[ARROW-2709] [Python] write_to_dataset poor performance when splitting

• Type: Improvement
• Status: Closed
• Priority: Critical
• Resolution: Duplicate
• Fix Version/s: 0.14.0
• Component/s: Python
• Labels: parquet pull-request-available
• External issue URL: https://github.com/apache/arrow/issues/19099
• Assignee: Unassigned
• Reporter: Olaf
• Created: 13/Jun/18 21:15
• Updated: 11/Jan/23 07:22
• Resolved: 29/Apr/19 11:51

Description

Hello,

Posting this from github (master wesmckinn asked for it  )

https://github.com/apache/arrow/issues/2138

 

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)

 

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')

 

this works but is inefficient memory-wise. The arrow table is a copy of the large pandas daframe and quickly saturates the RAM.

 

Thanks!

Issue Links

• is duplicated by: ARROW-2628 [Python] parquet.write_to_dataset is memory-hungry on large DataFrames
• links to: GitHub Pull Request #3344