Hive中跑MapReduce Job出现OOM问题分析及解决，mapreduceoom
Hive中跑MapReduce Job出现OOM问题分析及解决，mapreduceoom
一、引子
今天在跑一段很复杂而且涉及数据量10多年的N个表join的长SQL时，发生了OOM的异常。
由于一个map通常配置只有64MB或者128MB，则在Map阶段出现OOM的情况很少见。所以一般发生在reduce阶段。
但是今天这个异常详细的看后，会发现既不是map阶段，也不是reduce阶段，发现不是执行过程，而是driver提交job阶段就OOM了。
Hive中XMLEncoder序列化MapredWork引发OutOfMemoryError
XMLEncoder导致java.lang.OutOfMemoryError: GC overhead limit exceeded
二、概括回顾
先概括下，Hive中出现OOM的异常原因大致分为以下几种：1. Map阶段OOM。
2. Reduce阶段OOM。
3. Driver提交Job阶段OOM。
Map阶段OOM：
1. 发生OOM的几率很小，除非你程序的逻辑不正常，亦或是程序写的不高效，产生垃圾太多。Reduce阶段OOM：
1. data skew 数据倾斜
data skew是引发这个的一个原因。key分布不均匀，导致某一个reduce所处理的数据超过预期，导致jvm频繁GC。
2. value对象过多或者过大
某个reduce中的value堆积的对象过多，导致jvm频繁GC。
解决办法：
1. 增加reduce个数，set mapred.reduce.tasks=300，。
2. 在hive-site.xml中设置，或者在hive shell里设置 set mapred.child.java.opts = -Xmx512m
或者只设置reduce的最大heap为2G，并设置垃圾回收器的类型为并行标记回收器，这样可以显著减少GC停顿，但是稍微耗费CPU。
set mapred.reduce.child.java.opts=-Xmx2g -XX:+UseConcMarkSweepGC;
3. 使用map join 代替 common join. 可以set hive.auto.convert.join = true
4. 设置 hive.optimize.skewjoin = true 来解决数据倾斜问题
Driver提交job阶段OOM：
job产生的执行计划的条目太多，比如扫描的分区过多，上到4k-6k个分区的时候，并且是好几张表的分区都很多时，这时做join。
究其原因，是 因为序列化时，会将这些分区，即hdfs文件路径，封装为Path对象，这样，如果对象太多了，而且Driver启动的时候设置的heap size太小，则会导致在Driver内序列化这些MapRedWork时，生成的对象太多，导致频繁GC，则会引发如下异常:
java.lang.OutOfMemoryError: GC overhead limit exceeded at sun.nio.cs.UTF_8.newEncoder(UTF_8.java:53) at java.beans.XMLEncoder.createString(XMLEncoder.java:572)
三、诊断问题
如何诊断到了问题：
在网上搜异常，在Hive的IRA发现一个issues，和我的情况类似：问题描述：Use of XMLEncoder to serialize MapredWork causes OOM in hive cli
When running queries on tables with 6000 partitions, hive cli if configured with 128M runs into OOM. Heapdump showed 37MB occupied by one XMLEncoder object while the MapredWork was 500K which is highly inefficient. We should switch to using something more efficient like XStream.比较相近的解释：
I ran with 128M to investigate the OOM. We have resorted to running with 1G as XmX because we keep hitting OOM with bigger tables in hive. There were other things that contributed to the memory usage - mostly Path objects because of the higher number of partitions. But they are absolutely needed. XMLEncoder is something that created too much garbage in a very short span and caused GC. That would be something easy to change/fix without having to touch the core logic. We should be looking at fixing the root cause of the problem instead of keeping on increasing the memory requirements. Ours is a highly multi-tenant system and there are lot of other programs(pig,etc) running too in the gateway. So running with a lower memory(256-512MB) will help. Found two other reports of this issue: http://mail-archives.apache.org/mod_mbox/hive-user/201106.mbox/%3CBANLkTik4THLNkxV87UygvqhoLri3UL9R3Q@mail.gmail.com%3E https://issues.apache.org/jira/browse/HIVE-1316 This fix increased the max heap size of CLI client and disabled GC overhead limit.
