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3 Approaches to End-User Experience Monitoring

Sridhar Iyengar

The volume of transactions running through websites and mobile apps make customer-facing applications crucial to online businesses. If these applications perform well for their users, they generate revenue for the business. If they don't, they affect the credibility of the business, which in turn affects the overall revenue. It is therefore imperative that businesses understand how well their revenue-critical applications are behaving for their end users.

From an IT team's point of view, understanding the user experience of their applications is becoming challenging as technology evolves. Newer and more complex applications are being written using an assortment of languages. These applications are being deployed on a wide variety of infrastructure components. To add to that, today's users access these modern applications on a variety of devices such as the Web, smartphones, tablets and smart watches.

Fortunately, there are a few means available through which businesses can determine the user experience of their Web applications. Let's take a look at three common approaches:

Real User Monitoring (RUM)

Real user monitoring is a passive monitoring approach that involves collecting metrics at the browser level to accurately determine the application performance as perceived by the end users. Monitoring at the browser level is achieved by injecting JavaScript snippets into the header and footer of the HTML code of the Web application. This code will ascertain the full-page load experience — including downloading the assets from the content delivery network (CDN), rendering the page and executing the JavaScript from the browser's perspective. Additional instrumentation can be used to collect more metrics by injecting additional JavaScript code.

The data gathered through RUM provides answers to questions about user experience such as:

■ How long did it take to load the full page?

■ What is the response time from a network perspective (redirection time, DNS resolution time, connection time)?

■ What is the time interval between sending the request and receiving the first byte of response?

■ What is the time taken by the browser to receive the response and render the page?

■ Are there any problems on the page? If yes, what caused the problem?

■ How is the performance when the application is accessed from different countries?

■ What is the response time across different browsers? Do new application updates affect the performance in a specific version of the browser?

■ How does the application perform in different platforms such as desktop, Web and mobile?

The biggest advantage of monitoring real user data is that it relies on actual traffic to take measurements. There is no need to script the important use cases, which can save a lot of time and resources.

Real user monitoring captures everything as a user goes through the application, so performance data will be available irrespective of what pages the user sees. This is particularly useful for complex apps in which the functionality or content is dynamic.

Server-Side Monitoring

Although user experience is best tracked at the browser level, application performance monitoring at the server side also provides insight into end-user performance. Server-side monitoring is mostly used in conjunction with real user monitoring. This is because problems originating on the server side can only be efficiently detected using server-side monitoring.

Monitoring performance on the server side involves agent-based instrumentation technology for acquiring and transmitting data. This monitoring approach is used to watch user transactions in real time and troubleshoot in case of issues such as slowness or application bugs.

Developers have to install agents on the application server to help capture and visualize transactions end-to-end, with performance statistics across all components, from the URL down to the SQL level. This visual breakdown reveals the flow of all the user transactions being executed in each layer of the application infrastructure.

Server-side monitoring helps track response time and throughput taken by each application component, with the option to trace transactions end-to-end via code analysis. This helps the IT Operations/DevOps teams identify slow Web transactions and then isolate performance issues down to the level of the specific application code that caused them. The underlying database is also monitored most of the time to determine slow database calls, database usage and overall database performance. With server-side monitoring, users will be able to identify the SQL queries executed during a transaction and thus identify the worst performing queries.

Synthetic Transaction Monitoring

Synthetic transaction monitoring is an active monitoring technique based on the concept of simulating the actions of an end user on a Web application. This method involves the use of external monitoring agents executing pre-recorded scripts that mimic end-user behavior at regular time intervals. The monitoring agents are usually very light and do not create any additional load on network traffic.

Most application performance monitoring solutions provide recorder tools to capture the actions or paths a typical end user might take in an application, such as log in, view product, search and check out. These recordings are saved as scripts, which are then executed by the monitoring agents from different geographical locations.

Technically, there are two different approaches to generating requests. Some solutions replay recorded HTTP traffic patterns, while others drive real browser instances. The second approach is more useful for modern applications that make a lot of JavaScript, CSS and Ajax calls.

Since synthetic transaction monitoring involves sending requests across the network, it can measure the response time of application servers and network infrastructure. This type of monitoring does not require actual Web traffic, so you can use this approach to test your Web applications prior to launch — or anytime you like. Many companies use synthetic monitoring before entering production in the form of automated integration tests with Selenium.

Synthetic monitoring does have its limitations, though. Since the monitoring is based on pre-defined transactions, it does not monitor the perception of real end users. Transactions have to be “read-only” because they would otherwise set off real purchase processes. This limits the usage to a certain subset of your business-critical transactions.

The best approach is to use synthetic transaction monitoring as a reference measurement that will help identify performance degradation, detect network problems and notify in case of errors.

Every business is different and has its own requirements that can help to choose which type of monitoring to implement. An ideal strategy would be to use active and passive monitoring techniques side by side so that no stone is left unturned in the pursuit to monitor end-user experience.

