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meilisearch/meilisearch

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58,118 stars·2,569 forks·Rust·96 viewswww.meilisearch.com↗

Meilisearch

Meilisearch is a high-performance, developer-focused search engine designed to provide instant, typo-tolerant results for applications. It functions as a RESTful service that accepts JSON documents, organizing structured information into searchable collections to ensure rapid retrieval. The engine is built to be lightweight and easy to configure, minimizing maintenance overhead while integrating into existing software stacks.

The system distinguishes itself through specialized indexing and storage techniques, including the use of finite state transducers for memory-efficient lexicon storage and zero-copy memory mapping for direct data access. It employs a custom tokenizer pipeline to process text and utilizes processor-level vector instructions to accelerate ranking calculations. An asynchronous task queue manages indexing operations in the background, ensuring that search queries remain responsive and non-blocking even during database updates.

Beyond its core search capabilities, the engine supports complex querying requirements such as faceted filtering and content-heavy document retrieval. It is designed to facilitate immediate search experiences, allowing users to receive relevant results as they type. The software is distributed as a standalone service, with documentation available to guide the integration process.

Features

  • Developer-Focused Search Tools - Simplifies integration by offering a lightweight, drop-in search solution for modern application stacks.
  • Document Indexing Engines - Transforms raw information into structured, high-performance collections optimized for instant retrieval.
  • Full-Text - Delivers typo-tolerant text retrieval with advanced relevance ranking for high-performance search requirements.
  • RESTful Search Services - Exposes indexing and query functionality through standard HTTP interfaces for seamless network-based interaction.
  • Finite State Transducers - Compresses dictionary terms into graph-based structures to enable memory-efficient storage and rapid prefix lookups.

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  • Zero-Copy Memory Mappings - Maps database files directly into memory to eliminate redundant data copying between kernel and user space.
  • Tokenizers - Decomposes unstructured text into normalized tokens using language-specific rules to prepare data for indexing.
  • Instant Search Interfaces - Displays immediate, relevant results as users type to facilitate rapid navigation through large datasets.
  • Key-Value - Employs a memory-mapped storage engine to provide high-performance, atomic key-value persistence.
  • SIMD Accelerated Searchers - Leverages processor-level vector instructions to accelerate memory scanning and scoring calculations during search execution.
  • Machine Learning Operations - Fast, open-source search engine.
  • Enterprise Search - Typo-tolerant search engine for applications.
  • Search and Indexing - Fast, open-source search engine for applications.
  • API and Data Services - Delivers a fast, AI-powered search engine API.
  • Enterprise Search - Typo-tolerant search engine for web applications.
  • Web Framework Examples - Fast, typo-tolerant search engine.
  • Content Management Search - Optimizes the retrieval of articles and documents within content-heavy platforms through typo-tolerant search logic.
  • Product Discovery Engines - Facilitates product discovery through faceted filtering, relevance ranking, and highly responsive search interfaces.
  • Full-Text Search Integrations - Augments existing application databases with powerful, typo-tolerant search capabilities that exceed standard filtering.

    • Star history

    Star history chart for meilisearch/meilisearchStar history chart for meilisearch/meilisearch

    How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

    Frequently asked questions

    What does meilisearch/meilisearch do?

    Meilisearch is a high-performance, developer-focused search engine designed to provide instant, typo-tolerant results for applications. It functions as a RESTful service that accepts JSON documents, organizing structured information into searchable collections to ensure rapid retrieval. The engine is built to be lightweight and easy to configure, minimizing maintenance overhead while integrating into existing software stacks.

    What are the main features of meilisearch/meilisearch?

    The main features of meilisearch/meilisearch are: Developer-Focused Search Tools, Document Indexing Engines, Full-Text, RESTful Search Services, Finite State Transducers, Zero-Copy Memory Mappings, Tokenizers, Instant Search Interfaces.

    Which projects share features with meilisearch/meilisearch?

    Projects with overlapping indexed features include: typesense/typesense — Typesense is a distributed search engine designed to provide sub-millisecond query latency across massive datasets. It… spacejam/sled — Sled is an embedded key-value store and ACID-compliant database designed for high-performance data persistence. It… elastic/elasticsearch — Elasticsearch is a distributed search engine and document store designed for the high-performance indexing and… quickwit-oss/tantivy — Tantivy is a library for building full-text search engines and indexing frameworks. It provides the core components… redis/go-redis — This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive… qdrant/qdrant — Qdrant is a high-performance vector similarity database designed to store, index, and search high-dimensional vectors…

    Projects sharing features with Meilisearch

    These projects share indexed features with Meilisearch. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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      Typesense is a distributed search engine designed to provide sub-millisecond query latency across massive datasets. It functions as both a high-performance indexing and retrieval engine and a comprehensive search experience platform, offering built-in typo tolerance and tools for managing relevance through synonym configuration, result curation, and complex filtering. The platform distinguishes itself by utilizing in-memory indexing to maintain high-throughput data retrieval and integrating vector database capabilities to support semantic similarity searches. It ensures data consistency and h

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    • spacejam/sledspacejam avatar

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      Sled is an embedded key-value store and ACID-compliant database designed for high-performance data persistence. It functions as a log-structured storage engine that organizes data using B+ trees to support efficient range queries and prefix scans. The engine implements a zero-copy data store model, utilizing epoch-based reclamation to provide direct references to cached values without memory allocations. It distinguishes itself through a combination of write-ahead logging, page cache optimizations to reduce write amplification on flash storage, and serializable transactions for atomic multi-k

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    • elastic/elasticsearchelastic avatar

      elastic/elasticsearch

      77,012View on GitHub↗

      Elasticsearch is a distributed search engine and document store designed for the high-performance indexing and retrieval of massive volumes of unstructured data. It functions as a centralized analytics platform, providing a schema-flexible architecture that organizes information into searchable indices while maintaining global cluster state through a distributed consensus mechanism. The platform distinguishes itself through its integrated approach to observability, security, and advanced analytics. It combines full-text, vector, and hybrid search capabilities with machine learning-driven insi

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    • quickwit-oss/tantivyquickwit-oss avatar

      quickwit-oss/tantivy

      15,442View on GitHub↗

      Tantivy is a library for building full-text search engines and indexing frameworks. It provides the core components necessary to organize large collections of text data into searchable structures, enabling the execution of complex queries and the retrieval of information across structured document sets. The engine utilizes an inverted index architecture to map terms to document identifiers, supported by a segment-based storage model that balances search performance with write throughput. It incorporates specialized data structures, including finite state transducers for term dictionaries and

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