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https://issues.apache.org/jira/browse/ARROW-7305?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17000893#comment-17000893
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Bogdan Klichuk commented on ARROW-7305:
---------------------------------------
I have tried this in ubuntu docker and results for 0.14.1 vs 0.15.1 are pretty
interesting.
0.14.1:
{code:java}
Line # Mem usage Increment Line Contents
================================================
4 50.5 MiB 50.5 MiB @profile
5 def do():
6 99.9 MiB 49.4 MiB df = pd.read_csv('50mb.csv')
7 112.1 MiB 12.1 MiB df.to_parquet('test.parquet'){code}
0.15.1:
{code:java}
Line # Mem usage Increment Line Contents
================================================
4 50.5 MiB 50.5 MiB @profile
5 def do():
6 100.0 MiB 49.4 MiB df = pd.read_csv('50mb.csv')
7 401.4 MiB 301.4 MiB df.to_parquet('test.parquet') {code}
which besides the fact that 0.14.1 does indeed behave better on non-mac, also
shows that 0.15.1 requires much more memory to write.
> [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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