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https://issues.apache.org/jira/browse/ARROW-6985?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16959779#comment-16959779
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Casey commented on ARROW-6985:
------------------------------
Okay looks like the wide matrix case was explained in the ticket you linked. As
for the loop slowdown I'm seeing it gradually increase over time depending on
the data's shape.
Below are my results for a wide matrix, the wide matrix transposed, and the
matrix unraveled as a column. On the number of loops I tried this with, I see
about 2x in the wide and wide transpose cases though the trend of the line
indicates it will continue growing. Is this expected?
!image-2019-10-25-14-52-46-165.png|width=479,height=273!
!image-2019-10-25-14-53-37-623.png|width=483,height=253!
!image-2019-10-25-14-54-32-583.png|width=462,height=246!
> [Python] Steadily increasing time to load file using read_parquet
> -----------------------------------------------------------------
>
> Key: ARROW-6985
> URL: https://issues.apache.org/jira/browse/ARROW-6985
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 0.13.0, 0.14.0, 0.15.0
> Reporter: Casey
> Priority: Minor
> Attachments: image-2019-10-25-14-52-46-165.png,
> image-2019-10-25-14-53-37-623.png, image-2019-10-25-14-54-32-583.png
>
>
> I've noticed that reading from parquet using pandas read_parquet function is
> taking steadily longer with each invocation. I've seen the other ticket about
> memory usage but I'm seeing no memory impact just steadily increasing read
> time until I restart the python session.
> Below is some code to reproduce my results. I notice it's particularly bad on
> wide matrices, especially using pyarrow==0.15.0
> {code:python}
> import pyarrow.parquet as pq
> import pyarrow as pa
> import pandas as pd
> import os
> import numpy as np
> import time
> file = "skinny_matrix.pq"
> if not os.path.isfile(file):
> mat = np.zeros((6000, 26000))
> mat.ravel()[::100] = np.random.randn(60 * 26000)
> df = pd.DataFrame(mat.T)
> table = pa.Table.from_pandas(df)
> pq.write_table(table, file)
> n_timings = 50
> timings = np.empty(n_timings)
> for i in range(n_timings):
> start = time.time()
> new_df = pd.read_parquet(file)
> end = time.time()
> timings[i] = end - start
> {code}
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