lexindex

Compact, immutable string↔id indexes for huge catalogs, with a Rust core and Python bindings. Build once over a set of strings (entity names, document keys, vocabulary terms, cluster labels); query many times. Pairs naturally with betula-cluster — map string ids to cluster ids and back — but stands on its own.

Three complementary, build-once / query-many structures — pick by what you need to ask:

• StringIndex — an ordered index backed by a finite-state transducer (fst). Exact string → id and id → string, plus prefix, range, predecessor / successor (nearest key ≤ / ≥ a query), fuzzy (bounded Levenshtein edit distance), subsequence, and lazy full iteration — all driven by automata over the FST, never a full scan — in a compressed, serialisable, memory-mappable form. The only structure here that answers ordered and typo-tolerant queries. Use it for autocomplete, fuzzy search, browse, and ordered scans of a large catalog.
• CompactHashIndex — the smallest string → dense id map: a minimal perfect hash (ptr_hash) plus a small fingerprint per key, storing no keys at all. 1.30 bytes/key on real dictionary words — 2.3× smaller than marisa-trie and below every trie benchmarked (see Benchmarks) — at the cost of probabilistic membership (a tunable 256^-k false-positive rate) and no reverse lookup. Use it when a fixed vocabulary's footprint is paramount and rare false positives are acceptable.
• PerfectHashIndex — a minimal-perfect-hash dictionary with verified membership (id) and reverse lookup (key); the arena stores full keys, so it is exact but larger. For a known-closed vocabulary, id_unchecked skips the membership comparison and is faster than std::HashMap. Use it as a fixed-vocabulary token↔id map on a hot path when you need exact membership and id → key.

All three assign dense ids in [0, n) and serialise to a flat blob (save / load, or zero-copy load_mmap) — build once, persist, then reload and query many times. All are immutable after building. The mph feature (on by default) provides the two hash indexes; --no-default-features is fst-only.

Python

pip install lexindex

from lexindex import CompactHashIndex, PerfectHashIndex, StringIndex

idx = StringIndex(["apple", "apricot", "banana", "cherry"])
idx.id("banana")             # 2  (sorted rank)
idx.key(0)                   # "apple"  — reconstructed from the FST, no stored reverse map
idx.prefix("ap")             # [("apple", 0), ("apricot", 1)]
idx.fuzzy("aple", 1)         # [("apple", 0)]  — typo-tolerant
idx.successor("ba")          # ("banana", 2)   — nearest key >= query
idx.predecessor("ba")        # ("apricot", 1)  — nearest key <= query
list(idx)                    # [("apple", 0), ...]  — lazy iteration in sorted order
idx.ids_of(["apple", "x"])   # [0, None]  — batched: one FFI call, not one per key
idx.save("catalog.bix")      # persist; StringIndex.load("catalog.bix") reloads it

c = CompactHashIndex(["GET", "POST", "PUT", "DELETE"])  # smallest string->id (~1.3 B/key at scale)
c.id("POST")                 # dense id in [0, n); probabilistic membership, no id->key
c.id_unchecked("POST")       # fastest lookup for a known-closed vocabulary

d = PerfectHashIndex(["GET", "POST", "PUT", "DELETE"])
d.id("POST")                 # dense id in [0, n); membership verified, returns None if absent
d.key(d.id("POST"))          # "POST"  — exact reverse lookup (keys stored)

No runtime dependencies; a single abi3 wheel covers CPython 3.11+. See examples/quickstart.py for all three indexes end to end, and the documentation site.

Pairs with betula-cluster

lexindex owns the string id ↔ dense id mapping; betula-cluster clusters the numeric rows. Use the lexindex dense id as the embedding-matrix row index and you can go both ways — string id → cluster and cluster → string ids:

idx = PerfectHashIndex(doc_ids)                  # string id <-> dense [0, n) id
matrix[idx.id(doc_id)] = embedding[doc_id]       # row index == lexindex id
labels = betula_cluster.fit_predict(matrix, n_clusters=k)
cluster = labels[idx.id("doc-00042")]            # string id -> cluster
members = [idx.key(int(r)) for r in (labels == cluster).nonzero()[0]]  # cluster -> string ids

Runnable: examples/bridge_clustering.py.

Rust

[dependencies]
lexindex = { git = "https://github.com/ilgrad/lexindex" }
# fst-only (drop the ptr_hash dependency):
# lexindex = { git = "...", default-features = false }

Usage

use lexindex::StringIndex;

let idx = StringIndex::build(["apple", "apricot", "banana", "cherry"])?;

assert_eq!(idx.id("banana"), Some(2));     // string → id (sorted rank)
assert_eq!(idx.key(0).as_deref(), Some("apple")); // id → string
assert!(idx.contains("cherry"));

// prefix / range iteration, lexicographically ordered
let fruit: Vec<_> = idx.prefix("ap").into_iter().map(|(k, _)| k).collect();
assert_eq!(fruit, ["apple", "apricot"]);

