Spark 2.2排序因巨大的数据集而失败

时间：2019-05-07 11:34:11

标签： sorting apache-spark apache-spark-sql cluster-computing

在对基于4列的巨大数据集（ 1.2 T ）进行排序时，我遇到了一个问题。在排序之后，我还需要根据排序函数中使用的列之一在HDFS中写入最终数据集时对该数据集进行分区。

这是我几天前发布的一个stackoverflow帖子，它描述了我使用相同代码但关于连接两个数据集的另一个问题：

previous issue

我使用了这篇文章的答案来改进我的代码。现在联接可以正常工作了。

我测试了没有排序的代码，并且工作正常。为了执行排序，我考虑了基于四列对数据进行分区。

一个分区的大小为 500MB 。然后，我有2600=1.2T/500MB个分区。

执行火花作业时，出现shuffle.RetryingBlockFetcher错误（请参阅下面的错误日志）。

我的问题是：

  • 为避免乱序产生火花的最佳方式是什么？还是减少它？
  • 我可以对我的代码进行更正/添加改进以执行排序吗？
  • 我真的必须采用这种方式吗？我不能使用Group By等其他技术吗？

我的代码段：

已编辑

    val uh = uh_months
      .withColumn("UHDIN", datediff(to_date(unix_timestamp(col("UHDIN_YYYYMMDD"), "yyyyMMdd").cast(TimestampType)),
        to_date(unix_timestamp(col("january"), "yyyy-MM-dd").cast(TimestampType))))
      //      .withColumn("DVA_1", to_date((unix_timestamp(col("DVA"), "ddMMMyyyy")).cast(TimestampType)))
      .withColumn("DVA_1", date_format(col("DVA"), "dd/MM/yyyy"))
      .drop("UHDIN_YYYYMMDD")
      .drop("january")
      .drop("DVA").repartition(1300,col("MMED"),col("DEBCRED"),col("NMTGP"))//.repartition(1300,col("NO_NUM"))

    val uh_flag_comment = new TransactionType().transform(uh)
    val uh_repartitioned = uh_flag_comment.repartition(1300,col("NO_NUM"))

    val uh_joined = uh_repartitioned.join(broadcast(smallDF), "NO_NUM")
      .select(
        uh.col("*"),
        smallDF.col("PSP"),
        smallDF.col("minrel"),
        smallDF.col("Label"),
        smallDF.col("StartDate"))
      .withColumnRenamed("DVA_1", "DVA")

    val uh_final = uh_joined.repartition(1300, col("PSP")).sortWithinPartitions(col("NO_NUM"), col("UHDIN"), col("HOURMV"))

