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https://issues.apache.org/jira/browse/ARROW-13611?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Weston Pace updated ARROW-13611:
--------------------------------
Description:
I have a simple test case where I scan the batches of a 4GB dataset and print
out the currently used memory:
{code:python}
import pyarrow as pa
import pyarrow.dataset as ds
dataset = ds.dataset('/home/pace/dev/data/dataset/csv/5_big', format='csv')
num_rows = 0
for batch in dataset.to_batches():
print(pa.total_allocated_bytes())
num_rows += batch.num_rows
print(num_rows)
{code}
In pyarrow 3.0.0 this consumes just over 5MB. In pyarrow 4.0.0 and 5.0.0 this
consumes multiple GB of RAM.
was:
At the moment I'm not sure if the issue is in the C++ layer or the python
layer. I have a simple test case where I scan the batches of a 4GB dataset and
print out the currently used memory:
{code:python}
import pyarrow as pa
import pyarrow.dataset as ds
dataset = ds.dataset('/home/pace/dev/data/dataset/csv/5_big', format='csv')
num_rows = 0
for batch in dataset.to_batches():
print(pa.total_allocated_bytes())
num_rows += batch.num_rows
print(num_rows)
{code}
In pyarrow 3.0.0 this consumes just over 5MB. In pyarrow 4.0.0 and 5.0.0 this
consumes multiple GB of RAM.
> [C++] Scanning datasets does not enforce back pressure
> ------------------------------------------------------
>
> Key: ARROW-13611
> URL: https://issues.apache.org/jira/browse/ARROW-13611
> Project: Apache Arrow
> Issue Type: Bug
> Components: C++
> Affects Versions: 4.0.0, 5.0.0, 4.0.1
> Reporter: Weston Pace
> Priority: Major
> Fix For: 6.0.0
>
>
> I have a simple test case where I scan the batches of a 4GB dataset and print
> out the currently used memory:
> {code:python}
> import pyarrow as pa
> import pyarrow.dataset as ds
> dataset = ds.dataset('/home/pace/dev/data/dataset/csv/5_big', format='csv')
> num_rows = 0
> for batch in dataset.to_batches():
> print(pa.total_allocated_bytes())
> num_rows += batch.num_rows
> print(num_rows)
> {code}
> In pyarrow 3.0.0 this consumes just over 5MB. In pyarrow 4.0.0 and 5.0.0
> this consumes multiple GB of RAM.
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