[
https://issues.apache.org/jira/browse/ARROW-6985?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16960896#comment-16960896
]
Casey commented on ARROW-6985:
------------------------------
So it sounds like this is just a known use case where parquet is not well
suited. For my own knowledge, why exactly is the heap fragmenting? Shouldn't
the heap allocation just grab the same memory that was used in the previous
iteration?
Anyway, happy to have the issue closed as not needed and I'll restructure our
data to work within these limitations.
> [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}
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
