[jira] [Closed] (ARROW-6051) [C++][Python] Parquet float column of NaN writing performance regression from 0.13.0 to 0.14.1

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

     [ https://issues.apache.org/jira/browse/ARROW-6051?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Wes McKinney closed ARROW-6051.
-------------------------------
    Resolution: Not A Problem

So the plot thickens here a little bit

Arrow 0.13.0

{code}
import pyarrow as pa
import pyarrow.parquet as pq
import numpy as np
import pandas as pd
arr = pa.array([np.nan] * 10000000)
t = pa.Table.from_arrays([arr], names=['f0'])

In [7]: arr                                                                                                                                                                            
Out[7]: 
<pyarrow.lib.NullArray object at 0x7f4dd5b23138>
10000000 nulls
{code}

but in 0.14.1/master

{code}
In [2]: paste                                                                                                                                                                          
import pyarrow as pa
import pyarrow.parquet as pq
import numpy as np
import pandas as pd
arr = pa.array([np.nan] * 10000000)

## -- End pasted text --

In [3]: arr                                                                                                                                                                            
Out[3]: 
<pyarrow.lib.DoubleArray object at 0x7f2312461e08>
[
  nan,
  nan,
  nan,
  nan,
  nan,
  nan,
  nan,
  nan,
  nan,
  nan,
  ...
  nan,
  nan,
  nan,
  nan,
  nan,
  nan,
  nan,
  nan,
  nan,
  nan
]
{code}

So that explains the perf difference I saw. I used this code instead to make the table and didn't see a meaningful perf difference

{code}
import pyarrow as pa
import pyarrow.parquet as pq
import numpy as np
import pandas as pd

size = 10_000_000
values = np.random.randn(size)
values[::] = np.nan
arr = pa.array(values)
t = pa.Table.from_arrays([arr], names=['f0'])
{code}

Perhaps some slightly worse performance with NaN values in the hash table:

https://gist.github.com/wesm/436e37e398e61ca29031e43674084957

> [C++][Python] Parquet float column of NaN writing performance regression from 0.13.0 to 0.14.1
> ----------------------------------------------------------------------------------------------
>
>                 Key: ARROW-6051
>                 URL: https://issues.apache.org/jira/browse/ARROW-6051
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: C++
>            Reporter: Wes McKinney
>            Priority: Major
>              Labels: parquet
>         Attachments: perf.svg, perf_before.svg
>
>
> I'm not sure the origin of the regression but I have with
> pyarrow 0.13.0 from conda-forge
> {code}
> import pyarrow as pa
> import pyarrow.parquet as pq
> import numpy as np
> import pandas as pd
> arr = pa.array([np.nan] * 10000000)
> t = pa.Table.from_arrays([arr], names=['f0'])
> %timeit pq.write_table(t, '/home/wesm/tmp/nans.parquet')
> 28.7 ms ± 570 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
> {code}
> but in pyarrow 0.14.1 from conda-forge
> {code}
> %timeit pq.write_table(t, '/home/wesm/tmp/nans.parquet')
> 88.1 ms ± 1.68 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
> {code}
> I'm sorry to say, but this is what happens when benchmark data is not tracked and monitored



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