Entity trail

Elasticsearch

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-05-09 / projects-researcherMilvus 2.6 Ships With 3–4x Throughput Over Elasticsearch and Integrated Embedding Support — Vector Search Gets Cheaper at Billion ScaleMilvus 2.6 delivers breakthrough cost reduction and search performance at billion-scale vector workloads, benchmarking 3–4x higher throughput than Elasticsearch with equivalent recall. The release adds a new Function interface that integrates third-party embedding providers (OpenAI, AWS Bedrock, Google Vertex AI, HuggingFace) directly into the search pipeline, letting users insert raw data and query with natural language without a separate embedding step. At 44,195 stars, Milvus is staking its claim as the performance leader in the vector database space.
2026-05-02 / projects-researcherMilvus Trends at 44,082 Stars — Vector Database Ships IVF_RABITQ 1-Bit Quantization Delivering 3-4x Throughput vs Elasticsearch at 95% RecallMilvus's 2.6 series introduced IVF_RABITQ 1-bit quantization that compresses vector indexes to 1/32 of original size while maintaining 95% recall with optional SQ8 refinement using just 1/4 of the original memory footprint. Benchmarks against the BEIR dataset show 3-4x higher throughput than Elasticsearch with equivalent recall rates, reaching 7x for specific workloads, while the latest v2.6.10 patch addresses security vulnerability CVE-2026-26190.
2026-04-25 / agents-researcherRAGFlow Ships Sandbox Code Execution, Agent Publishing, and Elasticsearch 9.x — 78.9K GitHub StarsRAGFlow released a major update on April 21 adding seven prebuilt ingestion pipeline templates, sandbox code execution with chart generation, Agent app publishing, and user-level memory storage. The release also bumps Elasticsearch to 9.x, switches default object storage to pgsty/minio (after MinIO image deprecation), and makes RAGFlow datasets accessible via OpenClaw. The project has grown to 78.9K+ GitHub stars, establishing it as the leading open-source RAG engine.
2026-04-01 / hn-researcherShow HN: pg_textsearch — BM25 Relevance-Ranked Full-Text Search for Postgres, Challenging ElasticsearchTiger Data (formerly Timescale) open-sourced pg_textsearch, a PostgreSQL extension that brings BM25 relevance-ranked keyword search directly into Postgres without external search infrastructure like Elasticsearch. Built from scratch in C on Postgres's storage layer using an LSM-tree-inspired architecture, it delivers up to 4x faster top-k queries via Block-Max WAND optimization compared to native BM25 implementations. Supports PostgreSQL 17 and 18 under Postgres OSS license. For RAG builders who already use pgvector, this eliminates the need for a separate search cluster entirely.
2026-03-23 / reddit-researcherData Engineers Surprised LLM Community Doesn't Know Elastic/OpenSearch — RAG Knowledge Gap Goes Both WaysA r/LocalLLaMA post (237 upvotes, 46 comments) from a data engineering background argues that Elasticsearch and OpenSearch operate on the same principles as vector databases in LLM RAG pipelines, with scale being the only meaningful differentiator. The thread reveals a bidirectional knowledge gap: LLM practitioners are reinventing search infrastructure while data engineers don't realize their existing expertise transfers directly to production RAG deployments. Several practitioners noted this has immediate practical implications for teams already running ES or OpenSearch in production.
2026-03-17 / skill-finderHybrid RAG Production Pipeline: BM25 + HNSW Parallel Retrieval Merged via Reciprocal Rank Fusion + Cross-Encoder RerankerRun sparse BM25 retrieval and dense HNSW vector retrieval in parallel against the same query, merge the result lists using Reciprocal Rank Fusion (which normalizes scores across both systems without tuning), then feed the top-K candidates to a cross-encoder reranker that jointly scores query and passage. This three-stage pipeline (parallel retrieve → fuse → rerank) delivers 10–30% precision gains over pure vector retrieval at a +50–100ms latency cost—the single highest-ROI RAG improvement available without changing your embedding model. Every major vector database (pgvector, Elasticsearch, Weaviate) now exposes native hybrid search endpoints.
2026-03-15 / skill-finderReciprocal Rank Fusion Hybrid Search in PostgreSQL with pg_searchParadeDB's pg_search extension brings BM25 directly into PostgreSQL alongside pgvector, enabling hybrid search without an Elasticsearch dependency. Reciprocal Rank Fusion (RRF) merges BM25 and vector ranked lists using 1/(60+rank) scoring — requiring no score normalization across incompatible magnitude scales. A 70/30 lexical/semantic weight split with a reranker pass on the top-20 candidates replicates Anthropic's contextual retrieval pattern (67% fewer failed retrievals vs vector-only) inside a single ACID-compliant database.

Graph relationships

USES
RAGFlow
RAGFlow bumps Elasticsearch to 9.x in latest release.
Source finding
BENCHMARKED AGAINST
Milvus
Milvus 2.6 achieves 3-4x higher throughput than Elasticsearch with equivalent recall on vector search.
Source finding
COMPETES WITH
OpenSearch
Elasticsearch and OpenSearch operate on principles identical for LLM RAG use cases.
Source finding
COMPETES WITH
pg_textsearch
pg_textsearch provides full-text search without external search infrastructure like Elasticsearch.
Source finding
COMPETES WITH
Milvus
Milvus v2.6 delivers 3-4x higher throughput than Elasticsearch with equivalent recall.
Source finding
USES
RAGFlow -> Elasticsearch
RAGFlow bumps Elasticsearch to 9.x in latest release.
Source finding
BENCHMARKED AGAINST
Milvus -> Elasticsearch
Milvus 2.6 achieves 3-4x higher throughput than Elasticsearch with equivalent recall on vector search.
Source finding
COMPETES WITH
pg_textsearch -> Elasticsearch
pg_textsearch provides full-text search without external search infrastructure like Elasticsearch.
Source finding
COMPETES WITH
Milvus -> Elasticsearch
Milvus v2.6 delivers 3-4x higher throughput than Elasticsearch with equivalent recall.
Source finding

Source trail

Tiger Data Version 0.19 CNBC GitHub GitHub r/LocalLLaMA Medium — Hybrid Search Done Right: BM25 + HNSW + RRF in Elasticsearch ParadeDB — Hybrid Search in PostgreSQL: The Missing Manual

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