    return uh_final

TransactionType是一个类，其中我根据3列（uh，MMED，DEBCRED的值向我的NMTGP数据帧添加新列），使用正则表达式。

不进行排序，而是利用群集的全部容量，代码大约在1小时内运行。

执行计划

== Physical Plan ==
Exchange hashpartitioning(PSP#82, 2600)
+- *Sort [PSP#82 ASC NULLS FIRST, NO_NUM#252 ASC NULLS FIRST, UHDIN#547 ASC NULLS FIRST, HOURMV#175 ASC NULLS FIRST], true, 0
   +- Exchange rangepartitioning(PSP#82 ASC NULLS FIRST, NO_NUM#252 ASC NULLS FIRST, UHDIN#547 ASC NULLS FIRST, HOURMV#175 ASC NULLS FIRST, 200)
      +- Exchange hashpartitioning(PSP#82, NO_NUM#252, UHDIN#547, HOURMV#175, 2600)
         +- *Project [NO_NUM#252, DEV#153, DEBCRED#154, BDGRORI#155, BDGREUR#156, BEWC#157, MSG30_NL#158, SCAPMV#159, USERID#160, MMED#161, TNUM#162, NMTGP#163, BKA#164, CATEXT#165, SEQETAT#166, ACCTYPE#167, BRAND#168, FAMILY#169, SUBFAMILY#170, FORCED_DVA#172, BYBANK#173, CPTE_PROTEGE#174, HOURMV#175, RDFB#176, ... 30 more fields]
            +- *BroadcastHashJoin [NO_NUM#252], [NO_NUM#13], Inner, BuildRight
               :- Exchange hashpartitioning(NO_NUM#252, 1300)
               :  +- *Project [NUM#152 AS NO_NUM#252, DEV#153, DEBCRED#154, BDGRORI#155, BDGREUR#156, BEWC#157, MSG30_NL#158, SCAPMV#159, USERID#160, MMED#161, TNUM#162, NMTGP#163, BKA#164, CATEXT#165, SEQETAT#166, ACCTYPE#167, BRAND#168, FAMILY#169, SUBFAMILY#170, FORCED_DVA#172, BYBANK#173, CPTE_PROTEGE#174, HOURMV#175, RDFB#176, ... 26 more fields]
               :     +- *Filter (BEWC#157 INSET (25003,25302,25114,20113,12017,20108,25046,12018,15379,15358,11011,20114,10118,12003,25097,20106,20133,10133,10142,15402,25026,25345,28023,15376,25019,28004,21701,25001,11008,15310,15003,2SOMEPORT,22048,15470,25300,25514,25381,25339,15099,25301,28005,28026,25098,25018,15323,25376,15804,15414,25344,25102,15458,15313,28002,25385,22051,25214,15031,12005,15425,20145,22011,15304,25027,14020,11007,25901,15343,22049,20112,12031,20127,15339,25421,15432,28025,25340,25325,20150,28011,25368,25304,22501,25369,28022,15098,12032,15375,25002,25008,10116,10101,22502,25090,15004,20105,12030,22503,15095,22007,15809,15342,15311,25216,10103,20122,11019,20142,15097,20147,20149,25005,25205,25380,15380,10120,25015,15384,11003,10110,25016,15090,25307,15001,25390,15312,10115,25219,15806,15459,12016,15359,15395,15302,12021,11701,10111,10148,25379,15807,10102,25352,25355,12010,25095,25394,20101,25413,15385,25322,28027,11026,15533,25201,25371,10128,11028,12020,15819,10143,28028,10123,10125,11020,25029,10122,25343,15015,12033,25014,12012,25024,25375,11023,25501,25402,22001,15317,12014,16114,20501,15046,12001,12022,10104,10117,12002,25499,10145,10153,12011,15350,15300,10119,25305,15345,25374,11027,25430,28021,25202,10121,28024,25101,28001,15321,11025,25358,15333,15501,25533,15372,12008,11015,10114,10113,10112,15303,15320,28006,22002,25359,10132,15497,25353,11029,25425,15374,12019,25437,11022,15357,20148,20111,26114,25099,25354,10124,25303,11010,20120,20135,15820,15331,28029) && isnotnull(NUM#152))
               :        +- *FileScan csv [UHDIN_YYYYMMDD#151,NUM#152,DEV#153,DEBCRED#154,BDGRORI#155,BDGREUR#156,BEWC#157,MSG30_NL#158,SCAPMV#159,USERID#160,MMED#161,TNUM#162,NMTGP#163,BKA#164,CATEXT#165,SEQETAT#166,ACCTYPE#167,BRAND#168,FAMILY#169,SUBFAMILY#170,DVA#171,FORCED_DVA#172,BYBANK#173,CPTE_PROTEGE#174,... 26 more fields] Batched: false, Format: CSV, Location: InMemoryFileIndex[SOMEHOST:SOMEPORT/SOMEPATH, PartitionFilters: [], PushedFilters: [In(BEWC, [25003,25302,25114,20113,12017,20108,25046,12018,15379,15358,11011,20114,10118,12003,25..., ReadSchema: struct<UHDIN_YYYYMMDD:string,NUM:string,DEV:string,DEBCRED:string,BDGRORI:string,BDGREUR:string,B...
