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More on SolarWinds Prediction for APM in 2017

You Keep Using That Word
Leon Adato

As an IT professional, I'm used to words that mean different things to different people. For example, "log monitoring" could mean anything from simple text files to logfile aggregation systems. "Uptime" is also notoriously hard to nail down. Heck, even the word "monitoring" itself can be obscure.

To illustrate this phenomenon, I often bring up the (completely unrelated) classical Chinese poem Lion-Eating Poet in the Stone Den. Spoken out loud, every word is a version of the sound "shi." But as you can see, aside from the pronunciation, each word has extremely different meanings.

This is why I'm not surprised that application performance monitoring (APM) can mean so many different things depending on the context. But what is most confounding is that these usages are not mutually exclusive. There is overlap. This graphic demonstrates:


As you can see, there's code-centric APM (cAPM) where the focus is on code execution, transactions moving through the message queue, transforms, etc. This type of APM is often applied to custom developed code, or applications that are highly transactional in nature.

At the other end of the spectrum, there's operations-centric APM (oAPM). This type of APM is more concerned with what's often called "shrink-wrapped" software, which can be everything from single-purpose business utilities to enterprise class tools, such as Microsoft Exchange and even foundational things like the operating system itself. The point isn't that they are any less sophisticated than the programs that use code-centric APM, but the needs are different. More on this point in a moment.

There's also web-centric APM, or web performance monitoring (WPM), which, as the name implies, is focused on monitoring web applications. So it's less about the code execution or the stability of the underlying server application, and more about how the user of the web application is experiencing the service.

Finally, there's database-centric APM (dbAPM). In this iteration, it's all about the things that make your database go bump in the night: long running queries, locking, blocking, and wait states.

If you look at it closely, you can see the overlap. cAPM still cares that the application itself is healthy, and it can provide insight into things like services and processes, performance counters, and log messages. But that's not the primary focus. Similarly, oAPM has the ability to expose issues with transactions, but not to the level that cAPM does. Where it shines, however, is in operational metrics. And the same is true for WPM and dbAPM. 

This has all always been true, but it wasn't as clear until recently. The emergence (and convergence) of cloud, DevOps, hybrid IT, and everything-as-a-service (EaaS) has highlighted both the overlap and the differences. 

This is why I recently predicted that, "2017 will be the year of 'not just' in APM. As in 'not just agent-based transaction tracking' or 'not just for DevOps.' But most importantly, 'not just for home-grown code.' In the coming year, APM will fully embrace the words behind the acronym to include tools and techniques that allow management of all application types — from those developed in-house to customized-off-the-shelf ones, to pure shrink-wrap apps that enterprises purchase, install, and run as-is. Yes! Some of those really do still exist."

I'm looking forward to the time — in this coming year, if my prediction holds true — when IT professionals can say, "APM" and understand the nuances the same way students of Chinese literature understand that "shí shì shī shì shī shì" means, "A poet named Shi lived in a stone room."

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More on SolarWinds Prediction for APM in 2017

You Keep Using That Word
Leon Adato

As an IT professional, I'm used to words that mean different things to different people. For example, "log monitoring" could mean anything from simple text files to logfile aggregation systems. "Uptime" is also notoriously hard to nail down. Heck, even the word "monitoring" itself can be obscure.

To illustrate this phenomenon, I often bring up the (completely unrelated) classical Chinese poem Lion-Eating Poet in the Stone Den. Spoken out loud, every word is a version of the sound "shi." But as you can see, aside from the pronunciation, each word has extremely different meanings.

This is why I'm not surprised that application performance monitoring (APM) can mean so many different things depending on the context. But what is most confounding is that these usages are not mutually exclusive. There is overlap. This graphic demonstrates:


As you can see, there's code-centric APM (cAPM) where the focus is on code execution, transactions moving through the message queue, transforms, etc. This type of APM is often applied to custom developed code, or applications that are highly transactional in nature.

At the other end of the spectrum, there's operations-centric APM (oAPM). This type of APM is more concerned with what's often called "shrink-wrapped" software, which can be everything from single-purpose business utilities to enterprise class tools, such as Microsoft Exchange and even foundational things like the operating system itself. The point isn't that they are any less sophisticated than the programs that use code-centric APM, but the needs are different. More on this point in a moment.

There's also web-centric APM, or web performance monitoring (WPM), which, as the name implies, is focused on monitoring web applications. So it's less about the code execution or the stability of the underlying server application, and more about how the user of the web application is experiencing the service.

Finally, there's database-centric APM (dbAPM). In this iteration, it's all about the things that make your database go bump in the night: long running queries, locking, blocking, and wait states.

If you look at it closely, you can see the overlap. cAPM still cares that the application itself is healthy, and it can provide insight into things like services and processes, performance counters, and log messages. But that's not the primary focus. Similarly, oAPM has the ability to expose issues with transactions, but not to the level that cAPM does. Where it shines, however, is in operational metrics. And the same is true for WPM and dbAPM. 

This has all always been true, but it wasn't as clear until recently. The emergence (and convergence) of cloud, DevOps, hybrid IT, and everything-as-a-service (EaaS) has highlighted both the overlap and the differences. 

This is why I recently predicted that, "2017 will be the year of 'not just' in APM. As in 'not just agent-based transaction tracking' or 'not just for DevOps.' But most importantly, 'not just for home-grown code.' In the coming year, APM will fully embrace the words behind the acronym to include tools and techniques that allow management of all application types — from those developed in-house to customized-off-the-shelf ones, to pure shrink-wrap apps that enterprises purchase, install, and run as-is. Yes! Some of those really do still exist."

I'm looking forward to the time — in this coming year, if my prediction holds true — when IT professionals can say, "APM" and understand the nuances the same way students of Chinese literature understand that "shí shì shī shì shī shì" means, "A poet named Shi lived in a stone room."

Hot Topics

The Latest

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...