Application Performance Monitoring

In today’s always-on world, downtime isn’t just an inconvenience — it’s a liability. One missed alert, one overlooked spike, and suddenly your users are staring at error pages and your credibility is on the line. System reliability is the foundation of trust and business continuity and it starts with proactive monitoring and smart alerting. 📊 𝐊𝐞𝐲 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐌𝐞𝐭𝐫𝐢𝐜𝐬: 💻 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: 📌CPU, memory, disk usage: Think of these as your system’s vital signs. If they’re maxing out, trouble is likely around the corner. 📌Network traffic and errors: Sudden spikes or drops could mean a misbehaving service or something more malicious. 🌐 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧: 📌Request/response counts: Gauge system load and user engagement. 📌Latency (P50, P95, P99):  These help you understand not just the average experience, but the worst ones too. 📌Error rates: Your first hint that something in the code, config, or connection just broke. 📌Queue length and lag: Delayed processing? Might be a jam in the pipeline. 📦 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 (𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐨𝐫 𝐀𝐏𝐈𝐬): 📌Inter-service call latency: Detect bottlenecks between services. 📌Retry/failure counts: Spot instability in downstream service interactions. 📌Circuit breaker state: Watch for degraded service states due to repeated failures. 📂 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞: 📌Query latency: Identify slow queries that impact performance. 📌Connection pool usage: Monitor database connection limits and contention. 📌Cache hit/miss ratio: Ensure caching is reducing DB load effectively. 📌Slow queries: Flag expensive operations for optimization. 🔄 𝐁𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝 𝐉𝐨𝐛/𝐐𝐮𝐞𝐮𝐞: 📌Job success/failure rates: Failed jobs are often silent killers of user experience. 📌Processing latency: Measure how long jobs take to complete. 📌Queue length: Watch for backlogs that could impact system performance. 🔒 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: 📌Unauthorized access attempts: Don’t wait until a breach to care about this. 📌Unusual login activity: Catch compromised credentials early. 📌TLS cert expiry: Avoid outages and insecure connections due to expired certificates. ✅𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐟𝐨𝐫 𝐀𝐥𝐞𝐫𝐭𝐬: 📌Alert on symptoms, not causes. 📌Trigger alerts on significant deviations or trends, not only fixed metric limits. 📌Avoid alert flapping with buffers and stability checks to reduce noise. 📌Classify alerts by severity levels – Not everything is a page. Reserve those for critical issues. Slack or email can handle the rest. 📌Alerts should tell a story : what’s broken, where, and what to check next. Include links to dashboards, logs, and deploy history. 🛠 𝐓𝐨𝐨𝐥𝐬 𝐔𝐬𝐞𝐝: 📌 Metrics collection: Prometheus, Datadog, CloudWatch etc. 📌Alerting: PagerDuty, Opsgenie etc. 📌Visualization: Grafana, Kibana etc. 📌Log monitoring: Splunk, Loki etc. #tech #blog #devops #observability #monitoring #alerts

Last quarter, our team delivered a feature that looked perfect in testing. Users loved the functionality. But within weeks, complaints started pouring in about slow load times and timeouts during peak hours. That's when I realised functional testing alone wasn't enough. Here's what I learned about performance testing as an SDET: Why it matters beyond functional testing: Your code might work perfectly with 10 users. But what happens with 10,000? Performance testing shows you the real story - how your application handles the chaos of peak traffic. I've seen too many teams skip this step. They ship features that work great in staging, then watch them crumble in production. The metrics I track religiously: → Response time (sub-2 seconds keeps users happy) → Throughput (how many requests we can actually handle) → CPU/Memory usage (before the server gives up) → Error rates (the moment things start breaking) My JMeter workflow: Started using JMeter six months ago. Game changer. Set up realistic user scenarios, ramp up load gradually, and get detailed reports that actually make sense to stakeholders. The best part? It plugs right into our CI/CD pipeline. No more "it worked on my machine" excuses. Performance testing isn't glamorous work. But it's the difference between a product that works and a product that works when it matters most. Anyone else dealing with performance issues lately? What tools are working for you? -x-x- JMeter Load Testing & Distributed Performance Testing: https://lnkd.in/g4kxnMBB #SDET #japneetsachdeva

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. 