               +- BroadcastExchange HashedRelationBroadcastMode(List(input[0, string, true]))
                  +- *Project [NO_NUM#13, PSP#82, minrel#370, Label#105, StartDate#106]
                     +- *SortMergeJoin [PSP#381], [PSP#82], Inner
                        :- *Sort [PSP#381 ASC NULLS FIRST], false, 0
                        :  +- Exchange hashpartitioning(PSP#381, 200)
                        :     +- *Project [PSP#381, NO_NUM#13, minrel#370]
                        :        +- SortMergeJoin [PSP#381, C_SNUM#14, minrel#370, NO_NUM#13], [NO_PSP#47, C_SNUM_1#387, C_NRELPR#50, NO_NUM_1#400], LeftOuter
                        :           :- *Sort [PSP#381 ASC NULLS FIRST, C_SNUM#14 ASC NULLS FIRST, minrel#370 ASC NULLS FIRST, NO_NUM#13 ASC NULLS FIRST], false, 0
                        :           :  +- Exchange hashpartitioning(PSP#381, C_SNUM#14, minrel#370, NO_NUM#13, 200)
                        :           :     +- SortAggregate(key=[NO_PSP#12, C_SNUM#14, NO_NUM#13], functions=[min(C_NRELPR#15)])
                        :           :        +- *Sort [NO_PSP#12 ASC NULLS FIRST, C_SNUM#14 ASC NULLS FIRST, NO_NUM#13 ASC NULLS FIRST], false, 0
                        :           :           +- Exchange hashpartitioning(NO_PSP#12, C_SNUM#14, NO_NUM#13, 200)
                        :           :              +- SortAggregate(key=[NO_PSP#12, C_SNUM#14, NO_NUM#13], functions=[partial_min(C_NRELPR#15)])
                        :           :                 +- *Sort [NO_PSP#12 ASC NULLS FIRST, C_SNUM#14 ASC NULLS FIRST, NO_NUM#13 ASC NULLS FIRST], false, 0
                        :           :                    +- *Project [NO_PSP#12, C_SNUM#14, NO_NUM#13, C_NRELPR#15]
                        :           :                       +- *Filter (((C_NRELPR#15 IN (001,006) && C_SNUM#14 IN (030,033)) && isnotnull(NO_NUM#13)) && isnotnull(NO_PSP#12))
                        :           :                          +- *FileScan csv [NO_PSP#12,NO_NUM#13,C_SNUM#14,c_nrelpr#15] Batched: false, Format: CSV, Location: InMemoryFileIndex[SOMEHOST:SOMEPORT/SOMEPATH, PartitionFilters: [], PushedFilters: [In(c_nrelpr, [001,006]), In(C_SNUM, [030,033]), IsNotNull(NO_NUM), IsNotNull(NO_PSP)], ReadSchema: struct<NO_PSP:string,NO_NUM:string,C_SNUM:string,c_nrelpr:string>
                        :           +- *Sort [NO_PSP#47 ASC NULLS FIRST, C_SNUM_1#387 ASC NULLS FIRST, C_NRELPR#50 ASC NULLS FIRST, NO_NUM_1#400 ASC NULLS FIRST], false, 0
                        :              +- Exchange hashpartitioning(NO_PSP#47, C_SNUM_1#387, C_NRELPR#50, NO_NUM_1#400, 200)
                        :                 +- *Project [NO_PSP#47, NO_NUM#48 AS NO_NUM_1#400, C_SNUM#49 AS C_SNUM_1#387, c_nrelpr#50]
                        :                    +- *FileScan csv [NO_PSP#47,NO_NUM#48,C_SNUM#49,c_nrelpr#50] Batched: false, Format: CSV, Location: InMemoryFileIndex[SOMEHOST:SOMEPORT/SOMEPATH, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<NO_PSP:string,NO_NUM:string,C_SNUM:string,c_nrelpr:string>
                        +- *Sort [PSP#82 ASC NULLS FIRST], false, 0
                           +- Exchange hashpartitioning(PSP#82, 200)
                              +- *Project [PSP#82, Label#105, StartDate#106]
                                 +- *Filter isnotnull(PSP#82)
                                    +- *FileScan csv [PSP#82,Label#105,StartDate#106] Batched: false, Format: CSV, Location: InMemoryFileIndex[SOMEHOST:SOMEPORT/SOMEPATH, PartitionFilters: [], PushedFilters: [IsNotNull(PSP)], ReadSchema: struct<PSP:string,Label:string,StartDate:string>

