Open-source alternatives to Elasticsearch Php

This project is a Go client library and API wrapper for interacting with Elasticsearch clusters. It serves as a programmatic interface for managing documents, indices, and cluster health, allowing Go applications to perform search and indexing operations via the REST API. The library functions as a distributed search orchestrator, providing specialized tools for high-throughput data ingestion and cluster administration. It features a buffered bulk processor with exponential backoff retries for optimizing write performance and supports automated index lifecycle transitions and historical data

Spring Data Elasticsearch is a data access library that maps Java objects to Elasticsearch indices. It functions as an object mapper, a repository abstraction, and a query DSL wrapper, providing both a standard and a reactive client for executing asynchronous search and persistence operations. The project distinguishes itself by automating data access through repository interfaces, which generate query logic based on method naming conventions. It enables the construction of complex search queries using a domain-specific language and supports advanced search capabilities such as vector similar

This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive interface for managing remote data stores, enabling developers to execute standard database commands, handle complex data structures, and perform asynchronous operations within Go applications. The library distinguishes itself through its support for advanced Redis capabilities, including connection pooling, pipelining, and transactional integrity. It provides specialized primitives for managing distributed clusters, including automated topology updates and request routing to sha

elasticsearch-py is a distributed search API wrapper and cluster administrator. It serves as a secure connector for executing search queries and performing administration tasks against a search engine. The library provides a high-level interface for managing indices, documents, and node connections. It enables encrypted data transmission and authenticated communication with a search cluster. The project covers full-text search, data management, and cluster administration. It includes capabilities for managing document data, implementing complex search queries, and maintaining reliable commun

Infinity is a distributed vector database and multimodal vector store designed to manage large-scale datasets for retrieval and similarity search. It serves as a backend for large language model applications and retrieval augmented generation pipelines by storing and retrieving dense vectors, sparse vectors, and full-text data. The system functions as a hybrid search engine, combining vector embeddings and full-text search with reranking algorithms to identify the most relevant documents. It supports multimodal data storage, allowing the maintenance of diverse data types including tensors, st

The official Go client for Elasticsearch

zvec is an embedded vector database engine and indexing library designed for high-dimensional similarity search. It functions as a hybrid search engine and a retrieval-augmented generation knowledge base, allowing for the storage and retrieval of dense and sparse vectors. The system is distinguished by its hybrid retrieval pipeline, which fuses vector similarity, full-text keyword matching, and scalar metadata filtering into single query operations. It supports a plugin-based model integration system for registering custom embedding models and rerankers, as well as language bindings for nativ

LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters

Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations

ElasticHD is a management dashboard and monitoring tool for Elasticsearch clusters. It provides a web interface for tracking cluster health, managing indices, and performing real-time searches. The project features a translator that converts standard SQL queries into Elasticsearch Domain Specific Language to simplify the creation of complex search requests. It also includes a visual index manager for modifying index templates and updating type mappings without manual configuration file edits. The system covers cluster observability through health monitoring dashboards and metadata visualizat

Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer

This project is a full text search engine and enterprise search infrastructure designed for indexing and retrieving large sets of documents. It provides a comprehensive framework for information discovery using ranked results and linguistic analysis. The system integrates high-dimensional vector similarity search for semantic retrieval alongside traditional full-text capabilities. It distinguishes itself through support for geospatial data retrieval, multilingual text processing, and a search suggestion workflow that includes typo-tolerant query completion and spellchecking. The platform cov

Zombodb is a database extension and relational data indexer that integrates PostgreSQL with Elasticsearch. It provides a SQL search interface, allowing users to execute complex search queries and aggregations using standard SQL functions and syntax instead of native JSON APIs. The project synchronizes relational data from PostgreSQL to a remote search engine to enable high-performance full-text search and analytics. The system distinguishes itself by bridging relational structures with search engine capabilities, specifically through geospatial search integration for geometry and geography ty

Elasticvue is a management user interface and graphical dashboard for Elasticsearch clusters. It serves as a cluster browser and administration tool, providing a visual way to monitor cluster health, manage indices, and browse documents without writing raw JSON. The project is delivered as a cross-platform application, available as a browser extension, a standalone desktop application, or a containerized web service via Docker. The interface covers cluster administration, including the management of shards, aliases, and snapshot repositories for backups. It also includes data exploration too

This project is a reference implementation providing a collection of practical examples for data access patterns and repository abstractions within the Spring Data ecosystem. It serves as a comprehensive showcase for implementing consistent data layers across various relational and non-relational databases. The repository specifically demonstrates multi-store persistence by integrating relational, document, and vector databases within a single application. It includes implementations for vector search to manage high-dimensional embeddings and similarity searches across different database tech

The mongo-go-driver is a Go library for building applications that integrate with a MongoDB document store. It enables the storage and retrieval of flexible document data by providing a bridge between Go backends and the database. The driver implements specialized capabilities for semantic vector search, allowing the handling and execution of high-dimensional vector data for similarity-based retrieval. It also supports full-text search via linguistic analysis and programmatic search index management. The project covers a broad range of database operations, including document-based CRUD, bulk

