Olaf created ARROW-2709: --------------------------- Summary: 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 Reporter: Olaf
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)