于是亲自确认一下问题处在哪个地方：
因为这段复杂的sql有22个job，每次到第20个job的时候就会出现oom，所以我特意将数据置空，调试这在shell下跑。发现跑到第20个job，扫描的正是扫描了14年的注册表，推断是分区太多，造成的序列化时对象太多OOM。以下是调试图：
将查询计划打印出来，会发现很大，主要是因为有一个注册表，扫描了近14年的历史数据，14×365 = 5110 个分区。除去早些年份数据的不完整性，大约3138个分区。
每个分区都会生成一个Path对象，这样仅仅是这一个表，生成的查询计划被序列化为对象会耗去大半内存，如果在和其它的表好几年的数据继续做join的话，又会耗去更多内存来序列化成对象，这样就会频繁GC，GC不出来，就会抛出OOM的异常。
仅仅是一个查询，都打印出了25MB，说明扫描的数据量是在是太多了。
explain extended a_complex_hql_query > /app/hadoop/shengli/large_plan du -sh large_plan 25M large_plan
详细日志如下：
2014-11-21 13:13:28,334 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Hadoop job information for Stage-23: number of mappers: 12; number of reducers: 1 2014-11-21 13:13:28,354 INFO [Thread-2] AbstractBaseAction$StreamDrainer - 2014-11-21 13:13:28,347 Stage-23 map = 50%, reduce = 0%, Cumulative CPU 42.04 sec 2014-11-21 13:13:48,370 INFO [Thread-2] AbstractBaseAction$StreamDrainer - 2014-11-21 13:13:48,363 Stage-23 map = 100%, reduce = 33%, Cumulative CPU 262.39 sec 2014-11-21 13:14:29,228 INFO [Thread-2] AbstractBaseAction$StreamDrainer - 2014-11-21 13:14:29,222 Stage-23 map = 100%, reduce = 69%, Cumulative CPU 262.39 sec 2014-11-21 13:14:49,248 INFO [Thread-2] AbstractBaseAction$StreamDrainer - 2014-11-21 13:14:49,239 Stage-23 map = 100%, reduce = 92%, Cumulative CPU 324.73 sec 2014-11-21 13:15:11,952 INFO [Thread-2] AbstractBaseAction$StreamDrainer - 2014-11-21 13:15:11,944 Stage-23 map = 100%, reduce = 100%, Cumulative CPU 360.1 sec 2014-11-21 13:15:36,740 INFO [Thread-2] AbstractBaseAction$StreamDrainer - 2014-11-21 13:15:36,734 Stage-23 map = 100%, reduce = 100%, Cumulative CPU 360.1 sec 2014-11-21 13:15:36,741 INFO [Thread-2] AbstractBaseAction$StreamDrainer - MapReduce Total cumulative CPU time: 6 minutes 0 seconds 100 msec 2014-11-21 13:15:36,749 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Ended Job = job_201411141019_68657 2014-11-21 13:15:40,306 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Launching Job 20 out of 21 2014-11-21 13:15:42,162 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Number of reduce tasks not specified. Estimated from input data size: 252 2014-11-21 13:15:42,163 INFO [Thread-2] AbstractBaseAction$StreamDrainer - In order to change the average load for a reducer (in bytes): 2014-11-21 13:15:42,163 INFO [Thread-2] AbstractBaseAction$StreamDrainer - set hive.exec.reducers.bytes.per.reducer=<number> 2014-11-21 13:15:42,163 INFO [Thread-2] AbstractBaseAction$StreamDrainer - In order to limit the maximum number of reducers: 2014-11-21 13:15:42,163 INFO [Thread-2] AbstractBaseAction$StreamDrainer - set hive.exec.reducers.max=<number> 2014-11-21 13:15:42,163 INFO [Thread-2] AbstractBaseAction$StreamDrainer - In order to set a constant number of reducers: 2014-11-21 13:15:42,163 INFO [Thread-2] AbstractBaseAction$StreamDrainer - set mapred.reduce.tasks=<number> 2014-11-21 13:16:40,377 INFO [Thread-2] AbstractBaseAction$StreamDrainer - java.lang.OutOfMemoryError: GC overhead limit exceeded 2014-11-21 13:16:40,378 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at sun.nio.cs.UTF_8.newEncoder(UTF_8.java:53) 2014-11-21 13:16:40,378 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.createString(XMLEncoder.java:572) 2014-11-21 13:16:40,378 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputValue(XMLEncoder.java:543) 2014-11-21 13:16:40,378 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputStatement(XMLEncoder.java:682) 2014-11-21 13:16:40,378 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputStatement(XMLEncoder.java:687) 2014-11-21 13:16:40,378 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputValue(XMLEncoder.java:552) 2014-11-21 13:16:40,378 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputStatement(XMLEncoder.java:682) 2014-11-21 13:16:40,378 