Top Elasticsearch Use Cases

What Is Elasticsearch?

Elasticsearch is the distributed, RESTful search and analytics engine based on Apache Lucene, a full-text search-engine library. Elasticsearch was previously an open source project (prior to the beginning of 2021) and was developed in Java programming language. With Elasticsearch, you can store data which can be further processed in a data visualisation platform such as Kibana.

Elasticsearch allows you to store, search, retrieve, and analyses large volumes of data in split seconds with near real-time capability. Elasticsearch stores data and adds a searchable reference to the data in the cluster’s index. Data can then be searched and retrieved using the Elasticsearch API.

Elasticsearch has quickly become one of the most popular search and indexing engines to find specific logs and metrics as well as it's use in providing a search backend.

The Top Elasticsearch Use Cases

Search and data analysis are important features of today's software applications. The ability to scale Elasticsearch and its capability to handle large volumes of data in near real-time is a key requirement for monitoring many applications such as mobile apps, web, and data analytics applications.

Companies around the world are using Elasticsearch as a primary search platform for the access, storage and retrieval of their most vital machine data.

In this guide we have listed some of the most important use cases for Elasticsearch below to highlight what Elasticsearch is used for;

1. Log Analysis

Elasticsearch is a popular tool used for log analysis. As application generated data is often scattered across different parts of your infrastructure it can be very challenging to aggregate and use the data coming from multiple sources, especially when they are being produced at a very high volume.

Application-generated logs often contain a variety of information such as runtime errors, events, access requests, and other valuable insights. With Elasticsearch you have multiple ways of collecting data wherever it is located within your application's infrastructure. Elasticsearch centralises this data and gives you the automation to sort through all the logs and identify where the issues might be occurring in your application.

Elasticsearch's core function is its search engine, however, users have extended its use case for log analysis and data visualization using the ELK Stack (Elasticsearch, Logstash, Kibana). Elasticsearch indexes and stores the data, Logstash collects data and processes this, and Kibana provides a user interface for querying the data and visualising the data.

The ELK stack is a reliable tech stack used by leading businesses such as Facebook, Cisco, and Netflix to power various functions of their businesses. For instance, Netflix has over 800 production nodes spread across more than 85 Elasticsearch clusters and heavily relies on ELK to monitor and analyse customer service operations and security logs.

2. Elasticsearch As A Search Engine

Elasticsearch core search function includes full-text search which is able to handle many types of queries, from term, field, wildcard and range searches. Elasticsearch can also be used to power search on websites and perform a google-like search on the site contents for users.

GitHub, Wikipedia, Amazon, and other platforms, power their searches using Elasticsearch. Many leading content aggregation platforms are also powered using Elasticsearch.

Elasticsearch can be tooled to crawl multiple websites, index the web contents, and provide search functionality on the underlying content for users based on a query. Developers often use Elasticsearch to dive through and query millions of lines of codes in near real-time in order to pinpoint relevant logs to troubleshoot and resolve issues quicker.

The speed, reliability, and scalability of Elasticsearch are most relevant in today’s E-commerce business especially for platforms hosting thousands of products. Slow response times for products search queries is a leading cause of higher bounce rates on E-commerce platforms.

Elasticsearch is used to power product search and presents relevant products to customers based on ranking, price, or user-defined metrics in a fraction of a second.

Elasticsearch gives sub-seconds return to queries, its functionalities such as autocomplete, search term suggestion, fuzzy search keyword match, and instant searches give it an edge over other solutions. This use case is not limited to e-commerce websites as any application that needs to return the most relevant item from millions of data points can use Elasticsearch as a solution

3. Metrics Analysis

if you would like to analyse metrics data, Elasticsearch's rich aggregation API paired with other tools in the Elastic Stack (ELK) gives you excellent analytics capabilities.

Metrics data usually consists of numbered values, time-series data, and application events. Some examples of this include data generated by remote sensors, IoT devices, mobile devices metrics, hardware servers, virtual machines, routers, switchboards, load balancers, and so on.

With Elasticsearch and Kibana, data can be analysed and visualised along different dimensions to provide deep insights. From behaviour analytics to usage monitoring and performance evaluation, the use cases are endless.

Data is highly important for businesses conducting dynamic analysis who need to quickly respond to trends in user behaviour or changing markets. Using Elasticsearch data is immediately available for search and analytics for better decision making.

If you want to get started with using Elasticsearch for all of the use cases we've cited above then you'll likely require a platform to host your cluster. With Logit.io you can host, deploy and launch ELK within minutes.

To get started with using Logit.io simply sign up to our platform and experience 14-days of free access to create stacks backed by ELK, Grafana and Open Distro for Elasticsearch (ODFE).

If you enjoyed this article on the leading Elasticsearch use cases then why not brush up on your knowledge further with our post on the Top Elasticsearch interview questions or check out our updated guide on the best Kibana dashboards examples?