// typo-tolerant fuzzy lookup (Levenshtein edit distance ≤ 1) and subsequence match
let near: Vec<_> = idx.fuzzy("aple", 1)?.into_iter().map(|(k, _)| k).collect();
assert_eq!(near, ["apple"]);
let sub: Vec<_> = idx.subsequence("ap").into_iter().map(|(k, _)| k).collect();
assert_eq!(sub, ["apple", "apricot"]);

// serialise to a flat blob, then reload — or `load_mmap` to borrow it zero-copy from the file
idx.save("catalog.bix")?;
let idx = StringIndex::load_mmap("catalog.bix")?; // no read into RAM; pages shared across processes
# Ok::<(), lexindex::IndexError>(())

use lexindex::PerfectHashIndex;            // requires the default `mph` feature

let dict = PerfectHashIndex::build(["GET", "POST", "PUT", "DELETE"])?;
let id = dict.id("POST").unwrap();             // fastest exact lookup, dense id in [0, n)
assert_eq!(dict.key(id), Some("POST"));
assert_eq!(dict.id("PATCH"), None);            // membership is verified, not just hashed

// persist the MPH and reload it (the dense ids are preserved across save/load)
dict.save("verbs.bmp")?;
let dict = PerfectHashIndex::load("verbs.bmp")?;
assert_eq!(dict.id("POST"), Some(id));
# Ok::<(), lexindex::IndexError>(())

use lexindex::CompactHashIndex;           // requires the default `mph` feature

// The smallest string->id map: 1 fingerprint byte/key ⇒ ~1.3 B/key, ~0.4% membership false-positive.
let dict = CompactHashIndex::build(["GET", "POST", "PUT", "DELETE"], 1)?;
let id = dict.id("POST").unwrap();             // Some(slot); a non-member may rarely read as present
assert!(dict.contains("GET"));
let raw = dict.id_unchecked("POST");           // no fingerprint check — for a known-closed vocabulary
assert_eq!(raw, id);
// no key(id): CompactHashIndex stores no keys. Use PerfectHashIndex when you need id → string.
# Ok::<(), lexindex::IndexError>(())

Design notes

• StringIndex is the FST alone — id → key is reconstructed by a rank-walk, with no stored reverse map. Ids are the sorted rank of each key, which is exactly the FST's output value, so key(id) walks the automaton from the root, at each node taking the last transition whose accumulated output stays ≤ id, and returns the path once the outputs sum to exactly id. That is O(key length) and needs no auxiliary structure, so the serialised blob is just [magic "BIX4"][fst] — half the size of the 0.2.0 front-coded layout on real words (12.6 → 5.95 B/key) and simpler to reason about. from_bytes validates the magic and hands the rest to fst, which is itself bounds-checked, so loading an untrusted blob can fail but never corrupts.
• CompactHashIndex stores no keys — only a minimal perfect hash and one small fingerprint per slot. id(key) hashes the key to a slot (the MPH), then compares the key's independent k-byte fingerprint against the stored one; a match is a hit. Because the two hashes are independent, a non-member survives both only with probability 256^-k, the tunable false-positive rate. Dropping the key arena is what takes it below marisa-trie; the price is that membership is probabilistic and there is no id → key. The blob is [magic "BCH1"][n][fp_bytes][mph][fingerprints].
• PerfectHashIndex keys the MPH on a deterministic 64-bit hash of each string (so queries take &str without allocating), then verifies the hit against the stored key — an MPH returns a slot for any input, so verification is what turns it into a real membership test, and the stored keys give exact id → key. Build fails (rather than silently corrupting) on the astronomically rare 64-bit hash collision between two distinct keys. The hash is version-stable (FNV-1a + a splitmix64 finalizer, not std's DefaultHasher), so a saved MPH (the ptr_hash structure serialised via epserde, alongside the arena) reloads and queries identically on any build — the precondition for persistence. CompactHashIndex shares the same version-stable slot hash plus a second independent one for the fingerprint.
• Zero-copy load_mmap (the default mmap feature, memmap2) memory-maps a saved blob and borrows the index directly from the mapped pages — no read into RAM, so a multi-gigabyte index is ready instantly and the OS shares its pages across processes. StringIndex maps the whole FST; CompactHashIndex maps its fingerprint table; PerfectHashIndex maps the key arena (the bulk) and reads only the tiny MPH into memory. Every read is byte-wise, so there is no alignment gotcha; the one caveat is the usual mmap contract — the file must not be mutated while an index borrows it.
• mph is opt-in-by-default: with --no-default-features the crate depends only on fst (and keeps StringIndex). Enabling mph pulls ptr_hash and its dependency tree, which currently carries a few informational RustSec advisories (unmaintained / unsound) on transitive crates — cargo audit reports them as warnings, not vulnerabilities. The fst-only build is free of them.

