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Posted to issues@arrow.apache.org by "Wes McKinney (Jira)" <ji...@apache.org> on 2019/12/20 20:46:00 UTC
[jira] [Comment Edited] (ARROW-7305) [Python] High memory usage
writing pyarrow.Table with large strings to parquet
[ https://issues.apache.org/jira/browse/ARROW-7305?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17001208#comment-17001208 ]
Wes McKinney edited comment on ARROW-7305 at 12/20/19 8:45 PM:
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Thanks for the additional information. Someone (which can be you) will need to investigate. These issues are very time consuming to diagnose
was (Author: wesmckinn):
Thanks for the additional information. Someone will need to investigate. These issues are very time consuming to diagnose
> [Python] High memory usage writing pyarrow.Table with large strings to parquet
> ------------------------------------------------------------------------------
>
> Key: ARROW-7305
> URL: https://issues.apache.org/jira/browse/ARROW-7305
> Project: Apache Arrow
> Issue Type: Task
> Components: Python
> Affects Versions: 0.15.1
> Environment: Mac OSX
> Reporter: Bogdan Klichuk
> Priority: Major
> Labels: parquet
> Attachments: 50mb.csv.gz
>
>
> My case of datasets stored is specific. I have large strings (1-100MB each).
> Let's take for example a single row.
> 43mb.csv is a 1-row CSV with 10 columns. One column a 43mb string.
> When I read this csv with pandas and then dump to parquet, my script consumes 10x of the 43mb.
> With increasing amount of such rows memory footprint overhead diminishes, but I want to focus on this specific case.
> Here's the footprint after running using memory profiler:
> {code:java}
> Line # Mem usage Increment Line Contents
> ================================================
> 4 48.9 MiB 48.9 MiB @profile
> 5 def test():
> 6 143.7 MiB 94.7 MiB data = pd.read_csv('43mb.csv')
> 7 498.6 MiB 354.9 MiB data.to_parquet('out.parquet')
> {code}
> Is this typical for parquet in case of big strings?
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