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Advantages of Using Elasticsearch to Build Scalable Search System

In today’s digital age, search functionality is a critical component of web applications. Whether it’s a food ordering app, real estate marketplace, a job portal, or any similar platform, users expect to be able to find what they’re looking for quickly and easily. However, building a scalable search system can be a challenging task, and it’s crucial to choose the right technology to power your search functionality. Elasticsearch is a popular choice for building scalable search systems, and in this article, we’ll explore the advantages of using Elasticsearch for this purpose.

We’ll look at Elasticsearch’s architecture, search features, API, and monitoring tools, and highlight how they make Elasticsearch an ideal choice for a wide range of use cases. By the end of this article, you’ll have a clear understanding of why Elasticsearch is a powerful and scalable search engine that can help you build a search system that meets your needs.

What is Elasticsearch: A Peek into the Basics

Elasticsearch is an open-source, distributed search engine that provides a fast, efficient, and scalable search and analytics engine. It is built on top of the Lucene search library and is designed to handle large volumes of data and high query volumes’ is a distributed search engine, which means that it can be scaled horizontally by adding more nodes to the cluster, making it ideal for handling large volumes of data. It provides a rich set of search features out of the box, including full-text search, faceted search, and geo-search.

Elasticsearch also supports complex queries and aggregations, making it suitable for data analytics and business intelligence use cases. Moreover, it provides a RESTful API, making it easy to integrate with other systems and applications. It also provides client libraries for many programming languages, including Java, Python, and Ruby, making it easy to get started with the search engine.

“Efficient, reliable, and lightning-fast: Elasticsearch is the search engine of choice for modern businesses.”




Advantages of Elasticsearch

Advantages of Elasticsearch

There are several benefits of using Elasticsearch for building scalable search systems. Some of these benefits include:

  • Distributed Architecture

Distributed architecture is a system that can be deployed across multiple nodes or computers, where each node performs a specific function and communicates with others to achieve a common goal. This approach allows for the system to handle large amounts of data and traffic by scaling horizontally through the addition of nodes to the cluster. Elasticsearch is an example of a distributed architecture system that is optimized for managing large volumes of data and high query loads through horizontal scaling. This feature makes Elasticsearch a suitable choice for developing scalable search systems.

  • Rich Features

Elasticsearch offers a comprehensive set of advanced search features, collectively known as “rich search features”. These capabilities include full-text search, fuzzy search, geo-location search, and aggregations, which enable users to perform complex queries that can filter, sort, and analyze search results in real-time. Furthermore, it supports additional search features such as search suggestions, highlighting, and autocomplete, which provide personalized and relevant search results, improving the user experience. Its rich search features make it an excellent choice for developing search systems that require advanced search functionality.

“Elasticsearch offers capabilities for full-text search, fuzzy search, geo-location search, and aggregations, which enable users to perform complex queries.”




  • Analytics

One of the main advantages of Elasticsearch is that it offers robust analytics capabilities that enable users to extract valuable insights and patterns from data to inform decision-making. Elasticsearch’s analytic features allow users to visualize and analyze large datasets in real-time through the creation of custom dashboards, ad-hoc queries, and reports. Furthermore, it seamlessly integrates with popular visualization tools like Kibana, which provides interactive visualizations, graphs, and charts to represent data. These analytic capabilities make Elasticsearch an excellent choice for developing search systems that require real-time data analysis and visualization to enhance decision-making.

  • Schema-Free/User-Defined Schemas

Elasticsearch’s schema-free or user-defined schema architecture allows for flexibility in accommodating changes to the data structure without requiring predefined schema definitions. As a result, it can handle unstructured data with ease, making it an ideal choice for building search systems. This advantage of Elasticsearch having the schema-free architecture also enables it to adapt to changes in the data structure without the need for complex data migration processes. This makes it possible to add or remove fields in the data structure dynamically without having to redefine the schema. This also simplifies the development process and allows for faster iteration cycles, providing search systems with the flexibility required to handle changing data structures.

“From GitHub to Uber: Real companies trust Elasticsearch for their search needs!”




  • Restful API

Elasticsearch provides a Representational State Transfer Application Programming Interface (RESTful API), which enables developers to interact with Elasticsearch using HTTP requests. This standardized API allows developers to perform various operations such as indexing, searching, updating, and deleting data pro-grammatically. With Elasticsearch’s Restful API, it becomes easier to integrate it with other applications and services, and it supports different data formats, including JSON, making data exchange between different systems simple. The RESTful API is a key feature that makes Elasticsearch an ideal choice for building search systems that require smooth integration with other applications and services.

  • Monitoring and Management Tools

Monitoring and management tools are software tools used to monitor and manage a system’s performance. Elasticsearch offers a variety of powerful monitoring and management tools that simplify the monitoring and management of Elasticsearch clusters’ health and performance. The Elasticsearch Monitoring Plugin is an example of a dedicated monitoring and management tool that provides detailed metrics on cluster health, node performance, and query performance. Additionally, it integrates with popular monitoring and management tools such as Grafana and Prometheus, allowing users to monitor Elasticsearch alongside other system components. Hence, this makes it an ideal choice for building search systems that require high performance, reliability, and availability.

Use Cases of Elasticsearch 

  • GitHub

This popular code hosting platform uses Elasticsearch to power its search functionality. Elasticsearch’s fast and flexible search capabilities make it ideal for searching through vast amounts of code and related data.

  • Netflix

Netflix Use Cases of Elasticsearch

The popular streaming service, Netflix, uses Elasticsearch to power its search and recommendation systems, which help users discover new content based on their preferences and viewing history. Elasticsearch’s fast and flexible search capabilities make it an ideal choice for a platform like Netflix, where delivering personalized and relevant recommendations is crucial to the user experience.

  • Uber

Uber uses Elasticsearch’s geolocation queries for driver and passenger matching, ETA calculations, and fraud detection. Its real-time search and analytics capabilities enable Uber to process and analyze large volumes of data in real-time, providing efficient and reliable services.

  • Slack

The popular team communication and collaboration platform uses Elasticsearch for its search functionality. It is an excellent use case with unstructured data. Elasticsearch allows Slack users to quickly search through channels, messages, files, and other data stored in the platform. With Elasticsearch’s powerful search capabilities, Slack is able to provide a seamless and efficient search experience to its users, which is essential for a platform designed to improve team communication and productivity.

“Exper Labs leverages Elasticsearch to build high-performance search systems with latency SLA of less than a second.”




Final Thoughts

Elasticsearch is a powerful and flexible search engine that offers a wide range of features and benefits for building search systems. Its scalability, performance, real-time search and analytic capabilities, and user-defined schema make it an ideal choice for many companies and Exper Labs, we specialize in custom software development and have expertise in writing high-performance APIs following REST conventions in various languages such as Java, Python, Ruby, and more.

Our team has experience building scalable and reliable search systems using Elasticsearch for various industries, including hospitality, e-commerce, and healthcare. Whether you need to build a new search system from scratch or improve an existing one, we can provide you with tailored solutions that meet your business needs.

Contact us today to learn more about our services!