Benchmarks

Serialised size on real English words

python bench/compare.py on /usr/share/dict/words (479 823 words, 9.3 B/key raw). Keys are a real vocabulary, never a synthetic entity-{i} sequence — sequential keys collapse the FST to a near-regular automaton and report a misleading ~0 B/key, so the benchmark refuses them. Smaller is better; the capability columns are why you would still pick a larger one.

library	prefix	range	fuzzy	reverse id→str	exact membership	zero-copy mmap	bytes/key
lexindex CompactHashIndex (fp=1)	—	—	—	—	probabilistic	✅	1.30
lexindex CompactHashIndex (fp=2)	—	—	—	—	probabilistic	✅	2.30
marisa-trie	✅	—	—	✅	✅	✅	2.98
lexindex StringIndex	✅	✅	✅	✅	✅	✅	5.95
lexindex PerfectHashIndex	—	—	—	✅	✅	✅	17.62
DAWG (dawg2)	✅	—	—	—	✅	—	23.96
datrie	✅	—	—	—	✅	—	30.69

Two honest crowns. CompactHashIndex is the smallest string → dense id map — 2.3× below marisa-trie — when you can accept a bounded false-positive rate (256^-k: ≈0.4 % at 1 byte, ≈0.0015 % at 2, which the benchmark confirms) and don't need id → key. StringIndex is the only structure that answers fuzzy and range queries at all, at 4× below a plain DAWG. marisa-trie remains the pick when you need exact membership and ordering and the smallest such index — lexindex doesn't claim that particular cell (see below for why).

Against other Rust string indexes

marisa-trie is C++. Among ordered string indexes you can cargo add, none is smaller than StringIndex — the double-array tries trade space for lookup speed, and no succinct LOUDS trie (marisa / XCDAT / CoCo-trie-style) exists in Rust to depend on. So StringIndex at 5.95 B/key is the smallest ordered string → id index available in pure Rust — second only to a C++ library, and the only one of them that does fuzzy and range. Same real words:

Rust structure	bytes/key	vs marisa
marisa-trie (C++, reference)	2.98	1.0×
lexindex StringIndex (ordered + fuzzy + reverse)	5.95	2.0×
fst::Set (membership only — no ids, no reverse)	4.85	1.6×
yada (double-array)	15.98	5.4×
crawdad::MpTrie (minimal-prefix)	19.63	6.6×
crawdad::Trie (double-array)	26.22	8.8×

_{Measured with crawdad 0.4, yada 0.5, fst 0.4 over the same word list; size = serialised bytes (serialize_to_vec().len()) ÷ key count. Not lexindex dependencies — reproduce in a throwaway crate.}

Reaching marisa's 2.98 needs its recursive succinct-trie label nesting, which the byte-oriented fst automaton is ~1.6× away from by construction (even a bare fst::Set, which stores no ids at all, is 4.85) — so beating it on the ordered index means reimplementing marisa from scratch, not a bounded tweak. CompactHashIndex takes the size crown the other way: by dropping the keys entirely.

Point-lookup latency vs the standard library

cargo run --release --example bench (1 M keys). Absolute numbers are machine-dependent; the ratios are the point.

structure	build	lookup	note
lexindex PerfectHashIndex::id_unchecked	~310 ms	~232 ns	closed vocabulary, no membership check
std::HashMap<String, u32>	~205 ms	~290 ns	in-RAM, not serialisable
lexindex PerfectHashIndex::id (verified)	~376 ms	~377 ns	one extra cache line + key compare
lexindex StringIndex (FST)	~138 ms	~386 ns	and prefix / range / fuzzy
std::BTreeMap<String, u32>	~39 ms	~833 ns	in-RAM

Honest reading: for a fixed / closed vocabulary, PerfectHashIndex::id_unchecked is the fastest — ≈1.25× quicker than HashMap (no probing, no membership comparison) and compact + serialisable. CompactHashIndex shares that same MPH lookup while storing 13× less. Add membership verification (id) and you pay one extra cache line + a key comparison; use StringIndex and you trade more latency for ordered / prefix / range / fuzzy queries the hash maps cannot answer at all. So: CompactHashIndex when footprint dominates and a rare false positive is fine; PerfectHashIndex::id for exact membership + reverse; StringIndex when order or fuzzy/prefix matters; HashMap when you just need a general in-RAM map with nothing persisted.

Scaling to millions of keys

python bench/scale.py on real high-entropy keys (dictionary-word bigrams). Build time and memory grow linearly, lookups stay sub-microsecond, and CompactHashIndex's 1.30 bytes/key holds constant as n grows:

n	structure	build	bytes/key	peak RSS	lookup
1 M	CompactHashIndex	0.28 s	1.30	176 MB	232 ns
10 M	CompactHashIndex	4.4 s	1.30	1.5 GB	397 ns
10 M	StringIndex	4.7 s	2.00*	1.3 GB	797 ns

_{* bigram keys share more prefixes than single words, so StringIndex compresses below its 5.95 B/key on the raw dictionary — the honest single-word figure is in the size table above. Peak RSS includes the input key list. Linear extrapolation puts 100 M at ~50 s and ~15 GB (a big-memory box).}

License

MIT © Ilia Gradina