What is Elasticsearch? Perhaps you’ve heard the term ‘a search engine’, ‘an index’ or ‘a database’ being thrown around? The truth is, all of these answers are correct.
The more interesting question is how a seemingly simple search engine developed into one of the most popular available today? With its emphasis on speed, scale, and relevance, it’s safe to say Elasticsearch is transforming how the world uses data. Let’s take a deeper dive.
What exactly is Elasticsearch?
Released in 2010, Elasticsearch is a free, open-source distributed, RESTful, JSON-based search and analysis engine, which forms part of the Elastic Stack. It is used to search and analyse all kinds of data; textual, numerical, geospatial, structured and unstructured.
Elasticsearch ingests data from multiple sources simultaneously, stores and indexes it, making it easily searchable for users. The platform’s distributed architecture allows the search and analysis of huge volumes of data at lightning speed – and at scale.
How does it work?
Elasticsearch, like any other open-source technology, is very rapidly evolving, but the core fundamentals that power Elasticsearch doesn’t change.
Elasticsearch works by fetching and managing different types of data from multiple sources. This is achieved via Logstash (also part of the Elastic Stack), a server‑side data processing pipeline that takes the data, transforms it and stores it in Elasticsearch. All of Elasticsearch’s data is internally stored in Apache Lucene as an inverted index. Although the data is stored in Apache Lucene, Elasticsearch is what makes it distributed and provides the easy-to-use RESTFul API’s.
But what’s an inverted index? Simply put, an inverted index is a data structure that maps each unique ‘word’ to the list of documents containing that ‘word’. This inverted index is what makes Elasticsearch different from other traditional database systems. Using this structure allows Elasticsearch to efficiently find the best match for full-text searches from large sets of data accurately.
What are the benefits of Elasticsearch?
Search and analyse effectively
Have you ever wanted to ask your data all types of questions? Elasticsearch lets you perform and combine a multitude of searches — structured, unstructured, geo, metric — in any way you want.
While finding the results to match your search is one thing, making sense of a billion documents is another. Elasticsearch promotes analysis at scale by zooming out to explore trends and patterns in your data.
Relevance and Resilience
When searching and analysing reams of data, relevance is key. Elasticsearch allows you to rank your search results by frequency, recency and even popularity. You’re at liberty to fine-tune how your results are displayed to make analysis pain free.
Worried about data safety? No sweat. Elasticsearch detects failures to keep your cluster (and your data) safe and available. With cross-cluster replication, a secondary cluster can spring into action as a backup.
Speed and Scalability
Elasticsearch is seriously fast. Queries that would take 10 seconds using SQL, will take Elasticsearch less than 10 milliseconds to return results. The reason it’s so fast is because of the inverted indexing mentioned above. Beyond the technical jargon, speed is a critical feature for many forward-thinking businesses who seek to iterate and be agile.
What’s more? You can split data across multiple machines, scaling beyond what a single machine can do. As you add more data, you can scale horizontally, meaning you can handle tons of events per second, while automatically managing how indices and queries are distributed.
Elasticsearch Use Cases
As a result of its powerful search capabilities, Elasticsearch is often used as the underlying technology that powers applications with complex search requirements. eBay for example, uses Elasticsearch for application search, scouring across 800 million listings in milliseconds while maintaining a world-class end-user experience.
On the other hand, retailers are using Elasticsearch for instantaneous e-commerce searches. The platform seamlessly indexes inventory, alongside product attributes, so that when users search for a specific product attribute, the store can display the correct products instantly. Walgreens and Kreeger are retailers that are streamlining their online grocery experience with Elasticsearch.
Aside from search-oriented use cases, Elasticsearch is often used for storing data that needs to be grouped and analysed by various dimensions. Elasticsearch has helped companies like GoDaddy improve their user experience by processing billions of events and analysing logs to ensure consistent system performance. Alternatively, Sprint is using Elasticsearch for metric analysis. They are able to analyse over 3 billion events per day from logs, databases, emails, internal and vendor application APIs, in order to search for better retail operations insights.
With endless and versatile search and analytics capabilities, Elasticsearch provides an appealing solution for a number of growing critical business needs. Whether your business is starting up or your data stores have become too large to manage using traditional relational database queries, Realm Digital recommends Elasticsearch for in-depth search and analysis in near real-time.
Does your business need help in digitizing processes, or need guidance on identifying and implementing the tools to drive your business forward digitally? If you would like to know more about Elasticsearch or are interested in implementing it into your next project, contact Realm Digital today.