这是我在进行排序工作时遇到的主要错误：

19/05/06 18:02:25 ERROR shuffle.RetryingBlockFetcher: Exception while beginning fetch of 214 outstanding blocks 
java.io.IOException: Failed to connect to SOMEHOST/SOMEADDRESS:SOMEPORT
    at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:232)
    at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:182)
    at org.apache.spark.network.netty.NettyBlockTransferService$$anon$1.createAndStart(NettyBlockTransferService.scala:98)
    at org.apache.spark.network.shuffle.RetryingBlockFetcher.fetchAllOutstanding(RetryingBlockFetcher.java:141)
    at org.apache.spark.network.shuffle.RetryingBlockFetcher.start(RetryingBlockFetcher.java:121)
    at org.apache.spark.network.netty.NettyBlockTransferService.fetchBlocks(NettyBlockTransferService.scala:108)
    at org.apache.spark.storage.ShuffleBlockFetcherIterator.sendRequest(ShuffleBlockFetcherIterator.scala:228)
    at org.apache.spark.storage.ShuffleBlockFetcherIterator.fetchUpToMaxBytes(ShuffleBlockFetcherIterator.scala:435)
    at org.apache.spark.storage.ShuffleBlockFetcherIterator.initialize(ShuffleBlockFetcherIterator.scala:323)
    at org.apache.spark.storage.ShuffleBlockFetcherIterator.<init>(ShuffleBlockFetcherIterator.scala:140)
    at org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:45)
    at org.apache.spark.sql.execution.ShuffledRowRDD.compute(ShuffledRowRDD.scala:165)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
    at org.apache.spark.scheduler.Task.run(Task.scala:108)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
    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)
Caused by: io.netty.channel.AbstractChannel$AnnotatedConnectException: Connection refused: SOMEHOST/SOMEADDRESS:SOMEPORT
    at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
    at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:717)
    at io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(NioSocketChannel.java:257)
    at io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.finishConnect(AbstractNioChannel.java:291)
    at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:631)
    at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:566)
    at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:480)
    at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:442)
    at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:131)
    at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
    ... 1 more
19/05/06 18:02:25 INFO shuffle.RetryingBlockFetcher: Retrying fetch (1/3) for 214 outstanding blocks after 5000 ms
19/05/06 18:02:25 INFO storage.ShuffleBlockFetcherIterator: Started 6 remote fetches in 13 ms
19/05/06 18:02:28 INFO executor.Executor: Finished task 408.0 in stage 14.0 (TID 6696). 1733 bytes result sent to driver
19/05/06 18:02:28 INFO executor.CoarseGrainedExecutorBackend: Got assigned task 6816
19/05/06 18:02:28 INFO executor.Executor: Running task 466.1 in stage 14.0 (TID 6816)
19/05/06 18:02:28 INFO storage.ShuffleBlockFetcherIterator: Getting 5073 non-empty blocks out of 5089 blocks
19/05/06 18:02:28 INFO client.TransportClientFactory: Found inactive connection to SOMEHOST/SOMEADDRESS:SOMEPORT, creating a new one.
19/05/06 18:02:28 ERROR shuffle.RetryingBlockFetcher: Exception while beginning fetch of 82 outstanding blocks 
java.io.IOException: Failed to connect to SOMEHOST/SOMEADDRESS:SOMEPORT
    at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:232)
    at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:182)
    at org.apache.spark.network.netty.NettyBlockTransferService$$anon$1.createAndStart(NettyBlockTransferService.scala:98)
    at org.apache.spark.network.shuffle.RetryingBlockFetcher.fetchAllOutstanding(RetryingBlockFetcher.java:141)
    at org.apache.spark.network.shuffle.RetryingBlockFetcher.start(RetryingBlockFetcher.java:121)
    at org.apache.spark.network.netty.NettyBlockTransferService.fetchBlocks(NettyBlockTransferService.scala:108)
    at org.apache.spark.storage.ShuffleBlockFetcherIterator.sendRequest(ShuffleBlockFetcherIterator.scala:228)
    at org.apache.spark.storage.ShuffleBlockFetcherIterator.fetchUpToMaxBytes(ShuffleBlockFetcherIterator.scala:435)
    at org.apache.spark.storage.ShuffleBlockFetcherIterator.initialize(ShuffleBlockFetcherIterator.scala:323)
    at org.apache.spark.storage.ShuffleBlockFetcherIterator.<init>(ShuffleBlockFetcherIterator.scala:140)
    at org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:45)
    at org.apache.spark.sql.execution.ShuffledRowRDD.compute(ShuffledRowRDD.scala:165)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
    at org.apache.spark.scheduler.Task.run(Task.scala:108)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
Caused by: io.netty.channel.AbstractChannel$AnnotatedConnectException: Connection refused: SOMEHOST/SOMEADDRESS:SOMEPORT
    at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
    at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:717)
    at io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(NioSocketChannel.java:257)
    at io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.finishConnect(AbstractNioChannel.java:291)
    at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:631)
    at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:566)
    at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:480)
    at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:442)
    at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:131)
    at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
    ... 1 more

