SVEEP Pledge Portal: How Presear Softwares Managed 100 Entries Per Second with High Scalability Using Python and Django

SVEEP Pledge Portal: How Presear Softwares Managed 100 Entries Per Second with High Scalability Using Python and Django

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At Presear Softwares, we pride ourselves on delivering robust, scalable solutions that meet our clients' needs. One of our standout projects, the SVEEP Pledge Portal, in association with the Bilaspur Smart City Limited (BSCL), required us to handle a high volume of entries during peak time—up to 100 per second. This blog post showcases our approach and the technologies we used to ensure the portal's performance and scalability.


The SVEEP (Systematic Voters' Education and Electoral Participation) initiative by the Election Commission of India is designed to promote voter awareness and education. As part of this effort, the SVEEP Pledge Portal was created by Bilaspur Smart City Limited to allow citizens to pledge their participation in the democratic process. Given the expected high traffic, the portal needed to handle significant load efficiently. Presear Software rose to the challenge of using the Python Django stack. Here's how we did it.


  1. High Traffic: Managing up to 100 entries per second.

  2. Scalability: Ensuring the system scales dynamically with traffic.

  3. Data Integrity: Maintaining data integrity and consistency with high concurrent submissions.

  4. Performance: Ensuring fast response times under heavy load.

Solution Architecture

1. Django as the Core Framework

Django was chosen for its robustness, scalability, and security features. Its powerful ORM (Object-Relational Mapping) system provided a convenient and efficient way to interact with the database.

2. Database Optimization

  • Database Choice: We selected PostgreSQL for its performance, scalability, and advanced features like JSON support and indexing.

  • Indexing: Indexed critical fields to speed up queries.

  • Connection Pooling: Utilized connection pooling with libraries like django-redis to manage database connections efficiently.

3. Asynchronous Processing

  • Celery: Employed Celery, an asynchronous task queue, to handle background tasks. This ensured quick processing of user requests and offloaded heavy tasks like sending confirmation emails.

  • Redis: Used Redis as the message broker for Celery due to its speed and efficiency.

4. Caching

  • Memcached/Redis: Implemented caching for frequently accessed data to reduce database load and increase response times. Leveraged Django’s caching framework with Redis or Memcached as the backend.

5. Load Balancing

  • Nginx: Deployed Nginx as a reverse proxy and load balancer to distribute incoming traffic across multiple application instances, preventing any single server from becoming overwhelmed.

  • Horizontal Scaling: Designed the application to scale horizontally by adding more servers during peak traffic times.

6. Auto-scaling and Cloud Infrastructure

  • AWS EC2: Deployed the application on AWS EC2 instances, utilizing auto-scaling groups to automatically adjust the number of instances based on traffic demands.

  • AWS RDS: Leveraged Amazon RDS for PostgreSQL to handle database scaling and backups.

7. Monitoring and Logging

  • Prometheus & Grafana: Implemented monitoring with Prometheus and Grafana to track application performance and health metrics.

  • ELK Stack: Used the ELK stack (Elasticsearch, Logstash, Kibana) for centralized logging and real-time log analysis, enabling quick identification and resolution of issues.

Implementation Details

Database Schema Design

Our database schema was meticulously designed to handle high write loads efficiently. This included:

  • Efficient Table Design: Using appropriate data types and normalization techniques for quick insertions and lookups.

  • Partitioning: Implementing table partitioning to manage large datasets more effectively.

Code Optimization

  • Efficient Querying: Optimized Django’s ORM usage to prevent N+1 query problems and ensure efficient database queries.

  • Bulk Operations: Used bulk insert operations where possible to reduce database hits.

Caching Strategy

  • Page Caching: Cached entire pages where data was relatively static.

  • Query Caching: Cached results of expensive database queries to minimize database load.

Handling High Traffic

  • Rate Limiting: Implemented rate limiting to prevent abuse and ensure fair usage of the portal.

  • Throttling: Utilized Django Rest Framework’s throttling classes to limit request rates to the API.


The SVEEP Pledge Portal successfully handled the anticipated load, processing 100 entries per second smoothly. Key outcomes included:

  • High Availability: The portal maintained high availability even during peak loads.

  • Fast Response Times: Average response times remained within acceptable limits, ensuring a smooth user experience.

  • Scalability: The application dynamically scaled with traffic, preventing downtime and ensuring reliability.

In addition to that, The Bilaspur district administration has bagged a world record for the largest online pledge campaign for casting votes.


Handling high traffic for web applications is a complex challenge requiring a well-thought-out architecture and efficient implementation. At Presear Softwares, we leveraged Django’s powerful features, optimized our database, implemented asynchronous processing, and ensured proper load balancing and scaling to manage the SVEEP Pledge Portal successfully. This project is a testament to the robustness and scalability of the Python Django stack for building high-performance web applications.

This blog post illustrates our technical prowess and strategic approach at Presear Softwares in managing high-traffic applications, highlighting our commitment to delivering scalable and reliable solutions.