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Why is APM Important?

Anand Akela

The following is an excerpt from: An Introduction to Application Performance Management (APM).

It probably seems obvious to you that APM is important, but you will likely need to answer the question of APM importance to someone like your boss or the company CFO that wants to know why she must pay for it. In order to qualify the importance of APM, let's consider the alternatives to adopting an APM solution and assess the impact in terms of resolution effort and elapsed downtime.

First let's consider how we detect problems. An APM solution alerts you to the abnormal application behavior, but if you don't have an APM solution then you have a few options:

■ Build synthetic transactions

■ Manual instrumentation

■ Wait for your users to call customer support!?

A synthetic transaction is a transaction that you execute against your application and with which you measure performance. Depending on the complexity of your application, it is not difficult to build a small program that calls a service and validates the response. But what do you do with that program? If it runs on your machine then what happens when you're out of the office?

Furthermore, if you do detect a functional or performance issue, what do you do with that information? Do you connect to an email server and send alerts? How do you know if this is a real problem or a normal slowdown for your application at this hour and day of the week?

Finally, detecting the problem is one thing, how do you find the root cause of the problem?

The next option is manually instrumenting your application, which means that you add performance monitoring code directly to your application and record it somewhere like a database or a file system. Some challenges in manual instrumentation include:

What parts of my code do I instrument?

How do I analyze it?

How do I determine normalcy?

How do I propagate those problems up to someone to analyze?

What contextual information is important?

... and so forth. Plus you have introduced a new problem: you have introduced performance monitoring code into your application that you need to maintain.

Furthermore, can you dynamically turn it on and off so that your performance monitoring code does not negatively affect the performance of your application?

If you learn more about your application and identify additional metrics you want to capture, do you need to rebuild your application and redeploy it to production?

What if your performance monitoring code has bugs?

There are other technical options, but what I find most often is that companies are alerted to performance problems when their custom service organization receives complaints from users. I don't think I need to go into details about why this is a bad idea!

Next let's consider how we identify the root cause of a performance problem without an APM solution. Most often I have seen companies do one of two things:

■ Review runtime logs

■ Attempt to reproduce the problem in a development/test environment

Log files are great sources of information and many times they can identify functional defects in your application (by capturing exception stack traces), but when experiencing performance issues that do not raise exceptions, they typically only introduce additional confusion.

You may have heard of, or been directly involved in, a production war room. These war rooms are characterized by finger pointing and attempts to indemnify one's own components so that the pressure to resolve the issue falls on someone else. The bottom line is that these meetings are not fun and not productive.

Alternatively, and usually in parallel, the development team is tasked with reproducing the problem in a test environment. The challenge here is that you usually do not have enough context for these attempts to be fruitful. Furthermore, if you are able to reproduce the problem in a test environment, that is only the first step, now you need to identify the root cause of the problem and resolve it!

So to summarize, APM is important to you so that you can understand the behavior of your application, detect problems before your users are impacted, and rapidly resolve those issues. In business terms, an APM solution is important because it reduces your Mean Time To Resolution (MTTR), which means that performance issues are resolved quicker and more efficiently so that the impact to your business bottom line is reduced.

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Why is APM Important?

Anand Akela

The following is an excerpt from: An Introduction to Application Performance Management (APM).

It probably seems obvious to you that APM is important, but you will likely need to answer the question of APM importance to someone like your boss or the company CFO that wants to know why she must pay for it. In order to qualify the importance of APM, let's consider the alternatives to adopting an APM solution and assess the impact in terms of resolution effort and elapsed downtime.

First let's consider how we detect problems. An APM solution alerts you to the abnormal application behavior, but if you don't have an APM solution then you have a few options:

■ Build synthetic transactions

■ Manual instrumentation

■ Wait for your users to call customer support!?

A synthetic transaction is a transaction that you execute against your application and with which you measure performance. Depending on the complexity of your application, it is not difficult to build a small program that calls a service and validates the response. But what do you do with that program? If it runs on your machine then what happens when you're out of the office?

Furthermore, if you do detect a functional or performance issue, what do you do with that information? Do you connect to an email server and send alerts? How do you know if this is a real problem or a normal slowdown for your application at this hour and day of the week?

Finally, detecting the problem is one thing, how do you find the root cause of the problem?

The next option is manually instrumenting your application, which means that you add performance monitoring code directly to your application and record it somewhere like a database or a file system. Some challenges in manual instrumentation include:

What parts of my code do I instrument?

How do I analyze it?

How do I determine normalcy?

How do I propagate those problems up to someone to analyze?

What contextual information is important?

... and so forth. Plus you have introduced a new problem: you have introduced performance monitoring code into your application that you need to maintain.

Furthermore, can you dynamically turn it on and off so that your performance monitoring code does not negatively affect the performance of your application?

If you learn more about your application and identify additional metrics you want to capture, do you need to rebuild your application and redeploy it to production?

What if your performance monitoring code has bugs?

There are other technical options, but what I find most often is that companies are alerted to performance problems when their custom service organization receives complaints from users. I don't think I need to go into details about why this is a bad idea!

Next let's consider how we identify the root cause of a performance problem without an APM solution. Most often I have seen companies do one of two things:

■ Review runtime logs

■ Attempt to reproduce the problem in a development/test environment

Log files are great sources of information and many times they can identify functional defects in your application (by capturing exception stack traces), but when experiencing performance issues that do not raise exceptions, they typically only introduce additional confusion.

You may have heard of, or been directly involved in, a production war room. These war rooms are characterized by finger pointing and attempts to indemnify one's own components so that the pressure to resolve the issue falls on someone else. The bottom line is that these meetings are not fun and not productive.

Alternatively, and usually in parallel, the development team is tasked with reproducing the problem in a test environment. The challenge here is that you usually do not have enough context for these attempts to be fruitful. Furthermore, if you are able to reproduce the problem in a test environment, that is only the first step, now you need to identify the root cause of the problem and resolve it!

So to summarize, APM is important to you so that you can understand the behavior of your application, detect problems before your users are impacted, and rapidly resolve those issues. In business terms, an APM solution is important because it reduces your Mean Time To Resolution (MTTR), which means that performance issues are resolved quicker and more efficiently so that the impact to your business bottom line is reduced.

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The Latest

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 ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

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 ...