Addressing Software Performance Bottlenecks

I've debugged performance issues for some of the biggest brands out there on Salesforce Commerce Cloud, and here's the truth: 80% of site failures come from just a handful of repeat offenders. If you know where to look, you can fix them fast. 𝗠𝘆 𝗚𝗼-𝗧𝗼 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗧𝗿𝗶𝗰𝗸𝘀 (𝗧𝗵𝗮𝘁 𝗪𝗼𝗿𝗸 𝗘𝘃𝗲𝗿𝘆 𝗧𝗶𝗺𝗲): 1️⃣ 𝗖𝗵𝗲𝗰𝗸 𝘁𝗵𝗲 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗣𝗮𝗻𝗲𝗹 - Open DevTools → Network tab - Sort by load time - Identify the biggest offenders Most performance bottlenecks come from slow third-party scripts, oversized images, or unnecessary API calls. 2️⃣ 𝗥𝘂𝗻 𝗮𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘃𝗶𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗣𝗿𝗼𝗳𝗶𝗹𝗲𝗿 - Open Pipeline Profiler in SFCC Business Manager - Analyze controller response times against benchmarks: Search-Show: ≤400ms Product-Show: ≤300ms - Run the profiler after every deployment to detect regressions A slow or unoptimized controller can bring your storefront to a crawl (especially on PDPs and PLPs). 3️⃣ 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗔𝗰𝗰𝗲𝘀𝘀 & 𝗔𝗣𝗜 𝗨𝘀𝗮𝗴𝗲 - Use efficient APIs like 𝘗𝘳𝘰𝘥𝘶𝘤𝘵𝘚𝘦𝘢𝘳𝘤𝘩𝘔𝘰𝘥𝘦𝘭 for product searches instead of iterating through large data sets. - Minimize frequent calls to OnSession and OnRequest hooks - Batch database queries instead of querying one record at a time Excessive API calls and inefficient database access choke your site's performance. Optimize this, and your site will fly. A single bloated script can be the difference between high conversions and high bounce rates. ✅ 𝗥𝘂𝗻 𝘁𝗵𝗲𝘀𝗲 𝟯 𝗱𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗰𝗵𝗲𝗰𝗸𝘀 𝘁𝗼𝗱𝗮𝘆 - don't wait for customers to complain ✅ Fix the slowest controller, query, or API call you find ✅ 𝗕𝗼𝗼𝗸𝗺𝗮𝗿𝗸 𝘁𝗵𝗶𝘀 𝗽𝗼𝘀𝘁 - it'll help when Black Friday traffic hits Because in e-commerce, speed = conversions. And in SFCC, the brands that optimize first, win first. What's the worst SFCC performance issue you've had to fix? Drop it below.

API performance issues can silently erode user experience, strain resources, and ultimately impact your bottom line. I've grappled with these challenges firsthand. Here are the critical pain points I've encountered, and the solutions that turned things around: 𝗦𝗹𝘂𝗴𝗴𝗶𝘀𝗵 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗧𝗶𝗺𝗲𝘀 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗨𝘀𝗲𝗿𝘀 𝗔𝘄𝗮𝘆 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Users abandoning applications due to frustratingly slow API responses. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Implementing a robust caching strategy. Redis for server-side caching and proper use of HTTP caching headers dramatically reduced response times. 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 𝗕𝗿𝗶𝗻𝗴𝗶𝗻𝗴 𝗦𝗲𝗿𝘃𝗲𝗿𝘀 𝘁𝗼 𝗧𝗵𝗲𝗶𝗿 𝗞𝗻𝗲𝗲𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Complex queries causing significant lag and occasionally crashing our servers during peak loads. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Strategic indexing on frequently queried columns Rigorous query optimization using EXPLAIN Tackling the notorious N+1 query problem, especially in ORM usage 𝗕𝗮𝗻𝗱𝘄𝗶𝗱𝘁𝗵 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝗳𝗿𝗼𝗺 𝗕𝗹𝗼𝗮𝘁𝗲𝗱 𝗣𝗮𝘆𝗹𝗼𝗮𝗱𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Large data transfers eating up bandwidth and slowing down mobile users. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Adopting more efficient serialization methods. While JSON is the go-to, MessagePack significantly reduced payload sizes without sacrificing usability. 𝗔𝗣𝗜 𝗘𝗻𝗱𝗽𝗼𝗶𝗻𝘁𝘀 𝗕𝘂𝗰𝗸𝗹𝗶𝗻𝗴 𝗨𝗻𝗱𝗲𝗿 𝗛𝗲𝗮𝘃𝘆 𝗟𝗼𝗮𝗱𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Critical endpoints becoming unresponsive during traffic spikes. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Implementing asynchronous processing for resource-intensive tasks Designing a more thoughtful pagination and filtering system to manage large datasets efficiently 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗕𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀 𝗙𝗹𝘆𝗶𝗻𝗴 𝗨𝗻𝗱𝗲𝗿 𝘁𝗵𝗲 𝗥𝗮𝗱𝗮𝗿 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Struggling to identify and address performance issues before they impact users. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Establishing a comprehensive monitoring and profiling system to catch and diagnose issues early. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗮𝘀 𝗨𝘀𝗲𝗿 𝗕𝗮𝘀𝗲 𝗚𝗿𝗼𝘄𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: What worked for thousands of users started to crumble with millions. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Implementing effective load balancing Optimizing network performance with techniques like content compression Upgrading to HTTP/2 for improved multiplexing and reduced latency By addressing these pain points head-on, we can significantly improve user satisfaction and reduce operational costs. What challenges have you faced with API performance? How did you overcome them? Gif Credit - Nelson Djalo

𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸 𝗧𝗵𝗲 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆 Performance issues in Kubernetes can cascade from application-level problems to cluster-wide failures. Here's your systematic approach to identify and resolve them quickly. 𝗧𝗵𝗲 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝘆 Start with the application, work outward to infrastructure. 𝗦𝘁𝗲𝗽 𝟭: 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻-𝗟𝗲𝘃𝗲𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Check application metrics first:  • Response times and request throughput  • Error rates and success patterns  • Resource consumption trends  • Database connection efficiency Use kubectl top pods to identify resource-intensive applications immediately. 𝗦𝘁𝗲𝗽 𝟮: 𝗣𝗼𝗱-𝗟𝗲𝘃𝗲𝗹 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻 Examine container behavior:  • Memory leaks causing OOM kills  • CPU throttling from inadequate limits  • Storage I/O bottlenecks  • Network connectivity between services Check kubectl describe pod for recent events and resource constraints. 𝗦𝘁𝗲𝗽 𝟯: 𝗡𝗼𝗱𝗲-𝗟𝗲𝘃𝗲𝗹 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁  • Analyze worker node health:  • CPU and memory utilization patterns  • Disk I/O performance and capacity  • Network bandwidth consumption  • System processes competing for resources Use kubectl top nodes and node monitoring metrics for visibility. 𝗦𝘁𝗲𝗽 𝟰: 𝗖𝗹𝘂𝘀𝘁𝗲𝗿-𝗟𝗲𝘃𝗲𝗹 𝗥𝗲𝘃𝗶𝗲𝘄 Investigate control plane performance:  • API server response latency  • etcd performance and storage health  • Scheduler efficiency and placement decisions  • Network plugin overhead and CNI performance 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗜𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗖𝗼𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻: Multiple pods competing for node resources  𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴 𝗗𝗲𝗹𝗮𝘆𝘀: Pods stuck in pending state  𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗕𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀: Inter-node communication latency  𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Persistent volume response times 𝗪𝗵𝗮𝘁 𝗡𝗢𝗧 𝘁𝗼 𝗗𝗼 𝗗𝗼𝗻'𝘁 𝗴𝘂𝗲𝘀𝘀: Always use data-driven investigation  𝗔𝘃𝗼𝗶𝗱 𝗾𝘂𝗶𝗰𝗸 𝗳𝗶𝘅𝗲𝘀: Address root causes, not symptoms  𝗦𝗸𝗶𝗽 𝗯𝗮𝘀𝗲𝗹𝗶𝗻𝗲 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: Establish normal performance patterns first  𝗜𝗴𝗻𝗼𝗿𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀/𝗹𝗶𝗺𝗶𝘁𝘀: Properly configure container resources 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 Performance issues follow predictable patterns: application inefficiencies manifest as resource contention, which cascades to node-level problems, ultimately impacting cluster stability. Start small, think systematically, and always validate with metrics. #AWS #awscommunity #kubernetes