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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2017/06/19 11:16:00 UTC
[jira] [Closed] (SPARK-21137) Spark cannot read many small files
(wholeTextFiles)
[ https://issues.apache.org/jira/browse/SPARK-21137?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen closed SPARK-21137.
-----------------------------
> Spark cannot read many small files (wholeTextFiles)
> ---------------------------------------------------
>
> Key: SPARK-21137
> URL: https://issues.apache.org/jira/browse/SPARK-21137
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.2.1
> Reporter: sam
>
> A very common use case in big data is to read a large number of small files. For example the Enron email dataset has 1,227,645 small files.
> When one tries to read this data using Spark one will hit many issues. Firstly, even if the data is small (each file only say 1K) any job can take a very long time (I have a simple job that has been running for 3 hours and has not yet got to the point of starting any tasks, I doubt if it will ever finish).
> It seems all the code in Spark that manages file listing is single threaded and not well optimised. When I hand crank the code and don't use Spark, my job runs much faster.
> Is it possible that I'm missing some configuration option? It seems kinda surprising to me that Spark cannot read Enron data given that it's such a quintessential example.
> So it takes 1 hour to output a line "1,227,645 input paths to process", it then takes another hour to output the same line. Then it outputs a CSV of all the input paths (so creates a text storm).
> Now it's been stuck on the following:
> {code}
> 17/06/19 09:31:07 INFO LzoCodec: Successfully loaded & initialized native-lzo library [hadoop-lzo rev 154f1ef53e2d6ed126b0957d7995e0a610947608]
> {code}
> for 2.5 hours.
> All the app does is read the files, then try to output them again (escape the newlines and write one file per line).
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