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Critical metrics for monitoring Kubernetes

No matter what internal testing or error monitoring we do for our web services, our end users will interact with that service through a front end. It’s necessary to perform front end monitoring so that you’re not relying on users to report problems.

Frontend monitoring ensures seamless user experiences by observing and analyzing the performance and functionality of web applications. Edge case failures, bad 3rd party service interactions, and poor front end performance are all examples of issues that only direct front end monitoring can detect.

In this article, we’ll cover the basics of frontend monitoring, explore its various types, key metrics, benefits, and challenges, and review some top tools to help you manage and optimize your applications effectively.

Frontend monitoring involves tracking and analyzing the performance, reliability, and user experience of web applications from the user’s perspective. Frontend monitoring can either occur by observing users real interactions on the frontend (real user monitoring) or by sending an automated system to interact with your frontend (synthetic user monitoring).

It helps teams identify issues like slow page load times, JavaScript errors, or failed API calls that can negatively impact user satisfaction.

Proactive monitoring involves detecting potential issues before they affect users. Tools simulate user interactions to identify bottlenecks or vulnerabilities. For example, a proactive monitor might simulate slow network response times to check how a frontend will render when some components are slow to load. This is comparable to a ‘chaos monkey’ testing strategy for backend services.

Reactive monitoring focuses on capturing issues reported by users in real-time. It complements proactive efforts by highlighting problems occurring in production. Reactive monitoring may be as simple as catching JS errors that occur in a page and reporting them to an end service.

RUM tracks actual user interactions with your application, offering insights into how real users experience your site in different environments. In theory, RUM is the ideal way to find front end problems: simply track every user’s experience, everywhere. However this approach has several challenges:

• Failures of users expectations may go undetected - for example if a user searches for recent posts and gets posts from 7 years ago, no errors will be raised, and no problem will be tracked.
• Rum can impact browser performance for users.
• Transmitting data for every user every time is quite expensive, both for you the service provider and for the user’s browser performance and network bandwidth. The suggested solution for this known issue is to sample randomly: when a user starts a session on your site, they’re randomly assigned whether that session will be tracked in detail and transmitted to your observability service. This raises the issue of missing key failures: when a key client reports an error on your site, but no data was captured, you’re stuck trying to replicate an issue with only a user description.
• Difficulty finding patterns - user behavior is inherently inconsistent. It’s often very difficult to identify connected failures or trends based on multiple users’ inconsistent behavior. The situation is similar to the sampling problem: we’re left trying to guess what happened based on sketchy information.
• Complex implementation - from loading a javascript package to track user experience in the browser, to endpoints to collect that information, and a system to find patterns in stochastic user behavior, RUM is a complex technical challege with extensive techncial lift. If you’re trying to create a DIY solution for RUM, you’ll find that CORS: Cross-Origin Request Blocked errors are the first of many challenges.

Synthetic monitoring can be as simple as sending requests to a service and making sure the response has a 200 OK status code, but the term is generally used to describe an automated browser that can simulate user behavior. Checkly uses the open source Playwright framework to simulate user behavior in great detail, and make complex assertions about the results.

Synthetic monitoring solves many of the problems listed above with RUM:

• Engineers can create frontend tests to be run that mimic user behaviors and key user paths. By scripting things like user searches, these tests can exactly describe the expected output.
• Synthetic monitoring doesn’t impact performance for users.
• Synthetic monitoring’s costs are controlled from the outset, as you control the cadence of synthetic test runs.
• Patterns are readily identifiable since the behavior of a synthetic user is always the same. Further, by running tests on a cadence, the exact time that failures started is easier to find. This is expecially helpful if you’re trying to connect a new failure to a paticular deployment
• No implementation requirements - synthetic monitoring can be implemented as a 100% external service.

APM is a general term, usually referring to a combined solution that includes both front end monitoring and the measurement of performance of backend software through the use of installed software agents.

Frontend monitoring tools capture data through a combination of synthetic tests, browser instrumentation, and real user interactions. These tools generate insights by analyzing performance metrics, logging errors, and tracking user behavior.

• Performance Metrics: Monitor loading speeds, rendering times, and resource usage.
• Error Detection: Identify JavaScript errors, API failures, and crashes. Crash reporting can be a complex problem but it’s great if it’s possible to report some details from user browser crashes.
• User Experience Analysis: Assess user interactions, engagement, and satisfaction. This general concept is only sensible when performing Real User Monitoring, and may have significant overlap with business intelligence or business analytics. If you find yourself asking ‘what interface elements are most attractive to users?’ your use of the tool has shifted from monitoring to user analytics.