The MongoDB Python Driver is a client library and NoSQL database client used to execute CRUD operations and manage data within MongoDB databases using the Python programming language. It serves as a database connectivity library that handles authentication and connection pooling, while also providing a vector search client for managing embedding indexes and retrieving data based on semantic similarity. The driver supports both synchronous and asynchronous database driver models to perform non-blocking I/O operations and stream data from database clusters. It distinguishes itself through speci

Searchkick is an integration library and wrapper that connects application models to search engines such as Elasticsearch and OpenSearch. It functions as a search index synchronizer, automatically mirroring database records to a search server to enable full-text and vector retrieval. The project provides a high-level interface for implementing keyword search, semantic vector search, and hybrid search. It distinguishes itself through the ability to combine traditional keyword matching with vector embeddings using reranking and fusion techniques to improve precision. The library covers the end

GreptimeDB is a distributed, open-source time-series database built for unified observability. It stores and queries metrics, logs, and traces together in a single columnar engine, supporting both SQL and PromQL for analysis. The database is designed as a Kubernetes-native operator with a decoupled compute and storage architecture, enabling horizontal scaling and multi-region deployment. What distinguishes GreptimeDB is its role as a multi-protocol ingestion gateway, accepting data through OpenTelemetry, Prometheus Remote Write, InfluxDB, Loki, Elasticsearch, Kafka, and MQTT protocols without

Django Haystack is a search abstraction layer and framework for integrating full-text search into Django applications. It provides a unified interface for defining search schemas and managing how data models are indexed and stored across different search engines. The framework acts as a modular backend wrapper, allowing applications to switch between interchangeable search providers without rewriting core search logic. It standardizes interactions with external engines through a decoupled provider layer, facilitating search engine migration by changing configuration settings. The system cove

ParadeDB is a database extension that integrates full-text search, vector database capabilities, and real-time analytics directly into a relational engine. It functions as a plugin that adds new storage and query execution capabilities to an existing database architecture. The project distinguishes itself by supporting hybrid search workflows that combine lexical keyword matching with dense and sparse vector similarity in a single query. It utilizes reciprocal rank fusion to merge these ranked result sets and employs logical replication to synchronize data from external instances, removing th

Zinc is a high-performance full-text search engine written in Go. It provides a schema-less document index that organizes arbitrary datasets into searchable structures without requiring a predefined data format. The engine features an API compatible with Elasticsearch for indexing and querying data, which facilitates the ingestion of single and bulk records. It is designed as an in-process search engine that embeds indexing and retrieval logic within a single binary to operate with minimal system resource overhead. The system includes a built-in web-based management interface for executing s

Marqo is an ecommerce product discovery platform, multimodal vector database, and AI search merchandising tool. It provides infrastructure for implementing semantic search and recommendations, allowing shoppers to find products using natural language and images. The platform distinguishes itself through a hybrid ranking pipeline that combines neural semantic scores with business-defined boosting and pinning rules. It features a conversational commerce engine that uses large language models to process user intent and provides a search performance analytics suite for measuring conversion uplift

Kvrocks is a distributed key-value store and Redis-compatible NoSQL database. It utilizes a RocksDB storage engine to provide disk-based persistence, allowing for high-capacity data storage with reduced memory costs compared to in-memory systems. The system functions as a vector database and full-text search engine, supporting nearest-neighbor searches on vector embeddings and complex document queries via text matching. It employs a proxyless cluster architecture with slot-based routing to distribute data and scale capacity across multiple nodes. The platform covers a wide range of data mana

Manticoresearch is a high-performance search engine and database designed for indexing and retrieving large datasets. It functions as a full-text search engine, a vector search database, and a SQL-based search database, providing a distributed search cluster architecture. The system provides an alternative to the Elasticsearch stack, offering a compatible API for indexing and searching structured and unstructured data. It distinguishes itself by supporting multiple retrieval methods, including vector matching for similarity search, geospatial queries, and traditional full-text ranking. The p

DeepLake is AI data infrastructure consisting of a multimodal data lake, a hybrid search engine, and a serverless vector database. It provides a PostgreSQL-based AI data runtime that combines multimodal storage with streaming pipelines to load and shuffle datasets from cloud storage directly into deep learning training pipelines. The system utilizes lazy indexing to store and slice images, audio, and video without loading entire files into memory. It enables retrieval-augmented generation by persisting high-dimensional embeddings in a serverless vector store and implementing hybrid search tha

This project is a Node.js client and database driver for Elasticsearch. It acts as a REST API wrapper that translates JavaScript method calls into HTTP requests to index, search, and manage documents within a distributed search and analytics engine. The library provides a programmatic interface for cluster management and data integration. It includes tools for discovering cluster nodes and managing indices to maintain connection stability and organize data storage. The client covers a broad range of operations, including document lifecycle management for CRUD actions, search query execution,