Tips for Real-Time Performance Tracking

Why you should look for Spark UI when you are struggling with performance issues in your Spark Structured Streaming applications? 🤔 𝗙𝗶𝗿𝘀𝘁 𝗼𝗳 𝗮𝗹𝗹, 𝗪𝗵𝘆 𝗦𝗽𝗮𝗿𝗸 𝗨𝗜? ================== -> Spark UI is your window into the internals of Spark application. -> It provides real-time insights into your job's performance, resource utilization, and potential bottlenecks. ->For streaming applications, the Streaming tab is your go-to resource. 𝗞𝗲𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝘁𝗼 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 ----------------------- 𝟭. 𝗜𝗻𝗽𝘂𝘁 𝗥𝗮𝘁𝗲 𝘃𝘀. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗥𝗮𝘁𝗲   - Input Rate: How fast data is coming in   - Processing Rate: How fast your job is processing data   - 🚨 Alert: If Processing Rate < Input Rate, you're falling behind! 𝟮. 𝗕𝗮𝘁𝗰𝗵 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗧𝗶𝗺𝗲   - Shows how long each micro-batch takes to process   - 📈 Trend Analysis: Look for increasing trends over time 𝟯. 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴 𝗗𝗲𝗹𝗮𝘆   - Time between batch creation and the start of processing   - 🐢 High delay = Your system is overwhelmed 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗧𝗿𝗼𝘂𝗯𝗹𝗲𝘀𝗵𝗼𝗼𝘁𝗶𝗻𝗴 ------------------------ 1. Use the "min/max/avg" toggle   - Helps identify outliers in batch processing times 2. Check the DAG visualization   - Understand your job's logical and physical plans   - Spot bottlenecks in specific stages 3. Monitor Watermark Progress   - Ensure your watermark is advancing as expected   - Stalled watermark = potential state store bloat 4. Analyze Task Metrics   - Look for data skew in shuffle read/write sizes   - High GC time might indicate memory pressure 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: ---------- 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗸𝗲𝘄 𝗶𝗻 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 👉 Scenario:  ↳ Your spark click-stream analysis job is running slower than expected. 👉 Spark UI Action:  ↳ Check the "Executors" tab to see if some executors are processing significantly more data than others. 👉 Solution:  ↳ If skew is detected, implement salting techniques or adjust partitioning strategies to distribute data more evenly. #pyspark #apachespark #dataengineers #dataengineering

A Senior Data Engineer candidate was asked to design a real-time analytics pipeline during his interview at Netflix. Another candidate in a different loop at Uber got the same prompt. Real-time dashboards look simple until you add one layer of reality: – Add late arrivals? Now you need watermarks, session windows, and late-firing logic. – Add out-of-order events? Now event-time vs processing-time becomes your entire correctness model. – Add exactly-once semantics? Now idempotent sinks and transactional commits are non-negotiable. – Add backpressure? Now Kafka is lagging or your sink is choking and alerts are firing. – Add historical corrections? Now you're reconciling streaming state with batch recomputes. Here's my checklist of 15 things you must get right when building real-time analytics: 1. Start with your latency and correctness contract → Define what "real-time" actually means: sub-second? 5 minutes? End-to-end or just processing? And define correctness: approximate is fine, or must be exact? 2. Choose your processing model: Lambda vs Kappa → Lambda = separate batch + stream paths, eventually consistent. Kappa = stream-only, simpler but harder to backfill. Most companies say Kappa but run Lambda in disguise. 3. Pick your event-time strategy early → Use event timestamps, not processing timestamps. If events don't have timestamps, you're already behind. Decide: use producer time, log append time, or application time? 4. Design your windowing logic to match business semantics → Tumbling windows for fixed intervals. Hopping for overlapping aggregations. Session windows for user activity. Getting this wrong means your metrics lie. 5. Implement watermarking to handle late data → Watermark = "no events before this timestamp will arrive." But late data still arrives. Set your watermark delay based on observed lateness, not wishful thinking. 6. Build a late-firing strategy that doesn't break downstream → When late data arrives after the window closes, decide: update the past metric (retractions), append a correction, or drop it. Each has trade-offs for downstream consumers. 7. Handle out-of-order events with buffering and sorting → Events rarely arrive in order. Buffer and sort within your watermark delay. If you don't, your aggregations are wrong and nobody will notice until the CEO asks why revenue dropped. 8. Design for exactly-once semantics from source to sink → Kafka supports exactly-once within Kafka. Flink supports exactly-once with transactional sinks. But your sink (Postgres, Elasticsearch) must be idempotent or transactional too. 9. Make every sink operation idempotent → Assume every write happens twice. Use upsert patterns: INSERT ON CONFLICT, MERGE, or idempotency keys. Never use blind INSERT or INCREMENT operations. (Continued in comments)