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputStatement(XMLEncoder.java:687) 2014-11-21 13:16:40,379 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputValue(XMLEncoder.java:552) 2014-11-21 13:16:40,379 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputStatement(XMLEncoder.java:682) 2014-11-21 13:16:40,379 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputStatement(XMLEncoder.java:687) 2014-11-21 13:16:40,379 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputValue(XMLEncoder.java:552) 2014-11-21 13:16:40,379 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputStatement(XMLEncoder.java:682) 2014-11-21 13:16:40,379 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputStatement(XMLEncoder.java:687) 2014-11-21 13:16:40,379 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputValue(XMLEncoder.java:552) 2014-11-21 13:16:40,380 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputStatement(XMLEncoder.java:682) 2014-11-21 13:16:40,380 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputStatement(XMLEncoder.java:687) 2014-11-21 13:16:40,380 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.outputValue(XMLEncoder.java:552) 2014-11-21 13:16:40,380 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.flush(XMLEncoder.java:398) 2014-11-21 13:16:40,380 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at java.beans.XMLEncoder.close(XMLEncoder.java:429) 2014-11-21 13:16:40,380 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at org.apache.hadoop.hive.ql.exec.Utilities.serializeMapRedWork(Utilities.java:532) 2014-11-21 13:16:40,380 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at org.apache.hadoop.hive.ql.exec.Utilities.setMapRedWork(Utilities.java:376) 2014-11-21 13:16:40,381 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at org.apache.hadoop.hive.ql.exec.ExecDriver.execute(ExecDriver.java:418) 2014-11-21 13:16:40,381 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at org.apache.hadoop.hive.ql.exec.MapRedTask.execute(MapRedTask.java:138) 2014-11-21 13:16:40,381 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at org.apache.hadoop.hive.ql.exec.Task.executeTask(Task.java:144) 2014-11-21 13:16:40,381 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at org.apache.hadoop.hive.ql.exec.TaskRunner.runSequential(TaskRunner.java:57) 2014-11-21 13:16:40,381 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at org.apache.hadoop.hive.ql.Driver.launchTask(Driver.java:1355) 2014-11-21 13:16:40,381 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at org.apache.hadoop.hive.ql.Driver.execute(Driver.java:1139) 2014-11-21 13:16:40,381 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at org.apache.hadoop.hive.ql.Driver.run(Driver.java:945) 2014-11-21 13:16:40,381 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at org.apache.hadoop.hive.cli.CliDriver.processLocalCmd(CliDriver.java:259) 2014-11-21 13:16:40,381 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at org.apache.hadoop.hive.cli.CliDriver.processCmd(CliDriver.java:216) 2014-11-21 13:16:40,381 INFO [Thread-2] AbstractBaseAction$StreamDrainer - at org.apache.hadoop.hive.cli.CliDriver.processLine(CliDriver.java:413) 2014-11-21 13:16:40,381 INFO [Thread-2] AbstractBaseAction$StreamDrainer - FAILED: Execution Error, return code -101 from org.apache.hadoop.hive.ql.exec.MapRedTask 2014-11-21 13:16:40,382 INFO [Thread-2] AbstractBaseAction$StreamDrainer - MapReduce Jobs Launched: 2014-11-21 13:16:40,382 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 0: Map: 33 Reduce: 5 Cumulative CPU: 903.59 sec HDFS Read: 4225680985 HDFS Write: 123129169 S UCCESS 2014-11-21 13:16:40,382 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 1: Map: 192 Reduce: 4 Cumulative CPU: 3215.94 sec HDFS Read: 