10 Things Software Engineers Can Learn from Instagram’s Scaling to 2.5 Billion Users

Insights from Instagram’s Journey to Serving Over 2.5 Billion Users

Hello friends, Instagram’s journey from a simple location check-in app to a photo-sharing giant serving over 2.5 billion users is a testament to the importance of robust system design and scalability strategies.

Here are ten key lessons from Instagram’s scaling strategy that can guide anyone looking to understand system design or manage hypergrowth in their applications.

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10 Lessons Software Engineers Can Learn from Instagram’s Scaling to 2.5 Billion Users

Here are few important lessons you can learn from their journey:

1. Pivoting Can Be Powerful

Instagram began as a location check-in app, but the founders noticed that photo sharing was its most popular feature. This insight led them to pivot and focus solely on photo sharing, a decision that was instrumental in their rapid growth.

Lesson: Be flexible and responsive to user behavior and market demands.

2. Vertical Scaling Has Limits

In the early days, Instagram scaled vertically by adding more powerful hardware. However, this approach became prohibitively expensive as user numbers skyrocketed.

Lesson: Understand the limitations and costs of vertical scaling and be prepared to shift strategies as needed.

3. Embrace Horizontal Scaling

To accommodate their growing user base, Instagram transitioned to horizontal scaling by adding more servers.

This distributed the load and helped manage increased traffic.

Lesson: Horizontal scaling can be more cost-effective and scalable in the long run.

4. Optimize Resource Usage

Instagram found that their servers were underutilized. By optimizing their code, moving common data objects to shared memory, and reducing CPU instructions, they were able to maximize server usage and improve efficiency.

Lesson: Efficient resource usage can significantly enhance performance and reduce costs.

5. Ensure Data Consistency

With data centers spread across the globe, maintaining data consistency was crucial. Instagram used Cassandra for its user feed and ensured that data was locally consistent while managing latency.

Lesson: Choose the right database architecture to balance consistency and performance.

6. Leverage Localized Data Clusters

Instead of running a single global Cassandra cluster, Instagram used separate clusters for different continents, reducing latency and improving data access speed.

Lesson: Localized data clusters can enhance performance and user experience by minimizing latency.

7. Implement Smart Data Migration

When users moved between continents, Instagram’s system migrated their data to the nearest cluster. This involved tracking user access patterns and relocating data accordingly.

Lesson: Automated data migration based on user behavior can ensure optimal data placement and performance.

8. Balance Synchronous and Asynchronous Processing

To prevent server starvation, Instagram used asynchronous processing with Python async IO. This allowed their servers to handle more requests simultaneously without waiting for each external service to respond.

Lesson: Asynchronous processing can improve server utilization and reduce latency.

9. Use Caching Strategically

Instagram used Memcache to handle read requests and protect their PostgreSQL database from being overwhelmed. They also employed a cache invalidation strategy to ensure data freshness.

Lesson: Effective caching strategies can significantly enhance performance and scalability.

Here are top 5 cache strategies you can learn for system design interview, courtesy ByteByteGo

10. Avoid Thundering Herd Problem

To prevent a surge of requests overwhelming the database after a cache invalidation, Instagram implemented a Memcache lease system.

This allowed only one request to refresh the cache while others received stale data until the cache was updated.

Lesson: Implement mechanisms to prevent database overload during cache misses.

Conclusion

Instagram’s scaling journey highlights the importance of adapting and optimizing both infrastructure and code to handle hypergrowth.

Each architectural decision involves trade-offs, but with smart strategies, even simple, proven technologies like Python can be scaled to handle massive user bases.

Whether you’re a developer or a system architect, these lessons from Instagram’s experience offer valuable insights into building scalable, resilient systems.

For further reading and more in-depth information on Instagram’s scaling strategies, check out the references at the bottom of the page. If you found this article useful, consider sharing it with someone interested in system design.

References

10 Things Software Engineers Can Learn from Instagram’s Scaling to 2.5 Billion Users was originally published in Javarevisited on Medium, where people are continuing the conversation by highlighting and responding to this story.

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