[jira] [Commented] (ARROW-7043) [Python] pyarrow.csv.read_csv, memory consumed much larger than raw pandas.read_csv

["Antoine Pitrou (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/ARROW-7043?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16965089#comment-16965089 ] 

Antoine Pitrou commented on ARROW-7043:
---------------------------------------

Yes, can you upload the file?

> [Python] 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
>            Priority: Major
>
> 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, which will consume about *1GB memory*,
>  * 
> {code:java}
> import pandas as pd
> df = pd.read_csv(filename){code}
>  * using pyarrow bellow, which will consume about *3.6GB memory,* which really makes me confused.
>  * 
> {code:java}
> import pyarrow
> import pyarrow.csv
> table = pyarrow.csv.read_csv(filename){code}
>  
> problems:
>  * why pyarrow will need so much memory for reading just 1.5GB csv data, it really disappoints me.
>  * and when pyarrow is reading the file, my 8 Core CPU is full used.
>  
> environments:
>  * ubuntu 16
>  * python 3.5, ipython 6.5
>  * pandas, 0.20
>  * pyarrow, 0.15
>  * server 8 core, 16 GB
>  
> great thanks again.
> if needed, I can upload my 1.5GB file later.



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