sonic

{
  "aliases": [],
  "alternatives": [
    "elasticsearch",
    "meilisearch",
    "typesense",
    "whoosh"
  ],
  "build_dependencies": [
    "rust"
  ],
  "categories": [
    "search",
    "database",
    "server"
  ],
  "caveats": null,
  "conflicts_with": [],
  "dependencies": [],
  "deprecated": 0,
  "deprecation_reason": null,
  "desc": "Fast, lightweight \u0026 schema-less search backend",
  "disable_reason": null,
  "disabled": 0,
  "enrichment_fetched_at": "2026-06-20T23:41:16+00:00",
  "first_seen": "2026-06-20T23:34:18+00:00",
  "full_name": "sonic",
  "github_default_branch": "master",
  "github_last_commit_at": "2026-06-12T17:23:39Z",
  "github_readme_excerpt": "# Sonic\n\n[![Lint and Test](https://github.com/valeriansaliou/sonic/actions/workflows/test.yml/badge.svg?branch=master)](https://github.com/valeriansaliou/sonic/actions/workflows/test.yml) [![Build and Release](https://github.com/valeriansaliou/sonic/actions/workflows/release-server.yml/badge.svg?branch=master)](https://github.com/valeriansaliou/sonic/actions/workflows/release-server.yml) [![dependency status](https://deps.rs/repo/github/valeriansaliou/sonic/status.svg)](https://deps.rs/repo/github/valeriansaliou/sonic) [![Buy Me A Coffee](https://img.shields.io/badge/buy%20me%20a%20coffee-donate-yellow.svg)](https://www.buymeacoffee.com/valeriansaliou)\n\n**Sonic is a fast, lightweight and schema-less search backend. It ingests search texts and identifier tuples that can then be queried against in a microsecond\u0027s time.**\n\nSonic can be used as a simple alternative to super-heavy and full-featured search backends such as Elasticsearch in some use-cases. It is capable of normalizing natural language search queries, auto-completing a search query and providing the most relevant results for a query. Sonic is an identifier index, rather than a document index; when queried, it returns IDs that can then be used to refer to the matched documents in an external database.\n\nA strong attention to performance and code cleanliness has been given when designing Sonic. It aims at being crash-free, super-fast and puts minimum strain on server resources (our measurements have shown that Sonic - when under load - responds to search queries in the \u03bcs range, eats ~30MB RAM and has a low CPU footprint; [see our benchmarks](https://github.com/valeriansaliou/sonic#how-fast--lightweight-is-it)).\n\n_Tested at Rust version: `rustc 1.95.0 (59807616e 2026-04-14)`_\n\n**\ud83c\uddeb\ud83c\uddf7 Crafted in Nantes, France.**\n\n**\ud83d\udcf0 The Sonic project was initially announced in [a post on my personal journal](https://journal.valeriansaliou.name/announcing-sonic-a-super-light-alternative-to-elasticsearch/).**\n\n![Sonic](https://va",
  "github_repo": "valeriansaliou/sonic",
  "github_stars": 21258,
  "github_topics": [
    "backend",
    "database",
    "graph",
    "index",
    "infrastructure",
    "rust",
    "search",
    "search-engine",
    "search-server",
    "server"
  ],
  "homepage": "https://github.com/valeriansaliou/sonic",
  "homepage_og_description": null,
  "homepage_og_image": null,
  "homepage_title": null,
  "installs_30d": 1790,
  "installs_365d": 2122,
  "installs_90d": 1982,
  "keg_only": 0,
  "keg_only_reason": null,
  "last_seen": "2026-06-20T23:34:18+00:00",
  "license": "MPL-2.0",
  "llm_generated_at": "2026-06-20T23:48:34+00:00",
  "llm_model": "claude-haiku-4-5",
  "name": "sonic",
  "oldnames": [],
  "one_liner": "Fast, lightweight search backend written in Rust that indexes text and returns matching identifiers in microseconds.",
  "optional_dependencies": [],
  "rank_30d": 1031,
  "rank_365d": 2756,
  "rank_90d": 1603,
  "raw_hash": "81d0e742b7d29cb9",
  "recommended_dependencies": [],
  "revision": 0,
  "ruby_source_path": "Formula/s/sonic.rb",
  "tap": "homebrew/core",
  "test_dependencies": [],
  "uses_from_macos": [
    {
      "llvm": "build"
    }
  ],
  "version_head": null,
  "version_stable": "1.6.0",
  "versioned_formulae": [],
  "why_use_this": "Sonic is a schema-less search server designed as a lighter alternative to Elasticsearch for simple full-text search needs. It\u0027s single-binary, resource-efficient, and optimized for speed; use it when you need substring search, auto-completion, and ranked results without the overhead of a full-featured search engine. Returns identifier tuples rather than documents, making it ideal for pairing with an external database."
}