🔍 ARE YOU TRACKING YOUR AI TOKEN USAGE? If you're building AI applications with large language models, you're paying for every token - both input and output. But most developers have zero visibility into their usage patterns until that scary bill arrives at month-end. I just published a practical tutorial showing how to solve this problem by creating a complete monitoring solution using: - Spring Boot with Spring Actuator - Spring AI - Prometheus - Grafana This setup gives you real-time insights into: - Token usage per API call - Response times across different request types - Error rates and potential API failures - Projected costs based on current usage patterns The best part? It's surprisingly simple to set up. With Docker Desktop and a few configuration files, you can have a professional monitoring dashboard up and running in minutes. No more expensive surprises or guesswork about your AI application performance! Watch the full tutorial on YouTube: https://lnkd.in/eUeFp2h5 Get the full code and configuration on GitHub: https://lnkd.in/ecz9NYu8 Have you implemented monitoring for your AI applications? What metrics matter most to your team? Let me know in the comments! #AIEngineering #SpringBoot #LLM #Observability #SoftwareDevelopment

Let's say you commit a change that makes an application 0.05% slower. No big deal, right? Well, at the scale of Meta, it is a big deal--a small slowdown for a large application can waste thousands of servers. It's such a big deal that Meta needs a way to catch these performance regressions--to reliably figure out if an application is even a hundredth of a percent slower. The big problem with catching tiny performance regressions is that, well, they're tiny. It's easy for them to be lost in the noise. Any simple approach is going to return 99% false positives because most slowdowns are caused by transitory hardware issues, not actual code changes. Meta's solution to this problem has three high-level steps: 1. Look for performance regressions not in entire applications, but in individual subroutines. Applications have thousands of subroutines, so a small performance regression for an entire app is likely a large one for the affected subroutine. This way, you're looking for ~5% regressions, not ~0.05% regressions, which makes filtering out noise easier. 2. To make tracing at the coroutine level practical, do stack-trace sampling. Regularly collect stack traces of applications and use them to figure out in which subroutines applications spend their time. This is like conventional performance profiling, but done at massive scale. 3. When a regression is detected, do a root cause analysis to figure out if it's caused by transient issues or code changes. If it's caused by code changes, by analyzing the stack traces of the performance regression and comparing them to recent commits, it should be possible to automatically identify which changes caused the regression! What's the big takeaway? Performance really matters at scale, and small performance issues add up. The paper claims that this system saves millions of servers a year by detecting tiny performance regressions, which is a staggering number. If your code is used by billions of people, keep it optimized!

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.

AWS Observability Architecture Diagram This diagram represents a Centralized Observability Architecture. It splits the infrastructure into two separate AWS Accounts: Workload Account: Where the actual application runs. Observability Account: A dedicated environment for monitoring, logging, and security analysis.  1: User Access  2: Application Processing  3: Telemetry Collection (Inside EKS)  4: Cross-Account Transfer  5: Data Ingestion  6: Storage & Processing  7: Visualization & Alerting This creates a clean separation of concerns—even if the application crashes or is under heavy load, the monitoring tools remain safe in a separate account. Part 1: The Application Flow (Left Box - Workload Account)  1: User Access Action: Application Users send requests to the system. Component: The traffic hits the (ALB) located in the Public Subnets (accessible from the internet). Distribution: The ALB spreads the traffic across three Availability Zones (us-east-1a, 1b, 1c) for high availability.  2: Application Processing Action: The ALB forwards the secure traffic into the Private Subnets (not accessible directly from the internet). Component: The application runs on EKS  Data Storage: The application reads/writes data to the RDS at the bottom.  3: Telemetry Collection (Inside EKS) While the app is running, background tools inside the Kubernetes cluster are silently collecting data: FluentBit Agent (Daemon): Runs on every server node to collect Logs. OTEL Collector (SideCar): Runs alongside the application containers to collect Metrics (CPU/RAM usage) and Traces (code performance). Part 2: The Bridge (Networking)  4: Cross-Account Transfer Action: The telemetry data (logs, metrics, traces) needs to move from the Left Account to the Right Account. Component: VPC Peering. Purpose: This creates a private network tunnel between the two AWS accounts, allowing them to talk securely without going over the public internet. Part 3: The Monitoring Flow (Right Box - Observability Account)  5: Data Ingestion Action: The data enters the Pvt Subnets of the Observability Account. Component: It flows through VPC Endpoints and an EC2 (Data-Prepper) instance. Purpose: The Data Prepper likely formats or filters the raw data before sending it to the storage services.  6: Storage & Processing The data splits into two paths based on the arrows in the legend: Red Path (Metrics): Application metrics are sent to Amazon Managed Service for Prometheus. This is a database optimized for time-series data (like tracking CPU usage over time). Purple Path (Logs & Traces): Logs and trace data are sent to (AES) (now often called OpenSearch). This allows for deep text searching and complex data analysis.  7: Visualization & Alerting Action: Engineering & Operations Teams need to view this data to fix bugs or monitor health. Tools: They use Amazon Managed Grafana to visualize the metrics stored in Prometheus (graphs, charts, heatmaps). Summary of Benefits Security Scalability High Availability

Monitoring and visualizing application performance is critical, especially in distributed systems where multiple components interact. Recently, I worked on a project that showcased the power of AWS X-Ray for tracing and analyzing application requests. Here’s a detailed breakdown of what I learned and how X-Ray can make a significant difference in application monitoring. What is AWS X-Ray? AWS X-Ray provides tools to monitor, trace, and debug applications running in production or development environments. By capturing and analyzing application traces, X-Ray enables us to identify bottlenecks, understand dependencies, and ensure the overall health of the system. 1️⃣ Configured X-Ray in the Application Layer • Enabled the X-Ray SDK in the application code to capture traces. • Instrumented the application to capture SQL queries and HTTP requests for better visibility into performance. 2️⃣ Set Up X-Ray in the Web Layer • Integrated the X-Ray recorder with the web-tier application to track client-side interactions and their impact on the backend systems. 3️⃣ Deployed the X-Ray Daemon • Installed and configured the X-Ray daemon on the EC2 instances to process and send trace data to the X-Ray service. 4️⃣ Monitored the Trace Map • Generated a service map to visualize the flow of requests across the architecture, including the load balancers, EC2 instances, and Aurora database. • Used CloudWatch to complement X-Ray by analyzing metrics, response times, and any potential issues in real time. Key Features Explored: • Trace Map: A graphical representation of the application’s architecture, showing the interactions between various components. • Trace Details: Dive deep into individual requests to see how they flow through the system, from the client to the backend. • Raw Data Insights: Accessed JSON trace data for advanced debugging and detailed performance analysis. Why is X-Ray Important? • Provides end-to-end visibility into application performance. • Simplifies debugging in distributed systems by breaking down requests into segments and subsegments. • Highlights latency issues, slow queries, or misconfigurations in real time, enabling faster resolution. • Facilitates optimization by identifying dependencies and usage patterns. AWS X-Ray is an essential tool for any cloud-based architecture where observability and operational insights are critical. I created the architecture diagram using Cloudairy I would love to mention some amazing individuals who have inspired me and who I learn from and collaborate with: Neal K. Davis Steven Moran Eric Huerta Prasad Rao Azeez Salu Mike Hammond Teegan A. Bartos Kumail Rizvi Benjamin Muschko #AWS #CloudComputing #AWSXRay #Observability #ApplicationMonitoring #CloudArchitecture #CloudWatch #Metrics #diagrams

Dynatrace: Dynatrace is an AI-powered observability and Application Performance Monitoring (APM) platform that helps teams monitor, troubleshoot, and optimize applications and infrastructure in real time. Think of it as a single pane of glass that tells you what’s broken, why it’s broken, and how to fix it, automatically. What Dynatrace actually does (real-time) 📊 Monitors applications – response time, errors, user experience 🖥️ Tracks infrastructure – servers, VMs, containers, Kubernetes 🌐 Observes cloud services – AWS, Azure, GCP 🤖 Uses AI (Davis AI) – auto root-cause analysis (no guessing) Key features (simple words) APM – Finds slow APIs, DB queries, memory leaks Infrastructure monitoring – CPU, memory, disk, network Kubernetes & microservices – service-to-service tracing Real User Monitoring (RUM) – what real users are experiencing Synthetic monitoring – test apps before users complain Auto discovery – install once, it finds everything Real-time example (DevOps / SRE) Your app is slow after deployment Dynatrace detects response time spike AI traces it to a specific microservice Finds the root cause: slow DB query after code change Alerts the team with exact fix location No manual log digging. No finger-pointing. Why companies use Dynatrace 🚀 Faster incident resolution (MTTR ↓) 🔍 Full-stack visibility (end to end) 🤝 Dev + Ops + SRE + Security in one tool 📉 Less alert noise, more actionable insights One-line : Dynatrace is an AI-driven observability platform used to monitor application performance, infrastructure, and user experience with automated root-cause analysis. #Dynatrace #Observability #APM #ApplicationMonitoring #PerformanceMonitoring #DevOps #SRE #CloudComputing #CloudNative #Microservices #Kubernetes #AWS #Azure #GCP #AIOps #MonitoringTools #IncidentManagement #RootCauseAnalysis #EnterpriseMonitoring #ITInfrastructure #DigitalTransformation #TechCareers #C2C #C2H