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From "Eric Chu (JIRA)" <>
Subject [jira] [Commented] (HIVE-6134) Merging small files based on file size only works for CTAS queries
Date Tue, 07 Jan 2014 01:56:52 GMT


Eric Chu commented on HIVE-6134:

Good point [~xuefuz]. For the case of map-only jobs, we are considering to check that the
following are true
1. hconf.getBoolVar(ConfVars.HIVEMERGEMAPFILES)
2. currWork.getReduceWork() == null
3. currWork.getMapWork().getNumMapTasks() > [threshold], where [threshold] could be some
configurable value. We have observed that Hue starts to time out when number of output files
> 2000.

As for Ashutosh's comment, we could increase the limit on the timeout but it won't completely
solve the problem. We're talking about thousands to tens of thousands of output files from
a single query. Even if it doesn't timeout, it'll take a noticeably long time to download
the result, and UX will be horrible. 

> Merging small files based on file size only works for CTAS queries
> ------------------------------------------------------------------
>                 Key: HIVE-6134
>                 URL:
>             Project: Hive
>          Issue Type: Bug
>    Affects Versions: 0.8.0, 0.10.0, 0.11.0, 0.12.0
>            Reporter: Eric Chu
> According to the documentation, if we set hive.merge.mapfiles to true, Hive will launch
an additional MR job to merge the small output files at the end of a map-only job when the
average output file size is smaller than hive.merge.smallfiles.avgsize. Similarly, by setting
hive.merge.mapredfiles to true, Hive will merge the output files of a map-reduce job. 
> My expectation is that this is true for all MR queries. However, my observation is that
are only used if ((ctx.getMvTask() != null) && (!ctx.getMvTask().isEmpty())). So,
for a regular SELECT query that doesn't have move tasks, these properties are not used.
> Is my understanding correct and if so, what's the reasoning behind the logic of not supporting
this for regular SELECT queries? It seems to me that this should be supported for regular
SELECT queries as well. One scenario where this hits us hard is when users try to download
the result in HUE, and HUE times out b/c there are thousands of output files. The workaround
is to re-run the query as CTAS, but it's a significant time sink.

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