We use the z-Test for Proportions to test if two proportions are different from one another. A one-tailed distribution in statistics refers to a shaded area in one tail of a probability distribution. This chapter explains why you should read this book and how it can help you with any computer performance problem, now or in the future. Some have hundreds of pages of very difficult math that most people cana€™t do and most problems dona€™t require.
There are so many different technologies in your transaction path; to read all the necessary books would take longer than the average corporate lifetime.
Almost all manuals about a specific version of a technology were written under tremendous time pressure at about the same time the engineering was being completed; thus the engineers had little time to talk to the writers.
What Ia€™m about to tell you will work for any collection of computers, running any software that has ever been built. Whether you are studying the slow response time of a computer, the root cause of a traffic jam, or why you are always in the slowest line, understanding those forces gives you a big insight into why this is happening. Many parts of this book help you solve the diversity and complexity problems mentioned earlier by helping you quickly determine that large chunks of your computing infrastructure are not the problem.
Once focused, a large fraction of everyonea€™s effort is spent working in the right place and on the right problem. If you can replace S with the number 2 and U with the number 0.5 and calculate that R is equal to (spoiler alert) 4, then you have all the math you need for a long career in performance.
In the future, money will still be important, humans will still want to avoid risk, office politics will still matter, and there will be many ways that you can be right, but still fail because you did not pay attention to the people involved. Having worked many performance problems from problem discovery, through solution presentation to senior management, and then on to problem resolution, Ia€™ve made a few mistakes and learned a few tricks. If you are impressed with work experience, then read on as Ia€™ve spent the last 25 years teaching performance fundamentals, capacity planning, modeling, and performance testing of websites. If you are a person who is only impressed with academic qualifications then I have few to offer you.
Your business depends on a collection of computers, software, networking equipment, and specialized devices. All companies have an internal language and shorthand terms they use when describing the subset of the entire computing universe that determines their customera€™s experience.
In performance monitoring you need to know three things: the incoming workload, the resource consumption and what is normal.
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. Expected problems are when the users are simply asking the application for more things per second than it can do.
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. 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. With rare exception Ia€™ve found the lack of easily available workload information to be the single best predictor of how bad the situation is performance wise. The data you collect can also be used to predict the future with the other three tools in your bag: Capacity Planning, Load Testing, and Modeling. Capacity planning is the simple science of scaling the observed system so you can see if you have enough resources to handle the projected peak load, but it only works for resources you know about. Capacity planning is like a pre-party checklist where you check if there are enough: appetizers, drinks, glasses, places to sit, etc. Capacity planning starts by gathering key performance meters at a peak time on a reasonably busy day.
Now scale (multiply) the observed meters by the bossa€™s answer to get the projected peak utilization. In my experience, the application is rarely reengineered unless the inefficiency is egregious and the fix is easy and obvious.
Capacity planning can be that simple, but there are a few more things you need to consider. Even though you base your projections on one peak day, you should look at the data over a period of a few days to a few weeks.
Capacity planning can show you how busy key resources are at peak load, but it cana€™t tell you about response time changes as the load increases. Load testing is the art of creating artificially generated work that mimics the real work generated by the real users. Load tests depend on good performance monitoring to keep an eye on critical system resources as the load builds. For a load test to be really useful it must test the entire part of the transaction path that you care about.
Load testing can also help you find where subsystems break, and isolate the effect of one transaction type. Load testing can tell you many interesting things, but it can only give you data on the computing infrastructure you have now. If you need to predict a future that is different from your present, then you need to model.
When your boss asks you to predict future performance of some application, first see if you can do a simple capacity plan. This model of Hurricane Irene is accurate enough for anyone with a house on the Gulf of Mexico and it looks hopeful for Floridaa€™s Atlantic coast as well.

