1//! [![github]](https://github.com/dtolnay/unicode-ident) [![crates-io]](https://crates.io/crates/unicode-ident) [![docs-rs]](https://docs.rs/unicode-ident)
2//!
3//! [github]: https://img.shields.io/badge/github-8da0cb?style=for-the-badge&labelColor=555555&logo=github
4//! [crates-io]: https://img.shields.io/badge/crates.io-fc8d62?style=for-the-badge&labelColor=555555&logo=rust
5//! [docs-rs]: https://img.shields.io/badge/docs.rs-66c2a5?style=for-the-badge&labelColor=555555&logo=docs.rs
6//!
7//! <br>
8//!
9//! Implementation of [Unicode Standard Annex #31][tr31] for determining which
10//! `char` values are valid in programming language identifiers.
11//!
12//! [tr31]: https://www.unicode.org/reports/tr31/
13//!
14//! This crate is a better optimized implementation of the older `unicode-xid`
15//! crate. This crate uses less static storage, and is able to classify both
16//! ASCII and non-ASCII codepoints with better performance, 2–10×
17//! faster than `unicode-xid`.
18//!
19//! <br>
20//!
21//! ## Comparison of performance
22//!
23//! The following table shows a comparison between five Unicode identifier
24//! implementations.
25//!
26//! - `unicode-ident` is this crate;
27//! - [`unicode-xid`] is a widely used crate run by the "unicode-rs" org;
28//! - `ucd-trie` and `fst` are two data structures supported by the
29//! [`ucd-generate`] tool;
30//! - [`roaring`] is a Rust implementation of Roaring bitmap.
31//!
32//! The *static storage* column shows the total size of `static` tables that the
33//! crate bakes into your binary, measured in 1000s of bytes.
34//!
35//! The remaining columns show the **cost per call** to evaluate whether a
36//! single `char` has the XID\_Start or XID\_Continue Unicode property,
37//! comparing across different ratios of ASCII to non-ASCII codepoints in the
38//! input data.
39//!
40//! [`unicode-xid`]: https://github.com/unicode-rs/unicode-xid
41//! [`ucd-generate`]: https://github.com/BurntSushi/ucd-generate
42//! [`roaring`]: https://github.com/RoaringBitmap/roaring-rs
43//!
44//! | | static storage | 0% nonascii | 1% | 10% | 100% nonascii |
45//! |---|---|---|---|---|---|
46//! | **`unicode-ident`** | 10.1 K | 0.96 ns | 0.95 ns | 1.09 ns | 1.55 ns |
47//! | **`unicode-xid`** | 11.5 K | 1.88 ns | 2.14 ns | 3.48 ns | 15.63 ns |
48//! | **`ucd-trie`** | 10.2 K | 1.29 ns | 1.28 ns | 1.36 ns | 2.15 ns |
49//! | **`fst`** | 139 K | 55.1 ns | 54.9 ns | 53.2 ns | 28.5 ns |
50//! | **`roaring`** | 66.1 K | 2.78 ns | 3.09 ns | 3.37 ns | 4.70 ns |
51//!
52//! Source code for the benchmark is provided in the *bench* directory of this
53//! repo and may be repeated by running `cargo criterion`.
54//!
55//! <br>
56//!
57//! ## Comparison of data structures
58//!
59//! #### unicode-xid
60//!
61//! They use a sorted array of character ranges, and do a binary search to look
62//! up whether a given character lands inside one of those ranges.
63//!
64//! ```rust
65//! # const _: &str = stringify! {
66//! static XID_Continue_table: [(char, char); 763] = [
67//! ('\u{30}', '\u{39}'), // 0-9
68//! ('\u{41}', '\u{5a}'), // A-Z
69//! # "
70//! …
71//! # "
72//! ('\u{e0100}', '\u{e01ef}'),
73//! ];
74//! # };
75//! ```
76//!
77//! The static storage used by this data structure scales with the number of
78//! contiguous ranges of identifier codepoints in Unicode. Every table entry
79//! consumes 8 bytes, because it consists of a pair of 32-bit `char` values.
80//!
81//! In some ranges of the Unicode codepoint space, this is quite a sparse
82//! representation – there are some ranges where tens of thousands of
83//! adjacent codepoints are all valid identifier characters. In other places,
84//! the representation is quite inefficient. A characater like `µ` (U+00B5)
85//! which is surrounded by non-identifier codepoints consumes 64 bits in the
86//! table, while it would be just 1 bit in a dense bitmap.
87//!
88//! On a system with 64-byte cache lines, binary searching the table touches 7
89//! cache lines on average. Each cache line fits only 8 table entries.
