pitrou opened a new pull request #8777:
URL: https://github.com/apache/arrow/pull/8777
Instead of applying the same boolean filter to all N columns, first convert
to filter to take indices.
Selecting indices of a linear array is much faster, thanks to avoiding
bit-unpacking on the boolean filter.
Using the Python benchmark setup from ARROW-10569:
```python
import pandas as pd
import pyarrow as pa
import pyarrow.compute as pc
import numpy as np
num_rows = 10_000_000
data = np.random.randn(num_rows)
df = pd.DataFrame({'data{}'.format(i): data
for i in range(100)})
df['key'] = np.random.randint(0, 100, size=num_rows)
rb = pa.record_batch(df)
t = pa.table(df)
```
* before:
```python
>>> %timeit rb.filter(pc.equal(rb[-1], 5))
60 ms ± 509 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
>>> %timeit t.filter(pc.equal(t[-1], 5))
1.22 s ± 2.42 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
* after:
```python
>>> %timeit rb.filter(pc.equal(rb[-1], 5))
59.2 ms ± 583 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
>>> %timeit t.filter(pc.equal(t[-1], 5))
59.3 ms ± 339 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
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