V Luong created ARROW-6910:
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Summary: pyarrow.parquet.read_table(...) takes up lots of memory
which is not released until program exits
Key: ARROW-6910
URL: https://issues.apache.org/jira/browse/ARROW-6910
Project: Apache Arrow
Issue Type: Bug
Affects Versions: 0.15.0
Reporter: V Luong
I realize that when I read up a lot of Parquet files using
pyarrow.parquet.read_table(...), my program's memory usage becomes very
bloated, although I don't keep the table objects after converting them to
Pandas DFs.
You can try this in an interactive Python shell to reproduce this problem:
```{python}
from pyarrow.parquet import read_table
for path in paths_of_a_bunch_of_big_parquet_files:
read_table(path, use_threads=True, memory_map=False)
# note that I'm not assigning the read_table(...) result to anything, so
I'm not creating any new objects at all
```
After that For loop above, if you view the memory using (e.g. using htop
program), you'll see that the Python program has taken up a lot of memory. That
memory is only released when you exit() from Python.
This problem means that my compute jobs using PyArrow currently need to use
bigger server instances than I think is necessary, which translates to
significant extra cost.
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