When you ask people what elasticsearch is, the result is a wide variety of answers. Most of the answers, however, are not clear or concise. So, if you’re struggling with exactly what elasticsearch is, what it does, and more, then you’ve come to the right place!
Elasticsearch: What It Is
Elasticsearch is a distributed analytics and search engine built on Apache Lucene, a Java-based search and indexing library. It first began as a scalable version of the Lucene open-source framework. Later, it was given the ability to horizontally scale Lucene indexes.
Elasticsearch makes it easy to store, search, and analyse large amounts of data quickly and almost in real-time. It only takes a few milliseconds for the results. Results are reached much faster because it searches an index rather than searching directly for text.
What it all boils down to is that Elasticsearch is a server that can process JSON requests and provide you with JSON data.
How Does Elasticsearch Work?
In this section, we’ll take a look at the basic methods Elasticsearch uses to organise data. Understanding the different “pieces” of Elasticsearch will help you understand what it’s used for (discussed later in this article).
Logical Concepts
Documents
This is the basic type of information that can be indexed with Elasticsearch expressed in JSON. This is the global Internet data interchange format. However, in Elasticsearch, a document can be more than just text. It can be any structured data encoded in JSON. The data can be numbers, strings, or even dates.
What’s more, each document has a unique ID and a given data type—these work to categorise the document.
Indices
An index is a collection of documents with similar characteristics. This is the highest level entity that can be queried in Elasticsearch. An index is something like a database. Documents in the index are related logically.
For example, for an ecommerce website, indices may include customers, products, orders, and more.
Inverted Index
This can be somewhat confusing, but an index in Elasticsearch is actually called an inverted index. In fact, this is the method used by all search engines. The data structure stores mapping from content to its locations in a document (or a set of documents).
Inverted indices do not store strings directly. Instead, they split each document into individual search terms, which are then mapped to each search term in the documents.
This method makes it faster to look up information and search terms with a document. Elasticsearch quickly finds the best matches for full-text searches, even when using large data sets.
Backend Components
Cluster
A cluster is a group of one or more node instances that are connected. Elasticsearch clusters make it easier to distribute tasks, search, and index across all nodes in a cluster.
Node
This is a single server, which is part of a cluster. The node stores data and works in the cluster’s indexing and search capabilities. An Elasticsearch can be conducted in three different ways:
Master node: controls the Elasticsearch cluster and takes over all cluster operations (creating/deleting and index or adding/removing nodes)
Data node: stores data and conducts operations such as aggregation and search
Client node: works to forward cluster requests to the master node and data-related request so the data nodes
Shards
These are subdivisions of an index into multiple pieces, which are called shards. Each shard is a fully functional, independent index. These can be hosted on any node within a cluster. Distributing documents in an index across shards and then distributing these shards across multiple nodes allows Elasticsearch to ensure documents are not lost due to hardware failure and other problems.
Replicas
These are copies of an index’s shards, which are copies of the primary shard. Each document in an index belongs to a primary shard.
Replicas provide extra copies of your data, which protects the data against losses caused by hardware failure.
What is Elasticsearch Use For?
In this section, we’ll take a look at some of the primary uses of Elasticsearch.
Application search: for applications that rely heavily on a search platform for access, retrieval, and reporting of data.
Website search: can be used to search websites that contain large amounts of data. Elasticsearch is a great tool to make the searches faster and more accurate.
Enterprise search: Elasticsearch facilitates the search for documents across an enterprise. This is helpful for blogs, people searches, ecommerce product searches, and more. This is a great process to be used across company intranets.
Logging and log analytics: can be used to analyse log data in near-real-time. It also provides essential operational insights on log metrics, which drive actions.
Infrastructure metrics & container monitoring: Elastic search stack can be used to analyse a wide range of metrics.
Security analytics: Elasticsearch can be used to analyse access logs and other logs that are involved with system security.
Business analytics: the process is especially helpful where companies need to analyse multiple data sources.
Basically, Elasticsearch takes unstructured data from different locations and then indexes and stores the data according to user-specific mapping. The data then becomes more searchable. Elasticsearch also ensures copies of important data and documents are stored in case of hardware failure and other issues.
What’s more, Elasticsearch makes it much easier to search for and analyse large amounts of data in almost real-time. It’s also used to store data. The process can be done on the premises or even in the cloud. It can be hosted in-house or through specialised Elasticsearch services.
Summing It Up
Elasticsearch is a search engine, which uses specialised components and architecture to provide fast, scalable searches, analytics, data processing, and storage.
Elasticsearch makes everything easier when it comes to dealing with data. And you don’t have to worry about lost data and documents because Elasticsearch ensures copies are saved. This is a valuable tool that can be used by a large number of companies across many industries.