5 APM Techniques to Troubleshoot Application Slow Down in Minutes

Payal Chakravarty

Applications are getting more complex by the day. First you have the various hosting platforms that your app can span across like private cloud, public cloud, your own data center.

Second, you have applications for the web being accessed through different browsers and mobile apps being accessed from several hundred different devices and various device OSs.

Third, the same app is being accessed from around the world, 24X7.

Fourth, the number of users accessing apps have grown significantly requiring rapid scalability of the app's infrastructure.

To top it all, users, today, have very little patience to deal with poor performance.

Application Performance Management (APM) tools have evolved over the last decade to cater to this complexity and yet be able to troubleshoot application performance issues quickly. Let us look at some of the key features and visualization techniques that are enabling quicker troubleshooting:

1. End User Experience Metrics sliced by different dimensions

As an app developer or app owner, the first step to troubleshooting a performance problem is to narrow the scope of it. By comparing how long it is taking a web page to load for a user using your app through Firefox on Mac vs how long it is taking for the same web page to load for a user using Chrome on iOS, you can narrow down which browser and device to troubleshoot on. You could also compare how long the response time is for a user in California vs a user in Australia when accessing the same page and executing the same transaction. By slicing and dicing response time by various dimensions like geography, browser, device, network carrier etc isolation of problem areas have become easier.

2. Code level stack traces

For every business transaction that fails or is slow, you can find out what line of code is causing the slowdown by looking at its stack trace. APM tools today show the class name, method name and exact line of source code (e.g., SQL query, line number of code in a specific browser session trace) that led to a slow request. Further, you can see the pre- and post-code deployment patterns for your apps.

3. Transaction Topologies

Today, APM tools can automatically discover your end-to-end distributed application environment in minutes, showing you a topological view of all the components that your app depends on and hence aid visual detection of bottlenecks. A few of these tools not only show an aggregated transaction topology, but also show the detailed topological mapping for single transaction instances, capturing network hops and sub-transaction nodes to help you see where the time is spent during that instance. With the evolution of big data technologies, it is now possible to capture 100% transactions instead of sampling. This ensures you will not lose out on any key business transactions that may have failed.

4. Log analytics

Searching for errors across application stacks can be a laborious task. Earlier, while troubleshooting, operators, administrators and app owners would have to look through logs from different components independently, in silos. With integrated log analytics, you can now search for errors across log files for any component in your app stack in the context of the application. For example, you can correlate errors in your app server with an error in your database that may be impacting a transaction.

5. One pane-of-glass to view health of all components in the app stack

As opposed to looking at multiple panes of glass to see details of your application's health, today, at a glance in one UI you will be able to visualize the detailed health of all your app components. Spotting the problem area is as easy as spotting a color difference. For example, key metrics — like Garbage collection statistics from your code's runtime, memory usage of your VM, space utilization of your database server, bandwidth utilization of your network, http request response times of your web requests — can all be seen in one user interface.

With the evolution of big data, improved algorithms for search and correlation, smart dashboards/visualization and diagnostic capabilities, APM tools have matured to provide insights that you could never have before, thereby cutting troubleshooting time from days to minutes.

Payal Chakravarty is Senior Product Manager for IBM Application Performance Management.

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5 APM Techniques to Troubleshoot Application Slow Down in Minutes

Payal Chakravarty

Applications are getting more complex by the day. First you have the various hosting platforms that your app can span across like private cloud, public cloud, your own data center.

Second, you have applications for the web being accessed through different browsers and mobile apps being accessed from several hundred different devices and various device OSs.

Third, the same app is being accessed from around the world, 24X7.

Fourth, the number of users accessing apps have grown significantly requiring rapid scalability of the app's infrastructure.

To top it all, users, today, have very little patience to deal with poor performance.

