How to Address Performance Drops

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

With a background in data engineering and business analysis, I’ve consistently seen the immense impact of optimized SQL code on improving the performance and efficiency of database operations. It indirectly contributes to cost savings by reducing resource consumption. Here are some techniques that have proven invaluable in my experience: 1. Index Large Tables: Indexing tables with large datasets (>1,000,000 rows) greatly speeds up searches and enhances query performance. However, be cautious of over-indexing, as excessive indexes can degrade write operations. 2. Select Specific Fields: Choosing specific fields instead of using SELECT * reduces the amount of data transferred and processed, which improves speed and efficiency. 3. Replace Subqueries with Joins: Using joins instead of subqueries in the WHERE clause can improve performance. 4. Use UNION ALL Instead of UNION: UNION ALL is preferable over UNION because it does not involve the overhead of sorting and removing duplicates. 5. Optimize with WHERE Instead of HAVING: Filtering data with WHERE clauses before aggregation operations reduces the workload and speeds up query processing. 6. Utilize INNER JOIN Instead of WHERE for Joins: INNER JOINs help the query optimizer make better execution decisions than complex WHERE conditions. 7. Minimize Use of OR in Joins: Avoiding the OR operator in joins enhances performance by simplifying the conditions and potentially reducing the dataset earlier in the execution process. 8. Use Views: Creating views instead of results that can be accessed faster than recalculating the views each time they are needed. 9. Minimize the Number of Subqueries: Reducing the number of subqueries in your SQL statements can significantly enhance performance by decreasing the complexity of the query execution plan and reducing overhead. 10. Implement Partitioning: Partitioning large tables can improve query performance and manageability by logically dividing them into discrete segments. This allows SQL queries to process only the relevant portions of data. #SQL #DataOptimization #DatabaseManagement #PerformanceTuning #DataEngineering

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.

Understanding Data Science Case Study Rounds - Part 4/5 Root Cause Analysis (RCA) & Diagnosing Problems These questions present a problem scenario (e.g., performance drop, unexpected behavior) and ask you to diagnose the root cause. Example - "Our website traffic suddenly decreased by 20% last week. How would you investigate the cause?" "We've noticed a significant drop in the accuracy of our fraud detection model. What could be the reasons and how would you find out?" "Users are reporting slow loading times on our app. How would you troubleshoot this issue?" Framework you can use - Understand the Symptom Clearly: Pin down the exact problem. Ask clarifying questions: "When did the problem start?" "Is it affecting all users or a specific segment?" "Has anything changed recently (code deployments, data pipeline updates, external factors)?" Formulate Hypotheses (Brainstorm Potential Causes): Think broadly about possible reasons, categorizing them if helpful 👉 Data Issues - Data quality degradation (new data is noisy, biased, incomplete), Data pipeline failures, Data drift. 👉 Model Issues - Model decay (model performance degrading over time), Model retraining issues, Model deployment issues (incorrect model version deployed, configuration errors). 👉 System/Infrastructure Issues - Server outages, performance bottlenecks, Network issues, Database problems, Third-party API failures. 👉 External Factors - Seasonality, Marketing campaigns ending/changing., Competitor actions etc Prioritize Hypotheses & Investigate Systematically: Start investigating the most likely hypotheses based on: 👉 Recent Changes - Focus on things that changed around the time 👉 Ease of Investigation - Start with investigations that are quick and easy to perform. Data-Driven Investigation (Look at the Data & Logs): 👉 Monitoring Dashboards - Ask about what monitoring dashboards report 👉 Logs Analysis - Ask if application logs has error messages. 👉 Data Analysis - Analyze if data has changes in distributions, quality issues, or anomalies. Isolate the Root Cause: Through your investigation, aim to narrow down the cause to a specific issue. Propose Solutions & Preventative Measures: Once you've identified the root cause, suggest fixes and preventative measures to avoid recurrence in the future. What Interviewers are Looking For - 💡 Problem-Solving Skills: Ability to systematically diagnose and troubleshoot complex issues. 💡 Logical Reasoning: Formulating hypotheses and testing them in a structured way. 💡 Data Orientation: Using data and logs to guide the investigation. 💡 Practicality: Focusing on actionable steps and realistic solutions. 💡 Communication: Clearly explaining your diagnostic process and findings. 𝗥𝗲𝗽𝗼𝘀𝘁 with comments to grow your own network! 𝐂𝐨𝐦𝐦𝐞𝐧𝐭 your opinions/questions below! 𝗟𝗶𝗸𝗲 for more such content. Follow Karun Thankachan to land your next Data Science role