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3 Approaches to End-User Experience Monitoring

Sridhar Iyengar

The volume of transactions running through websites and mobile apps make customer-facing applications crucial to online businesses. If these applications perform well for their users, they generate revenue for the business. If they don't, they affect the credibility of the business, which in turn affects the overall revenue. It is therefore imperative that businesses understand how well their revenue-critical applications are behaving for their end users.

From an IT team's point of view, understanding the user experience of their applications is becoming challenging as technology evolves. Newer and more complex applications are being written using an assortment of languages. These applications are being deployed on a wide variety of infrastructure components. To add to that, today's users access these modern applications on a variety of devices such as the Web, smartphones, tablets and smart watches.

Fortunately, there are a few means available through which businesses can determine the user experience of their Web applications. Let's take a look at three common approaches:

Real User Monitoring (RUM)

Real user monitoring is a passive monitoring approach that involves collecting metrics at the browser level to accurately determine the application performance as perceived by the end users. Monitoring at the browser level is achieved by injecting JavaScript snippets into the header and footer of the HTML code of the Web application. This code will ascertain the full-page load experience — including downloading the assets from the content delivery network (CDN), rendering the page and executing the JavaScript from the browser's perspective. Additional instrumentation can be used to collect more metrics by injecting additional JavaScript code.

The data gathered through RUM provides answers to questions about user experience such as:

■ How long did it take to load the full page?

■ What is the response time from a network perspective (redirection time, DNS resolution time, connection time)?

■ What is the time interval between sending the request and receiving the first byte of response?

■ What is the time taken by the browser to receive the response and render the page?

■ Are there any problems on the page? If yes, what caused the problem?

■ How is the performance when the application is accessed from different countries?

■ What is the response time across different browsers? Do new application updates affect the performance in a specific version of the browser?

■ How does the application perform in different platforms such as desktop, Web and mobile?

The biggest advantage of monitoring real user data is that it relies on actual traffic to take measurements. There is no need to script the important use cases, which can save a lot of time and resources.

Real user monitoring captures everything as a user goes through the application, so performance data will be available irrespective of what pages the user sees. This is particularly useful for complex apps in which the functionality or content is dynamic.

Server-Side Monitoring

Although user experience is best tracked at the browser level, application performance monitoring at the server side also provides insight into end-user performance. Server-side monitoring is mostly used in conjunction with real user monitoring. This is because problems originating on the server side can only be efficiently detected using server-side monitoring.

Monitoring performance on the server side involves agent-based instrumentation technology for acquiring and transmitting data. This monitoring approach is used to watch user transactions in real time and troubleshoot in case of issues such as slowness or application bugs.

Developers have to install agents on the application server to help capture and visualize transactions end-to-end, with performance statistics across all components, from the URL down to the SQL level. This visual breakdown reveals the flow of all the user transactions being executed in each layer of the application infrastructure.

Server-side monitoring helps track response time and throughput taken by each application component, with the option to trace transactions end-to-end via code analysis. This helps the IT Operations/DevOps teams identify slow Web transactions and then isolate performance issues down to the level of the specific application code that caused them. The underlying database is also monitored most of the time to determine slow database calls, database usage and overall database performance. With server-side monitoring, users will be able to identify the SQL queries executed during a transaction and thus identify the worst performing queries.

Synthetic Transaction Monitoring

Synthetic transaction monitoring is an active monitoring technique based on the concept of simulating the actions of an end user on a Web application. This method involves the use of external monitoring agents executing pre-recorded scripts that mimic end-user behavior at regular time intervals. The monitoring agents are usually very light and do not create any additional load on network traffic.

Most application performance monitoring solutions provide recorder tools to capture the actions or paths a typical end user might take in an application, such as log in, view product, search and check out. These recordings are saved as scripts, which are then executed by the monitoring agents from different geographical locations.

Technically, there are two different approaches to generating requests. Some solutions replay recorded HTTP traffic patterns, while others drive real browser instances. The second approach is more useful for modern applications that make a lot of JavaScript, CSS and Ajax calls.

Since synthetic transaction monitoring involves sending requests across the network, it can measure the response time of application servers and network infrastructure. This type of monitoring does not require actual Web traffic, so you can use this approach to test your Web applications prior to launch — or anytime you like. Many companies use synthetic monitoring before entering production in the form of automated integration tests with Selenium.

Synthetic monitoring does have its limitations, though. Since the monitoring is based on pre-defined transactions, it does not monitor the perception of real end users. Transactions have to be “read-only” because they would otherwise set off real purchase processes. This limits the usage to a certain subset of your business-critical transactions.

The best approach is to use synthetic transaction monitoring as a reference measurement that will help identify performance degradation, detect network problems and notify in case of errors.

Every business is different and has its own requirements that can help to choose which type of monitoring to implement. An ideal strategy would be to use active and passive monitoring techniques side by side so that no stone is left unturned in the pursuit to monitor end-user experience.

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...