![]() In the context of an e-commerce website, for example, you can have an index for Customers, one for Products, one for Orders, and so on. Any documents in an index are typically logically related. You can think of the index as being similar to a database in a relational database schema. An index is the highest level entity that you can query against in Elasticsearch. IndicesĪn index is a collection of documents that have similar characteristics. For example, a document can represent an encyclopedia article or log entries from a web server. Each document has a unique ID and a given data type, which describes what kind of entity the document is. That data can be things like numbers, strings, and dates. In Elasticsearch, a document can be more than just text, it can be any structured data encoded in JSON. You can think of a document like a row in a relational database, representing a given entity - the thing you’re searching for. ![]() Logical Concepts Documentsĭocuments are the basic unit of information that can be indexed in Elasticsearch expressed in JSON, which is the global internet data interchange format. To better understand how Elasticsearch works, let’s cover some basic concepts of how it organizes data and its backend components. At its core, you can think of Elasticsearch as a server that can process JSON requests and give you back JSON data. It uses a structure based on documents instead of tables and schemas and comes with extensive REST APIs for storing and searching the data. It’s able to achieve fast search responses because instead of searching the text directly, it searches an index. Elasticsearch allows you to store, search, and analyze huge volumes of data quickly and in near real-time and give back answers in milliseconds. It started as a scalable version of the Lucene open-source search framework then added the ability to horizontally scale Lucene indices. What is Elasticsearch?Īt its core, you can think of Elasticsearch as a server that can process JSON requests and give you back JSON data.Įlasticsearch is a distributed, open-source search and analytics engine built on Apache Lucene and developed in Java. So how did a simple search engine created by Elastic co-founder Shay Bannon for his wife’s cooking recipes grow to become today’s most popular enterprise search engine and one of the 10 most popular DBMS? We’ll answer that in this post by understanding what Elasticsearch is, how it works, and how it’s used. Over the years, Elasticsearch and the ecosystem of components that’s grown around it called the “Elastic Stack” has been used for a growing number of use cases, from simple search on a website or document, collecting and analyzing log data, to a business intelligence tool for data analysis and visualization. But the truth is, all of these answers are correct and that’s part of the appeal of Elasticsearch. Depending on your level of familiarity with this technology, these answers may either bring you closer to an ah-ha moment or further confuse you. When people ask, “what is Elasticsearch?”, some may answer that it’s “an index”, “a search engine”, an “analytics database”, “a big data solution”, that “it’s fast and scalable”, or that “it’s kind of like Google”. You can also set up a 15 minute call with a member of our team to see if Knowi may be a good BI solution for your project. Before we jump into it, if you have a project and are trying to visualize your Elasticsearch data, take a look at our Elasticsearch Analytics page.
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