Want your XR app to have the best user experience? Performance monitoring tools are key to identifying bottlenecks & optimizing performance. Here's how to leverage them effectively 🧵 1/ First, establish KPIs to track for your XR app. Frame rate, GPU utilization, memory usage, load times are all critical metrics. The right tool will monitor these in real-time as users interact with your app. 2/ For VR, aim for a stable 90 FPS to avoid motion sickness. AR apps should target 60 FPS. Monitor frame rates under various conditions (low power mode, heavy usage) to gauge real-world performance. Tools like Intel GPA are ideal for this. 3/ GPU utilization is another key metric, especially for graphics-heavy XR apps. You want the GPU working hard but not constantly maxed out. Tools like Unity Profiler or Unreal Insights identify GPU-intensive areas to optimize. 4/ Memory management is crucial in XR to avoid crashes & stutters. Track memory usage/leaks over time with tools like Visual Studio or Xcode. Look for assets/areas using excessive memory and optimize resource loading. 5/ Don't forget to monitor load times, especially for asset-rich XR apps. Use profiling tools to see what's causing long loads - large textures, unoptimized models, too many objects, etc. Optimize based on these insights. 6/ Regularly test on a range of devices to gauge real-world performance. Automated performance tests help identify regressions. Many tools can test XR apps on farms of physical devices for comprehensive insights. 7/ Lastly, don't just rely on tools - actively seek user feedback on app performance. Prompt users to report any slowdowns, stutters, or instability they encounter. Combine this qualitative data with quantitative metrics for the full picture. 8/ Optimization is a pain and a half. But, the upfront effort pays dividends in user experience and engagement. Work on it until no-one mentions stutters or frame drops.

We've all seen brilliant AIoT concepts stumble when they hit the real world. Why? Often, it's not a flaw in the core idea, but a failure to truly 'see' how the system is performing once it's deployed. I've personally advised teams who spent months debugging what they thought was a model issue, only to find their connectivity was silently failing, or their sensors were feeding garbage data. It's a costly lesson, and it taught me that tracking the right metrics isn't just good practice; it's the difference between a robust, intelligent system and one that's effectively operating blindly. Here are the 10 non-negotiable metrics you need to monitor for reliable, safe, and intelligent AIoT performance: ➞ Latency (Response Time): How fast your system reacts from sensing to action. This directly impacts user experience and, in critical applications, safety. ➞ Inference Accuracy: Are your models making the right decisions in the wild? Constantly monitor prediction errors and drift, because poor accuracy erodes user trust and leads to ineffective automation. ➞ Power Consumption: AI tasks can be power hungry. Track energy use per component for efficiency, especially critical for edge devices where battery life is paramount. ➞ Connectivity Stability: Dropped connections lead to broken experiences and missed data. Monitor retries, latency spikes, and outright failures – a device that can't connect can't act or learn. ➞ Sensor Data Quality: Garbage in, garbage out is true for AIoT. Ensure your input data isn’t noisy, missing, or faulty at the source, as it’s foundational for meaningful AI outcomes. ➞ Model Drift: The real world changes, and so does your AI's optimal performance. Track drift to avoid outdated, inaccurate AI behavior, which can make your system progressively worse. ➞ Override Rate: How often do users override AI actions or suggestions? A high override rate is a clear signal of lacking trust or that your AI needs recalibration to better meet user needs. ➞ Anomaly Detection Frequency: This metric shows how often your system spots unusual behavior and, importantly, how quickly and effectively it resolves it. It's a key indicator of proactive system health. ➞ Model Update Success Rate: Essential for continuous improvement, security, and adaptability. Track update logs and rollback issues diligently, as failed updates can leave devices vulnerable or stagnant. ➞ User Engagement Level: Measure how users interact with your AIoT automation and interfaces. Drop-offs or low engagement often reveal friction or critical gaps in the system's value proposition. These metrics aren't just numbers; they're your AIoT system's eyes and ears in the real world. Without them, you're flying blind. 💬 Which of these do you find most challenging to track effectively? 🔁 Repost if you're building for the real world, not just connected demos. ➕ Follow me, Nick Tudor, for more insights on AI + IoT that actually ship.

