ElasticSearch Platform

Context:
- It can (typically) be based on Apache Lucene.
- It can (typically) function as a NoSQL Database, featuring schema flexibility and distributed architecture.
- It can (typically) organize data into ElasticSearch Indexes (possibly inverted indices for full-text querying and BKD trees for storing numeric and geo data).
- It can (typically) support ElasticSearch Queries through an ElasticSearch RESTful API.
- It can (typically) support a Query DSL (Domain Specific Language) for complex queries.
- It can (often) use JSON documents for data representation and is compatible with various programming languages.
- It can (often) be combined with tools such as: Kibana, Logstash, and Beats for data visualization, processing, and shipping.
- It can (often) support ElasticSearch Plugins, such as Kibana.
- It can (often) support Vector DBMS Features, such as storing high-dimensional vector data, supporting K-Nearest Neighbor (KNN) searches, and computing cosine similarity and Euclidean distance between document vectors.
- It can be implemented into an ElasticSearch-based System.
- ...
Example(s):
- ElasticSearch, v8.12.0 [1] (~2024-01-17)
- ElasticSearch, v7.6.2 [2] (~2020-03-31)
- ElasticSearch, v5.2 (~2017-01-31)
- ElasticSearch, v2.0 (~2015-10-28)
- …
Counter-Example(s):
- Apache SOLR Software.
- CouchDB.
- MongoDB.
See: Full-Text Indexing, JSON Document Storage, Multitenancy, Schema-Free Database, Shard (Database Architecture).

References

(Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Elasticsearch Retrieved:2024-1-22.
- Elasticsearch is a search engine based on the Lucene library. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch is developed in Java and is dual-licensed under the source-available Server Side Public License and the Elastic license, while other parts fall under the proprietary (source-available) Elastic License. Official clients are available in Java,^[1] .NET^[2] (C#), PHP,^[3] Python,^[4] Ruby^[5] and many other languages.^[6] According to the DB-Engines ranking, Elasticsearch is the most popular enterprise search engine.

↑ Cite error: Invalid <ref> tag; no text was provided for refs named elasticsearch-java
↑ Cite error: Invalid <ref> tag; no text was provided for refs named elasticsearch-net
↑ Cite error: Invalid <ref> tag; no text was provided for refs named elasticsearch-php
↑ Cite error: Invalid <ref> tag; no text was provided for refs named elasticsearch-py
↑ Cite error: Invalid <ref> tag; no text was provided for refs named elasticsearch-ruby
↑ Cite error: Invalid <ref> tag; no text was provided for refs named programming-language-clients

(Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Elasticsearch#Overview Retrieved:2017-1-31.
- Elasticsearch can be used to search all kinds of documents. It provides scalable search, has near real-time search, and supports multitenancy. "Elasticsearch is distributed, which means that indices can be divided into shards and each shard can have zero or more replicas. Each node hosts one or more shards, and acts as a coordinator to delegate operations to the correct shard(s). Rebalancing and routing are done automatically [...]". Related data is often stored in the same index, which consists of one or more primary shards, and zero or more replica shards. Once an index has been created, the number of primary shards cannot be changed. Elasticsearch uses Lucene and tries to make all its features available through the JSON and Java API. It supports facetting and percolating, which can be useful for notifying if new documents match for registered queries. Another feature is called "gateway" and handles the long-term persistence of the index; for example, an index can be recovered from the gateway in the event of a server crash. Elasticsearch supports real-time GET requests, which makes it suitable as a NoSQL datastore, but it lacks distributed transactions.

How HipChat scales to 1 Billion Messages By zuhaib http://blog.hipchat.com/2013/10/16/how-hipchat-scales-to-1-billion-messages/
- QUOTE: In this post, we’ll highlight how we use CouchDB, ElasticSearch, and Redis to handle our load and make sure we provide as reliable a service for our users as possible. …