Spark-隨機播放“打開的文件太多”

Question

使用Spark 1.1

我有2個數據集。 一個很大，另一個減小（使用了1：100過濾）到較小的比例。 我只需要通過將較小列表中的那些項與它們在較大列表中的對應項相連接（那些列表包含具有相互聯接字段的元素）來將大型數據集縮小到相同的比例。

我正在使用以下代碼：

• “ if（joinKeys！= null）”部分是相關部分

• 較小的列表是“ joinKeys”，較大的列表是“ keyedEvents”

   private static JavaRDD<ObjectNode> createOutputType(JavaRDD jsonsList, final String type, String outputPath,JavaPairRDD<String,String> joinKeys) { outputPath = outputPath + "/" + type; JavaRDD events = jsonsList.filter(new TypeFilter(type)); // This is in case we need to narrow the list to match some other list of ids... Recommendation List, for example... :) if(joinKeys != null) { JavaPairRDD<String,ObjectNode> keyedEvents = events.mapToPair(new KeyAdder("requestId")); JavaRDD < ObjectNode > joinedEvents = joinKeys.join(keyedEvents).values().map(new PairToSecond()); events = joinedEvents; } JavaPairRDD<String,Iterable<ObjectNode>> groupedEvents = events.mapToPair(new KeyAdder("sliceKey")).groupByKey(); // Add convert jsons to strings and add "\\n" at the end of each JavaPairRDD<String, String> groupedStrings = groupedEvents.mapToPair(new JsonsToStrings()); groupedStrings.saveAsHadoopFile(outputPath, String.class, String.class, KeyBasedMultipleTextOutputFormat.class); return events; } 

問題是在運行此作業時，我總是會遇到相同的錯誤：

Exception in thread "main" java.lang.reflect.InvocationTargetException
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.deploy.worker.DriverWrapper$.main(DriverWrapper.scala:40)
at org.apache.spark.deploy.worker.DriverWrapper.main(DriverWrapper.scala)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 2757 in stage 13.0 failed 4 times, most recent failure: Lost task 2757.3 in stage 13.0 (TID 47681, hadoop-w-175.c.taboola-qa-01.internal): java.io.FileNotFoundException: /hadoop/spark/tmp/spark-local-20141201184944-ba09/36/shuffle_6_2757_2762 (Too many open files)
    java.io.FileOutputStream.open(Native Method)
    java.io.FileOutputStream.<init>(FileOutputStream.java:221)
    org.apache.spark.storage.DiskBlockObjectWriter.open(BlockObjectWriter.scala:123)
    org.apache.spark.storage.DiskBlockObjectWriter.write(BlockObjectWriter.scala:192)
    org.apache.spark.shuffle.hash.HashShuffleWriter$$anonfun$write$1.apply(HashShuffleWriter.scala:67)
    org.apache.spark.shuffle.hash.HashShuffleWriter$$anonfun$write$1.apply(HashShuffleWriter.scala:65)
    scala.collection.Iterator$class.foreach(Iterator.scala:727)
    scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    org.apache.spark.shuffle.hash.HashShuffleWriter.write(HashShuffleWriter.scala:65)
    org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
    org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
    org.apache.spark.scheduler.Task.run(Task.scala:54)
    org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:177)
    java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    java.lang.Thread.run(Thread.java:745)

通過在所有群集計算機上執行以下操作，我已經增加了ulimit：

echo "* soft nofile 900000" >> /etc/security/limits.conf
echo "root soft nofile 900000" >> /etc/security/limits.conf
echo "* hard nofile 990000" >> /etc/security/limits.conf
echo "root hard nofile 990000" >> /etc/security/limits.conf
echo "session required pam_limits.so" >> /etc/pam.d/common-session
echo "session required pam_limits.so" >> /etc/pam.d/common-session-noninteractive

但是並不能解決我的問題...

Answer 1

bdutil框架的工作方式是用戶“ hadoop”是運行作業的人。 部署集群的腳本創建了一個文件/etc/security/limits.d/hadoop.conf，該文件覆蓋了我不知道的“ hadoop”用戶的ulimit設置。 通過刪除此文件或在此處設置所需的ulimit，我能夠解決問題。