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Out of memory due to hash maps used in map-side aggregation解决办法

在运行一个group by的sql时,抛出以下错误信息:

Task with the most failures(4): 

-----
Task ID:
  task_201411191723_723592_m_000004


URL:
  http://DDS0204.dratio:50030/taskdetails.jsp?jobid=job_201411191723_723592&tipid=task_201411191723_723592_m_000004


Possible error:
  Out of memory due to hash maps used in map-side aggregation.


Solution:
  Currently hive.map.aggr.hash.percentmemory is set to 0.25. Try setting it to a lower value. i.e 'set hive.map.aggr.hash.percentmemory = 0.125;'
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Diagnostic Messages for this Task:


FAILED: Execution Error, return code 2 from org.apache.hadoop.hive.ql.exec.mr.MapRedTask
MapReduce Jobs Launched: 
Job 0: Map: 12  Reduce: 1   Cumulative CPU: 164.04 sec   HDFS Read: 0 HDFS Write: 0 FAIL

Total MapReduce CPU Time Spent: 2 minutes 44 seconds 40 msec


原因是在map端进行了聚合,超过hash map的大小

终极解决办法:set hive.map.aggr=false 或者更改为子sql 或者尝试更改以下参数


备注:

与mapjoin和map aggregate相关的优化参数有:

①.hive.map.aggr 是否关闭关掉map端的aggregation,sethive.map.aggr=false就关闭map端的聚合了

②.hive.map.aggr.hash.min.reduction如果内存Map超过一定大小,就关闭MapAggregation功能,比如set hive.map.aggr.hash.min.reduction=0.5;

③.hive.map.aggr.hash.percentmemory

 当内存的Map大小,占到jsm配置的Map进程的25%(设置sethive.map.aggr.hash.percentmemory = 0.25)的时候(默认是50%),就将这个数据flush到reducer去,以释放内存Map的空间。

④.hive.groupby.skewindata数据据倾斜的时候进行负载均衡,当hive.groupby.skewindata=true,生成的查询计划会有两个 mr job。第一个mr中,每个map的输出结果集合会随机分布到reduce中,reduce做部分聚合操作。第二个mr再根据上个mr的数据结果按照group by key分布到 reduce中完成最终的聚合操作。

参考:

http://dev.bizo.com/2013/02/map-side-aggregations-in-apache-hive.html




posted @ 2015-01-28 16:14  程序优化  阅读(366)  评论(0编辑  收藏  举报