How to Boost Pipeline Performance

A sluggish API isn't just a technical hiccup – it's the difference between retaining and losing users to competitors. Let me share some battle-tested strategies that have helped many  achieve 10x performance improvements: 1. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Not just any caching – but strategic implementation. Think Redis or Memcached for frequently accessed data. The key is identifying what to cache and for how long. We've seen response times drop from seconds to milliseconds by implementing smart cache invalidation patterns and cache-aside strategies. 2. 𝗦𝗺𝗮𝗿𝘁 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Large datasets need careful handling. Whether you're using cursor-based or offset pagination, the secret lies in optimizing page sizes and implementing infinite scroll efficiently. Pro tip: Always include total count and metadata in your pagination response for better frontend handling. 3. 𝗝𝗦𝗢𝗡 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 This is often overlooked, but crucial. Using efficient serializers (like MessagePack or Protocol Buffers as alternatives), removing unnecessary fields, and implementing partial response patterns can significantly reduce payload size. I've seen API response sizes shrink by 60% through careful serialization optimization. 4. 𝗧𝗵𝗲 𝗡+𝟭 𝗤𝘂𝗲𝗿𝘆 𝗞𝗶𝗹𝗹𝗲𝗿 This is the silent performance killer in many APIs. Using eager loading, implementing GraphQL for flexible data fetching, or utilizing batch loading techniques (like DataLoader pattern) can transform your API's database interaction patterns. 5. 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 GZIP or Brotli compression isn't just about smaller payloads – it's about finding the right balance between CPU usage and transfer size. Modern compression algorithms can reduce payload size by up to 70% with minimal CPU overhead. 6. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗼𝗹 A well-configured connection pool is your API's best friend. Whether it's database connections or HTTP clients, maintaining an optimal pool size based on your infrastructure capabilities can prevent connection bottlenecks and reduce latency spikes. 7. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗟𝗼𝗮𝗱 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 Beyond simple round-robin – implement adaptive load balancing that considers server health, current load, and geographical proximity. Tools like Kubernetes horizontal pod autoscaling can help automatically adjust resources based on real-time demand. In my experience, implementing these techniques reduces average response times from 800ms to under 100ms and helps handle 10x more traffic with the same infrastructure. Which of these techniques made the most significant impact on your API optimization journey?

Want real change? Think 10X, not 10%. —𝘮𝘺 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭 𝘷𝘪𝘦𝘸; 𝘰𝘱𝘪𝘯𝘪𝘰𝘯𝘴 𝘢𝘳𝘦 𝘮𝘺 𝘰𝘸𝘯. Our ADAS integration pipeline was stuck at 2–3 hours per build. Developers were frustrated. Productivity dropped. The easy move? Tweak a few configs, save 10%, celebrate. But we asked: what if we aimed for 10X better? We dug deep, found hidden bottlenecks—no artifact sharing, inefficient build farm, heavy test workflows. We rebuilt from the ground up: • Introduced ccache on all servers • Parallelized builds • Streamlined test and A2L generation Result? Builds dropped to 20–30 minutes. And we even added more tests—including hardware-in-the-loop. The lesson: The 10X mindset isn’t about chasing perfection. It’s about escaping incremental thinking and forcing real transformation. Are you still chasing “better”? Or are you pushing for breakthrough? #Automotive #ContinuousIntegration #10XMindset #DevOps #SoftwareEngineering

𝟗 𝐊𝐞𝐲 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐭𝐨 𝐁𝐨𝐨𝐬𝐭 𝐀𝐏𝐈 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 ◾ Use Caching Store frequently accessed data in memory so you don’t have to fetch it from the database or other slow sources repeatedly. This drastically cuts down on response time. ◾ Minimize Payload Size Send only the necessary data in responses. Avoid sending large, unneeded chunks of data by filtering fields or compressing the payload, which reduces bandwidth usage and speeds up responses. ◾ Use Asynchronous Processing For tasks that don’t need an immediate response (like sending emails or processing large data sets), use asynchronous methods. This keeps the API responsive while the heavy work happens in the background. ◾ Load Balancing Distribute incoming API requests across multiple servers to prevent any single server from becoming overloaded. This improves availability and handles more traffic efficiently. ◾ Optimize Data Formats Use lightweight data formats like JSON or Protocol Buffers instead of XML. Smaller data formats reduce the time spent parsing and transmitting data. ◾ Connection Pooling Reuse existing connections to the database or other services rather than opening a new one for each request. Connection pooling significantly reduces the overhead of establishing connections. ◾ Use Content Delivery Networks (CDNs) For APIs serving static content (such as images or scripts), use CDNs to deliver content more quickly by caching it closer to the user’s location, thereby reducing latency. ◾ Implement API Gateway An API Gateway can help in routing requests, handling authentication, rate limiting, and caching. By offloading these tasks from your API, you can improve its overall performance. ◾ Avoid Overfetching and Underfetching Design your API endpoints to return the exact amount of data required. GraphQL, for example, allows clients to request exactly what they need, thereby avoiding the issues of overfetching and underfetching common in REST APIs. 📌 Liked this post? Join my free newsletter: https://lnkd.in/gpHAFd9t 📌Reference: Grokking the System Design Interview - https://lnkd.in/giwyzfkT #api #systemdesign #rest #grpc