In the quietest corners of our digital workspaces, progress hums along, often unnoticed. But what if we could see it, feel it, without disrupting its flow? The daily standup, once a revolution, now feels like a relic. It's time for a change. Here are five ways to track progress that respect your team's time and talent: 𝟭. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗰𝗵𝗲𝗰𝗸-𝗶𝗻𝘀: Imagine a friendly bot that pings your team daily. "What did you accomplish? What's next? Any roadblocks?" Simple questions, powerful insights. No meetings, no time zones to juggle. Just a moment of reflection that keeps everyone aligned. 𝟮. 𝗩𝗶𝘀𝘂𝗮𝗹 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: A digital Kanban board where tasks are easily dragged from "To Do" to "Done." See progress unfold in real-time. It's not just a tool; it's a window into your team's momentum. 𝟯. 𝗖𝗼𝗱𝗲 𝗮𝘀 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Every commit tells a story. By linking code changes to project tasks, we turn the act of coding into a form of progress tracking. It's subtle, seamless, and speaks the language developers already use. 𝟰. 𝗣𝘂𝗹𝘀𝗲 𝘀𝘂𝗿𝘃𝗲𝘆𝘀: Quick, focused questions that take the team's temperature. "How's your workload? Feel supported? Any hidden obstacles?" It's not just about tasks; it's about the humans behind them. Catch issues before they become problems. 𝟱. 𝗔𝘀𝘆𝗻𝗰 𝘃𝗶𝗱𝗲𝗼 𝘂𝗽𝗱𝗮𝘁𝗲𝘀: Sixty seconds of face time, without the meeting. Team members share quick video updates on their own time. It adds a human touch to remote work, conveying nuances that text can't capture. It's not just progress tracking; it's team building. 𝙒𝙝𝙮 𝙙𝙤𝙚𝙨 𝙩𝙝𝙞𝙨 𝙢𝙖𝙩𝙩𝙚𝙧? - Because 20% of productivity evaporates when priorities blur in distributed teams. - Because teams with clear tracking are 50% more likely to retain their best. - Because 87% of distributed teams move 30% faster with robust tracking. 𝘽𝙪𝙩 𝙢𝙤𝙧𝙚 𝙩𝙝𝙖𝙣 𝙣𝙪𝙢𝙗𝙚𝙧𝙨, 𝙞𝙩'𝙨 𝙖𝙗𝙤𝙪𝙩 𝙧𝙚𝙨𝙥𝙚𝙘𝙩. - Respect for the craft. - Respect for the creators. - Respect for the quiet moments where brilliance blooms. The best progress tracking doesn't feel like tracking at all. It feels like clarity. Like purpose. Like forward motion. What if your team's progress was as clear as day, without casting a single shadow on their work? That's not just efficiency. That's empowerment. What's your next step toward invisible, impactful progress tracking?

