From 6cd5a2b6a203498aacce361ee86bdac837710577 Mon Sep 17 00:00:00 2001 From: Jeff Zarnett Date: Sat, 20 Jul 2024 21:59:28 -0400 Subject: [PATCH] Merge what was L26 into revised L25, will need new plan for 26 --- lectures/L25.tex | 156 +++++++----------- lectures/L26.tex | 133 ++++++++++----- .../L25-Profiling_and_Scalability.pdf | Bin 240528 -> 0 bytes lectures/compiled/L26-Load_Testing.pdf | Bin 200479 -> 0 bytes 4 files changed, 152 insertions(+), 137 deletions(-) delete mode 100644 lectures/compiled/L25-Profiling_and_Scalability.pdf delete mode 100644 lectures/compiled/L26-Load_Testing.pdf diff --git a/lectures/L25.tex b/lectures/L25.tex index cb9fd98a..829da395 100644 --- a/lectures/L25.tex +++ b/lectures/L25.tex @@ -2,9 +2,9 @@ \begin{document} -\lecture{25 --- Profiling and Scalability}{\term}{Jeff Zarnett} +\lecture{25 --- Load Testing}{\term}{Jeff Zarnett} -\section*{Profiling and Scalability} +\section*{Load Testing} We've had a long look at the subject of observation -- identifying areas in the execution of our program that are a potential issue. Most of our upcoming discussion will be around the idea of CPU profiling, which is to say, examining where CPU time is going. That's generally predicated on the idea that CPU time is limiting factor. But maybe it isn't, and before we go about focusing on how to improve this aspect, let's check that assumption and consider what else it could be. @@ -12,145 +12,101 @@ \section*{Profiling and Scalability} With that said, if you identify a problem in a production system and we want to experiment on it with a dev machine, or if we have an application developed and we want to estimate what its performance and limiting factors would be, there are a few things we need to worry about. So we should respect the following principles, as outlined in~\cite{swps}. -\paragraph{Hardware Principle.} -Scalability testing is very different from QA testing (you test your code, right?) in that you will do development and QA on your local computer and all you really care about is whether the program produces the correct output ``fast enough''. That's fine, but it's no way to test if it scales. If you actually want to test for scalability and real world performance, you should be doing it on the machines that are going to run the program in the live/production environment. Why? Well, low-end systems have very different limiting factors. You might be limited by the 16GB of RAM in your laptop and that would go away in the 64GB of RAM server you're using. So you might spend a great deal of time worrying about RAM usage when it turns out it doesn't matter. - -\paragraph{Reality Principle.} -It would be a good idea to use a ``real'' workload, as much as one can possibly simulate. Legal reasons might prevent you from using actual customer data, but you should be trying to use the best approximation of it that you have. - -If you only generate some test data, it might not be representative of the actual data: you might say 1\% of your customers are on the annual subscription but if it's really 10\% that might make a difference in how long it takes to run an analysis. On the other hand, your test data might be much larger than in production because your tests create (and don't delete) entities in the test system database every time you run. -This isn't theoretical: I [JZ] have been the incident responder on an incident where an app doesn't start up because the database migration takes too long and gets killed. This issue wasn't caught in the test environment because the size of data there was much smaller than the live database. So of course the migration was finished well under the time limit during testing... and failed when going live. Oops. +\paragraph{Start with why.} The most important question is: why are we doing this? A few possible answers~\cite{hitchhiking}: +\begin{itemize} + \item A new system is being developed, and management wants to understand the limits and the risks. + \item The workload is expected to increase, and we (the CFO) want(s) to be sure the system can handle it. + \item A sudden spike in workload is expected, like tax season, and we want to know we can handle it. + \item The system has high uptime requirements, like 99.99\%, and must work over an extended period of time. + \item The project plan has a checkbox ``performance testing'', even though nobody has a real idea of what it means. +\end{itemize} -Related to that, user behaviour is hard to simulate, too. If you are making threads that pretend to be users to simulate concurrent usage of the system, the scripts those threads run may not accurately represent the way the users really behave. Your test example scenario may assume that company managers want to run the report once a month... your users might run them every hour. That one really happened to me [JZ] as well. The manager was using the report to keep an eye on what his team members were doing all day! +Leaving aside the last ``reason'', each of the actual reasons why implies a different kind of testing is required. If it's a new system, maybe we just need to understand the average workload (plus, perhaps, a buffer for safety) and make a plan for that. If the workload is expected to increase, like having ten times the number of users, we just need to establish our bottlenecks as in the previous topic to identify what we think the limit is. If it's the spike situation, we need to find a way to generate that spike behaviour to see how the system responds, and the hardest part might actually be in how to create the test. If our reason is uptime requirements, then in addition to putting a lot of load on the system, we have to maintain it for an extended period to make sure there's no performance degradation -- a test of endurance. -\paragraph{Volume Principle.} -``More is the new more.'' It's okay to use lighter workloads for regression testing and things like that, but if you actually want to see how your system performs under pressure, you actually need to put it under pressure! You can simulate pressure by limiting RAM or running something very CPU-intensive concurrently, but it's not quite the same as having an actually-high workload. +\paragraph{Stress Tests?