Spark-随机播放“打开的文件太多”

Question

Using Spark 1.1

I have 2 datasets. One is very large and the other was reduced (using some 1:100 filtering) to much smaller scale. I need to reduce the large dataset to the same scale, by joining only those items from the smaller list with their corresponding counterparts in the larger list (those lists contain elements that have a mutual join field).

I am doing that using the following code:

• The "if(joinKeys != null)" part is the relevant part

• Smaller list is "joinKeys", larger list is "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; } 

Thing is when running this job, I always get the same error:

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)

I already increased my ulimits, by doing the following on all cluster machines:

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

But doesn't fix my problem...

Answer 1

The bdutil framework works in a way the user "hadoop" is the one running the job. The script that deploys the cluster, created a file /etc/security/limits.d/hadoop.conf that overrided the ulimit settings for "hadoop" user, which I wasn't aware of. By deleting this file, or alternatively setting the desired ulimits there, I was able to resolve the problem.