Dear colleagues, When it comes to scaling the system, I believe performance testing is essential for providing a clearer answer to the question of what specifically needs to be scaled and where bottlenecks might exist. In my experience, testing in a controlled, non-production environment helps us secure our real customers from potential issues caused by unsuccessful attempts. To simulate the kind of load we need in a test environment, having a performance testing framework in place is crucial. While this requires some upfront investment, it’s far more cost-effective than running experiments in production and risking potential reputation damage. Let me know what you think, and I'd be happy to dive deeper into this approach. Dmytro

In my experience, we act according to the following plan: 1. Infrastructure part: a) If the scaling is required immediately - we try to scale vertically (if possible) to buy us some time. That's usually a sign of bad planning, but not always. b) Try to scale different app layers independently: - DB: move to a separate server, increase existing server capacity, add more nodes to the cluster, split write and read loads, optimize queries; - app: scale horizontally if possible (if app structure allows), add load balancing, etc. 2. App architecture part: a) implement different caching strategies b) use async processing c) use queues and scale their workers separately d) split the monolithic app into independent services and scale them

Horizontal Scaling: Add more servers or instances to distribute load across multiple machines. Caching: Implement caching at different layers (e.g., database, application, CDN) to reduce load on the primary system and improve response times. Load Balancing: Use load balancers to evenly distribute incoming requests across servers, preventing any single server from becoming overloaded. Database Optimization: Optimize queries, introduce indexing, use read replicas, and partition data for better database performance under heavy loads. Microservices Architecture. Asynchronous Processing. Auto-scaling. Monitoring and Profiling.

When scalability challenges hit, I always start by looking for quick wins in optimization. In one project, we tackled database bottlenecks first by fine-tuning queries and indexing the most frequently accessed data. Next, we implemented caching at both the database and application layers, significantly reducing redundant requests. We also shifted to a scalable cloud infrastructure, allowing us to autoscale during traffic spikes. Load balancing came into play to ensure even distribution across servers. The key is to take a layered approach—optimize what you have, then scale infrastructure to support future growth, all while keeping performance front and center.