Elasticsearch
Elasticsearch 9.4 - the world's leading distributed search and analytics engine powering full-text search, log observability, and AI-driven semantic retrieval at any scale.
Elasticsearch is the world's most widely adopted distributed search and analytics engine, built on Apache Lucene and delivered via a schema-free RESTful JSON API. Version 9.4 (May 2026) brings native semantic search, ES|QL Views, vector kNN acceleration, and LogsDB storage compression — making it the single platform teams reach for across full-text search, log observability, and AI-powered retrieval.
Elasticsearch at a glance
Why teams choose it
Near real-time search
Inverted index and HNSW graph structures return results within milliseconds across billions of documents, with index changes visible in under one second.
Horizontal scalability
Automatic shard allocation across nodes means you add capacity simply by joining new nodes — no application changes. It handles petabyte-scale datasets in production.
AI-ready retrieval
Dense vector (kNN) and sparse vector (ELSER) fields enable semantic search natively alongside BM25 keyword matching — no external vector database needed.
Rich aggregations
Bucket, metric, pipeline, and matrix aggregations run directly against indexed data, powering dashboards and alerts without separate OLAP infrastructure.
Built-in security
TLS encryption, role-based access control, field- and document-level security, and audit logging have been included free since Elasticsearch 7.1.
Open ecosystem
2,400+ open-source contributors, official clients for every major language, and seamless integration with Kibana, Logstash, Beats, OpenTelemetry, and Kafka.
How Elasticsearch works
Data flows in as JSON, is analysed and indexed, then served back through keyword, vector, or hybrid queries — all across a self-healing cluster of shards and replicas:
From ingest to answer — the Elasticsearch data path
Data becomes searchable in a second
Documents are submitted as JSON over HTTP. Elasticsearch applies the configured mapping — field types, analyzers, tokenizers — builds an inverted index for text fields, and makes documents searchable within roughly one second. Ingest pipelines with built-in processors (grok, enrich, script) transform and enrich data at write time. LogsDB mode reduces log storage by up to 65% via synthetic source and columnar compression.
One engine, hundreds of query types
The Query DSL exposes hundreds of query types — match, multi_match, bool, term, range, geo_distance, and percolate. ES|QL, now with Views support in 9.x, provides a SQL-like pipe syntax optimised for log analytics and exploration directly in the engine. Relevance uses BM25 by default and can be boosted with function_score or rank_feature queries.
Resilience without application complexity
Each index is divided into primary shards and one or more replicas for fault tolerance. Shard allocation is handled automatically by the raft-based cluster coordinator, and a primary-shard failure triggers automatic promotion of a replica within seconds. Cross-cluster replication (CCR) and cross-cluster search (CCS) support multi-region and multi-cloud topologies with no application-layer plumbing.
AI retrieval built into the platform
Dense vector fields (up to 4,096 dimensions) use HNSW graphs for approximate k-nearest-neighbour search, and Elasticsearch 9.x delivers roughly 30% faster HNSW queries versus 8.8 benchmarks. ELSER provides out-of-the-box semantic retrieval with no externally managed embedding models, and hybrid search with Reciprocal Rank Fusion (RRF) combines keyword and vector scores in a single request.
Adoption & comparison
Cumulative downloads have climbed steadily past 1.4 billion, reflecting how deeply Elasticsearch has embedded itself across search, observability, and AI workloads.
Elasticsearch cumulative downloads (billions) — 2019 to 2025
| Capability | Elasticsearch 9.x | OpenSearch 2.x | Apache Solr 9.x | Algolia |
|---|---|---|---|---|
| Full-text search | ||||
| Native vector / kNN search | — | Partial | ||
| Semantic search (built-in model) | ELSER | Partial | — | — |
| Log & observability at scale | LogsDB | Partial | — | — |
| Aggregations / analytics | Limited | — | ||
| ES|QL / SQL interface | Partial (PPL) | — | — | |
| Self-hosted & cloud options | Cloud only | |||
| SIEM / security analytics | Partial | — | — |
Where we use it at ELIVTECH
E-commerce & product search
Faceted filtering, synonym dictionaries, typo-tolerant fuzzy matching, and relevance tuning drive product discovery and personalised ranking at catalogue scale.
Log analytics & observability
Centralised ingestion of application, infrastructure, and security logs via Elastic Agent or Beats. LogsDB cuts storage costs by up to 65% while retaining full query capability.
Security & SIEM
Real-time correlation of security events, threat-intelligence enrichment, and ML-based anomaly detection power SIEM and SOAR workflows in regulated industries.
Enterprise knowledge search
Unified search across SharePoint, Confluence, Salesforce, and internal databases surfaces the right document or answer from any authoritative source in milliseconds.
RAG & AI assistant retrieval
Hybrid BM25 + kNN retrieval feeds grounded context into LLM pipelines, reducing hallucinations and ensuring responses are backed by your proprietary data.
Geospatial & location services
Geo-point and geo-shape fields enable proximity search, bounding-box filtering, and geospatial aggregations for logistics, mapping, and location-aware applications.
How we integrate Elasticsearch
Discover
We audit your data sources, query patterns, and SLAs to size the cluster and shape the index model correctly.
Integrate
We set up mappings, ingest pipelines, and the client library, connecting Elasticsearch to your application stack.
Optimise
We tune relevance, add ILM tiering, and build monitoring dashboards so search stays fast and cost-efficient.
Scale
As data and traffic grow, we add nodes and replicas with zero downtime and no application changes.
Where Elasticsearch shines
- Full-text search with language analysis, fuzzy matching, and relevance scoring
- Centralised, queryable log storage at multi-terabyte scale
- Semantic and vector search without deploying a separate vector database
- Rich aggregation analytics without a separate data warehouse
- Battle-tested SIEM and security-analytics back-ends
- Self-hosted, cloud, or hybrid deployment flexibility
What this means for your business
Beyond the engineering, Elasticsearch with ELIVTECH delivers outcomes your whole organisation can feel:
Customers find what they want
Fast, typo-tolerant, relevant search turns browsing into buying — users reach the right product or answer in milliseconds instead of giving up.
Lower storage costs
LogsDB compression cuts log storage by up to 65%, so you keep more history for compliance and troubleshooting while spending less on infrastructure.
Faster answers, fewer risks
Real-time dashboards and anomaly detection surface issues and threats early, protecting uptime, data integrity and the trust your customers place in the service.
AI grounded in your data
Semantic and hybrid retrieval let AI assistants answer from your own trusted content, making generative features accurate and safe to ship.
Build your next product on Elasticsearch
Our engineers ship production-grade Elasticsearch solutions. Let's scope yours.
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