[jira] [Assigned] (ARROW-13187) [c++][python] Possibly memory not deallocated when reading in CSV

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

     [ https://issues.apache.org/jira/browse/ARROW-13187?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Antoine Pitrou reassigned ARROW-13187:
--------------------------------------

    Assignee: Antoine Pitrou

> [c++][python] Possibly memory not deallocated when reading in CSV
> -----------------------------------------------------------------
>
>                 Key: ARROW-13187
>                 URL: https://issues.apache.org/jira/browse/ARROW-13187
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: C++, Python
>    Affects Versions: 4.0.1
>            Reporter: Simon
>            Assignee: Antoine Pitrou
>            Priority: Minor
>         Attachments: backward-refs.png, forward-refs.png
>
>
> When one reads in a table from CSV in pyarrow version 4.0.1, it appears that the read-in table variable is not freed (or not fast enough). I'm unsure if this is because of pyarrow or because of the way pyarrow memory allocation interacts with Python memory allocation. I encountered it when processing many large CSVs sequentially.
> When I run the following piece of code, the RAM memory usage increases quite rapidly until it runs out of memory.
> {code:python}
> import pyarrow as pa
> import pyarrow.csv
> # Generate some CSV file to read in
> print("Generating CSV")
> with open("example.csv", "w+") as f_out:
>     for i in range(0, 10000000):
>         f_out.write("123456789,abc def ghi jkl\n")
> def read_in_the_csv():
>     table = pa.csv.read_csv("example.csv")
>     print(table)  # Not strictly necessary to replicate bug, table can also be an unused variable
>     # This will free up the memory, as a workaround:
>     # table = table.slice(0, 0)
> # Read in the CSV many times
> print("Reading in a CSV many times")
> for j in range(100000):
>     read_in_the_csv()
> {code}



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