.SparkHiveDynamicPartitionWriterContainer.writeToFile(hiveWriterContainers.scala:286)
... 8 more
Caused by: java.lang.IllegalArgumentException: Too large frame: 5587345928
at org.spark_project.guava.base.Preconditions.checkArgument(Preconditions.java:119)
at org.apache.spark.network.util.TransportFrameDecoder.decodeNext(TransportFrameDecoder.java:133)
at org.apache.spark.network.util.TransportFrameDecoder.channelRead(TransportFrameDecoder.java:81)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:346)
at io.netty.channel.DefaultChannelPipeline$HeadContext.channelRead(DefaultChannelPipeline.java:1294)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:911)
at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:131)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:652)
at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:575)
at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:489)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:451)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:140)
at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
... 1 more
这是我的spark-submit:spark-submit --class com.generic.MSSQLHiveIngestion --master yarn --num-executors 8 --executor-cores 2 --executor-memory 16G --driver-memory 8G --driver- cores 4 --conf spark.yarn.executor.memoryOverhead = 1G data-ingestion.jar

以下是我的sudo代码:

//create spark session first
val spark = SparkSession.builder()
.appName("MSSQLIngestion")
.master("yarn")
.config("spark.sql.caseSensitive", "false")
.config("spark.sql.shuffle.partitions", "1000")
.config("spark.shuffle.spill", "true")
.config("spark.executor.extraJavaOptions", "-XX:+UseG1GC")
.config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
.config("hive.exec.dynamic.partition", "true")
.config("hive.exec.dynamic.partition.mode", "nonstrict")
.enableHiveSupport()
.getOrCreate();

spark.sql("set hive.exec.parallel=true")

// Create a Properties() object to hold the parameters.
val connectionProperties = new Properties()
connectionProperties.setProperty("Driver", driverClass)
connectionProperties.setProperty("fetchSize", "100000")

// read data from JDBC server and construct a dataframe
val jdbcDF1 = spark.read.jdbc(url = jdbcUrl, table = (select * from jdbcTable) e, properties = connectionProperties)

val jdbcDF = jdbcDF1.repartition(1000)

val count = jdbcDF.count()

println("red "+count+" records from sql server and started loading into hive")

// if count > 0 then insert the records into Hive
if (count > 0) {
// create spark temporary table
jdbcDF.createOrReplaceTempView("sparkTempTable")
// insert into Hive external table
spark.sql("insert into externalTable partition (hivePartitionCol) select * from sparkTempTable distribute by hivePartitionCol ")
}
println("completed the job for loading the data into hive")

spark.stop()

SQL 分布式计算 资源调度 Java 数据库连接 Apache Scala HIVE Spark
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  • 社区小助手
    2019-07-17 23:18:31

    出现此问题的原因是对象太大而无法进行随机播放。

    你能尝试增加后缀分区吗?

    .config("spark.sql.shuffle.partitions", "1000")

    或者您可以尝试添加此配置:

    .config("spark.shuffle.spill.compress", true)

    .config("spark.shuffle.compress", true)

    或者您可以降低块大小以降低随机内存使用量

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