Elasticsearch 8.6.2

Elasticsearch 8.6.2


Elasticsearch is a powerful and flexible search and analytics engine designed for all types of data. It is built on top of the popular Apache Lucene library, which provides low-level search and indexing capabilities. Elasticsearch enhances these capabilities by providing a distributed, scalable, and highly available search and analytics platform that can handle large volumes of data. One of Elasticsearch's key features is its real-time search and analytics capabilities, which allow users to quickly and easily search through large volumes of data and retrieve relevant information. This is achieved through a distributed architecture that enables Elasticsearch to store, index, and search data across multiple nodes in a cluster, ensuring high availability and fault tolerance.

  • Distributed and scalable: Elasticsearch is designed to be distributed and scalable, allowing you to store and search massive amounts of data across a cluster of nodes.
  • Real-time search and analytics: Elasticsearch provides real-time search and analytics capabilities, allowing you to perform complex queries and aggregations on large datasets in near real-time.
  • Multitenancy: Elasticsearch provides multitenancy support, allowing you to securely isolate and search data across multiple tenants or customers.
  • Full-text search: Elasticsearch provides a powerful full-text search engine that can be used to search and analyze text data, including support for advanced search features such as fuzzy search and phonetic matching.
  • Analytics and visualization: Elasticsearch provides built-in support for data analytics and visualization, allowing you to explore and understand your data with features such as aggregations, histograms, and geo-visualization.

  • Log analytics: Elasticsearch is commonly used for log analytics, allowing you to collect, index, and search logs in real-time across multiple servers or applications. This makes it easy to troubleshoot issues and gain insights into application performance and user behavior.
  • E-commerce search: Elasticsearch is also used in e-commerce applications to provide fast and accurate search capabilities for product catalogs. This enables users to quickly find the products they are looking for, and provides businesses with insights into user behavior and product popularity.

  • Install and configure Elasticsearch on a cluster of nodes.
  • Index data into Elasticsearch using APIs or integrations with other data sources.
  • Search and analyze data using Elasticsearch's query DSL and aggregation framework.
  • Visualize data using Kibana, which is tightly integrated with Elasticsearch.

  • Elasticsearch is written in Java and uses the Apache Lucene library for indexing and searching.
  • It provides a RESTful API that can be accessed using HTTP requests.
  • Elasticsearch uses a distributed, sharded architecture to provide scalability and high availability.
  • It provides advanced search features such as full-text search, fuzzy search, and phonetic matching.
  • Elasticsearch can be extended through plugins, which provide additional functionality such as analysis, ingest pipelines, and security.

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