Preset-Optimized Engine

Setting Preset on AhoCorasickArgs builds a local automaton next to the Redis collection: writes still go to Redis atomically, reads never touch it. This page is about picking a preset. How the engine stays in sync across instances is Redis-Backed Engine.

ac, err := acor.Create(&acor.AhoCorasickArgs{
    Addr:          "localhost:6379",
    Name:          "my-collection",
    Preset:        acor.PresetBalanced,
    CaseSensitive: false, // default; matching is case-insensitive
})
if err != nil {
    panic(err)
}
defer ac.Close()

The preset is fixed at creation. Info() reports the resulting Keywords, Nodes, MemoryBytes, and TrieDepth.

The three presets

Preset	Engine	Best for	Costs
`PresetSpeed`	Full DFA + flat array trie + compact alphabet map	Packet inspection, high-rate log scanning, latency-critical paths	Memory proportional to states × alphabet size
`PresetBalanced`	Double-Array Trie + Banded DFA + output link compression	General backend filtering, search	Neither extreme
`PresetMemoryEfficient`	Map-based sparse trie + Bloom pre-filter + NFA	Millions of patterns under a memory cap	Slower search: failure-link traversal and map lookups

Start with PresetBalanced. Move to PresetSpeed when latency is the constraint and memory is not; to PresetMemoryEfficient when it is the other way round.

PresetSpeed measured fastest on every query shape on the benchmarks page, and PresetBalanced gives up some of that for a much smaller transition table. Measure your own corpus before choosing by name.

Refresh behavior

Stale reads share one reload and each observes its own cancellation. A failed reload returns an error to the search rather than silently serving the old engine, and a later search retries. Optional version polling recovers from missed Pub/Sub messages; its interval is not a freshness bound. Full lifecycle and failure counters: invalidation safety.