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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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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 ...

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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 ...

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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

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 ...