In a computer context, imagine your model shows that at peak load this servera€™s CPU is 20% busy.
Unfortunately, the above model of Hurricane Irene, at that moment, could not see far enough into the future to predict the devastating flooding Irene would cause in my home state of Vermont.
If you respect these limits, modeling is doable, useful and has its place when you are asking questions about the future.
This chapter shows you how to gain confidence you will make it smoothly though a projected future peak load by scaling up your current observed load. This chapter gives you practical advice on how to load test an existing application to see if can really handle the upcoming peak.
This chapter dispels the myth of modeling complexity and shows how modeling performance can save you a lot of time and money.
This chapter teaches you how to present your results so that they are understood and are believable.
This book is concise, useful, and occasionally funny.A  It gives you the tools needed to explore, and discover the hidden truths about your computing world. Unfortunately, the above model of Hurricane Irene, at that moment, could not see far enough into the future to predict the devastating flooding Irene would cause in my home state of Vermont.A  All models have limits.
A critical value often represents a rejection region cut-off value for a hypothesis test – also called a zc value for a confidence interval.
For confidence intervals and two-tailed z-tests, you can use the zTable to determine the critical values (zc). The index lists seven possible types of area, including two tailed, one tailed, and areas to the left and right of z. That’s because the area in the right-hand z-table is the area between the mean and the z-score. Everything (hardware, operating systems, networking, applications, users, corporate goals, org charts, priorities, and budgets) changes over time and with change comes new performance challenges. It will continue to be useful 100 years from now when todaya€™s technology, if it runs at all, will look as quaint as a mechanical cuckoo clock.
They help you make sense out of the performance meters you do understand and help keep you from making big mistakes. It shows you how to use the scientific method to decipher what they mean and how to find patterns in the raw data. This rapidly focuses your companya€™s full and undivided attention on the small part of your computing world (System Z) that is causing the performance problem.
Now, the right experts can be consulted and business decisions can be made to fix or work around the problem.
I spent about 15 of those years also doing performance work on live systems and critical applications for customers around the world.
Ia€™ve had plenty of practice in my standup teaching as well as when working with customers.
Back in the 70a€™s he wrote this wonderful little book about repairing literally any bicycle.
It usually includes dealing with immediate performance problems or collecting data that will be used by the other three tools to plan for future peak loads.
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. 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. 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.
Assuming you know how many guests will show up and have a reasonable understanding of what they will consume, everything you checked should be fine. Almost any day will do, as long as the system load is high enough to clearly differentiate it from the idle system load.
In most situations, people tend to value application stability more than the money they will spend to solve the problem with more hardware.
This gives you a clear picture where the usual daily peaks are and if there are things going on at odd times that you have to factor into any changes you propose. Even if you had perfect metering, doing the math would be a staggering challenge and, even, then youa€™d be gambling the business on reams of calculations.
These resources take a little more work and creativity to capacity plan for, but this is completely doable.
It can be done in a few days, presented in a few slides and prepared with few additional costs or risks.
The overall load starts off low and then increases in stages to the point where you achieved your goal or you fail because some resource has hit a limit and has become a bottleneck. For any test you need to know how much work you are sending into the system, the throughput and response time for the completed work, and how the system resources are responding under that load. If your product is your website then you need to test from where your users are: all the way in and all the way back to where they live. If you only got half way to your goal before you hit a bottleneck, then every measured resource is going to be doing twice the work once you fix the current bottleneck. Imagine your workload is an even mix of Red and Blue transactions, but it is the Blue transactions that really exercise some key component of your computing world. To use a metaphor, running a race tells you about your current aerobic capacity, not the capacity you will have after six more months of training. To model, you are going to need the data you collected with performance monitoring and maybe some data from load tests.

Everyday, in companies all over the world, regular people build simple models that answer important business questions. If circumstances change so much that you lack confidence in that prediction, then try modeling. Either you can directly measure the performance of a live system under load, or you have to guess about a theoretical future situation. It doesna€™t take a genius to understand that your model could be off by a lot and the server would still have plenty of CPU power left. This chapter gives you the keys to understanding all computer performance problems and provides an important foundation for the next four chapters.
With it, and some work on your part, youa€™ll be able to solve performance problems and to walk into the CIOa€™s office with confidence. 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. A useful answer is: accurate enough to answer your question with a reasonable margin of safety.
These are the same as the rejection region z-value cut-offs for a two-tailed z test with alpha = .05. There are always things to do, things to learn, things to prepare for, and experiments to run.
These patterns can tell you useful things about whata€™s going on inside the giant black box that is your computing world.
I had three majors (Wildlife Biology, Botany and Computer Science) but I have no degree as I left college when I ran out of money and got my first job in the computer industry. Mostly Ia€™ve learned from other performance wizards, programmers, system administrators and a few generally smart people Ia€™ve known.
Clarity is essential when explaining to senior management that they need to spend large amounts of money. There are many other parts that you dona€™t control, cana€™t meter, and have no clue how they do their work.
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.
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 a€?normala€? was, so everything the register told him had to be taken at face value. 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.
You will want to search this data by time (What happened at 10:30?), so be sure to include timestamps. However, even if you miss something, you are still better off having planned for reasonable amounts of the key resources. Ia€™m very sure the CIO looking at all this complex math would feel very uneasy betting his or her job on these calculations.
If you need more confidence in your plan, or you need to hold the response time down to a reasonable level, or if the future you are planning for includes significant changes to the transaction mix or vital systems, then you need to do either Load Testing or Modeling. When that happens, throughput stops increasing, response times climb to an unacceptably painful place and things break. If you (and your boss) only have responsibility for a small subset of the entire transaction path then thata€™s all you really need to test. So take all the peak measurements and double them to get a good idea of what you are likely to run out of as you push the test to the goal load. Create a load test that sends in all Blue transactions and you can collect the data you need without the interference of the Red transactions. Examples of big changes that preclude simple capacity planning might be things like, large application or middleware changes or a big change in the incoming workload. The closer to the utilization limit (or the projected storm track), the more precise you have to make your model. As the commemorative plate infomercial says: a€?Collect them all!a€?A  Please dona€™t wait for perfection. I would arrive knowing almost nothing about their business and their problems and leave having given them a clear path to follow.
An engaging style is essential when teaching multi-day classes, especially just after lunch. Furthermore, there are limits placed on you due to politics, time, budget, legal restrictions, and the tools you have available.
Modeling is the only way to project the performance of an application in the design stage where there is no performance data to scale. Start somewhere, collect something and, as you explore and discover, add this to your collection. I like people and if I couldna€™t make my classes useful, interesting, and relevant, then I would have stopped teaching a long time ago. As the commemorative plate infomercial says: a€?Collect them all!a€? Please dona€™t wait for perfection.

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