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https://issues.apache.org/jira/browse/ARROW-2709?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Alex Hagerman updated ARROW-2709:
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Component/s: 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
>
> 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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