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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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...