Use cases for Elasticsearch

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If you’re looking for a fast, easy and scalable solution to improve your search functionality, Elasticsearch is a natural choice. When organizations are considering Elasticsearch for their applications, a common question is: “What is Elasticsearch best used for?”. While the various uses of Elasticsearch continue to grow and evolve, there are a few specific trends among customers that really showcase the product’s greatest strengths. In this article, we’ll take a closer look at Elasticsearch’s features and functionality and discuss some common use cases for Elasticsearch.

What is Elasticsearch

Before we dive into some possible use cases for Elasticsearch, let’s take a step back and talk a little bit about Elasticsearch itself, its history, and its features. Elasticsearch is an open-source, distributed, highly scalable search engine built on the Lucene library. The first version of Elasticsearch was released in 2010, but its history and evolution can be traced back to its precursor, Compass, which was created in 2004. Elasticsearch is a central part of the “Elastic Stack”, which includes Logstash, Kibana and Beats. A few notable features of the product include:

  • Speed: To put it simply, Elasticsearch is really fast. It can process queries far more quickly than MySQL or other relational databases, especially for full-text searching. This faster performance is due to Elasticsearch’s document-based approach to indexing, which is very different from a typical relational database structure of tables made up of columns and rows.

  • Scalability: You can run Elasticsearch on a single laptop, or you can set up a cluster with a few hundred nodes. Elasticsearch’s horizontal scaling allows the system to handle as much load as you can throw at it; the system’s ability to automatically manage and distribute queries across a cluster keeps operations running smoothly at all times.

  • Resiliency: Even the most well-maintained systems occasionally have problems. Fortunately, Elasticsearch is able to detect failures and take action to keep your data safe. You can take advantage of cross-cluster replication to have a secondary cluster ready as a backup as needed.

Use Cases for Elasticsearch

Now that we’ve provided some background information on Elasticsearch, we can talk about some common use cases. Some of the most popular uses of Elasticsearch are for the following purposes:

  • Full-text search: Traditional relational database management systems aren’t always the best choice for applications that are heavy on text searching, but this is an area where Elasticsearch really shines. The ability to handle fuzzy searches and “search as you type” autocomplete functionality improves overall search quality even more. The website Instructables saw the difference in their site search when they switched to Elasticsearch. Queries that had previously returned minimal or no results at all began returning relevant, high-quality results.

  • Logging and analysis: Elasticsearch is widely used by organizations to store and analyze log data. For example, the SaaS company Paylocity was having difficulty keeping up with their logs as their business underwent massive growth. With the help of the Elastic Stack, they’re now able to index and analyze 500 million logs per day.

  • Scraping remote data from multiple sources: Scraping and indexing public data is a breeze with the Elastic Stack. Elasticsearch’s document-based approach, which lacks the strict schema found in relational databases, makes it easy to pull in data from a wide variety of sources while keeping it all searchable. With the help of Elasticsearch, you can analyze and make sense of large volumes of data scraped from the web.

  • Metrics analysis: Another area where Elasticsearch really stands out is metrics. The National Energy Research Computing Center (NERCC) made good use of the Elastic Stack to crunch through over 1.2 billion documents per day, analyzing metrics on system load, power usage KPIs, building air temperature, and much more. The Elastic Stack even has machine learning capabilities that can spot anomalies. Once it learns what’s normal in your data, it can identify issues and alert you when anomalies are detected.


While the use cases we described don’t cover every possible implementation of Elasticsearch, they do represent some of the most common scenarios for using the product. With its speedy performance, high scalability and distributed nature, Elasticsearch is a powerful and versatile product. Whether used on it own or in conjunction with the other components of the Elastic Stack, Elasticsearch can be put to good use in a wide variety of applications and industries.

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