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https://issues.apache.org/jira/browse/SPARK-21137?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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sam updated SPARK-21137:
------------------------
Description:
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).
So I've provided full reproduce steps here (including code and cluster setup)
https://github.com/samthebest/scenron, scroll down to "Bug In Spark". You can
easily just clone, and follow the README to reproduce exactly!
was:
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).
> 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).
> So I've provided full reproduce steps here (including code and cluster setup)
> https://github.com/samthebest/scenron, scroll down to "Bug In Spark". You can
> easily just clone, and follow the README to reproduce exactly!
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