Case Study - Improving System Performance with Gearman for Asynchronous Job Queue Processing

By using Gearman for work queue processing, we improved system performance by offloading resource-intensive jobs, resulting in smooth and responsive operations even with high data volumes and frequent updates.

Overview

To boost efficiency and scalability, we added Gearman into our application to handle asynchronous operations more efficiently. The system had to handle a tremendous volume of data, particularly while syncing between the database and Elasticsearch. Gearman assisted us in removing these time-consuming activities, freeing up resources for real-time operations and enhancing overall system responsiveness.

Challenge
Our client's application was heavily loaded due to the need to synchronise data between their traditional relational database and Elasticsearch. Because Elasticsearch performed better when querying large datasets, it became the primary data source for displaying leads in the system. However, keeping Elasticsearch in sync with the database posed challenges. Processing these sync jobs synchronously would put unnecessary strain on the system, affecting its performance. We needed a solution that could handle these tasks asynchronously, allowing the system to remain responsive and efficient during high-demand periods.

Solution
We opted to use Gearman, a distributed work queue system, to handle data synchronisation between the database and Elasticsearch. Gearman enables us to offload time-consuming operations to background workers, so that they do not interfere with the performance of real-time user requests. We installed Gearman on a virtual machine (VM) and configured it to run jobs asynchronously. When a lead was created or updated in the database, Gearman would manage the synchronisation of that data with Elasticsearch in the background. This allowed the application to continue running normally while Elasticsearch kept up with the latest updates.

Results
With Gearman in place, we saw a significant improvement in system performance. The application became more responsive, as heavy processing tasks no longer blocked real-time operations. Gearman allowed for asynchronous job execution, which optimized resource utilization and reduced system load. The synchronization between the database and Elasticsearch became seamless, ensuring that the leads data was always fresh without compromising the user experience.

Impact
The integration of Gearman resulted in numerous important benefits.
  1. Improved System Performance: Offloading demanding tasks to Gearman allowed the program to process user requests efficiently and without delays.
  2. Scalability: Gearman's scalability allows for job processing among several workers, maintaining continuous performance even as the system grows.
  3. Reduced Latency: Asynchronous processing reduces latency and ensures lead data is available in Elasticsearch without affecting delays in other areas of the application.
  4. What we did

    • Job Queue Management
    • Virtual Machine
    Improvement in System Response Time
    45%
    Reduction in System Load During High Traffic Periods
    60%
    Client Satisfaction with Improved Data Synchronization and Performance
    95%

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