Unlike on other OS, on Windows there is no wildcard expansion/globbing
by the shell. Instead the application has to handle this. Therefore I
used the `glob` package to handle wildcards on Windows.
I also had to make the parent directory check more strict due to the
glob package resolving `..` in the middle of the path as well.
This closes#2695.
Export `SourcemapVisualizer` from codegen, it will be used oxc and
rolldown sourcemap test, so it support multiply source print, it will
using sourcemap `sourcesContent` as original source.
Add NodeJS parser to benchmarks.
Previous attempt #2724 did not work due CodSpeed producing very
inaccurate results (https://github.com/CodSpeedHQ/action/issues/96).
This version runs the actual benchmarks without CodSpeed's
instrumentation. Then another faux-benchmark runs within Codspeed's
instrumented action and just performs meaningless calculations in a loop
for as long as is required to take same amount of time as the original
uninstrumented benchmarks took.
It's unfortunate that we therefore don't get flame graphs on CodSpeed,
but this seems to be the best we can do for now.
This PR merges the previous confusing features `serde` and `wasm` into a
single `serialize` feature.
We'll eventually do serialize + type information for both wasm and napi
targets.
`oxc_macros` is removed from `oxc_ast`'s dependency because it requires
`syn` and friends, which goes against our policy ["Third-party
dependencies should be
minimal."](https://oxc-project.github.io/docs/contribute/rules.html#development-policy)
Closes#2641.
Also added `tsify` attribute to the `SerAttrs` derive macro, so `#[cfg_attr(feature = "wasm", tsify(...))]` can also be reduced to `#[tsify(...)]`.
I should've done a better job at selecting features. Every feature
requires more than just code to get it right.
linting by codeowners' files sounds good on paper but actually not that
useful.
The runtime performance gains does not out weight the compilation speed from
building the custom allocators, which takes about a minute to build on
slower machines.
Consume multi-line comments faster.
* Initially search for `*/`, `\r`, `\n` or `0xE2` (first byte of
irregular line breaks).
* Once a line break is found, switch to faster search which only looks
for `*/`, as it's not relevant whether there are more line breaks or
not.
Using `memchr` for the 2nd simpler search, as it's efficient for a
search with only one "needle".
Initializing `memchr::memmem::Finder` is fairly expensive, and tried
numerous ways to handle it. This is most performant way I could find.
Any ideas how to avoid re-creating it for each Lexer pass? (it can't be
a `static` as `Finder::new` is not a const function, and `lazy_static!`
is too costly)
This PR re-implements lexing identifiers with a fast path for the most common case - identifiers which are pure ASCII characters, using the new `Source` / `SourcePosition` APIs.
Lexing identifiers is a hot path, and accounts for the majority of the time the Lexer spends. The performance bump from this change is (if I do say so myself!) quite decent.
I've spent a lot of time tuning the implementation, which gained a further 10-15% on the Lexer benchmarks compared to my first, simpler attempt. Some of the design decisions, if they look odd, are likely motivated by gains in performance.
### Techniques
This implementation uses a few different strategies for performance:
* Search byte-by-byte, not char-by-char.
* Process batches of 32 bytes at a time to reduce bounds checks.
* Mark uncommon paths `#[cold]`.
### Structure
The implementation is built in 3 layers:
1. ASCII characters only.
2. ASCII and Unicode characters.
3. `\` escape sequences (and all the above).
`identifier_name_handler` starts at the top layer, and is optimized for consuming ASCII as fast as possible. Each "layer" is considered more uncommon than the previous, and dropping down a layer is a de-opt.
I'm assuming that 95%+ of JavaScript code does not include either Unicode characters or escapes in identifiers, so the speed of the fast path is prioritised.
That said, once a Unicode character is encountered, the next layer does expect to find further Unicode characters, rather than de-opting over and over again. If an identifier *starts* with a Unicode character, it enters the code straight on the 2nd layer, so is not penalised by going through a `#[cold]` boundary. Lexing Unicode is never going to be as fast as ASCII, but still I felt it was important not to penalise it unnecessarily, so as not to be Anglo-centric.
### ASCII search macro
The main ASCII search is implemented as a macro. I found that, for reasons I don't understand, it's significantly faster to have all the code in a single function, even compared to multiple functions marked `#[inline]` or `#[inline(always)]`. The fastest implementation also requires some code to be repeated twice, which is nicer to do with a macro.
This macro, and the `ByteMatchTable` types that go with it, are designed to be re-usable. Next step will be to apply them for whitespace and strings, which should be fairly simple.
Searching in batches of 32 bytes is also designed to be forward-compatible with SIMD.
### Bye bye `AutoCow`
`AutoCow` is removed. Instead, a string-builder is only created if it's needed, when a `\` escape is first encountered. The string builder is also more efficient than `AutoCow` was, as it copies bytes in chunks, rather than 1-by-1.
This won't make much difference for identifiers, as escapes are so rare anyway, but this same technique can be used for strings, where they're more common.
closes#1803
This string is currently unsafe, but I want to get miri working before
introducing more changes.
I want to make a progress from memory leak to unsafe then to safety.
It's harder to do the steps in one go.
