Elasticsearch is a highly scalable open-source full-text search and analytics engine built on top of Apache Lucene. It is commonly used for log and event data analysis, real-time application monitoring, and full-text search scenarios. Elasticsearch is part of the Elastic Stack, which also includes Logstash for log data ingestion and transformation, and Kibana for data visualization and exploration.
Here are some key features of Elasticsearch:
- Full-text search: Elasticsearch allows you to index and search textual data efficiently, supporting various types of queries including keyword searches, phrase searches, fuzzy searches, and more.
- Distributed architecture: Elasticsearch is designed to be distributed, meaning it can scale horizontally across multiple nodes to handle large amounts of data and queries.
- Near real-time search: Elasticsearch provides near real-time search capabilities, meaning that indexed documents are searchable almost immediately after being indexed.
- Multi-tenancy: It supports multiple indices and types, allowing you to segregate data and perform searches across specific indices or types.
- Schema-free: Elasticsearch is schema-free, which means you can index documents without predefining a schema. It dynamically maps data types based on the structure of the documents.
- RESTful API: Elasticsearch provides a RESTful API for interacting with the cluster, making it easy to integrate with various programming languages and frameworks.
- Aggregations: It offers powerful aggregations for data analysis, allowing you to compute metrics, histograms, and statistical summaries over large datasets.
- Document-oriented: Data in Elasticsearch is stored as JSON documents, making it easy to work with structured and semi-structured data.
Overall, Elasticsearch is a versatile and powerful tool for building search and analytics applications, and it is widely used across various industries for a range of use cases including log analysis, e-commerce search, and real-time analytics.