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From "taotao li (Jira)" <j...@apache.org>
Subject [jira] [Created] (ARROW-7043) pyarrow.csv.read_csv, memory consumed much larger than raw pandas.read_csv
Date Fri, 01 Nov 2019 09:44:00 GMT
taotao li created ARROW-7043:
--------------------------------

             Summary: pyarrow.csv.read_csv, memory consumed much larger than raw pandas.read_csv
                 Key: ARROW-7043
                 URL: https://issues.apache.org/jira/browse/ARROW-7043
             Project: Apache Arrow
          Issue Type: Test
          Components: Python
    Affects Versions: 0.15.0
            Reporter: taotao li


Hi, thanks great for building Arrow firstly, I find this project from wes's post : [https://wesmckinney.com/blog/apache-arrow-pandas-internals/]

his ambition on building arrow for fixing problems in pandas really attract my eyes.

bellow is my problems:

background:
 * Our team's analytic work deeply rely on pandas, we often read large csv files into memory
and do kinds of analytic work.
 * We have faced problems which mentioned in wes's post, espcially `pandas rule of thumb:
have 5 to 10 times as much RAM as the size of your dataset`
 * We are looking for some technics which can help us on load our csv(or other format, like
msgpack, parquet, or something else), using as little as memory.

 

experiment:
 * luckily I find arrow, and I did a simple test.
 * input file: a 1.5GB csv file, around 6 million records, 15 columns;
 * using pandas bellow:
 * 
{code:java}
import pandas as pd{code}

 * 
 * 
{code:java}
 {code}



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