Elastic Database
Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. As the heart of the Elastic Stack, it centrally stores your data so you can discover the expected and uncover the unexpected.
- Since:2009
- Discord:@elastic
- Dockerhub:elasticsearch
- Docs:www.elastic.co
- Github Topic:elasticsearch
- Official:www.elastic.co
- Reddit:r/elasticsearch
- Repository:github.com
- StackOverflow:[elasticsearch]
- Twitter:@elastic
- Wikipedia:Elasticsearch
#What is Elastic?
Elasticsearch is a distributed, open-source search and analytics engine based on the Lucene search engine library. It provides a full-text search engine that can index and search unstructured data, such as log files or documents, with high speed and flexibility. Elasticsearch is built for scalability and fault tolerance, making it suitable for use cases that require real-time search and analysis of large datasets.
#Elastic Key Features
Some of the most recognizable features of Elasticsearch include:
- Full-text search: Elasticsearch uses the Apache Lucene library to provide powerful full-text search capabilities, including support for fuzzy matching, phrase matching, and proximity searches.
- Distributed architecture: Elasticsearch is designed to scale horizontally across multiple nodes, allowing it to handle large volumes of data and search queries.
- RESTful API: Elasticsearch provides a simple and intuitive RESTful API that can be used to perform CRUD (create, read, update, delete) operations and execute search queries.
- Real-time analytics: Elasticsearch supports real-time analytics with its aggregation framework, which allows you to perform complex calculations and data analysis on your data.
- Document-oriented storage: Elasticsearch stores data in a document-oriented format, making it easy to index and search unstructured data such as JSON or XML documents.
- Integrations: Elasticsearch integrates with a wide range of other technologies, including log collection tools, messaging systems, and data visualization tools.
#Elastic Use-Cases
Some common use cases for Elasticsearch include:
- Log analysis and monitoring: Elasticsearch is commonly used to analyze and search log files from web servers, applications, and infrastructure components.
- E-commerce search: Elasticsearch can be used to power search and product recommendations for e-commerce websites.
- Enterprise search: Elasticsearch can be used to provide a unified search experience across multiple data sources within an organization.
- Security analytics: Elasticsearch can be used to analyze security-related data such as network traffic logs and system event logs.
- Business intelligence: Elasticsearch can be used to provide real-time analytics and data visualization for business intelligence applications.
- Geospatial search: Elasticsearch provides support for geospatial search, making it useful for location-based applications.
#Elastic Summary
Elasticsearch is an open-source, distributed search and analytics engine designed for real-time search and analysis of large datasets, with features such as full-text search, distributed architecture, RESTful API, real-time analytics, document-oriented storage, and integrations, and can be used for use cases such as log analysis, e-commerce search, enterprise search, security analytics, business intelligence, and geospatial search.
