Elasticsearch is an open-source search engine and analytics engine made to handle all kinds of structured and unstructured data including textual data, numerical data, and even geospatial data. The engine was built on the Apache Lucene project and was initially released by Elastic in 2010. Elasticsearch is the heart of the Elastic Stack, also called the ELK Stack, which contains the Elasticsearch, Logstash, and Kibana tools. With full-text search capabilities, this engine is the easiest way for enterprise-level companies to search and analyze large amounts of data in a manageable time frame.
What is it used for?
The speed and scalability offered by Elastic Search mean that it can be used for a variety of tasks. Common examples include enterprise and application search, business analytics, security analytics, visualization, and infrastructure monitoring. The Elasticsearch service is used by thousands of well-known companies such as Uber, Shopify, and Slack, and it’s great support for any large organization that needs to quickly and seamlessly access and log data across its entire database. Elasticsearch can even help your customers by making it easy to search your entire site and get near-instant results for queries.
How does it work?
Before detailing how exactly Elasticsearch works, it’s important to cover some of the basic terminology involved.
Nodes: These are single servers that contain relevant data for searching and indexing. Elasticsearch’s scalability means it can add and use more nodes as its datasets grow.
Clusters: Collections of nodes that contain entire datasets.
Index: Collection of documents with shared characteristics. The name of each index should reveal its purpose. Indices can be lists containing customer data, product information, or employee information, for example. Elastic stores these indices as JSON documents.
Shards: Subcategories that are kept within indices. The original data is called the primary shard, and backup replica shards are created to guard against hardware failure. Replicas ensure that data is available for retrieval at all times.
Essentially, Elasticsearch collects data from your web applications, log analytics, and system metrics before indexing the data so it can be easily retrieved via search queries. These clusters are made via a process known as data ingestion, which is typically powered by Logstash. This is a server-side data processing pipeline that simultaneously ingests data from multiple sources and lets you easily transform and store it. Thanks to the final component of the ELK stack, Kibana, you can even create visual representations of your data including pie charts, line graphs, histograms, and more.
Elasticsearch is able to work so efficiently thanks to its use of Restful API. APIs are basically software solutions that let different applications communicate with each other. REST stands for Representational State Transfer and is a type of architecture that many systems that utilize simultaneous information are built on. Consult a REST API tutorial for beginners for additional information.
Deploying Elasticsearch via the cloud gives you access to the entire ELK stack at once instead of having to go through an individual installation process for each component. Cloud infrastructure is also practically limitless, so you won’t have to worry about running out of space for Elasticsearch documentation, and scaling will be effortless. Automatic cloud updates also ensure your tools are secure and that you’re always working with the latest versions.
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.