blackblitz opened a new issue, #37766:
URL: https://github.com/apache/arrow/issues/37766
### Describe the bug, including details regarding any error messages,
version, and platform.
I created a random array and wrote it repeatedly to an Arrow IPC file so
that the whole array was too large to fit in RAM. Then, I read it by memory
mapping. I could `slice` it without any problem, but when I tried to access the
rows based on an arbitrary list of indices by using `take`, the RAM usage went
up until the computer hung. The code is as follows (in which the array length
and the number of writes may be adjusted according to your disk space and RAM
size):
```python
import numpy as np
import pyarrow as pa
from pyarrow import feather
rng = np.random.default_rng(1337)
data = rng.normal(size=(1000000,))
table = pa.table({'data': data})
sink = pa.output_stream('data.feather')
schema = pa.schema([('data', pa.float64())])
with pa.ipc.new_file(sink, schema) as writer:
for i in range(1000):
writer.write_table(table)
table = feather.read_table('data.feather', memory_map=True)
print(table.take([0]))
```
### Component(s)
Python
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