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Package probabilistic-php
Short Description Pure-PHP probabilistic data structures: Bloom Filter, Counting Bloom Filter, Cuckoo Filter, Count-Min Sketch, and HyperLogLog. Zero dependencies.
License MIT
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Informations about the package probabilistic-php
probabilistic-php
Pure-PHP probabilistic data structures: Bloom Filter, Counting Bloom Filter, Cuckoo Filter, Count-Min Sketch, and HyperLogLog. No runtime dependencies, no Redis — everything runs in plain PHP memory.
Why this exists
JavaScript has bloom-filters and Go has BoomFilters — single, well-maintained libraries covering this whole family. PHP has had no equivalent: the existing Packagist options are either a bare Bloom filter, abandoned since the PHP 5.x–7.1 era, or require a running Redis server to do anything at all. This package fills that gap with a modern, fully tested, pure in-memory implementation for PHP 8.2+.
These are probabilistic structures
Every structure here trades a little accuracy for a large amount of memory. That is the point, not a bug:
- A Bloom / Counting Bloom / Cuckoo filter answers "have I probably seen this?" It never reports a false negative, but it may report a false positive at a rate you configure.
- A Count-Min Sketch answers "roughly how many times?" It never undercounts, but it may overcount.
- A HyperLogLog answers "roughly how many distinct things?" with a small, statistically bounded error (roughly 0.3–3.6% at the default precision in testing), not an exact count.
If you need exact answers, use a real Set or a database. If you need to track membership or counts over millions of items in kilobytes of memory, these are the right tool.
Installation
Requires PHP 8.2+ and the ext-hash extension (bundled with PHP core).
Usage
Bloom Filter — membership testing, no false negatives
mightContain() returning false is a guarantee the item was never added. true means it probably was, with at most the configured false-positive rate.
Counting Bloom Filter — Bloom Filter with removal
Each slot is a counter (capped at 255) instead of a single bit, which is what allows removal. Removing an item that was never added throws — it would corrupt the counts of other items sharing those slots.
Cuckoo Filter — space-efficient membership with removal
Often more space-efficient than a Bloom filter for the same false-positive rate, with deletion built in. A FilterFullException is thrown if the filter is overfilled well past its expectedItems — create a larger one.
Count-Min Sketch — approximate frequency counting
Counts are never underestimated. Wider/deeper sketches reduce overestimation at the cost of memory. Two sketches of identical dimensions can be combined with merge().
HyperLogLog — approximate distinct-count (cardinality)
Estimates the cardinality of a stream using a tiny, fixed amount of memory regardless of how many items pass through. Two estimators of equal precision can be combined with merge(), giving exactly what a single estimator over the union would have produced.
Choosing a structure
| You want to… | Use | Removal? | Error |
|---|---|---|---|
| Test membership in minimal memory | BloomFilter |
No | Configurable false positives |
| …and also remove items | CountingBloomFilter |
Yes | Configurable false positives |
| …with better space efficiency and removal | CuckooFilter |
Yes | Low, fixed false positives |
| Count how often each item appears | CountMinSketch |
No | Never under, may over |
| Count how many distinct items appear | HyperLogLog |
No | 0.3–3.6% at precision 14 |
Error handling
Every exception this library throws implements Probabilistic\Exception\ExceptionInterface, so you can catch all of them in one place:
Each one also extends the closest SPL exception — for example InvalidConfigurationException extends \InvalidArgumentException — so existing catch blocks for the standard types keep working.
Testing
Because these structures are probabilistic, the test suite mixes two styles: exact assertions for the hard guarantees (a Bloom filter never reports a false negative; a Count-Min Sketch never underestimates) and statistical-tolerance assertions for the rest (observed false-positive and error rates stay within generous, meaningful bounds across many items). If you contribute, follow the same pattern rather than asserting exact equality on inherently approximate outputs.
Contributing
composer.lock is intentionally not committed: this is a library, so consumers resolve their own compatible dependency versions. Run composer check before opening a pull request — analysis, lint, and tests must all pass.
Framework integrations
Ready-made wiring to use these structures as named, pre-configured services in your framework of choice:
- probabilistic-laravel — Laravel integration via the service container and a Facade.
- probabilistic-bundle — Symfony bundle exposing them as configured services.
Other projects
More of my packages are listed on my GitHub profile.
License
MIT — see LICENSE.
All versions of probabilistic-php with dependencies
ext-hash Version *