This is a cache of https://www.elastic.co/guide/en/elasticsearch/reference/current/elasticsearch-intro-what-is-es.html. It is a snapshot of the page at 2024-10-02T00:57:46.025+0000.
What is Elasticsearch? | Elasticsearch Guide [8.15] | Elastic
« Elasticsearch basics Run Elasticsearch »
Elastic Docs ›Elasticsearch Guide [8.15] ›Elasticsearch basics

What is Elasticsearch?

edit

What is Elasticsearch?

edit

Elasticsearch is a distributed search and analytics engine, scalable data store, and vector database built on Apache lucene. It’s optimized for speed and relevance on production-scale workloads. Use Elasticsearch to search, index, store, and analyze data of all shapes and sizes in near real time.

Elasticsearch is the heart of the Elastic Stack. Combined with Kibana, it powers the following Elastic solutions:

Elasticsearch has a lot of features. Explore the full list on the product webpage.

What is the Elastic Stack?

Elasticsearch is the core component of the Elastic Stack, a suite of products for collecting, storing, searching, and visualizing data. Learn more about the Elastic Stack.

Use cases

edit

Elasticsearch is used for a wide and growing range of use cases. Here are a few examples:

Observability

  • Logs, metrics, and traces: Collect, store, and analyze logs, metrics, and traces from applications, systems, and services.
  • Application performance monitoring (APM): Monitor and analyze the performance of business-critical software applications.
  • Real user monitoring (RUM): Monitor, quantify, and analyze user interactions with web applications.
  • OpenTelemetry: Reuse your existing instrumentation to send telemetry data to the Elastic Stack using the OpenTelemetry standard.

Search

  • Full-text search: Build a fast, relevant full-text search solution using inverted indexes, tokenization, and text analysis.
  • Vector database: Store and search vectorized data, and create vector embeddings with built-in and third-party natural language processing (NLP) models.
  • Semantic search: Understand the intent and contextual meaning behind search queries using tools like synonyms, dense vector embeddings, and learned sparse query-document expansion.
  • Hybrid search: Combine full-text search with vector search using state-of-the-art ranking algorithms.
  • Build search experiences: Add hybrid search capabilities to apps or websites, or build enterprise search engines over your organization’s internal data sources.
  • Retrieval augmented generation (RAG): Use Elasticsearch as a retrieval engine to supplement generative AI models with more relevant, up-to-date, or proprietary data for a range of use cases.
  • Geospatial search: Search for locations and calculate spatial relationships using geospatial queries.

Security

  • Security information and event management (SIEM): Collect, store, and analyze security data from applications, systems, and services.
  • Endpoint security: Monitor and analyze endpoint security data.
  • Threat hunting: Search and analyze data to detect and respond to security threats.

This is just a sample of search, observability, and security use cases enabled by Elasticsearch. Refer to Elastic customer success stories for concrete examples across a range of industries.

« Elasticsearch basics Run Elasticsearch »