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From "Dapeng Sun (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (HIVE-15527) Memory usage is unbound in SortByShuffler for Spark
Date Wed, 18 Jan 2017 03:32:26 GMT

    [ https://issues.apache.org/jira/browse/HIVE-15527?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15827376#comment-15827376
] 

Dapeng Sun edited comment on HIVE-15527 at 1/18/17 3:32 AM:
------------------------------------------------------------

Thank [~csun] and [~Ferd], here is the detail log:
{noformat}
17/01/17 xx:xx:xx INFO client.RemoteDriver: Failed to run job xxxxxxxxxxxxxxxxxxxx
java.lang.NumberFormatException: null
        at java.lang.Long.parseLong(Long.java:552)
        at java.lang.Long.parseLong(Long.java:631)
        at org.apache.hadoop.hive.ql.exec.spark.SparkPlanGenerator.generate(SparkPlanGenerator.java:202)
        at org.apache.hadoop.hive.ql.exec.spark.SparkPlanGenerator.generateParentTran(SparkPlanGenerator.java:141)
        at org.apache.hadoop.hive.ql.exec.spark.SparkPlanGenerator.generate(SparkPlanGenerator.java:109)
        at org.apache.hadoop.hive.ql.exec.spark.RemoteHiveSparkClient$JobStatusJob.call(RemoteHiveSparkClient.java:335)
        at org.apache.hive.spark.client.RemoteDriver$JobWrapper.call(RemoteDriver.java:366)
        at org.apache.hive.spark.client.RemoteDriver$JobWrapper.call(RemoteDriver.java:335)
        at java.util.concurrent.FutureTask.run(FutureTask.java:266)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        at java.lang.Thread.run(Thread.java:745)
17/01/17 xx:xx:xx INFO client.RemoteDriver: Shutting down remote driver.
{noformat}


was (Author: dapengsun):
Thank [~csun] and [~Ferd], here is the detail log:
17/01/17 xx:xx:xx INFO client.RemoteDriver: Failed to run job xxxxxxxxxxxxxxxxxxxx
java.lang.NumberFormatException: null
        at java.lang.Long.parseLong(Long.java:552)
        at java.lang.Long.parseLong(Long.java:631)
        at org.apache.hadoop.hive.ql.exec.spark.SparkPlanGenerator.generate(SparkPlanGenerator.java:202)
        at org.apache.hadoop.hive.ql.exec.spark.SparkPlanGenerator.generateParentTran(SparkPlanGenerator.java:141)
        at org.apache.hadoop.hive.ql.exec.spark.SparkPlanGenerator.generate(SparkPlanGenerator.java:109)
        at org.apache.hadoop.hive.ql.exec.spark.RemoteHiveSparkClient$JobStatusJob.call(RemoteHiveSparkClient.java:335)
        at org.apache.hive.spark.client.RemoteDriver$JobWrapper.call(RemoteDriver.java:366)
        at org.apache.hive.spark.client.RemoteDriver$JobWrapper.call(RemoteDriver.java:335)
        at java.util.concurrent.FutureTask.run(FutureTask.java:266)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        at java.lang.Thread.run(Thread.java:745)
17/01/17 xx:xx:xx INFO client.RemoteDriver: Shutting down remote driver.


> Memory usage is unbound in SortByShuffler for Spark
> ---------------------------------------------------
>
>                 Key: HIVE-15527
>                 URL: https://issues.apache.org/jira/browse/HIVE-15527
>             Project: Hive
>          Issue Type: Improvement
>          Components: Spark
>    Affects Versions: 1.1.0
>            Reporter: Xuefu Zhang
>            Assignee: Chao Sun
>         Attachments: HIVE-15527.0.patch, HIVE-15527.0.patch, HIVE-15527.1.patch, HIVE-15527.2.patch,
HIVE-15527.3.patch, HIVE-15527.4.patch, HIVE-15527.5.patch, HIVE-15527.6.patch, HIVE-15527.7.patch,
HIVE-15527.8.patch, HIVE-15527.patch
>
>
> In SortByShuffler.java, an ArrayList is used to back the iterator for values that have
the same key in shuffled result produced by spark transformation sortByKey. It's possible
that memory can be exhausted because of a large key group.
> {code}
>             @Override
>             public Tuple2<HiveKey, Iterable<BytesWritable>> next() {
>               // TODO: implement this by accumulating rows with the same key into a list.
>               // Note that this list needs to improved to prevent excessive memory usage,
but this
>               // can be done in later phase.
>               while (it.hasNext()) {
>                 Tuple2<HiveKey, BytesWritable> pair = it.next();
>                 if (curKey != null && !curKey.equals(pair._1())) {
>                   HiveKey key = curKey;
>                   List<BytesWritable> values = curValues;
>                   curKey = pair._1();
>                   curValues = new ArrayList<BytesWritable>();
>                   curValues.add(pair._2());
>                   return new Tuple2<HiveKey, Iterable<BytesWritable>>(key,
values);
>                 }
>                 curKey = pair._1();
>                 curValues.add(pair._2());
>               }
>               if (curKey == null) {
>                 throw new NoSuchElementException();
>               }
>               // if we get here, this should be the last element we have
>               HiveKey key = curKey;
>               curKey = null;
>               return new Tuple2<HiveKey, Iterable<BytesWritable>>(key, curValues);
>             }
> {code}
> Since the output from sortByKey is already sorted on key, it's possible to backup the
value iterable using the same input iterator.



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