[ https://issues.apache.org/jira/browse/ARROW-2709?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Alex Hagerman updated ARROW-2709: --------------------------------- Summary: [Python] write_to_dataset poor performance when splitting (was: write_to_dataset poor performance when splitting) > [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 > > 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!}} -- This message was sent by Atlassian JIRA (v7.6.3#76005)