How to Scale an App to 100 Million Users on GCP 🚀

Executive Summary

This guide outlines a step-by-step approach to scaling an application from 1,000 to 100 million users on Google Cloud Platform (GCP). It covers architectural evolution, key GCP services, and scalability strategies, emphasizing simplicity, resilience, and cost-efficiency.

Core Technical Concepts/Technologies

• GCP Services: Cloud DNS, Managed Instance Groups (MIG), Cloud Load Balancer, Cloud Spanner, Cloud CDN, Pub/Sub, BigQuery.
• Architectures: Monolith, three-tier, microservices.
• Scalability Tools: Autoscaling, leader-follower replication, Kubernetes, CI/CD.
• Data Management: Redis caching, Clickstream analytics.

Main Points

1. Initial Setup (1K Users)

   • Monolithic architecture on a single VM with MySQL.
   • Deployed via Google Cloud Shell; traffic routed via Cloud DNS.
   • Single-region deployment (North America) for low latency/cost.

2. 5K Users: Resilience & Automation

   • Single point of failure addressed using Managed Instance Groups (MIG) for autoscaling/healing.
   • Load balancer distributes traffic; CI/CD ensures reliable releases.

3. 10K Users: Decoupling & Global Reach

   • Shift to three-tier architecture (frontend/backend/database on separate VMs).
   • Multi-region deployment to reduce latency for global users.
   • MySQL leader-follower replication for high availability.

4. 100K Users: Database Optimization

   • Migrated to managed relational DB (auto-scaling disks).
   • Added Redis caching to reduce DB load.

5. 1M Users: Traffic & Content Delivery

   • Global load balancer replaces DNS Geo routing for failover.
   • CDN + Cloud Storage for static content; Cloud Spanner for scalable SQL.

6. 10M Users: Microservices

   • Monolith split into microservices (containerized, managed via Kubernetes).

7. 100M Users: Analytics

   • Clickstream data processed via Pub/Sub → BigQuery for user insights.

Technical Specifications

• Autoscaling: Based on CPU/memory metrics (MIG).
• Replication: MySQL leader-follower topology.
• Caching: Redis for frequent queries.
• Code Example: Not provided, but references GCP’s native tools (e.g., Cloud Spanner, Pub/Sub).

Key Takeaways

1. Start Simple: Begin with a monolith, then decouple as needed (three-tier → microservices).
2. Leverage Managed Services: Reduce operational overhead (e.g., Cloud Spanner, MIG).
3. Prioritize Resilience: Use multi-zone/region deployments, load balancers, and replication.
4. Optimize Data Layer: Caching (Redis) and managed databases prevent bottlenecks.
5. Monitor & Adapt: Clickstream analytics drive iterative improvements.

Limitations & Caveats

• Cost: Global infrastructure and managed services (e.g., Spanner) can be expensive.
• Complexity: Microservices/Kubernetes introduce operational overhead.
• Further Exploration: Deep dives into cost optimization, security, and advanced Kubernetes configurations.