The key metrics for frontend monitoring—performance, user interactions, errors, and availability—are crucial for understanding and optimizing the user experience. Performance metrics, including page load time and Core Web Vitals (e.g., Largest Contentful Paint, Interaction to Next Paint), measure how fast and smoothly content is delivered to users. User interactions capture events like clicks, form submissions, and navigations to gauge how users engage with the application. Errors track JavaScript exceptions, resource failures, and API issues, helping teams identify and resolve defects impacting functionality. Availability monitors uptime and service reachability to ensure the application is consistently accessible. Together, these metrics provide a comprehensive view of application health, enabling teams to improve performance, address issues proactively, and enhance the overall user experience.

Key metrics in detail:

• Core Web Vitals: Largest Contentful Paint (LCP), Interaction to Next Pain (INP), Cumulative Layout Shift (CLS). These metrics are considered to be quite critical for search engine optimization.
• JavaScript Error Rates: Frequency of client-side code failures. You may need to implement filtering for common errors.
• API Response Times: Speed and reliability of API calls. This may be called ‘heartbeat monitoring’ if you’re only measuring the reliability of straightforward get requests.
• Network Request Failures: Broken or delayed network requests
• Errors and Crashes: Stability of the application under different conditions
• User Interactions and Engagement: Clicks, scrolls, and session durations

• Monitoring Page Load Times: Ensure optimal page rendering speed.
• Tracking Third-Party Services: No amount of pre-deploy testing with stubs of third party services can find all the possible interaction problems with those services. Frontend monitoring can detect issues caused by external libraries or APIs.
• User Interaction Monitoring: Analyze behavior patterns and engagement levels.
• Analyzing Client-Side Errors: Identify and resolve JavaScript issues, especially those happening on single platforms or with particular browser versions.

Frontend monitoring empowers teams to detect potential problems before they escalate into critical failures. By observing patterns such as sudden increases in error rates or performance degradation, teams can investigate and resolve issues early, often before users are impacted. This phase of the monitoring journey focuses on “known knowns,” enabling developers to answer key questions like, “What happened during a spike in errors?” By building a narrative around historical data, teams can use these insights to improve system resilience.

Performance monitoring provides actionable data that highlights bottlenecks and inefficiencies in applications. Metrics such as Largest Contentful Paint or Interaction to Next Paint help developers understand where delays occur and prioritize optimization efforts. These insights shift the focus from merely reacting to known issues toward analyzing anomalies, such as unexpectedly fast or slow responses. This phase aligns with “known unknowns,” where developers explore statistical questions to assess how normal or abnormal system behavior is.

Frontend monitoring enables developers to track user interactions, such as clicks, form submissions, or navigation paths, providing visibility into how users engage with an application. These insights help identify friction points in real-time, allowing teams to address usability challenges proactively. By adding context-specific data, such as user or session identifiers, developers can refine their analysis and enhance the customer experience. This phase blends analysis with experimentation, helping developers test hypotheses about what improves engagement or usability.

By identifying specific areas that require attention, monitoring data enables teams to focus their efforts where they matter most. For instance, if metrics reveal that a checkout flow is a primary source of performance complaints, resources can be redirected toward optimizing that functionality. This approach not only improves the end-user experience but also ensures the efficient use of time and budget. This aligns with the later phases of the observability journey, where teams experiment with targeted solutions and evaluate the ROI of their changes.

The abundance of frontend monitoring tools can create silos, making it challenging to consolidate insights across systems. Teams often struggle to integrate disparate tools like performance trackers, error loggers, and user interaction analytics. This fragmentation can hinder the ability to see the full picture, leaving blind spots in observability. Addressing this challenge requires adopting systems that unify logs, metrics, and traces to provide a cohesive view of the application’s behavior.

Applications are accessed through a wide array of devices, browsers, and network conditions, each introducing unique challenges. For example, a feature that works seamlessly on one browser may fail on another, and network latency can vary dramatically across regions. Monitoring tools must account for this diversity by capturing data that reflects real-world conditions. Understanding these “known unknowns” helps developers identify anomalies across different environments and adapt their solutions to improve the experience for all users.

The fast pace of evolution in frontend technologies, including frameworks and browser standards, makes it difficult to maintain effective monitoring. What works for today’s stack might become obsolete as new features and tools emerge. Teams must stay agile, continually updating their instrumentation and observability practices to align with the latest advancements. This requires a culture of experimentation and learning, allowing developers to test and adopt new solutions without losing focus on system stability.

Entry-level monitoring tools often focus on surface-level insights, such as error counts or simple performance metrics, but fail to provide the deeper diagnostics needed to identify root causes. For example, they may show that an API call failed but not explain why. This limitation makes it difficult to move from the “what” to the “why,” hindering the ability to address systemic issues. Advanced observability tools that provide context-rich data and enable correlation across systems are essential for overcoming this challenge.

Frontend monitoring alone cannot provide a complete view of an application’s health. Many issues arise at the intersection of the frontend and backend, requiring integrated observability across the entire stack. Without this integration, teams risk spending excessive time proving whether an issue is frontend- or backend-related. Implementing context propagation and unified tracing, such as through OpenTelemetry, helps connect frontend events to backend processes, enabling a more holistic understanding of system behavior and streamlining troubleshooting efforts. For more detail on connecting backend OpenTelemetry traces with frontend performance see how Checkly Traces connects data from across your stack.