已编辑

另一种错误类型：

19/05/06 18:06:16 ERROR executor.Executor: Exception in task 309.1 in stage 13.1 (TID 7592)
java.io.FileNotFoundException: /applis/hadoop/yarn/local/usercache/MYUSER/appcache/application_1555263602441_0123/blockmgr-aa586b76-ff58-4f88-b168-288c3e1b9f61/3c/temp_shuffle_ea967624-f633-4481-9a05-249b561e3c38 (No such file or directory)
    at java.io.FileInputStream.open0(Native Method)
    at java.io.FileInputStream.open(FileInputStream.java:195)
    at java.io.FileInputStream.<init>(FileInputStream.java:138)
    at org.spark_project.guava.io.Files$FileByteSource.openStream(Files.java:124)
    at org.spark_project.guava.io.Files$FileByteSource.openStream(Files.java:114)
    at org.spark_project.guava.io.ByteSource.copyTo(ByteSource.java:202)
    at org.spark_project.guava.io.Files.copy(Files.java:436)
    at org.spark_project.guava.io.Files.move(Files.java:651)
    at org.apache.spark.shuffle.sort.UnsafeShuffleWriter.mergeSpills(UnsafeShuffleWriter.java:277)
    at org.apache.spark.shuffle.sort.UnsafeShuffleWriter.closeAndWriteOutput(UnsafeShuffleWriter.java:216)
    at org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:169)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
    at org.apache.spark.scheduler.Task.run(Task.scala:108)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
19/05/06 18:06:16 ERROR executor.Executor: Exception in task 502.1 in stage 13.1 (TID 7599)
java.io.FileNotFoundException: /applis/hadoop/yarn/local/usercache/MYUSER/appcache/application_1555263602441_0123/blockmgr-aa586b76-ff58-4f88-b168-288c3e1b9f61/34/temp_shuffle_dd202cd1-ad8f-41c4-b4d1-d79621cd169e (No such file or directory)
    at java.io.FileOutputStream.open0(Native Method)
    at java.io.FileOutputStream.open(FileOutputStream.java:270)
    at java.io.FileOutputStream.<init>(FileOutputStream.java:213)
    at org.apache.spark.storage.DiskBlockObjectWriter.initialize(DiskBlockObjectWriter.scala:102)
    at org.apache.spark.storage.DiskBlockObjectWriter.open(DiskBlockObjectWriter.scala:115)
    at org.apache.spark.storage.DiskBlockObjectWriter.write(DiskBlockObjectWriter.scala:247)
    at org.apache.spark.shuffle.sort.ShuffleExternalSorter.writeSortedFile(ShuffleExternalSorter.java:201)
    at org.apache.spark.shuffle.sort.ShuffleExternalSorter.closeAndGetSpills(ShuffleExternalSorter.java:405)
    at org.apache.spark.shuffle.sort.UnsafeShuffleWriter.closeAndWriteOutput(UnsafeShuffleWriter.java:209)
    at org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:169)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
    at org.apache.spark.scheduler.Task.run(Task.scala:108)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
19/05/06 18:06:16 INFO executor.Executor: Finished task 200.2 in stage 13.1 (TID 7568). 2826 bytes result sent to driver
19/05/06 18:06:16 ERROR executor.CoarseGrainedExecutorBackend: RECEIVED SIGNAL TERM
19/05/06 18:06:16 INFO util.ShutdownHookManager: Shutdown hook called

某些信息/上下文：

我正在生产环境中工作（请参阅下面的集群配置）。我无法升级我的Spark版本。我没有Spark UI或yarn UI来监视我的工作。我所能找到的只是纱线记录。

Spark版本：2.2

集群配置：

  • 21个计算节点（工作人员）
    • 每个
  • 8个核心
  • 每个节点64 GB RAM

当前Spark配置：

-主：纱

-执行者内存：42G

-executor-cores：5

-驱动程序内存：42G

-num-executors：32

-spark.sql.broadcastTimeout = 3600

-spark.kryoserializer.buffer.max = 512

-spark.yarn.executor.memoryOverhead = 2400

-spark.driver.maxResultSize = 500m

-spark.memory.storageFraction = 0.3

-spark.memory.fraction = 0.9

-spark.hadoop.fs.permissions.umask-mode = 007

工作如何执行：

我们使用IntelliJ构建工件（jar），然后将其发送到服务器。然后执行bash脚本。该脚本：

  • 导出一些环境变量（SPARK_HOME，HADOOP_CONF_DIR，PATH和SPARK_LOCAL_DIRS）

  • 使用上面的spark配置中定义的所有参数启动spark-submit命令

  • 检索该应用程序的纱线日志

2 个答案:

答案 0 :(得分：1)

以下是针对您的情况的一些建议：

  • 更改1 ：基于生成的较大数据集1.2TB进行分区。另外，我此时还删除了entry.getValue().getKey().longitude，因为它将被下一个分区（“ NO_NUM”）覆盖，因此是多余的。

  • 更改2 ：使用persist保存我们刚刚分区的数据，以避免对同一数据框反复进行分区（请检查上一篇文章中的链接，了解如何可行）

  • 更改3 ：已删除repartition(col("NO_NUM"), col("UHDIN"), col("HOURMV"))，因为它对我来说似乎很多余。尽管仅在 uh_flag_comment.repartition(1300,col("NO_NUM"))导致重新组合时才有用，例如在内部进行join或groupBy！这样的操作将修改我们在上一步中使用TransactionType().transform(uh)设置的分区键。

  • 更改4 ：使用repartition(2600, col("NO_NUM")进行分区，因为这将是orderBy将使用的分区键，因此这两个应该相同

  • 更改5 ：使用col("NO_NUM"), col("UHDIN"), col("HOURMV")

  • 更改6 ：将执行程序数量增加到40 

col("NO_NUM"), col("UHDIN"), col("HOURMV")

祝你好运，如果您有任何疑问，请告诉我

答案 1 :(得分：0)

我使用@Alexandros发布的大部分答案成功地对数据进行了排序（然后运行整个代码）。

但是我在集群配置方面做了一些更改：

  • 我将执行程序的内存增加到45 G（原来是42 G）
  • 我还在每个节点上增加了/applis/hadoop/yarn/local/usercache/MYUSER/的磁盘空间，并添加了20-25G（此文件夹的每个节点上的可用空间不足50 G）。这是yarn usercache，Spark在其中写入中间改组的数据块。因为我有一个1.2T的数据集，并且有21个节点，所以当数据分布在各个节点上时，每个节点上大约需要60-65G磁盘空间。

我还使用了sortWithinPartition函数（此函数可以正常工作，但经典的sort函数不能运行）。此外，由于我是基于PSP进行分区，因此我只需要对每个分区进行排序（如果数据集不是基于PSP进行排序，就可以了。）

下面是代码：

val uh = uh_months
  .withColumn("UHDIN", datediff(to_date(unix_timestamp(col("UHDIN_YYYYMMDD"), "yyyyMMdd").cast(TimestampType)),
    to_date(unix_timestamp(col("january"), "yyyy-MM-dd").cast(TimestampType))))
  //      .withColumn("DVA_1", to_date((unix_timestamp(col("DVA"), "ddMMMyyyy")).cast(TimestampType)))
  .withColumn("DVA_1", date_format(col("DVA"), "dd/MM/yyyy"))
  .drop("UHDIN_YYYYMMDD")
  .drop("january")
  .drop("DVA")
  .repartition(3000, col("NO_NUM"))
  .persist()

val uh_flag_comment = new TransactionType().transform(uh)

val uh_joined = uh_flag_comment.join(broadcast(smallDF), "NO_NUM")
  .select(
    uh_flag_comment.col("*"),
    kl_holdmand_pruned.col("PSP"),
    kl_holdmand_pruned.col("minrel"),
    kl_holdmand_pruned.col("TerroLabel"),
    kl_holdmand_pruned.col("TerroStartDate"))
  .withColumnRenamed("DVA_1", "DVA")

smallDF.unpersist()
uh.unpersist()

val uh_to_be_sorted = uh_joined.repartition(3000, col("PSP"))
val uh_final = uh_to_be_sorted.sortWithinPartitions(col("NO_NUM"), col("UHDIN"), col("HOURMV"))

uh_final
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