Ready to start web application monitoring today? check out our practical guide on how to start web application monitoring with Checkly
Web application monitoring is a highly generalized term, and distinctions between, for example, ‘metrics’ and ‘monitoring;’ and ‘error tracking’ versus ‘performance monitoring’ are often distinctions without a difference. The question we’re trying to answer with Web Application Monitoring is: “how well is our application performing for users?” Observations that don’t relate to this question, for example the popularity of a single post on our social media site, or how well our live-generated site follows brand standards, are outside the scope of web application monitoring.
How Web Application Monitoring Works
Monitoring tools collect data from your web application, servers, and user interactions. They process this data to generate insights about application health, performance, and user behavior. Alerts, dashboards, and reports provide actionable insights for resolving issues and optimizing performance.Types of Web Application Monitoring
Monitoring a web application requires understanding various dimensions of performance, usability, and security. Each type of monitoring addresses specific aspects of the application to ensure seamless operation. Let’s explore these types in greater detail. Synthetic Monitoring Synthetic monitoring uses pre-recorded scripts to simulate user interactions with your web application. By performing these synthetic transactions, you can test key functionalities such as page load times, form submissions, or API calls without relying on real user activity.- Benefits: It is proactive, enabling teams to detect and resolve issues before users are impacted. Synthetic monitoring is particularly effective for testing uptime, availability during off-hours, and the impact of new deployments.
- Example Use Case: Running tests on an e-commerce checkout page to ensure it processes payments correctly after a new update.
- Challenges: It doesn’t capture real-world user behaviors, so it should complement, not replace, real user monitoring.
Real User Monitoring (RUM) Real User Monitoring (RUM) captures real-time data from actual users as they interact with your application. By embedding a lightweight tracking code into the application, RUM collects metrics like page load times, errors, and user interactions.
- Benefits: It provides insights into actual user experiences, helping identify regional performance variations, device-specific issues, or areas of improvement in UX design.
- Example Use Case: Tracking how mobile users from Europe experience an application compared to desktop users in North America.
- Challenges: RUM may require substantial data processing infrastructure to analyze the vast volume of user interaction data in real time.
Application Performance Monitoring (APM) APM focuses on monitoring and optimizing application-level metrics such as response times, throughput, memory consumption, and database query performance. APM tools provide deep visibility into application behavior, often by instrumenting code to measure key metrics.
- Benefits: APM is essential for identifying performance bottlenecks, such as slow database queries, inefficient APIs, or memory leaks.
- Example Use Case: Diagnosing why a specific API endpoint is causing latency spikes under heavy load.
- Challenges: Implementing APM requires careful planning, as excessive instrumentation can add overhead to the application.
- Pitfalls of Defining Errors:
- What is an error? The definition varies widely. For some, an error might include user-facing error messages or unhandled exceptions. For others, it might extend to slow-loading assets or deprecated API warnings.
- Signaling Theory: Effective error tracking relies on understanding what each sensor (or monitoring system) is intended to capture. Without clear definitions, teams risk alert fatigue or missing critical issues.
- Benefits: Centralized logging reduces the time to identify the root cause of an issue, while structured error tracking can prioritize issues affecting user experience.
- Example Use Case: Monitoring uncaught exceptions in a JavaScript application and prioritizing fixes for errors impacting 10% of users.
- Challenges: Over-logging can lead to excessive noise, making it harder to find actionable insights.
200 OK HTTP status.
- Benefits: Uptime monitoring offers a straightforward way to track availability, often serving as the first line of defense against outages.
- Example Use Case: Monitoring whether an online banking platform’s login page is accessible to users.
- Challenges: Simple uptime checks don’t account for partial outages or degraded performance (e.g., slow response times). Or for responses that look okay to a script, but not the users (e.g., the page loads but the only text says ‘server error’).
- Benefits: Essential for ensuring that the underlying infrastructure can meet application demands, especially during peak loads.
- Example Use Case: Monitoring CPU usage to detect bottlenecks during a holiday sale on an e-commerce site.
- Challenges: Infrastructure monitoring can produce misleading signals in scenarios like network outages that reduce user traffic but increase resource availability, falsely suggesting optimal performance.
Key Metrics to Monitor
- Response Time: Time taken by the application to respond to requests.
- Error Rate: Frequency of errors in the application.
- Throughput: Number of requests processed over a time frame.
- Uptime and Availability: Percentage of time the application is operational.
Benefits of Web Application Monitoring
- Instant Downtime Alerts
- Find trends before they become problems
Limitations of Web Application Monitoring
- Dynamic Content
- Cross-purposes
Best Practices for Monitoring Web Applications
Effective monitoring is essential to maintaining the performance, availability, and security of web applications. However, monitoring is more than just collecting data; it’s a strategic process of learning and acting based on insights. By following these best practices, organizations can ensure their monitoring efforts are both meaningful and actionable.- Set Clear Objectives
- Why It Matters: Objectives guide what data you collect and how you interpret it. For example, minimizing downtime might focus on uptime monitoring and incident alerts, while improving user experience might emphasize performance metrics like load time and responsiveness.
- How to Do It: Align your objectives with business goals. For instance, if your goal is to increase conversion rates, focus on monitoring checkout processes and page performance.
- Choose the Right Monitoring Tools
- Why It Matters: A tool tailored to your application architecture provides more relevant data and reduces noise.
