lexindex 0.1.0

Compact, immutable string<->id indexes for huge catalogs: ordered FST lookup with prefix/range/fuzzy, plus a minimal-perfect-hash dictionary.

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.

Two complementary, build-once / query-many structures:

• StringIndex — an ordered index backed by a finite-state transducer (fst). Exact string → id and id → string, plus prefix, range, fuzzy (bounded Levenshtein edit distance), and subsequence iteration — all driven by automata over the FST, never a full scan — in a compressed, serialisable (and memory-mappable-by-blob) form. Use it for autocomplete, typo-tolerant search, browse, and ordered scans of a large catalog.
• PerfectHashIndex — a minimal-perfect-hash dictionary backed by ptr_hash (the mph feature, on by default). Exact string → dense id with verified membership (id) and reverse lookup; no ordering. For a known-closed vocabulary, id_unchecked skips the membership comparison and is faster than std::HashMap (see Benchmarks). Use it as a fixed-vocabulary token↔id map on a hot path.

Both assign dense ids in [0, n), support reverse lookup, and serialise to a flat blob (save/load) — build once, persist, then load/mmap and query many times. Both are immutable after building.

Python

pip install lexindex

from lexindex import PerfectHashIndex, StringIndex

idx = StringIndex(["apple", "apricot", "banana", "cherry"])
idx.id("banana")             # 2  (sorted rank)
idx.key(0)                   # "apple"
idx.prefix("ap")             # [("apple", 0), ("apricot", 1)]
idx.fuzzy("aple", 1)         # [("apple", 0)]  — typo-tolerant
idx.save("catalog.bix")      # persist; StringIndex.load("catalog.bix") reloads it

d = PerfectHashIndex(["GET", "POST", "PUT", "DELETE"])
d.id("POST")                 # dense id in [0, n); membership verified, returns None if absent
d.id_unchecked("POST")       # fastest lookup for a known-closed vocabulary

No runtime dependencies; a single abi3 wheel covers CPython 3.11+.

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), 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 and reload (e.g. mmap the file, then `from_bytes`)
idx.save("catalog.bix")?;
let idx = StringIndex::load("catalog.bix")?;
# 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>(())

Design notes

• StringIndex keeps the FST (key → id, prefix/range) plus a string arena (id → key: one contiguous byte buffer + offsets, no per-String overhead). Ids are the sorted rank of each key, so they are stable for the same key set. The serialised blob is [magic][fst][arena]; from_bytes validates every length and offset, so loading an untrusted blob can fail but never corrupts.
• 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. 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.
• mph is opt-in-by-default: with --no-default-features the crate depends only on fst. 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

cargo run --release --example bench (1 M keys, 19 bytes each). Absolute numbers are machine-dependent; the ratios and the trade-off are the point.

structure	build	lookup	serialised
lexindex PerfectHashIndex::id_unchecked	~310 ms	~232 ns	27 B/key
std::HashMap<String, u32>	~205 ms	~290 ns	— (in-RAM, not serialisable)
lexindex PerfectHashIndex::id (verified)	~376 ms	~377 ns	27 B/key
lexindex StringIndex (FST)	~138 ms	~386 ns	27 B/key
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. 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: id_unchecked for a known-closed token→id map on a hot path; StringIndex when order or fuzzy/prefix matters; HashMap when you just need a general in-RAM map with membership and nothing persisted.

License

MIT © Ilia Gradina