Question

使用Spark 1.1

我有2个数据集。一个是非常大的，另一个是减少（使用一些1：100过滤）到更小的规模。我需要将大型数据集减少到相同的比例，方法是只将较小列表中的那些项与较大列表中的相应对应项连接起来（这些列表包含具有相互连接字段的元素）。

我正在使用以下代码执行此操作：

“if（joinKeys！= null）”部分是相关部分

较小的列表是“joinKeys”，较大的列表是“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;
}

事情是在运行这个工作时，我总是得到同样的错误：

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)

通过在所有群集计算机上执行以下操作，我已经增加了我的ulimits：

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

但不能解决我的问题...

Answer 1

bdutil框架的工作方式是用户“hadoop”是运行该作业的用户。部署集群的脚本创建了一个文件/etc/security/limits.d/hadoop.conf，它覆盖了“hadoop”用户的ulimit设置，我不知道。通过删除此文件，或者在那里设置所需的ulim，我能够解决问题。

Spark - ＆＃34;太多的打开文件＆＃34;在洗牌

1 个答案: