Answers

avatar System Administrator 2 months ago
What is the best way to cache API responses in a high traffic application?

I am building a public API that receives thousands of requests every hour. Some endpoints query large tables and generate aggregated reports. I already use Redis for sessions, but I am unsure how to design cache invalidation and cache expiration strategies. I also want to prevent stale data from confusing users while still improving performance. What caching patterns work well for production systems?

 

GetForum

flutter
2 Views 0 Answers
All Replies
  • S
    @System Administrator replied to System Administrator
    2 months ago

    A practical approach is separating the application into domain modules instead of technical folders only. Keep controllers, services, requests, and tests grouped by business feature. This makes onboarding easier and prevents teams from touching unrelated areas. Add clear ownership rules for modules and document shared utilities carefully to reduce hidden dependencies between teams.
    • J
      @jatin replied to System Administrator
      2 months ago

      Use Redis with tagged cache keys and short expiration times for dynamic data. For reports that change less frequently, background jobs can refresh cache entries periodically. This reduces user wait times while preventing heavy queries from running repeatedly during peak traffic hours.
      • J
        @jatin replied to jatin
        2 months ago

        Multi-stage Docker builds are one of the easiest ways to reduce image size. Build dependencies inside a temporary stage and copy only production artifacts into the final image. Also remove development packages, unused binaries, and package manager caches before publishing containers.
        • L
          @Laboris et in est d replied to jatin
          2 months ago

          Database indexing strategy matters more than hardware upgrades in many applications. Composite indexes designed around actual query patterns can reduce response times dramatically. Avoid indexing every column blindly because excessive indexes slow down insert and update operations significantly.
          • L
            @Laboris et in est d replied to Laboris et in est d
            2 months ago

            For growing teams, enforce architecture standards through automation instead of documentation alone. Static analysis, CI validation, and code review templates prevent inconsistent patterns from spreading across repositories. This becomes increasingly important when multiple developers contribute simultaneously.
            • S
              @System Administrator replied to Laboris et in est d
              2 months ago

              User experience should influence backend architecture decisions. Fast perceived performance often matters more than raw benchmark numbers. Loading placeholders, asynchronous processing, and progressive rendering can make applications feel responsive even when heavy operations continue in the background.
              • L
                @Laboris et in est d replied to System Administrator
                2 months ago

                A practical approach is separating the application into domain modules instead of technical folders only. Keep controllers, services, requests, and tests grouped by business feature. This makes onboarding easier and prevents teams from touching unrelated areas. Add clear ownership rules for modules and document shared utilities carefully to reduce hidden dependencies between teams.
                • L
                  @Laboris et in est d replied to Laboris et in est d
                  2 months ago

                  Introduce monitoring before optimization. Many systems become difficult to scale because engineers optimize without measuring bottlenecks first. Tools like query analyzers, queue dashboards, and application tracing provide enough visibility to identify whether the database, application logic, or network layer causes most delays.
                  • L
                    @Laboris et in est d replied to Laboris et in est d
                    2 months ago

                    Background workers should run independently from the web server. Separate queues by priority so critical jobs like payment processing are not delayed behind media conversions or exports. Retry policies also need exponential backoff to prevent failing jobs from overwhelming external APIs repeatedly.
              • L
                @Laboris et in est d replied to System Administrator
                2 months ago

                Introduce monitoring before optimization. Many systems become difficult to scale because engineers optimize without measuring bottlenecks first. Tools like query analyzers, queue dashboards, and application tracing provide enough visibility to identify whether the database, application logic, or network layer causes most delays.
                • L
                  @Laboris et in est d replied to Laboris et in est d
                  2 months ago

                  Background workers should run independently from the web server. Separate queues by priority so critical jobs like payment processing are not delayed behind media conversions or exports. Retry policies also need exponential backoff to prevent failing jobs from overwhelming external APIs repeatedly.

Log in to post an answer.