Minute Grocery Delivery App: Challenges, Tech Stack, and Key Decisions

Rapid Grocery Delivery App: A 10-Minute Challenge

Quick-commerce apps like Blinkit, Zepto, and Swiggy Instamart have exploded in popularity. To understand the complexities of such services, I built a similar app focused on delivering groceries within 10 minutes.

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The Core Problem

These apps are essentially single-vendor e-commerce platforms prioritizing rapid delivery (sub-one-day). The biggest hurdle? Efficiently connecting delivery agents with orders in real-time. Other e-commerce features remain standard. As a freelancer with extensive e-commerce development experience, this project presented a familiar yet challenging landscape.

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Technology Stack

Leveraging my expertise, I opted for a Django (backend) and React Native (frontend) architecture. This choice aligns with my previous work on Class To Cloud. PostgreSQL serves as the primary database for structured data, complemented by Redis for in-memory caching.

Backend

• Framework: Django
• Database: PostgreSQL (structured data)
• Cache: Redis (fast data retrieval)

Frontend

• Framework: React Native

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Inventory Data: The Scraping Solution

I rapidly implemented the core e-commerce functionality (product and category listings). To populate the app with realistic data, I employed data scraping using HAR files (details available in a separate article). This data informed the app's design, drawing inspiration from Figma templates and existing apps like Blinkit and Zepto.

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Design Inspiration & Screens

The app's design blends Figma templates with design elements from Blinkit and Zepto.

Key Screens

• Home Screen
• Live Location Tracking

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Real-Time Location Tracking: A Custom Solution

Lacking prior experience with mobile architectures and GPS, I researched extensively. Many solutions involved Kafka for location updates. However, to avoid the overhead of adding Kafka to this monolithic application, I developed a custom solution using Django's caching system with Redis. While functional for a small user base, this approach may need refinement for larger-scale deployments. I'll explore better solutions as needed.

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Key Learnings

1. Tech Stack Selection: Choosing the right tech stack requires balancing complexity and performance. Prioritize solutions that meet your needs and align with your skillset.
2. Real-Time Challenges: Real-time updates necessitate careful system synchronization. Securing and ensuring reliability, especially handling scenarios with no available drivers, remains a focus for future development.
3. Modular Design: Modular architecture is crucial for scalability and rapid deployment. A modular design makes scaling (e.g., adding an EC2 instance) much easier.

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Future Enhancements

Currently, the app focuses on order delivery and database storage. Future improvements could include:

• Analytics: Adding comprehensive analytics.
• Admin App: Developing a companion admin app for mobile data access.
• White-Labeling: Enabling white-labeling for broader client use.

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Conclusion

Creating a 10-minute grocery delivery app presents significant challenges. By strategically addressing operational and technical hurdles and making informed tech stack decisions, this project provides a solid foundation. Future iterations will incorporate advanced features and address scalability to meet growing demand.

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Source Code

[Link to Source Code]

Connect with Me

Feel free to leave comments or contact me to share your experiences or ask questions!

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