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Posted to issues@arrow.apache.org by "Joris Van den Bossche (JIRA)" <ji...@apache.org> on 2019/04/29 11:51:00 UTC
[jira] [Commented] (ARROW-2709) [Python] write_to_dataset poor
performance when splitting
[ https://issues.apache.org/jira/browse/ARROW-2709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16829173#comment-16829173 ]
Joris Van den Bossche commented on ARROW-2709:
----------------------------------------------
This seems a duplicate of ARROW-2628, so closing this issue (both are about the (memory) performance issues due to the usage of pandas' groupby functionality). I will update the other issue with some of the discussion in the closed PR.
> [Python] write_to_dataset poor performance when splitting
> ---------------------------------------------------------
>
> Key: ARROW-2709
> URL: https://issues.apache.org/jira/browse/ARROW-2709
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Python
> Reporter: Olaf
> Priority: Critical
> Labels: parquet, pull-request-available
> Fix For: 0.14.0
>
> Time Spent: 2h 50m
> Remaining Estimate: 0h
>
> Hello,
> Posting this from github (master [~wesmckinn] asked for it :) )
> [https://github.com/apache/arrow/issues/2138]
>
> {code:java}
> import pandas as pd
> import numpy as np
> import pyarrow.parquet as pq
> import pyarrow as pa
> idx = pd.date_range('2017-01-01 12:00:00.000', '2017-03-01 12:00:00.000', freq = 'T')
> dataframe = pd.DataFrame({'numeric_col' : np.random.rand(len(idx)),
> 'string_col' : pd.util.testing.rands_array(8,len(idx))},
> index = idx){code}
>
> {code:java}
> df["dt"] = df.index
> df["dt"] = df["dt"].dt.date
> table = pa.Table.from_pandas(df)
> pq.write_to_dataset(table, root_path='dataset_name', partition_cols=['dt'], flavor='spark'){code}
>
> {{this works but is inefficient memory-wise. The arrow table is a copy of the large pandas daframe and quickly saturates the RAM.}}
>
> {{Thanks!}}
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