3036813878 HDFS Write: 322647287 SUCCESS 2014-11-21 13:16:40,382 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 2: Map: 52 Reduce: 4 Cumulative CPU: 1314.04 sec HDFS Read: 3278902794 HDFS Write: 435711878 SUCCESS 2014-11-21 13:16:40,382 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 3: Map: 959 Reduce: 70 Cumulative CPU: 55831.24 sec HDFS Read: 69728403753 HDFS Write: 457377 8468 SUCCESS 2014-11-21 13:16:40,382 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 4: Map: 154 Reduce: 1 Cumulative CPU: 1809.45 sec HDFS Read: 483233734 HDFS Write: 25999761 S UCCESS 2014-11-21 13:16:40,382 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 5: Map: 84 Reduce: 6 Cumulative CPU: 2466.01 sec HDFS Read: 5618486947 HDFS Write: 1649704865 SUCCESS 2014-11-21 13:16:40,382 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 6: Map: 450 Reduce: 55 Cumulative CPU: 22239.14 sec HDFS Read: 54635746333 HDFS Write: 728231 5124 SUCCESS 2014-11-21 13:16:40,382 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 7: Map: 359 Reduce: 27 Cumulative CPU: 14699.06 sec HDFS Read: 26702083597 HDFS Write: 236403 3090 SUCCESS 2014-11-21 13:16:40,382 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 8: Map: 83 Reduce: 6 Cumulative CPU: 2410.03 sec HDFS Read: 5514015601 HDFS Write: 628742985 SUCCESS 2014-11-21 13:16:40,382 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 9: Map: 84 Reduce: 6 Cumulative CPU: 2200.0 sec HDFS Read: 5618486947 HDFS Write: 853325331 S UCCESS 2014-11-21 13:16:40,383 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 10: Map: 365 Reduce: 27 Cumulative CPU: 13274.58 sec HDFS Read: 27143622450 HDFS Write: 29912 35104 SUCCESS 2014-11-21 13:16:40,383 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 11: Map: 959 Reduce: 70 Cumulative CPU: 55145.65 sec HDFS Read: 69728403753 HDFS Write: 43358 51625 SUCCESS 2014-11-21 13:16:40,383 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 12: Map: 172 Reduce: 1 Cumulative CPU: 1561.64 sec HDFS Read: 945050606 HDFS Write: 40445679 SUCCESS 2014-11-21 13:16:40,383 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 13: Map: 260 Reduce: 9 Cumulative CPU: 5965.67 sec HDFS Read: 8639664681 HDFS Write: 38277094 9 SUCCESS 2014-11-21 13:16:40,383 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 14: Map: 212 Reduce: 15 Cumulative CPU: 7573.68 sec HDFS Read: 14849298755 HDFS Write: 109518 4574 SUCCESS 2014-11-21 13:16:40,383 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 15: Map: 16 Reduce: 2 Cumulative CPU: 981.37 sec HDFS Read: 1660614485 HDFS Write: 795259370 SUCCESS 2014-11-21 13:16:40,383 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 16: Map: 27 Reduce: 3 Cumulative CPU: 1357.34 sec HDFS Read: 2991246795 HDFS Write: 238860030 1 SUCCESS 2014-11-21 13:16:40,383 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 17: Map: 70 Reduce: 5 Cumulative CPU: 2138.48 sec HDFS Read: 4573808778 HDFS Write: 290957162 1 SUCCESS 2014-11-21 13:16:40,383 INFO [Thread-2] AbstractBaseAction$StreamDrainer - Job 18: Map: 12 Reduce: 1 Cumulative CPU: 360.1 sec HDFS Read: 853346317 HDFS Write: 705875733 SU CCESS
三、Driver阶段OOM解决方案：
原因找到了，是因为扫描的表的分区太多，上到3千到6千个分区，这样在对计划进行序列化时，仅仅是路径对象Path就会耗去大半Driver，如果Driver设置的heap太小，甚至都会OOM。
解决思路：
1. 减少分区数量，将历史数据做成一张整合表，做成增量数据表，这样分区就很少了。
2. 调大Hive CLI Driver的heap size, 默认是256MB，调节成512MB或者更大。
具体做法是在bin/hive bin/hive-config里可以找到启动CLI的JVM OPTIONS。
这里我们设置
export HADOOP_HEAPSIZE=512
我的解决方法是，双管齐下。
即做成了整合，方便使用，又调节了Hive CLI Driver的heap size，保证线上的运行稳定。
四、总结：
写在结尾，今天只顾折腾这个问题了，遇到这种问题： 一是HiveQL的写法上，尽量少的扫描同一张表，并且尽量少的扫描分区。扫太多，一是job数多，慢，二是耗费网络资源，慢。 二是Hive的参数调优和JVM的参数调优，尽量在每个阶段，选择合适的jvm max heap size来应对OOM的问题。——EOF——原创文章，转载请注明出自：http://blog.csdn.net/oopsoom/article/details/41356251
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