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Performance Monitoring: Understanding What's Happening Right Now

Insights from The Every Computer Performance Book

Performance monitoring is about understanding what's happening right now. It usually includes dealing with immediate performance problems or collecting data that will be used by the other performance tools (such as capacity planning) to plan for future peak loads.

In performance monitoring you need to know three things:

- The incoming workload

- The resulting resource consumption

- What is normal under this load

Without these three things you can only solve the most obvious performance problems and have to rely on tools outside the scientific realm (such as a Ouija Board, or a Magic 8 Ball) to predict the future.

You need to know the incoming workload (what the users are asking your system to do) because all computers run just fine under no load. Performance problems crop up as the load goes up. These performance problems come in two basic flavors: Expected and Unexpected.

Expected problems are when the users are simply asking the application for more things per second than it can do. You see this during an expected peak in demand like the biggest shopping day of the year. Expected problems are no fun, but they can be foreseen and, depending on the situation, your response might be to endure them, because money is tight or because the fix might introduce too much risk.

Unexpected problems are when the incoming workload should be well within the capabilities of the application, but something is wrong and either the end-user performance is bad or some performance meter makes no sense. Unexpected problems cause much unpleasantness and demand rapid diagnosis and repair.

Know What is Normal

The key to all performance work is to know what is normal. Let me illustrate that with a trip to the grocery store.

Image removed.

One day I was buying three potatoes and an onion for a soup I was making. The new kid behind the cash register looked at me and said: “That will be $22.50.” What surprised me was the total lack of internal error checking at this outrageous price (in 2012) for three potatoes and an onion. This could be a simple case of them not caring about doing a good job, but my more charitable assessment is that he had no idea what “normal” was, so everything the register told him had to be taken at face value. Don't be like that kid.

On any given day you, as the performance person, should be able to have a fairly good idea of how much work the users are asking the system to do and what the major performance meters are showing. If you have a good sense of what is normal for your situation, then any abnormality will jump right out at you in the same way you notice subtle changes in a loved one that a stranger would miss. This can save your bacon because if you spot the unexpected utilization before the peak occurs, then you have time to find and fix the problem before the system comes under a peak load.

There are some challenges in getting this data. For example:

- There is no workload data.

- The only workload data available (ex: per day transaction volume) is at too low a resolution to be any good for rapid performance changes.

- The workload is made of many different transaction types (buy, sell, etc.) It's not clear what to meter.

With rare exception I've found the lack of easily available workload information to be the single best predictor of how bad the overall situation is performance wise. Over the years as I visited company after company this led me to develop Bob's First Rule of Performance Work: “The less a company knows about the work their system did in the last five minutes, the more deeply screwed up they are.”

What meters should you collect? Meters fall into big categories. There are utilization meters that tell you how busy a resource is, there are count meters that count interesting events (some good, some bad), and there are duration meters that tell you how long something took. As the commemorative plate infomercial says: “Collect them all!” Please don't wait for perfection. Start somewhere, collect something and, as you explore and discover, add newly discovered meters to your collection.

When should you run the meters? Your meters should be running all the time (like bank security cameras) so that when weird things happen you have a multitude of clues to look at. You will want to search this data by time (What happened at 10:30?), so be sure to include timestamps.

The data you collect can also be used to predict the future with tools like: Capacity Planning, Load Testing, and Modeling.

This blog is based on: The Every Computer Performance Book available from Amazon and on iTunes.

ABOUT Bob Wescott

Bob Wescott is the author of The Every Computer Performance Book. Since 1987, Wescott has worked in the field of computer performance, doing professional services work and teaching how to do capacity planning, load testing, simulation modeling and web performance for Gomez/Compuware, HyPerformix/CA and Stratus Computer/Technologies. Now, Wescott is mostly retired, and his job is to give back what he has been given. His latest project is The Every Computer Performance Blog based on the book.

Related Links:

The Every Computer Performance Blog

The Every Computer Performance Book

Image removed.

