MeiliSearch

MeiliSearch is a high‑performance, open‑source search engine designed to be embedded directly into web and mobile applications. It exposes a simple RESTful API that returns results in under 50 ms, enabling search‑as‑you‑type experiences without the need for complex query planners or heavy infrastructure. The engine is written in Rust, giving it low‑latency memory safety and a small binary footprint that can run on commodity hardware or in containerized environments.

Key Features

• Full‑text search with typo tolerance, stop‑word removal, and custom ranking rules.
• Faceting & filtering support out of the box for building rich, drill‑down UIs.
• Geosearch and hybrid search (combining full‑text with semantic embeddings) allow developers to layer AI retrieval on top of classic indexing.
• Real‑time updates: documents can be added, updated or deleted via the API and are immediately searchable.
• Zero‑config presets: a “quick start” mode automatically builds an index from incoming documents, ideal for rapid prototyping.

Technical Stack & Architecture

The engine’s architecture follows a single‑threaded event loop model that avoids locking contention. Indexes are kept in memory for fast lookup, with a background writer persisting changes to disk asynchronously. This design eliminates the need for external search back‑ends like Elasticsearch or Solr.

Core Capabilities & APIs

• Document CRUD: POST /indexes/{uid}/documents, DELETE /indexes/{uid}/documents/{id}.
• Search: GET /indexes/{uid}/search with support for filters, facets, sort, and pagination.
• Index management: create, update, delete indexes; configure ranking rules and stop‑words.
• Synonyms & Stop‑words: define custom synonym sets or exclude terms from indexing.
• Batch operations: POST /indexes/{uid}/documents accepts arrays for bulk ingestion.
• Health & metrics: /health, /metrics expose Prometheus‑compatible endpoints.

Deployment & Infrastructure

• Self‑hosted: A single binary can be run on Linux, macOS, or Windows.
• Containerization: Official Docker images support --restart=unless-stopped and bind‑mounts for data persistence.
• Scalability: Horizontal scaling is achieved via sharding at the application layer; MeiliSearch does not support built‑in clustering, so developers typically deploy multiple instances behind a load balancer and merge results client‑side.
• Resource footprint: ~50 MB RAM for a moderate index; CPU usage remains low (<5 % on typical workloads).
• High availability: Pair with a reverse proxy and persistent storage; use Docker Compose or Kubernetes for orchestration.

Integration & Extensibility

• SDKs: Official clients in JavaScript, TypeScript, Python, Go, Ruby, PHP, and Swift simplify integration.
• Webhooks: Subscribe to events like index_updated or document_deleted.
• Plugins: Extend the engine with custom ranking functions or data sources via Rust crates; community plugins exist for integrating with GraphQL, Redis, and more.
• Hybrid Search: Combine MeiliSearch’s full‑text engine with OpenAI embeddings or other LLMs; developers can inject semantic vectors into the index and query them alongside textual terms.

Developer Experience

• Configuration: A single meilisearch.toml file controls data paths, port, and API key.
• Documentation: Comprehensive guides, API reference, and example projects are available on the official site.
• Community: Active GitHub repo (MIT license), Discord channel, and frequent roadmap updates.
• Testing: The Rust codebase includes extensive unit tests; developers can run the test suite locally with cargo test.

Use Cases

Advantages Over Alternatives

• Speed: Sub‑50 ms responses outperform Elasticsearch or Solr for typical workloads.
• Simplicity: Zero‑config mode and a single binary eliminate the operational overhead of distributed clusters.
• License: MIT license allows unrestricted use in commercial products without licensing fees.
• Rust Performance: Memory