Application Performance Management (APM) tools have evolved over the last decade to cater to this complexity and yet be able to troubleshoot application performance issues quickly. Let us look at some of the key features and visualization techniques that are enabling quicker troubleshooting:

1. End User Experience Metrics sliced by different dimensions

As an app developer or app owner, the first step to troubleshooting a performance problem is to narrow the scope of it. By comparing how long it is taking a web page to load for a user using your app through Firefox on Mac vs how long it is taking for the same web page to load for a user using Chrome on iOS, you can narrow down which browser and device to troubleshoot on. You could also compare how long the response time is for a user in California vs a user in Australia when accessing the same page and executing the same transaction. By slicing and dicing response time by various dimensions like geography, browser, device, network carrier etc isolation of problem areas have become easier.

2. Code level stack traces

For every business transaction that fails or is slow, you can find out what line of code is causing the slowdown by looking at its stack trace. APM tools today show the class name, method name and exact line of source code (e.g., SQL query, line number of code in a specific browser session trace) that led to a slow request. Further, you can see the pre- and post-code deployment patterns for your apps.

3. Transaction Topologies

Today, APM tools can automatically discover your end-to-end distributed application environment in minutes, showing you a topological view of all the components that your app depends on and hence aid visual detection of bottlenecks. A few of these tools not only show an aggregated transaction topology, but also show the detailed topological mapping for single transaction instances, capturing network hops and sub-transaction nodes to help you see where the time is spent during that instance. With the evolution of big data technologies, it is now possible to capture 100% transactions instead of sampling. This ensures you will not lose out on any key business transactions that may have failed.

4. Log analytics

Searching for errors across application stacks can be a laborious task. Earlier, while troubleshooting, operators, administrators and app owners would have to look through logs from different components independently, in silos. With integrated log analytics, you can now search for errors across log files for any component in your app stack in the context of the application. For example, you can correlate errors in your app server with an error in your database that may be impacting a transaction.

5. One pane-of-glass to view health of all components in the app stack

As opposed to looking at multiple panes of glass to see details of your application's health, today, at a glance in one UI you will be able to visualize the detailed health of all your app components. Spotting the problem area is as easy as spotting a color difference. For example, key metrics — like Garbage collection statistics from your code's runtime, memory usage of your VM, space utilization of your database server, bandwidth utilization of your network, http request response times of your web requests — can all be seen in one user interface.

With the evolution of big data, improved algorithms for search and correlation, smart dashboards/visualization and diagnostic capabilities, APM tools have matured to provide insights that you could never have before, thereby cutting troubleshooting time from days to minutes.

Payal Chakravarty is Senior Product Manager for IBM Application Performance Management.

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Understanding Zero-Day Vulnerabilities: Why They Matter and How to Stay Protected

Cyber threats are growing more sophisticated every day, and at their forefront are zero-day vulnerabilities. These elusive security gaps are exploited before a fix becomes available, making them among the most dangerous threats in today's digital landscape ... This guide will explore what these vulnerabilities are, how they work, why they pose such a significant threat, and how modern organizations can stay protected ...

Data Center Outages Becoming Less Frequent

The prevention of data center outages continues to be a strategic priority for data center owners and operators. Infrastructure equipment has improved, but the complexity of modern architectures and evolving external threats presents new risks that operators must actively manage, according to the Data Center Outage Analysis 2025 from Uptime Institute ...

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As observability engineers, we navigate a sea of telemetry daily. We instrument our applications, configure collectors, and build dashboards, all in pursuit of understanding our complex distributed systems. Yet, amidst this flood of data, a critical question often remains unspoken, or at best, answered by gut feeling: "Is our telemetry actually good?" ... We're inviting you to participate in shaping a foundational element for better observability: the Instrumentation Score ...

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Much like a traditional factory turns raw materials into finished products, the AI factory turns vast datasets into actionable business outcomes through advanced models, inferences, and automation. From the earliest data inputs to the final token output, this process must be reliable, repeatable, and scalable. That requires industrializing the way AI is developed, deployed, and managed ...

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Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

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