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What is Elasticsearch?
Elasticsearch is a distributed, free and open search and analysis engine for all types of data, including textual, numeric, geospatial, structured and unstructured. Elasticsearch is based on Apache Lucene and was first released in 2010 by Elasticsearch NV (now known as Elastic).
Known for its simple REST API, the distributed nature, speed and scalability, Elasticsearch is the core component of Elastic Stack, a free and open set of tools for data acquisition, enrichment, archiving, analysis and visualization. Commonly referred to as ELK Stack (after Elasticsearch, Logstash, and Kibana), Elastic Stack now includes a rich collection of light shippers known as Beats for submitting data to Elasticsearch.
Elasticsearch can be used to search for any type of document and provides a scalable, near real-time search system with support for multitenancy. “Elasticsearch is distributed; this means that the indices can be divided into shard, each with the possibility of replication. Each node contains one or more shards, and is able to act as a coordinator, delegating the necessary operations to the correct shard (or shards). Routing and rebalancing are done automatically
What is Elasticsearch used for?
Elasticsearch's impressive speed and scalability and its ability to index many types of content mean it can be used for a variety of use cases:
- Search for applications
- Search the website
- Corporate research
- Registration and analysis of logs
- Infrastructure metrics and container monitoring
- Application performance monitoring
- Analysis and visualization of geospatial data
- Security analysis
- Business analysis
How does Elasticsearch work?
Raw data flows into Elasticsearch from a variety of sources, including logs, system metrics, and web applications. THE Data acquisition is the process by which this raw data is analyzed, normalized and enriched before it is indexed in Elasticsearch. Once indexed in Elasticsearch, users can perform complex queries on their data and use aggregations to retrieve complex summaries of their data. From Kibana, users can create powerful views of their data, share dashboards, and manage Elastic Stack.
What is an Elasticsearch index?
Un index Elasticsearch is a collection of related documents. Elasticsearch stores data as JSON documents. Each document relates a set of keys (field or property names) with their corresponding values (strings, numbers, booleans, dates, arrays of values , geolocations or other types of data).
Elasticsearch uses a data structure called inverted index , designed to allow very fast full-text searches. An inverted index lists each unique word that appears in any document and identifies all documents in which each word occurs.
During the indexing process, Elasticsearch archives documents and creates an inverted index to make the document data searchable in near real time. Indexing is initiated with the index API, through which you can add or update a JSON document in a specific index.
Why use Elasticsearch?
Elasticsearch is fast. Since Elasticsearch is based on Lucene, it excels at full-text search. Elasticsearch is also a near real-time search platform, which means that the latency from the time a document is indexed until it becomes searchable is very short, typically a second. Consequently, Elasticsearch is suitable for time-sensitive use cases such as security analysis and infrastructure monitoring.
Elasticsearch is distributed by nature. Documents stored in Elasticsearch are spread across several containers known as shard , which are duplicated to provide redundant copies of data in the event of a hardware failure. The distributed nature of Elasticsearch allows you to scale up to hundreds (or even thousands) of servers and manage petabytes of data.
Elasticsearch comes with a wealth of features. In addition to its speed, scalability and resilience, Elasticsearch has a number of powerful built-in features that make data storage and search even more efficient, such as data rollups and index lifecycle management.
Elastic Stack makes it easy to capture, view and report data. Integration with Beats and Logstash simplifies data processing before indexing in Elasticsearch. And Kibana provides real-time visualization of Elasticsearch data and user interfaces to quickly access application performance monitoring (APM), logs and infrastructure metrics data.
What programming languages does Elasticsearch support?
Elasticsearch supports a variety of languages and the official clients are available for:
- .NET (C #)
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