Everyone talks about what you should do before you push to production, but software engineers, what about after? The job doesn’t end once you’ve deployed; you must monitor, log, and alert. ♠ 1. Logging Logging captures and records events, activities, and data generated by your system, applications, or services. This includes everything from user interactions to system errors. ◄Why do you need it? To capture crucial data that provides insight into system health user behavior and aids in debugging. ◄Best practices • Structured Logging: Use a consistent format for your logs to make it easier to parse and analyze. • Log Levels: Utilize different log levels (info, warning, error, etc.) to differentiate the importance and urgency of logged events. • Sensitive Data: Avoid logging sensitive information like passwords or personal data to maintain security and privacy. • Retention Policy: Implement a log retention policy to manage the storage of logs, ensuring old logs are archived or deleted as needed. ♠ 2.Monitoring It’s observing and analyzing system performance, behavior, and health using the data collected from logs. It involves tracking key metrics and generating insights from real-time and historical data. ◄Why do you need it? To detect real-time issues, monitor trends, and ensure your system runs smoothly. ◄Best practices: • Dashboard Visualization: Use monitoring tools that offer dashboards to present data in a clear, human-readable format, making it easier to spot trends and issues. • Key Metrics: Monitor critical metrics like response times, error rates, CPU/memory usage, and request throughput to ensure overall system health. • Automated Analysis: Implement automated systems to analyze logs and metrics, alerting you to potential issues without constant manual checks. 3. Alerting It’s all about notifying relevant stakeholders when certain conditions or thresholds are met within the monitored system. This ensures that critical issues are addressed as soon as they arise. ◄Why do you need it? To promptly address critical issues like high latency or system failures, preventing downtime. ◄Best practices: •Thresholds: Set clear thresholds for alerts based on what’s acceptable for your system’s performance. For instance, set an alert if latency exceeds 500ms or if error rates rise above 2%. • Alert Fatigue: To prevent desensitization, avoid setting too many alerts. Focus on the most critical metrics to ensure that alerts are meaningful and actionable. • Escalation Policies: Define an escalation path for alerts so that if an issue isn’t resolved promptly, it is automatically escalated to higher levels of support. Without these 3, no one would know there’s a problem until the user calls you themselves. 

This is something every affiliate team runs into. “What actually drove revenue this week?” “Which creators are trending right now, not last month?” Until now, you were stuck guessing or pulling messy reports. Cruva just fixed that. Introducing Performance Time Range. - Zoom in on any window: day, week, custom range. - Rank affiliates by real GMV in that period. - Spot breakout videos as they’re happening. - Export clean data instantly. All-time stats look nice. But they hide what actually matters. If you’re not tracking time-based performance, you’re optimizing blind.

Expecting your team to perform better without tracking KPIs is like expecting a plant to grow without water. If you’re not tracking KPIs, you’re not managing your team—you’re just hoping they’ll figure it out. And I hate to break it to you, but hope isn’t a strategy. In fact, it’s setting you and your team up for a major faceplant. KPIs aren’t just numbers. They’re the pulse of your business. The roadmap. They tell you exactly where you’re winning, where you’re tanking, and where your team might be grinding their wheels without making progress. And without them? Well, you’re essentially steering your business in the dark. Here’s why KPIs are everything and some dead-simple tips to make tracking them second nature. 1. 𝗠𝗮𝗸𝗲 𝗞𝗣𝗜𝘀 𝗦𝘁𝘂𝗽𝗶𝗱𝗹𝘆 𝗖𝗹𝗲𝗮𝗿 Your team shouldn’t have to decode some corporate lingo to understand what you’re tracking. If the KPI is “grow revenue,” spell it out: “Increase monthly revenue by 20%.” No jargon. No fluff. Just a clear goal. 2. 𝗧𝗿𝗮𝗰𝗸 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗶𝗻 𝗥𝗲𝗮𝗹 𝗧𝗶𝗺𝗲 KPIs only work if they’re actionable. If you’re checking on them quarterly or even monthly, it’s too late to course-correct. By then, the damage is done. Set up a weekly KPI dashboard. Use a tool like ClickUp or even Google Sheets. Update it regularly so your team can see exactly where they stand every week. 3. 𝗖𝗿𝗲𝗮𝘁𝗲 𝗮 “𝗟𝗲𝗮𝗱𝗶𝗻𝗴 𝘃𝘀. 𝗟𝗮𝗴𝗴𝗶𝗻𝗴” 𝗞𝗣𝗜 𝗦𝘆𝘀𝘁𝗲𝗺 Most people track “lagging indicators,” like last month’s sales. That’s fine, but it’s backward-looking. You need leading indicators to know where you’re headed. ➝ Leading KPIs predict future success. Think daily sales calls, conversion rates, or leads generated. ➝ Lagging KPIs confirm what already happened. This includes revenue, monthly profit, and churn rate. You want a high-performing team? Start by giving them a map and a compass. That’s what KPIs are. Because at the end of the day, you can’t measure what you don’t track—and you can’t grow what you don’t measure.

