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Elasticsearch: History, Architecture and features & Overview

Elasticsearch is a source-available search engine developed by Elastic. It is based on Apache Lucene and provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. Official clients are available in Java, C#, PHP, Python, Ruby, and other languages. According to the DB-Engines ranking…

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Elasticsearch topic overview

The analysis highlights History, Architecture and features and Overview as prominent areas in the source structure around Elasticsearch.

Related topics
46
Source areas
3
Connected nodes
49
Extracted relationships
63
Concept neighborhoods
27
Bridge connections
49

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

History · 19 topics
Overview · 18 topics
Architecture and features · 9 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

9.5.x
9.5.2 / 20 August 2026; 4 days ago (20 August 2026)
Developer
Elastic NV
License
Triple-licensed Elastic License (proprietary; source-available), Server Side Public License (proprietary; source-available) and Affero General Public License (free and open-source)
Operating system
Cross-platform
Original author
Shay Banon
Release
8 February 2010; 16 years ago (2010-02-08)

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

History

Architecture and features

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Elasticsearch connects Entity context

The extracted context around Elasticsearch shows recurring relationship patterns in the source. For example, Elasticsearch → Aiven, Amazon Web Services, Apache, Apache License, April, AWS, Beats, CrateDB, Critics, Due, Elastic, Elastic License, In January, Kibana, Logstash, Logz, OpenSearch, Other, Server Side Public License Another extracted example is Elasticsearch → Benchmark Capital, Compass, Developing, Elastic NV, February, He, HTTP, In June, Index Ventures, JSON, New Enterprise Associates, Series, Shay Banon. Use these groups to spot repeated connection types before inspecting the individual relationships.

Elasticsearch

Top relations

related to Licensing changes · 19
Elasticsearch → Aiven, Amazon Web Services, Apache, Apache License, April, AWS, Beats, CrateDB, Critics, Due, Elastic, Elastic License, In January, Kibana, Logstash, Logz, OpenSearch, Other, Server Side Public License
related to history · 13
Elasticsearch → Benchmark Capital, Compass, Developing, Elastic NV, February, He, HTTP, In June, Index Ventures, JSON, New Enterprise Associates, Series, Shay Banon
related to Architecture and features · 10
Elasticsearch → Apache Lucene, Documents, Java API, JSON, Lucene's, NoSQL, Once, Real-time GET, Routing, The
related to Elastic Stack · 6
Elasticsearch → Beats, Elastic Stack, ELK Stack, Kibana, Logstash, The
is a · 2
Elasticsearch → most popular enterprise search engine.Elasticsearch is distributed and uses JSON documents stored in indices divided into shards, source-available search engine developed by Elastic
see also · 2
Elasticsearch → AWSGraylog, Information
9.5.x · 1
Elasticsearch → 9.5.2 / 20 August 2026; 4 days ago (20 August 2026)
Developer · 1
Elasticsearch → Elastic NV
License · 1
Elasticsearch → Triple-licensed Elastic License (proprietary; source-available), Server Side Public License (proprietary; source-available) and Affero General Public License (free and open-source)
Operating system · 1
Elasticsearch → Cross-platform

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

elastic search kibana engine json distributed java stack license apache documents fork source-available available software logstash developed shards index also

Elasticsearch relationships Subject–Predicate–Object triples

TTTA extracted 63 structured relationships around Elasticsearch. Examples in this analysis include Elasticsearch → 9.5.x → 9.5.2 / 20 August 2026; 4 days ago (20 August 2026) and Elasticsearch → Developer → Elastic NV. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
Elasticsearch9.5.x9.5.2 / 20 August 2026; 4 days ago (20 August 2026)1.00infobox
ElasticsearchDeveloperElastic NV1.00infobox
ElasticsearchLicenseTriple-licensed Elastic License (proprietary; source-available), Server Side Public License (proprietary; source-available) and Affero General Public License (free and open-source)1.00infobox
ElasticsearchOperating systemCross-platform1.00infobox
ElasticsearchOriginal authorShay Banon1.00infobox
ElasticsearchRelease8 February 2010; 16 years ago (2010-02-08)1.00infobox
ElasticsearchRepositorygithub.com/elastic/elasticsearch1.00infobox
ElasticsearchStable release9.5.x9.5.2 / 20 August 2026; 4 days ago (20 August 2026)1.00infobox
ElasticsearchTypeSearch and index1.00infobox
ElasticsearchWebsitewww.elastic.co/elasticsearch/1.00infobox
ElasticsearchWritten inJava1.00infobox
Elasticsearchis asource-available search engine developed by Elastic0.90text
Elasticsearchis amost popular enterprise search engine.Elasticsearch is distributed and uses JSON documents stored in indices divided into shards0.90text

Related concept clusters Concept neighborhoods

The concept neighborhoods around Elasticsearch bring nearby vocabulary together. In this analysis, examples include Elastic, Kibana and Also. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Elasticsearch
    • Elastic
    • Kibana
    • Also
    • Licensing
    • Aws
    • Fork
    • Logstash
    • Open-source
    • Public
    • Version
    • Apache
    • Engine
  • elasticsearch
    • Elastic
    • Kibana
    • Also
    • Licensing
    • Aws
    • Fork
    • Logstash
    • Open-source
    • Public
    • Version
    • Apache
    • Engine
  • search engine
    • Engine
    • Search
    • Documents
    • Full-text
    • Http
    • Source-available
    • Json
    • Distributed
    • Lucene
    • Alongside
    • Beats
    • Stored
  • elastic
    • Elasticsearch
    • Kibana
    • Public
    • Stack
    • License
    • Licensing
    • Offering
    • Search
    • Beats
    • Source-available
    • Also
    • Available
  • apache license 2.0
    • Open-source
    • Public
    • Lucene
    • Json
    • Licensing
    • Source-available
    • Version
    • License
    • Kibana
    • Full-text
    • Http
    • Beats
  • apache lucene
    • Json
    • Lucene
    • Http
    • License
    • Documents
    • Java
    • Kibana
    • Full-text
    • Beats
    • Licensing
    • Elasticsearch
    • Aws
  • full-text search
    • Engine
    • Http
    • Lucene
    • Full-text
    • Search
    • Source-available
    • Documents
    • Json
    • Distributed
    • Licensing
    • Stored
    • Also
  • faceted search
    • Engine
    • Full-text
    • Http
    • Source-available
    • Documents
    • Json
    • Distributed
    • Lucene
    • Licensing
    • Stored
    • Also
    • Index

Connections between topic areas Semantic bridges

For Elasticsearch, one of the stronger structural bridges in this analysis connects Elasticsearch with History. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
Elasticsearch — History · splits 30 ⟂ 20
Elasticsearch — Overview · splits 31 ⟂ 19
Elasticsearch — Architecture and features · splits 40 ⟂ 10

Map overview Semantic statistics

Elasticsearch

Nodes50
Edges49
Triples63
Avg. degree1.96
Density0.04
Components1

Source & methodology

TTTA analyzes the structure around Elasticsearch to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Architecture and features & Overview, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Elasticsearch · EN edition · Analysis: TopicsToTalkAbout

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