- How to Do It: Assess your application stack (e.g., serverless, microservices, or monolithic), the types of metrics you need, and your team’s familiarity with specific tools. For example, use Prometheus for metrics, Loki for logs, and Jaeger for tracing in a Kubernetes-based application.
- Define Key Performance Indicators (KPIs)
- Why It Matters: Without KPIs, monitoring efforts can lack focus, leading to wasted resources and misaligned priorities.
- How to Do It: Identify KPIs that directly affect user experience or business outcomes, such as uptime, response time, error rates, or user engagement metrics. For example, define a goal like “99.9% uptime for key services over a month.”
Monitor User Experience Modern monitoring goes beyond infrastructure to focus on the end-user journey. Users don’t care if your CPU is underutilized—they care if your site loads quickly and works smoothly.
- Why It Matters: User experience (UX) monitoring ensures that technical performance aligns with user satisfaction and retention.
- How to Do It: Combine Real User Monitoring (RUM) and Synthetic Monitoring to capture both actual and simulated user interactions. Focus on load times, time to interact (TTI), and error rates that directly impact UX.
Implement Continuous Monitoring Web applications operate in dynamic environments, where issues can arise at any time. Continuous monitoring ensures constant vigilance.
- Why It Matters: Continuous monitoring helps teams catch problems early, reducing downtime and improving system reliability.
- How to Do It: Automate monitoring across all layers of your stack—servers, APIs, front-end performance, and user interactions. Use tools like CI/CD integrations to monitor deployments for potential issues.
Be Proactive with Alerting and Notifications Alert fatigue is a common problem in monitoring, where too many notifications desensitize teams. A proactive approach focuses on actionable alerts.
- Why It Matters: Timely and meaningful alerts enable faster incident resolution while avoiding unnecessary noise.
- How to Do It: Configure alerts for critical thresholds and anomalies. For instance, set alerts for unusual spikes in response time or memory usage, but suppress notifications for predictable auto-scaling events.
Analyze and Act on Monitoring Data Data alone is not valuable unless it leads to action. Effective monitoring transforms raw data into insights that drive meaningful improvements.
- Why It Matters: Many organizations collect vast amounts of monitoring data but fail to act on it, leaving potential optimizations on the table.
- How to Do It: Establish regular review processes to analyze trends, identify recurring issues, and implement fixes. For example, a monthly review of error logs can reveal patterns like frequently failing endpoints.
Implement Synthetic Monitoring Synthetic monitoring simulates user activity to proactively identify potential issues.
- Why It Matters: This type of monitoring allows teams to test functionality and performance before users are affected.
- How to Do It: Use scripts to mimic common user actions, such as navigating pages, submitting forms, or using APIs. Test critical user paths regularly, especially after updates or deployments.
Leverage Real User Monitoring (RUM) RUM provides insights based on actual user interactions, capturing the diversity of real-world experiences.
- Why It Matters: Real user data reflects the performance users experience, including regional differences, device-specific issues, and varying network conditions.
- How to Do It: Deploy lightweight tracking scripts to collect metrics such as page load time, interaction speed, and error rates. Segment data by user demographics or device type for targeted improvements.
Conduct Regular Performance Audits Periodic audits ensure that your monitoring strategy remains effective and that your application continues to meet performance expectations.
- Why It Matters: Web applications evolve over time, and so do the challenges they face. Regular audits help identify outdated metrics, unnecessary alerts, and new performance bottlenecks.
- How to Do It: Schedule audits to review KPIs, monitoring coverage, and tool configurations. For instance, ensure your monitoring setup includes new microservices added to your architecture.
Tie Best Practices to Business Goals Effective monitoring isn’t just about data collection—it’s about using data to improve your application and achieve your organization’s goals. By integrating these best practices into your strategy, you ensure that monitoring becomes a driver of growth, user satisfaction, and operational excellence.
Top 9 Web Application Monitoring Tools
-
- Datadog
-
- New Relic
-
- Logz.io
-
- Sentry
-
- Icinga
-
- Site24x7
-
- Raygun
-
- AppDynamics
-
- IBM Instana
Open Source Monitoring: a key part of web application monitoring
Open source monitoring plays a pivotal role in modern cloud-native web application monitoring by leveraging community-driven tools to provide robust, scalable, and accessible monitoring solutions. These tools empower organizations to manage the complexity of distributed systems without being locked into proprietary solutions. Challenges in Cloud-Native Observability Cloud-native applications, by design, introduce new complexities:- Obfuscation: Dependencies across microservices, Kubernetes orchestration, and cloud-managed services obscure system behavior.
- Dynamic Dependencies: The interactions among thousands of microservices, infrastructure layers, and APIs shift dynamically with scaling and updates.
- Data Volume: High data granularity across logs, metrics, traces, and flows creates immense operational overhead to derive actionable insights.
- Metrics Collection with Prometheus Prometheus is the cornerstone of open source monitoring, providing time-series data collection and querying capabilities. With exporters like Node Exporter and Kubernetes State Metrics (KSM), it gathers metrics at both the system and application levels.
- Log Aggregation with Loki Loki collects and queries logs efficiently, ensuring contextual insights alongside metrics. Integrated with Prometheus and Kubernetes, Loki enables rapid troubleshooting.
- Distributed Tracing with Jaeger Jaeger offers a standardized approach to tracing requests across microservices, enabling visibility into service-to-service interactions and latency.
- Kernel-Level Observability with eBPF The extended Berkeley Packet Filter (eBPF) collects real-time data on network flows and application behavior, bypassing traditional agents and minimizing performance overhead.