Algolia
Hosted search-as-a-service platform delivering real-time, full-text search for apps and websites
Elasticsearch
Distributed search and analytics engine for production-scale workloads
Elasticsearch is a scalable, distributed search engine and vector database optimized for speed and relevance across massive datasets, powering full-text search, logs, metrics, APM, and AI applications.
Overview
Overview
Elasticsearch is a distributed search and analytics engine designed for production-scale workloads. It serves as a scalable data store and vector database, delivering near real-time search performance across massive datasets. As the foundation of Elastic's Stack platform, it powers full-text search, log aggregation, metrics analysis, application performance monitoring (APM), security logs, and generative AI integrations.
Capabilities & Deployment
Elasticsearch indexes structured and unstructured data—text, numerical values, and geospatial information—optimizing storage for fast retrieval. Documents are indexed and available for search in near real-time. Users interact with Elasticsearch through REST APIs, language clients (Python, Java, JavaScript, etc.), or Kibana's Dev Tools Console. Data can be added as single documents or in bulk using newline-delimited JSON.
Deployment options include managed Elasticsearch Service on Elastic Cloud or self-managed installations. For local development, the start-local script provisions Dockerized Elasticsearch and Kibana instances with basic authentication and a one-month trial license that includes all features before reverting to the Basic tier.
Audience
Elasticsearch serves developers building search-driven applications, DevOps teams managing observability pipelines, data engineers handling analytics workloads, and organizations integrating vector search for AI use cases.
Highlights
Near real-time search and indexing across distributed clusters
Vector database capabilities for AI and machine learning workflows
REST API and multi-language client support for flexible integration
Scalable architecture handling logs, metrics, APM, and security data
Pros
- Proven scalability for production workloads with distributed architecture
- Rich ecosystem including Kibana for visualization and exploration
- Flexible deployment: managed cloud service or self-hosted installations
- Strong full-text search with support for structured and unstructured data
Considerations
- Complexity increases with cluster management and tuning at scale
- Resource-intensive; requires careful capacity planning for large datasets
- Advanced features require paid licensing after trial period expires
- Learning curve for optimizing queries and index configurations
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Fit guide
Great for
- Organizations needing full-text search across large document repositories
- DevOps teams centralizing logs, metrics, and APM data for observability
- Applications requiring vector search and generative AI integration
- Teams seeking a unified platform for search and analytics workloads
Not ideal when
- Simple use cases where a lightweight search library suffices
- Projects with strict constraints on infrastructure complexity or cost
- Teams lacking expertise in distributed systems and cluster operations
- Scenarios requiring ACID transactions or relational database guarantees
How teams use it
Centralized Log Management
Aggregate logs from distributed services into Elasticsearch, enabling real-time search, filtering, and anomaly detection across infrastructure and application layers.
E-commerce Product Search
Deliver fast, relevant full-text search with faceted filtering and typo tolerance, improving customer experience and conversion rates on product catalogs.
Security Information and Event Management (SIEM)
Ingest security logs and events at scale, perform correlation analysis, and detect threats in near real-time with customizable alerting rules.
AI-Powered Semantic Search
Leverage vector database capabilities to implement semantic search and retrieval-augmented generation (RAG) for generative AI applications.
Tech snapshot
Java99%
Groovy1%
StringTemplate1%
Shell1%
ANTLR1%
Dockerfile1%
Frequently asked questions
What is the difference between Elasticsearch and the Elastic Stack?
Elasticsearch is the core search and analytics engine. The Elastic Stack includes Elasticsearch plus Kibana (visualization), Logstash (data ingestion), and Beats (lightweight shippers) for a complete observability and search platform.
Can I run Elasticsearch for free?
Yes. Elasticsearch offers a Free and open Basic tier with core search and analytics features. A one-month trial provides access to advanced features; after expiration, the license reverts to Basic unless upgraded.
How do I connect to Elasticsearch from my application?
Use the REST API directly via HTTP clients like curl, or integrate official language clients (Python, Java, JavaScript, Go, etc.) with basic authentication or API keys for programmatic access.
Is the start-local setup suitable for production?
No. The start-local script is designed exclusively for local development and testing. It disables HTTPS and uses basic authentication, making it unsuitable for production deployments.
What deployment options are available?
Deploy Elasticsearch as a managed service on Elastic Cloud for simplified operations, or self-host by downloading binaries and managing infrastructure yourself. Local Docker setups are available for development.
Project at a glance
Active- Stars
- 75,932
- Watchers
- 75,932
- Forks
- 25,797
Repo age15 years old
Last commit5 hours ago
Self-hostingSupported
Primary languageJava
Last synced 4 hours ago