Consider features like synthetic monitoring, RUM, session replay, language compatibility, and security. Evaluate pricing, ease of use, and whether an agent or agentless setup fits your needs.

Frontend monitoring has historically lagged behind backend systems in sophistication and integration. With OpenTelemetry (OTel), frontend monitoring can now achieve deeper insights into user experiences. However, it also brings unique challenges that require careful consideration.

While OpenTelemetry provides out-of-the-box instrumentation, setting it up for a browser-based application requires upfront effort. Instrumentation code must load before the application initializes to capture critical spans like document load times. Ensuring this is correctly implemented across environments can be a source of frustration.

Instrumenting a frontend app involves adding listeners for browser events, fetching metrics, and propagating trace headers. Over-instrumentation or poorly optimized spans can degrade application performance, especially for resource-intensive pages or on devices with limited capabilities.

Browsers have unique behaviors that can make instrumentation challenging. For example:

• Redirects and Network Timing: JavaScript doesn’t have access to certain browser-level events, like pre-redirect network timing. Combining OpenTelemetry’s network instrumentation with browser APIs like PerformanceObserver can help, but it requires extra configuration.
• Clock Synchronization: Distributed tracing relies on accurate timestamps. However, clock drift between client devices and servers can result in misaligned spans. Proxying timestamp corrections through an OpenTelemetry Collector is often necessary.

Frontend telemetry can quickly generate a high volume of spans, especially on high-traffic applications. Without rate limiting or filtering, this data can overwhelm storage systems or increase monitoring costs. Developers must design selective instrumentation strategies to focus on high-value spans.

While auto-instrumentation provides useful baselines like resource fetch times and click events, it lacks application-specific context. Developers need to enhance these spans with attributes that matter to their business logic—such as user IDs, session data, or interaction details. Without this, telemetry risks becoming just noise.

Traditional frontend monitoring tools often report what is happening (e.g., a page is slow), but lack insights into why. OpenTelemetry addresses this by correlating frontend spans with backend traces. However, this requires propagation of trace headers (traceparent) between the frontend and backend, which can be technically challenging in distributed systems.

OpenTelemetry opens a new frontier for frontend observability, but it’s not a plug-and-play solution. Teams must balance instrumentation depth with performance and focus on collecting the data that best illuminates user experiences. By addressing these challenges head-on, developers can achieve a robust, connected view of their systems and elevate the standard of frontend monitoring.

1. Identify Key Metrics: Focus on what matters most to your application.
2. Select Tools: Choose tools that integrate with your tech stack.
3. Monitor During High-Usage Periods: Analyze performance under peak load.
4. Integrate Insights: Align findings with business goals.

Checkly performs synthetic monitoring with the power of Playwright for robust testing of key workflows. By simulating real-world scenarios, it ensures applications remain functional and performant under varying conditions.

Effective frontend monitoring ensures the seamless performance and reliability of websites and applications, delivering a strong user experience. Checkly excels in this area by combining the power of end-to-end (E2E) monitoring and modern development workflows to proactively identify and resolve issues before they impact users.

Checkly enables developers to monitor critical user flows—like login, search, and checkout—in real-time. By leveraging tools like Playwright, Checkly runs automated scripts simulating real user interactions. This synthetic monitoring approach provides continuous feedback on the health of frontend applications, revealing issues that often go undetected in pre-production testing.

For example, Checkly allows teams to:

• Run E2E tests on a cadence to catch edge cases and unpredictable failures.
• Test frontend flows directly in production, monitoring the behavior users actually experience.
• Extend monitoring to include application performance metrics like load times and rendering speed.

1. Integration with Existing Workflows

   Checkly fits seamlessly into CI/CD pipelines, enabling checks to trigger automatically on deployments or pull requests. This “monitoring as code” (MaC) approach allows developers to maintain monitoring scripts alongside the application codebase.

2. Resource-Efficient Testing

   By utilizing headless browser automation tools, Checkly achieves higher stability and faster execution compared to traditional headful testing methods. This means frontend monitoring can run efficiently in cloud environments without consuming excessive resources.

3. Real-Time Alerting

   When checks fail, Checkly alerts teams immediately through preferred channels like Slack, PagerDuty, or email. This ensures swift action to resolve issues and minimize user impact.

4. Global Monitoring Capability

   Checkly’s cloud-based infrastructure lets teams run tests from multiple geographic locations, ensuring frontend performance remains consistent for users worldwide.

Frontend monitoring is essential for maintaining application reliability and delivering exceptional user experiences. By leveraging the right tools and strategies, teams can proactively address issues, optimize performance, and meet the expectations of modern web users. Consider tools like Checkly to simplify and strengthen your monitoring efforts.