Richard Shin created ARROW-2449:
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Summary: [Python] Efficiently serialize functions containing NumPy
arrays
Key: ARROW-2449
URL: https://issues.apache.org/jira/browse/ARROW-2449
Project: Apache Arrow
Issue Type: Improvement
Affects Versions: 0.9.0
Reporter: Richard Shin
It is my understanding that pyarrow falls back to serializing functions (and
other complex Python objects) using cloudpickle, which means that the contents
of those functions are also serialized using the fallback method, rather than
the efficient method described in
[https://ray-project.github.io/2017/10/15/fast-python-serialization-with-ray-and-arrow.html.]
It would be good to get the benefit of fast zero-copy (de)serialization for
objects like NumPy arrays contained inside functions.
{code}
In [1]: import numpy as np, pyarrow as pa
In [2]: pa.__version__
Out[2]: '0.9.0'
In [3]: arr = np.random.rand(10000)
In [4]: %timeit pa.deserialize(pa.serialize(arr).to_buffer())
The slowest run took 38.29 times longer than the fastest. This could mean that
an intermediate result is being cached.
10000 loops, best of 3: 68.7 µs per loop
In [5]: def arr_f(): return arr
In [6]: %timeit pa.deserialize(pa.serialize(arr_f).to_buffer())
The slowest run took 5.89 times longer than the fastest. This could mean that
an intermediate result is being cached.
1000 loops, best of 3: 539 µs per loop
{code}
For comparison:
{code}
In [7]: %timeit cloudpickle.loads(cloudpickle.dumps(arr))
1000 loops, best of 3: 193 µs per loop
In [8]: %timeit cloudpickle.loads(cloudpickle.dumps(arr_f))
The slowest run took 4.02 times longer than the fastest. This could mean that
an intermediate result is being cached.
1000 loops, best of 3: 429 µs per loop
{code}
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