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https://issues.apache.org/jira/browse/ARROW-2400?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16426749#comment-16426749
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Antoine Pitrou commented on ARROW-2400:
---------------------------------------
The figures above were with gcc 4.9. If I switch to clang 5.0 the slowdown is
much smaller, but still exists:
* with the normal destructor:
{code:shell}
$ python -m timeit -s "import pyarrow as pa; data = [b'xx' for i in
range(10000)]" "pa.array(data, type=pa.binary())"
1000 loops, best of 3: 546 usec per loop
{code}
* with an empty destructor:
{code:shell}
$ python -m timeit -s "import pyarrow as pa; data = [b'xx' for i in
range(10000)]" "pa.array(data, type=pa.binary())"
1000 loops, best of 3: 520 usec per loop
{code}
> [C++] Status destructor is expensive
> ------------------------------------
>
> Key: ARROW-2400
> URL: https://issues.apache.org/jira/browse/ARROW-2400
> Project: Apache Arrow
> Issue Type: Improvement
> Affects Versions: 0.9.0
> Reporter: Antoine Pitrou
> Priority: Major
>
> Let's take the following micro-benchmark (in Python):
> {code:bash}
> $ python -m timeit -s "import pyarrow as pa; data = [b'xx' for i in
> range(10000)]" "pa.array(data, type=pa.binary())"
> 1000 loops, best of 3: 784 usec per loop
> {code}
> If I replace the Status destructor with a no-op:
> {code:c++}
> ~Status() { }
> {code}
> then the benchmark result becomes:
> {code:bash}
> $ python -m timeit -s "import pyarrow as pa; data = [b'xx' for i in
> range(10000)]" "pa.array(data, type=pa.binary())"
> 1000 loops, best of 3: 561 usec per loop
> {code}
> This is almost a 30% win. I get similar results on the conversion benchmarks
> in the benchmark suite.
> I'm unsure about the explanation. In the common case, {{delete _state}}
> should be extremely fast, since the state is NULL. Yet, it seems it adds
> significant overhead. Perhaps because of exception handling?
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