90//! Additionally, the branching performed during the binary search is probably
91//! mostly unpredictable to the branch predictor.
92//!
93//! Overall, the crate ends up being about 10× slower on non-ASCII input
94//! compared to the fastest crate.
95//!
96//! A potential improvement would be to pack the table entries more compactly.
97//! Rust's `char` type is a 21-bit integer padded to 32 bits, which means every
98//! table entry is holding 22 bits of wasted space, adding up to 3.9 K. They
99//! could instead fit every table entry into 6 bytes, leaving out some of the
100//! padding, for a 25% improvement in space used. With some cleverness it may be
101//! possible to fit in 5 bytes or even 4 bytes by storing a low char and an
102//! extent, instead of low char and high char. I don't expect that performance
103//! would improve much but this could be the most efficient for space across all
104//! the libraries, needing only about 7 K to store.
105//!
106//! #### ucd-trie
107//!
108//! Their data structure is a compressed trie set specifically tailored for
109//! Unicode codepoints. The design is credited to Raph Levien in
110//! [rust-lang/rust#33098].
111//!
112//! [rust-lang/rust#33098]: https://github.com/rust-lang/rust/pull/33098
113//!
114//! ```rust
115//! pub struct TrieSet {
116//! tree1_level1: &'static [u64; 32],
117//! tree2_level1: &'static [u8; 992],
118//! tree2_level2: &'static [u64],
119//! tree3_level1: &'static [u8; 256],
120//! tree3_level2: &'static [u8],
121//! tree3_level3: &'static [u64],
122//! }
123//! ```
124//!
125//! It represents codepoint sets using a trie to achieve prefix compression. The
126//! final states of the trie are embedded in leaves or "chunks", where each
127//! chunk is a 64-bit integer. Each bit position of the integer corresponds to
128//! whether a particular codepoint is in the set or not. These chunks are not
129//! just a compact representation of the final states of the trie, but are also
130//! a form of suffix compression. In particular, if multiple ranges of 64
131//! contiguous codepoints have the same Unicode properties, then they all map to
132//! the same chunk in the final level of the trie.
133//!
134//! Being tailored for Unicode codepoints, this trie is partitioned into three
135//! disjoint sets: tree1, tree2, tree3. The first set corresponds to codepoints
136//! \[0, 0x800), the second \[0x800, 0x10000) and the third \[0x10000,
137//! 0x110000). These partitions conveniently correspond to the space of 1 or 2
138//! byte UTF-8 encoded codepoints, 3 byte UTF-8 encoded codepoints and 4 byte
139//! UTF-8 encoded codepoints, respectively.
140//!
141//! Lookups in this data structure are significantly more efficient than binary
142//! search. A lookup touches either 1, 2, or 3 cache lines based on which of the
143//! trie partitions is being accessed.
144//!
145//! One possible performance improvement would be for this crate to expose a way
146//! to query based on a UTF-8 encoded string, returning the Unicode property
147//! corresponding to the first character in the string. Without such an API, the
148//! caller is required to tokenize their UTF-8 encoded input data into `char`,
149//! hand the `char` into `ucd-trie`, only for `ucd-trie` to undo that work by
150//! converting back into the variable-length representation for trie traversal.
151//!
152//! #### fst
153//!
154//! Uses a [finite state transducer][fst]. This representation is built into
155//! [ucd-generate] but I am not aware of any advantage over the `ucd-trie`
156//! representation. In particular `ucd-trie` is optimized for storing Unicode
157//! properties while `fst` is not.
158//!
159//! [fst]: https://github.com/BurntSushi/fst
160//! [ucd-generate]: https://github.com/BurntSushi/ucd-generate
161//!
162//! As far as I can tell, the main thing that causes `fst` to have large size
163//! and slow lookups for this use case relative to `ucd-trie` is that it does
164//! not specialize for the fact that only 21 of the 32 bits in a `char` are
165//! meaningful. There are some dense arrays in the structure with large ranges
166//! that could never possibly be used.
167//!
168//! #### roaring
169//!
170//! This crate is a pure-Rust implementation of [Roaring Bitmap], a data
171//! structure designed for storing sets of 32-bit unsigned integers.
172//!
173//! [Roaring Bitmap]: https://roaringbitmap.org/about/
174//!
175//! Roaring bitmaps are compressed bitmaps which tend to outperform conventional
176//! compressed bitmaps such as WAH, EWAH or Concise. In some instances, they can
177//! be hundreds of times faster and they often offer significantly better
178//! compression.
179//!
180//! In this use case the performance was reasonably competitive but still
181//! substantially slower than the Unicode-optimized crates. Meanwhile the
182//! compression was significantly worse, requiring 6× as much storage for
183//! the data structure.
