High availability for a Spring Boot e-commerce application during peak traffic comes from a scalable microservices architecture combined with load balancing, caching, replication, and geographic redundancy, so no single component or region is a failure point.
Key Points: • Break the monolith into independently scalable microservices so bottlenecked components, like checkout or search, can scale without scaling everything else. • Use load balancers to distribute traffic evenly and route around unhealthy instances automatically. • Apply caching for read-heavy data like product catalogs to reduce database load during traffic spikes. • Use database replication, with read replicas handling query load separately from the primary write instance. • Deploy across multiple availability zones or regions so a single data center outage doesn't take down the whole platform.
Example: During a major sale, the product catalog service auto-scales independently of the checkout service, cached product data serves most read traffic without hitting the database, and traffic is spread across two regions so a regional outage doesn't take the storefront offline.
Interview Tip: A concise interview answer is:
"I'd break the application into independently scalable microservices, put a load balancer in front with auto-scaling, cache read-heavy data like the product catalog, and use database read replicas to spread query load. Deploying across multiple availability zones or regions adds redundancy so peak traffic or a localized outage doesn't take the whole platform down."