[ https://issues.apache.org/jira/browse/ARROW-2709?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
ASF GitHub Bot updated ARROW-2709: ---------------------------------- Labels: parquet pull-request-available (was: parquet) > [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 > > 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)