GCP Web Application Architecture

What this diagram shows

This diagram represents a production-grade web application deployed on Google Cloud Platform using serverless and managed services. User requests flow through Cloud CDN and Cloud Load Balancing to reach stateless Cloud Run container services, which query Cloud SQL for persistent data and Memorystore for cached results. Asynchronous workloads are decoupled via Pub/Sub messaging, allowing background processors to handle tasks independently. This architecture emphasizes horizontal scalability, minimal operational overhead, and separation of concerns between synchronous request handling and asynchronous event processing.

Key components

• Cloud Load Balancer — Routes incoming HTTPS traffic across geographic regions and backends, terminating SSL/TLS connections.
• Cloud CDN — Caches static and dynamic content at Google's edge locations to reduce latency and origin traffic.
• Cloud Run — Executes stateless containerized application code on-demand, scaling from zero to thousands of concurrent requests.
• Cloud SQL — Provides managed relational database storage with automatic backups, replication, and failover capabilities.
• Memorystore — Delivers in-memory caching via Redis or Memcached to accelerate frequent queries and session storage.
• Pub/Sub — Decouples asynchronous workloads by publishing events that background services consume independently.
• Cloud Storage — Stores unstructured data such as user uploads, logs, and static assets with configurable retention and access policies.

When to use it

Use this template for web applications requiring high availability, elastic scaling, and minimal infrastructure management. It suits microservices architectures, content-heavy platforms, and systems with variable traffic patterns. Ideal when your team prioritises rapid deployment and operational simplicity over custom infrastructure tuning. Works well for applications handling both synchronous API requests and long-running background jobs.

Common mistakes

• Placing business logic in the load balancer or assuming CDN caching will solve database performance without also implementing Memorystore.
• Treating Cloud Run services as stateful by storing session data locally instead of routing it through Memorystore or session management middleware.
• Failing to configure Pub/Sub dead-letter topics and monitoring, leading to silently dropped messages when background workers fail.

Adapting it to your system

Replace Cloud Run with Cloud Functions if your workloads are under 15 minutes or purely event-driven. Substitute Cloud SQL with Firestore or Spanner depending on consistency requirements and schema flexibility. Use BigQuery instead of Pub/Sub for analytical event pipelines. Adjust CDN cache policies based on your content type: enable for static assets, use short TTLs for dynamic content, and bypass caching for sensitive data. Document your retry policies for Pub/Sub subscribers and set resource limits on Cloud Run to prevent runaway costs.