[jira] [Closed] (ARROW-2709) [Python] write_to_dataset poor performance when splitting

["Joris Van den Bossche (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-2709?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Joris Van den Bossche closed ARROW-2709.
----------------------------------------
    Resolution: Duplicate

> [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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