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Performance Monitoring: Understanding What's Happening Right Now

Insights from The Every Computer Performance Book

Performance monitoring is about understanding what's happening right now. It usually includes dealing with immediate performance problems or collecting data that will be used by the other performance tools (such as capacity planning) to plan for future peak loads.

In performance monitoring you need to know three things:

- The incoming workload

- The resulting resource consumption

- What is normal under this load

Without these three things you can only solve the most obvious performance problems and have to rely on tools outside the scientific realm (such as a Ouija Board, or a Magic 8 Ball) to predict the future.

You need to know the incoming workload (what the users are asking your system to do) because all computers run just fine under no load. Performance problems crop up as the load goes up. These performance problems come in two basic flavors: Expected and Unexpected.

Expected problems are when the users are simply asking the application for more things per second than it can do. You see this during an expected peak in demand like the biggest shopping day of the year. Expected problems are no fun, but they can be foreseen and, depending on the situation, your response might be to endure them, because money is tight or because the fix might introduce too much risk.

Unexpected problems are when the incoming workload should be well within the capabilities of the application, but something is wrong and either the end-user performance is bad or some performance meter makes no sense. Unexpected problems cause much unpleasantness and demand rapid diagnosis and repair.

Know What is Normal

The key to all performance work is to know what is normal. Let me illustrate that with a trip to the grocery store.

Image removed.

One day I was buying three potatoes and an onion for a soup I was making. The new kid behind the cash register looked at me and said: “That will be $22.50.” What surprised me was the total lack of internal error checking at this outrageous price (in 2012) for three potatoes and an onion. This could be a simple case of them not caring about doing a good job, but my more charitable assessment is that he had no idea what “normal” was, so everything the register told him had to be taken at face value. Don't be like that kid.

On any given day you, as the performance person, should be able to have a fairly good idea of how much work the users are asking the system to do and what the major performance meters are showing. If you have a good sense of what is normal for your situation, then any abnormality will jump right out at you in the same way you notice subtle changes in a loved one that a stranger would miss. This can save your bacon because if you spot the unexpected utilization before the peak occurs, then you have time to find and fix the problem before the system comes under a peak load.

There are some challenges in getting this data. For example:

- There is no workload data.

- The only workload data available (ex: per day transaction volume) is at too low a resolution to be any good for rapid performance changes.

- The workload is made of many different transaction types (buy, sell, etc.) It's not clear what to meter.

With rare exception I've found the lack of easily available workload information to be the single best predictor of how bad the overall situation is performance wise. Over the years as I visited company after company this led me to develop Bob's First Rule of Performance Work: “The less a company knows about the work their system did in the last five minutes, the more deeply screwed up they are.”

What meters should you collect? Meters fall into big categories. There are utilization meters that tell you how busy a resource is, there are count meters that count interesting events (some good, some bad), and there are duration meters that tell you how long something took. As the commemorative plate infomercial says: “Collect them all!” Please don't wait for perfection. Start somewhere, collect something and, as you explore and discover, add newly discovered meters to your collection.

When should you run the meters? Your meters should be running all the time (like bank security cameras) so that when weird things happen you have a multitude of clues to look at. You will want to search this data by time (What happened at 10:30?), so be sure to include timestamps.

The data you collect can also be used to predict the future with tools like: Capacity Planning, Load Testing, and Modeling.

This blog is based on: The Every Computer Performance Book available from Amazon and on iTunes.

ABOUT Bob Wescott

Bob Wescott is the author of The Every Computer Performance Book. Since 1987, Wescott has worked in the field of computer performance, doing professional services work and teaching how to do capacity planning, load testing, simulation modeling and web performance for Gomez/Compuware, HyPerformix/CA and Stratus Computer/Technologies. Now, Wescott is mostly retired, and his job is to give back what he has been given. His latest project is The Every Computer Performance Blog based on the book.

Related Links:

The Every Computer Performance Blog

The Every Computer Performance Book

Image removed.

Hot Topics

The Latest

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...