Show HN: PHP-fts – Full-text search engine in pure PHP, no extensions

php-fts

A self-contained full-text search engine written in pure PHP.
No extensions. No external services. No dependencies. Just files.

Who is this for?

php-fts is designed for projects where deploying a dedicated search service is not an option — shared hosting, small VPS, or simply situations where you want to keep your stack minimal and portable.

If you have access to Elasticsearch, Meilisearch or Typesense and the infrastructure to run them, use those. They are more powerful and built for high-traffic, large-scale workloads.

If you don't — or if you'd rather not — php-fts gives you solid full-text search with ranked results, filters, and tolerant matching, with nothing to install and nothing to configure beyond a directory path.

It is a good fit if:

You are on shared hosting (OVH, Infomaniak, o2switch, etc.)
You want zero infrastructure overhead
Your dataset is in the range of hundreds to tens of thousands of documents
You index offline or on a schedule, and serve searches at runtime

It is not a good fit if:

You need real-time indexing under heavy concurrent write load
Your dataset is in the millions of documents
You need geo search or multi-tenant isolation

Features

Full-text search with trigram indexing — tolerant to typos and partial matches
BM25 + IDF scoring — industry-standard relevance ranking (same algorithm as Lucene / Elasticsearch)
Per-document score — exposed in results, usable to build facet counts, sorting, or custom ranking
Field boosting — weight some fields (e.g. title) more than others
Filters — exact match, comparisons, range, in, not in, contains on array fields
Combined AND / OR filtering — flexible condition logic
Bulk insertion — up to 2.4× faster than individual inserts, single lock for the whole batch
Soft delete with tombstones — fast deletes, cleaned up on compaction
Atomic update — soft delete + re-insert in a single lock
Compaction — rebuilds index files cleanly, removes deleted documents
Fragmentation monitoring — know when to compact
Binary file storage — portable across servers, no rebuild needed
O(1) trigram lookup — fixed-size index (~810 KB), no tree traversal
No extensions required — runs on any standard PHP 8.1+ installation

Requirements

PHP 8.1 or higher
Read/write access to a directory for index files

Installation

Via Composer

composer require ols/php-fts

Manual install — if you are not using Composer, copy the src/ directory into your project and include the autoloader:

require '/path/to/php-fts/src/autoload.php';

Quick start

use Ols\PhpFts\SearchEngine; $engine = new SearchEngine(); $engine->open('./search_data'); // Insert a document $docId = $engine->insert([ 'title' => 'Brown leather shoe', 'description' => 'Elegant city shoe in soft leather', 'price' => 129.90, 'stock' => 42, 'active' => true, 'category' => 'Shoes', 'brand' => 'Adidas', 'tags' => ['summer', 'luxury', 'city'], ]); // Search $results = $engine->search('leather shoe', limit: 20, boosts: [ 'title' => 3.0, 'description' => 1.0, ]); foreach ($results as $result) { echo $result['document']['title'] . ' — score: ' . $result['score'] . PHP_EOL; } $engine->close();

API Reference

Open / Close

$engine->open('./search_data'); // Creates directory and files if they don't exist $engine->close(); // Flushes and closes all file handles

Insert

// Single document — returns the doc ID (binary offset, keep it if you need update/delete) $docId = $engine->insert([ 'title' => 'My product', 'price' => 49.90, 'active' => true, 'tags' => ['new', 'sale'], ]); // Bulk insert — one lock for the entire batch, significantly faster $docIds = $engine->insertBulk([ ['title' => 'Product A', 'price' => 29.90], ['title' => 'Product B', 'price' => 59.90], ]);

Supported field types: string, int, float, bool, array of strings.

Search

$results = $engine->search( query: 'leather shoe', limit: 20, maxCandidates: 5000, boosts: ['title' => 3.0, 'description' => 1.0], filters: [...], );

Each result:

[ 'docId' => 942222, // document identifier 'score' => 43.74, // BM25+IDF relevance score, 0-100 'document' => [...], // original document array ]

The score field is available on every result and can be used to build facet counts, custom sorting, or relevance thresholds.

