wesm commented on pull request #7442:
URL: https://github.com/apache/arrow/pull/7442#issuecomment-644513681
To show some simple numbers to show the perf before and after in Python,
this example has a high selectivity (all but one value selected) and low
selectivity filter (only 1% of values selected):
```
import numpy as np
import pandas as pd
import pyarrow as pa
import pyarrow.compute as pc
string_values = pa.array([pd.util.testing.rands(16)
for i in range(10000)] * 100)
double_values = pa.array(np.random.randn(1000000))
all_but_one = np.ones(len(string_values), dtype=bool)
all_but_one[500000] = False
only_1pct = np.array(np.random.binomial(1, 0.01, size=1000000), dtype=bool)
```
before:
```
In [4]: timeit pc.filter(double_values, only_1pct)
2.01 ms ± 7.89 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [5]: timeit pc.filter(double_values, all_but_one)
5.74 ms ± 17.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [6]: timeit pc.filter(string_values, only_1pct)
2.21 ms ± 6.87 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [7]: timeit pc.filter(string_values, all_but_one)
11.4 ms ± 142 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
after
```
In [29]: timeit pc.filter(double_values, only_1pct)
1.43 ms ± 3.79 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [30]: timeit pc.filter(double_values, all_but_one)
1.81 ms ± 20.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [31]: timeit pc.filter(string_values, only_1pct)
1.57 ms ± 4.06 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [32]: timeit pc.filter(string_values, all_but_one)
6.66 ms ± 39.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
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