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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For all the attention AI receives in corporate slide decks and strategic roadmaps, many businesses are struggling to translate that ambition into something that holds up at scale. At least, that's the picture that emerged from a recent Forrester study commissioned by Tines ...

Not All Networks Are Built for the Edge

From smart factories and autonomous vehicles to real-time analytics and intelligent building systems, the demand for instant, local data processing is exploding. To meet these needs, organizations are leaning into edge computing. The promise? Faster performance, reduced latency and less strain on centralized infrastructure. But there's a catch: Not every network is ready to support edge deployments ...

Why Observability Is the Missing Piece in Your Business Growth

Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...

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Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...

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Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...

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