Identify Cause — If it is a web application and the load issue happens during peak traffic, Identify any bottlenecks My initial step would involve examining server logs and inspecting system metrics (such as CPU, memory, database queries) to identify potential bottlenecks. If it were a resource problem I would either resize up or scale-out to add more servers and balance the traffic. Improving speed can be done via code optimization, reducing HTTP requests, and caching data that gets requested often. I would also be looking at somewhere to get a CDN and load static content out. Other steps such as optimizing database queries through profiling, and making asynchronous processing possible can also significantly lower the latency.

I focus on these: Identify and optimize bottlenecks: Use profiling tools to analyse and optimize slow database queries, code inefficiencies, and server resource usage. Scale dynamically: Leverage auto-scaling cloud services to increase resources during traffic spikes and scale down when demand drops. Implement robust caching: Use caching layers (e.g., CDN, database query caching) to reduce load on servers and speed up page delivery. Load testing and monitoring: Run stress tests before peak periods to identify vulnerabilities and set up real-time monitoring to detect issues as they arise. Prioritise critical paths: Optimize the most important features or user flows first to ensure core functionality remains fast during high traffic.

In this case: -Using CDN and data caching -Caching data that changes in the long term -Use and implementation of Load Balancing -Server and web application monitoring to identify bottlenecks

To handle performance issues during peak traffic, I would: Identify Bottlenecks: Use performance monitoring tools (e.g., Lighthouse, New Relic) to find slow-loading components. Optimize Assets: Minify CSS/JS, compress images, and implement lazy loading. Code Splitting: Use dynamic imports to load only necessary parts of the application. Caching: Implement browser and server-side caching (e.g., CDN). Debounce/Throttle Requests: Reduce API call frequency with techniques like debouncing or throttling. Optimize API Calls: Batch requests or use GraphQL to fetch specific data. Scalability: Ensure backend supports scaling with traffic spikes (e.g., load balancers).

Peak traffic performance issues? Welcome to the stress test in real life. First, scale up—spin those extra servers or boost your cloud resources. Next, check your database; slow queries love showing up uninvited. Cache everything you can—pages, queries, even snacks (you’ll need the energy). Monitor in real time and kill non-essential processes—your debug logs don’t need VIP treatment right now. If all else fails, queue requests and display a friendly “Hold tight, we’re on it!” message. Remember, a slow app is better than a crashed one, and caffeine helps solve both.

Addressing web application performance issues during peak traffic requires both immediate action and long-term strategies. A real-world example: during a high-traffic sale event, an e-commerce client implemented a Content Delivery Network (CDN) to cache static assets like images and scripts, significantly reducing server load and improving response times. In parallel, they optimized their database by indexing frequently queried fields and archiving older data, speeding up query execution. For scalability, autoscaling on AWS ensured the system adapted to traffic spikes in real-time. These combined efforts ensured a seamless experience for users, even under heavy demand.