Olaf created ARROW-2709:
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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!}}
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