184//!
185//! I also benchmarked the [`croaring`] crate which is an FFI wrapper around the
186//! C reference implementation of Roaring Bitmap. This crate was consistently
187//! about 15% slower than pure-Rust `roaring`, which could just be FFI overhead.
188//! I did not investigate further.
189//!
190//! [`croaring`]: https://crates.io/crates/croaring
191//!
192//! #### unicode-ident
193//!
194//! This crate is most similar to the `ucd-trie` library, in that it's based on
195//! bitmaps stored in the leafs of a trie representation, achieving both prefix
196//! compression and suffix compression.
197//!
198//! The key differences are:
199//!
200//! - Uses a single 2-level trie, rather than 3 disjoint partitions of different
201//! depth each.
202//! - Uses significantly larger chunks: 512 bits rather than 64 bits.
203//! - Compresses the XID\_Start and XID\_Continue properties together
204//! simultaneously, rather than duplicating identical trie leaf chunks across
205//! the two.
206//!
207//! The following diagram show the XID\_Start and XID\_Continue Unicode boolean
208//! properties in uncompressed form, in row-major order:
209//!
210//! <table>
211//! <tr><th>XID_Start</th><th>XID_Continue</th></tr>
212//! <tr>
213//! <td><img alt="XID_Start bitmap" width="256" src="https://user-images.githubusercontent.com/1940490/168647353-c6eeb922-afec-49b2-9ef5-c03e9d1e0760.png"></td>
214//! <td><img alt="XID_Continue bitmap" width="256" src="https://user-images.githubusercontent.com/1940490/168647367-f447cca7-2362-4d7d-8cd7-d21c011d329b.png"></td>
215//! </tr>
216//! </table>
217//!
218//! Uncompressed, these would take 140 K to store, which is beyond what would be
219//! reasonable. However, as you can see there is a large degree of similarity
220//! between the two bitmaps and across the rows, which lends well to
221//! compression.
222//!
223//! This crate stores one 512-bit "row" of the above bitmaps in the leaf level
224//! of a trie, and a single additional level to index into the leafs. It turns
225//! out there are 124 unique 512-bit chunks across the two bitmaps so 7 bits are
226//! sufficient to index them.
227//!
228//! The chunk size of 512 bits is selected as the size that minimizes the total
229//! size of the data structure. A smaller chunk, like 256 or 128 bits, would
230//! achieve better deduplication but require a larger index. A larger chunk
231//! would increase redundancy in the leaf bitmaps. 512 bit chunks are the
232//! optimum for total size of the index plus leaf bitmaps.
233//!
234//! In fact since there are only 124 unique chunks, we can use an 8-bit index
235//! with a spare bit to index at the half-chunk level. This achieves an
236//! additional 8.5% compression by eliminating redundancies between the second
237//! half of any chunk and the first half of any other chunk. Note that this is
238//! not the same as using chunks which are half the size, because it does not
239//! necessitate raising the size of the trie's first level.
240//!
241//! In contrast to binary search or the `ucd-trie` crate, performing lookups in
242//! this data structure is straight-line code with no need for branching.
243
244#![no_std]
245#![doc(html_root_url = "https://docs.rs/unicode-ident/1.0.12")]
246#![allow(clippy::doc_markdown, clippy::must_use_candidate)]
247
248#[rustfmt::skip]
249mod tables;
250
251use crate::tables::{ASCII_CONTINUE, ASCII_START, CHUNK, LEAF, TRIE_CONTINUE, TRIE_START};
252
253pub fn is_xid_start(ch: char) -> bool {
254 if ch.is_ascii() {
255 return ASCII_START.0[ch as usize];
256 }
257 let chunk = *TRIE_START.0.get(ch as usize / 8 / CHUNK).unwrap_or(&0);
258 let offset = chunk as usize * CHUNK / 2 + ch as usize / 8 % CHUNK;
259 unsafe { LEAF.0.get_unchecked(offset) }.wrapping_shr(ch as u32 % 8) & 1 != 0
260}
261
262pub fn is_xid_continue(ch: char) -> bool {
263 if ch.is_ascii() {
264 return ASCII_CONTINUE.0[ch as usize];
265 }
266 let chunk = *TRIE_CONTINUE.0.get(ch as usize / 8 / CHUNK).unwrap_or(&0);
267 let offset = chunk as usize * CHUNK / 2 + ch as usize / 8 % CHUNK;
268 unsafe { LEAF.0.get_unchecked(offset) }.wrapping_shr(ch as u32 % 8) & 1 != 0
269}
270