Elasticsearch

BM25是一种基于词频统计（TF-IDF改进版）的经典信息检索算法，属于稀疏检索方法

BM25 remains the default ranking algorithm for mainstream search engines such as Elasticsearch and Lucene.

RAGFlow is deployed based on Docker containerization and runs five containers upon startup: Redis, MinIO, MySQL, Elasticsearch, and RAGFlow Server.

RAGFlow依赖Elasticsearch（全文检索）、Redis（缓存）、MySQL（元数据存储）等多个服务

iNaturalist的v1 API基于Elasticsearch构建，支持复杂的地理空间查询，单次请求最多返回200条记录并支持分页游标

Spring Boot、MyBatis、Kafka、Elasticsearch等企业级框架构成了国内绝大多数互联网公司的技术底座

BM25 is a probabilistic retrieval model proposed by Robertson et al. in the 1990s and is the core ranking algorithm of search engines such as Elasticsearch.

In Booking.com's search scenario, Elasticsearch handles full-text search and geographic filtering, maintaining eventual consistency with PostgreSQL through CDC, with PostgreSQL as the single source of truth.

After evaluating at the scale of a single VPS with several hundred thousand documents, LectuLibre concluded that PostgreSQL's built-in full-text search is sufficient, with no need to introduce Elasticsearch.

RRF（倒数排名融合）是一种无需训练、无需归一化的多路检索结果融合算法，公式为score = Σ weight_r / (k + rank_r(d))，k通常取60，被Elasticsearch和Weaviate等系统采纳为默认融合策略。

Winora supports retrieval across multiple vector stores including pgvector, Elasticsearch, Milvus, and Qdrant, and adopts a parent-child chunking strategy to preserve contextual semantics.

Milvus is currently the most widely used vector database, with other options including PostgreSQL (pgvector), Redis, and Elasticsearch

Traditional relational databases and ES search engines are good at exact keyword matching but cannot understand the true intent behind natural language questions

Mainstream data systems such as Elasticsearch, ClickHouse, and MongoDB have successively added vector index support.

In a dual-system architecture, ETL pipelines are typically required to synchronize data between full-text search and analytical query systems, introducing data latency and consistency issues.

SereneDB is compatible with both the Postgres and Elasticsearch protocols.

According to official public benchmark tests, SereneDB outperforms Elasticsearch, ClickHouse, and Postgres search extensions in full-text search and analytics scenarios.

Separating search functionality from the primary database into a standalone search engine introduces operational overhead and data synchronization consistency issues.

Although Elasticsearch supports dynamic mapping, it triggers mapping conflict errors and can even cause data loss when the same field is written with different types across different logs.

Elasticsearch in production typically requires configuring dedicated Master nodes, Data nodes, and Ingest nodes, along with manually planning shard counts—too many shards slow down queries, while too few limit write throughput.