Case Study
Here's the Case-Study of some of our works we'd like you to see...
Gyanmandir Library
Our client, who manages a vast database of over 2 million records of Sanskrit books, approached us with a critical performance issue impacting their application’s search functionality. The inefficiency in handling such a large data set and the inability to accommodate user input errors in search queries had led to a frustrating user experience.
The Challenge
Performance Issues with a Large Dataset: The client's application struggled with performance due to the size of the dataset, which contained more than 2 million records of books, all written in Sanskrit. The SQL Server backend was unable to efficiently manage the search queries on such a large scale, resulting in significant delays. Users often experienced long wait times before search results were displayed, leading to dissatisfaction and hampering the overall usability of the application.
Inaccurate and Incomplete Search Queries: Another major challenge was the accuracy of search queries. Users frequently entered incorrect or incomplete book titles, making it difficult for them to find the correct records. The existing search functionality lacked tolerance for minor errors, such as spelling mistakes or partial inputs, which caused searches to fail when users did not provide the exact match. As a result, users became frustrated when they couldn’t retrieve the relevant books, especially when they made minor input errors.
The Solution
Implementation of Elasticsearch for Enhanced Performance: To resolve the performance bottleneck, we integrated Elasticsearch as the primary search engine for the application. By replacing the traditional SQL search approach with Elasticsearch, we achieved significant performance improvements. Searches that once took an excessive amount of time were now carried out up to 10 times faster. This optimization provided users with a far more efficient and responsive search experience, significantly improving the application's overall performance.
Leveraging Elasticsearch for Fuzzy and Partial Matching: To address the issue of inaccurate and incomplete search queries, we utilized Elasticsearch’s advanced fuzzy search capabilities. By incorporating this functionality, the application became more forgiving of minor inaccuracies in search terms, enabling users to retrieve relevant results even if their input was not an exact match. This enhancement greatly improved the usability of the search feature, empowering users to find the books they were looking for more easily, regardless of minor input errors.
The implementation of Elasticsearch transformed the application’s search functionality. The performance improvements made searches 10 times faster, delivering near-instantaneous results, and significantly enhancing user satisfaction. Moreover, by enabling fuzzy and partial searches, users were no longer hindered by minor input mistakes, resulting in more successful searches and a much-improved user experience.
This solution not only resolved the client’s performance issues but also enhanced the overall usability of their application, allowing them to provide a more reliable and efficient search tool for their users.
Technology Stack
