Speed up parseDateTime[InJodaSyntax]Or(Null|Zero) on invalid inputs

[liuneng1994]

This is a re-implementation of #62538 based on #62538 (comment)

parseDateTime previously always threw an exception when there was a parse error. This error was handled in the top-level loop and rethrown (for standard parseDateTime) or suppressed (*OrNull/*OrZero variants). With this PR, we no longer throw internal exceptions, instead faults are communicated back as error code.

In my local measurements, runtime to parse 100k invalid date/time strings (max_threads = 1) goes down from 2.1 sec to 0.0015 sec.

Changelog category (leave one):

• Performance Improvement

Changelog entry (a user-readable short description of the changes that goes to CHANGELOG.md):

Functions parseDateTimeOrNull, parseDateTimeOrZero, parseDateTimeInJodaSyntaxOrNull and parseDateTimeInJodaSyntaxOrZero now run significantly faster (10x - 1000x) when the input contains mostly non-parseable values.

Documentation entry for user-facing changes

• Documentation is written (mandatory for new features)

[rschu1ze]

Thanks, I'll check, but please expect a bit of delay as I am working in the next 1.5 weeks from a remote location with only random access to my laptop.

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[rschu1ze]

@liuneng1994 Thanks a lot. I fixed a few things:

• I added [[nodiscard]] everywhere because it is easy to forget checking the return code. There was actually one place where a return code was not checked.
• I reduced the number of thread in the performance tests (back) from 10 to 1. It is not guaranteed that every machine in CI has 10 cores. Also, performance improvements (because we no longer unwind the stack due to exception handling once per row), will also be visible for single-thead execution.

[rschu1ze]

When the new use case is run in a single thread, a timeout will occur. Is there any way to increase the timeout of this use case? Otherwise, the number of test records needs to be reduced.

The only way to deal with that is to reduce the dataset size... I also did that (1 mio rows --> 100k rows).