Popular search engines like Google and Bing collect and index data from the internet to make it available via search queries. Most people use these search results to find answers to questions and help them make decisions every day. Elasticsearch is a type of search engine used by enterprise-level organizations who need to sort through several petabytes of data in a manageable amount of time. Whether your company is using big data to gain insights for business decisions or to develop new features for web applications or your site to improve the user experience, Elasticsearch helps you collect and index multiple data types from different sources simultaneously.
Elasticsearch is the chief component of the Elastic Stack, also known as the ELK Stack, which includes Elasticsearch, Logstash, and Kibana. The search and analytics engine was built on Apache Lucene and released by Elastic in 2010 as an open-source engine. Elasticsearch is scalable, and different tools in the stack can be used to rapidly ingest data and even create visual representations. It’s used by thousands of well-known organizations, such as Shopify, Netflix, and Uber, and is an excellent solution for any company that needs to near-instantly log and retrieve large amounts of data under conditions of any kind.
How does it work?
Elasticsearch absorbs raw data simultaneously from multiple different data sources via a process known as data ingestion. This data ingestion is handled by the Logstash component of the ELK stack, which is a server-side data processing pipeline that lets you transform data before it’s put in an Elasticsearch index. Once data is indexed, users can input queries of varying complexity to retrieve summaries of relevant data.
An index in Elasticsearch is a collection of related documents that are stored as JSON documents. These indices of JSON documents may contain data like username lists, customer information, or product lists to give a few possible examples. Indices are stored in nodes, which are single servers that hold data for indexing and searching. These nodes make up Elasticsearch clusters, which represent an entire dataset. All indices have subsets called shards. A primary shard is the original set of data, and each one is backed up by replica shards to protect against hardware failures and ensure data is always available for use. Each Elasticsearch document can be utilized by an inverted index, enabling rapid full-text search that allows users to retrieve results for complex Elasticsearch queries in near real-time. Once data is stored and indexed, the Kibana component can even create visual summaries of data in forms including histograms, pie charts, graphs, and more.
What is it for?
As you can probably imagine, Elasticsearch is used primarily for its near-instantaneous search applications with big data. Thanks to its ability to simultaneously draw from multiple data sources, searches of any complexity level rarely take longer than one second. This is invaluable for accurately searching large databases and for providing a good user experience in applications that rely on search platforms. The Elasticsearch service can improve the quality of life for both your organization and your customers.
Other applications include security analysis since the ELK stack is capable of rapidly analyzing security logs, as well as business analytics, though this application has a steep learning curve and isn’t necessarily recommended for beginners.
Compatibility and Deployment
Elasticsearch is a trademark of Elasticsearch B.V., registered in the U.S. and in other countries. Apache, Apache Lucene, Apache Hadoop, Hadoop, HDFS, and the yellow elephant logo are trademarks of the Apache Software Foundation in the United States and/or other countries.