Filters

$results = $engine->search('shoe', filters: [ 'and' => [ ['field' => 'active', 'op' => '=', 'value' => true], ['field' => 'stock', 'op' => '>', 'value' => 0], ['field' => 'price', 'op' => '<=', 'value' => 300], ['field' => 'category', 'op' => 'in', 'value' => ['Shoes', 'Sport']], ['field' => 'tags', 'op' => 'contains', 'value' => 'luxury'], ], 'or' => [ ['field' => 'brand', 'op' => '=', 'value' => 'Adidas'], ['field' => 'brand', 'op' => '=', 'value' => 'Puma'], ], ]);

Both and and or are optional, but at least one must be present.
When both are used: all AND conditions must pass and at least one OR condition must pass.
A document missing a filtered field is excluded from results.

Operator	Supported types
= !=	int, float, bool, string
> >= < <=	int, float
in not in	int, float, string
contains not contains	array (document field)

Update / Delete

// Atomic update: soft delete + re-insert in a single lock $newDocId = $engine->update($docId, ['title' => 'Updated title', 'price' => 149.90]); // Soft delete (cleaned up on compaction) $engine->delete($docId);

Maintenance

$count = $engine->count(); // Number of live documents $rate = $engine->fragmentationRate(); // Fragmentation percentage (0 = clean, 100 = all deleted) if ($engine->fragmentationRate() > 20) { $engine->compact(); // Rebuild index files, remove deleted documents } $engine->reset(); // Wipe all index files and start fresh

Index files

search_data/ documents.bin — serialized documents (JSON, binary format) trigrams.bin — fixed-size trigram index ~810 KB (37^3 entries, O(1) access) postings.bin — doc_id lists per trigram tombstones.bin — deleted doc_ids (cleared on compaction)

Files are fully portable — copy them between servers without rebuilding.

Scoring

Relevance is computed using BM25 + IDF:

BM25 — term frequency saturation (a word appearing 10x doesn't score 10x higher) and document length normalization. Parameters: k1 = 1.5, b = 0.75 (standard Lucene defaults).
IDF — a trigram present in every document contributes little; a rare trigram contributes a lot.
The final score is normalized between 0 and 100.

Benchmark

Benchmarks were run on two environments:

Windows 11 — local machine, NVMe SSD, PHP 8.3
Linux (OVH shared hosting) — standard shared plan, PHP 8.3

Insertion

Volume	insert() Windows	insert() Linux	insertBulk() Windows	insertBulk() Linux
1 000	5.3 s	7.3 s	3.0 s	3.0 s
5 000	—	33.5 s	—	14.8 s
10 000	53.0 s	63.4 s	30.5 s	29.4 s
50 000	282.2 s	—	157.8 s	—

Insertion is an offline operation — indexing is typically done via a scheduled job, not at request time.
Always prefer insertBulk() in production: it acquires a single lock for the entire batch and is consistently ~2x faster.

Index size

Volume	Index size
1 000	2.8 MB
10 000	21.7 MB
50 000	106.0 MB

Search — Linux shared hosting, 10 000 documents

Metric	Value
Median	3.2 ms
Average	4.9 ms
P95	12.5 ms
P99	22.9 ms
Min / Max	1.3 ms / 37.1 ms

200 queries, 10 distinct queries in rotation (including typos and out-of-corpus queries).
Measured with hrtime() on a live shared hosting environment under normal load.

Example application

The gif below shows one possible use of php-fts — a product search interface with filters and ranked results, built on top of a fake shoe catalogue.

It is just an illustration. php-fts is an engine, not an interface. You can use it to power a product search, a documentation search, an admin filter, a CLI tool, or anything else that needs full-text matching over a set of documents.

To run it locally:

php demo/seed.php php -S localhost:8000 -t demo

No database. No external service. The filters, scores, and result counts are all computed by the engine.

License

MIT — see LICENSE.