We built Quest Nutrition to $1B without perfect financial data. Sadly, most founders think they need complete accuracy to make decisions. Here's the system we used to grow 57,000% in 3 years with imperfect data: The FAST system has 4 components: • Focus: Simple dashboards with 5-10 key metrics • Anticipate: Track leading indicators • Sync: Compare against benchmarks • Transform: Convert data to action Let me break down each step: Step 1: Focus Build a 30-second dashboard. Create 3 tabs in a spreadsheet: • Inflows (revenue streams) • Outflows (expenses) • Summary (key metrics) Create a column for each of your 5-10 most critical metrics. Your summary should track: Revenue, Margins, Cash, CAC, LTV, MRR, and Churn. The key to Focus is ruthless simplification. Your dashboard should show your business health in 30 seconds flat. If you need more time to understand the numbers, you're tracking the wrong metrics. Think of it as your business cockpit. Step 2: Anticipate Most founders stare at lagging indicators (revenue, profit). That's like driving by looking in the rearview mirror. Instead, track leading indicators: • Leads in pipeline • Customer satisfaction • Social media follower growth Work with each department to identify their 2-3 most predictive metrics. Example for sales: • Demos booked • Deal size • Weeks to close Place these next to your lagging indicators. Step 3: Sync Give context to your numbers — a 12% margin means nothing without context. Compare each metric against: • Your 90-day baseline • Your targets/budget • Industry averages This shows exactly where to investigate and adjust. Step 4: Transform Let this data fuel team discussions. At least once a week, run a 30-minute meeting: • 10min: Report numbers • 10min: Diagnose issues • 10min: Make 2-3 decisions Then, revisit those decisions in next week's dashboard. Remember: Perfect accuracy is the enemy of good decisions. The FAST system isn't about getting every decimal right. It's about making confident moves with "good enough" data in real-time. Because speed beats perfection in business every single time. For more ideas like this to help you scale your business, my team and I created a custom GPT that you can ask ANYTHING. You can get access right here: https://buff.ly/OBkuddm

Last week, I reviewed a project dashboard. I realized... Your project isn’t failing… You’re just not seeing it early enough. In 30 seconds, everything became clear: • Budget: $42M • Forecast: $43.7M → Already heading toward a $1.7M loss • Schedule: 3 weeks behind • Problem areas: piling, trenching, overheads • QA: lagging behind construction • RFIs: piling up • Variations: still stuck in approval No long meetings. No endless spreadsheets. No guessing. Just clarity. Here’s the truth most teams ignore: Projects don’t go wrong overnight. They drift… slowly… silently… until it’s too late. And the biggest mistake? You’re tracking data… but not turning it into decisions. The best construction teams I’ve seen don’t just “track everything” They focus on 7 things that actually matter: Cost → Are we still making money? Schedule → Are we falling behind? Procurement → Did we buy smart? Quality → Is work keeping up with standards? Variations → Are we protecting margin? RFIs → Are delays building up? Risk → What can still go wrong? But here’s where it gets powerful: A dashboard isn’t just for reporting. It changes behavior. When your team knows: • Targets are visible • Performance is tracked • Problems are obvious They don’t wait to be told. They act. Simple example: If your team knows they must hit “250 piles per day” That number alone will drive performance more than any meeting ever will. What gets measured… gets managed. But more importantly: What gets seen… gets fixed. If you’re still relying on scattered Excel sheets and weekly reports… You’re reacting. Not managing. Curious question: Do you currently have a project dashboard that gives you clarity in under 60 seconds… or are you still digging through data to figure out what’s going wrong? #construction #projectmanagement #costestimation #engineering #constructionmanagement #projectcontrols #estimating #productivity