[jira] [Commented] (ARROW-7059) Reading parquet file with many columns is still slow for 0.15.1

["Wes McKinney (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/ARROW-7059?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16967226#comment-16967226 ] 

Wes McKinney commented on ARROW-7059:
-------------------------------------

I can confirm that the performance in 0.15.0/0.15.1 is slower. I'm not able to spend any more time on this issue myself so improvements for the many-columns case will have to come from new volunteers. For what it's worth, more than 1000 columns or so is considered to be an off-label use case for Parquet files 

> Reading parquet file with many columns is still slow for 0.15.1
> ---------------------------------------------------------------
>
>                 Key: ARROW-7059
>                 URL: https://issues.apache.org/jira/browse/ARROW-7059
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: Python
>    Affects Versions: 0.15.1
>         Environment: Linux OS with RHEL 7.7 distribution
> blkcqas037:~$ lscpu
> Architecture:          x86_64
> CPU op-mode(s):        32-bit, 64-bit
> Byte Order:            Little Endian
> CPU(s):                32
> On-line CPU(s) list:   0-31
> Thread(s) per core:    2
> Core(s) per socket:    8
> Socket(s):             2
> NUMA node(s):          2
> Vendor ID:             GenuineIntel
> CPU family:            6
> Model:                 79
> Model name:            Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
>            Reporter: Eric Kisslinger
>            Priority: Major
>              Labels: performance
>
> Reading Parquet files with large number of columns still seems to be very slow in 0.15.1 compared to 0.14.1. I using the same test used in https://issues.apache.org/jira/browse/ARROW-6876 except I set {{use_threads=False}} to make for an apples-to-apples comparison with respect to # of CPUs.
> {{import numpy as np}}
> {{import pyarrow as pa}}
> {{import pyarrow.parquet as pq}}
> {{table = pa.table(\{'c' + str(i): np.random.randn(10) for i in range(10000)})}}
> {{pq.write_table(table, "test_wide.parquet")}}
> {{res = pq.read_table("test_wide.parquet")}}
> {{print(pa.__version__)}}
> use_threads=False
> {{%time res = pq.read_table("test_wide.parquet", use_threads=False)}}
> *In 0.14.1 with use_threads=False:*
> {{0.14.1}}
> {{CPU times: user 515 ms, sys: 9.3 ms, total: 524 ms}}
> {{Wall time: 525 ms}}
> **
> *In 0.15.1 with* *use_threads=False**:*
> {{0.15.1}}
> {{CPU times: user 9.89 s, sys: 37.8 ms, total: 9.93 s}}
> {{Wall time: 9.93 s}}
> {{}} 



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