} Load testing is not the same thing as \textit{stress testing}. Load testing involves having a specific target and the goal is to demonstrate that the application can handle that amount. Stress testing is about turning up the pressure (load) until things break, or at least stop working well. We are not going to go into stress testing in this topic, though it should be possible to take the load testing lessons and repurpose them by simply turning the setting up to even-bigger numbers. -Why not? The issue isn't so much the test as it is what you're not seeing. If I want to test the app on my laptop under CPU pressure I can just run the app while I repeatedly convert a lecture video just to consume CPU cycles. That does have the effect of limited CPU time available and we can see how the app performs when CPU time is maxed out. But that's not the same as what happens if you have 500 users concurrently, because you might not get to full CPU usage with 500 users due to other reasons, such as lock contention. +\subsection*{Plans Are Useless, Planning Is Essential} The previously-cited book suggests making a plan that answers the questions of \textit{who, what, where, when, \& why}. We just covered the ``why'' question, and understood how it points us towards the answer of ``what'' -- what kind of load testing are we intending to do here. That leaves ``who'' and ``when''. In a company (or FYDP) situation, there might be reason for debate or discussion around who should do the tests and when they should take place. For our purposes the answers are: we are going to do them, and we're going to do them now. -These tests, incidentally, are of great interest to the customers and C-Levels, who would like to know that you can deliver promised levels of performance or throughput, or what the current maximum is. +How detailed the plan needs to be is going to depend on your organization, and the same applies for how many sign-offs (if any!) you need on the plan. Some companies have a high need for justification of the time invested in this, after all, because time is money (developer salary) and there are opportunity costs (what work are we not doing in favour of this). There's lots of literature out there about how to justify technical investments to senior management and let's not get sidetracked. -\paragraph{Reproducibility and Regression Testing.} -Your results will need to be reproducible. Remember that good programming practice says that tests should be run, and re-run to make sure it all works. The same is true in your performance tests. Just as we don't want to see old bugs cropping up again, old (solved) performance issues are not nice to see either. Or a new change that slows the whole program down is not a success either. So please, have regression testing and performance regression testing. Real-life example: \url{https://arewefastyet.com} tracks regressions/improvements in Firefox performance. +The load testing plan needs at least a definition of what will be tested, how it will be tested, and how we'll know if the test passes or fails. A simple scenario could look something like: we will test saving things to the database by generating 10~000 update-account requests, submit them all at once, and the the total time to complete all transactions should not exceed 15 seconds. That's all made up and probably even a little bit silly, but it meets the important criteria of explaining what to test, how to test it, and how to evaluate success. Let's expand on this. -\section*{Characterizing Performance \& Scalability Problems} -\begin{quote} -\textit{It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.} -\end{quote} -\hfill - Sherlock Holmes (\textit{A Scandal in Bohemia}; Sir Arthur Conan Doyle) +\subsection*{What Workflows to Test?} +While we might have a clear direction about the kind of test we want from why, the question still remains about what workflows are going to be tested. We cannot test everything -- aiming for 100\% coverage is unnecessary, but load testing should be reserved for only where it's truly needed because of its high cost and effort requirements. -Keeping the wisdom of Mr. Holmes in mind, we need to collect evidence before reaching conclusions. At a high level we probably have five potential culprits to start with: -\begin{enumerate} - \item CPU - \item Memory - \item Disk - \item Network - \item Locks -\end{enumerate} +If we already know which ones are slow (rate-limiting) or on the critical path, then we can certainly start there. Ideally, the observability/monitoring (you have it, right?) gives us the guidance needed here. If monitoring doesn't exist, that might need to get addressed first. -These are, obviously, more categories than specific causes, but they are starting points for further investigation. They are listed in some numerical order, but there is no reason why one would have to investigate them in the order defined there. +If it's a new (as-yet-unimplemented) system, which are the critical workflows for this application? Critical is determined by the nature of the product and what you want it to do. If something is computationally intensive, like compiling analytical data, then that workflow is important. In other scenarios, user experience determines what's critical for load testing: if we have information that says users quit the signup process if the signup page takes more than two seconds to load, we care about making sure the signup time doesn't exceed that limit even when the system is busy. There can also be external requirements that require load testing: if you are processing a transaction and you must return a yes or no response within 1 second, anything that is part of coming up with this answer is critical and worth testing. -\paragraph{CPU.} CPU is probably the easiest of these to diagnose. Something like \texttt{top} or Task Manager will tell you pretty quickly if the CPU is busy. You can look at the \%CPU columns and see where all your CPU is going. Still, that tells you about right now; what about the long term average? Checking with my machine ``Loki'', that used to donate its free CPU cycles to world community grid (I'm singlehandedly saving the world, you see.): +If your current utilization is low, you might not know what the rate-limiting steps are at a glance. You can take a guess, but be prepared to revise those guesses partway through the process as you ramp up the load. Actually, you might need to do that even if utilization isn't low; you may find new things along the way that turn out to be the real limiting factors. -\begin{verbatim} -top - 07:28:19 up 151 days, 23:38, 8 users, load average: 0.87, 0.92, 0.91 -\end{verbatim} +In the event of the uptime requirements, the tests likely look the same as the increased-workload situation -- we just run them for longer. Endurance tests have significant overlap with load tests, but are not exactly the same. We'll come back to figuring out how long an endurance test should be shortly. -Those last three numbers are the one, five, and fifteen minute averages of CPU load, respectively. Lower numbers mean less CPU usage and a less busy machine. A small guide on how to interpret this, from~\cite{scout}. -Picture a single core of a CPU as a lane of traffic. You are a bridge operator and so you need to monitor how many cars are waiting to cross that bridge. If no cars are waiting, traffic is good and drivers are happy. If there is a backup of cars, then there will be delays. Our numbering scheme corresponds to this: +\subsection*{How to Test Them} +Given a workflow and the kind of test that we want to do, then we just need to think about how to test it. Carrying out these tests might require provisioning additional (virtual) hardware, particularly if they are long-running. The typical test harness tools used for unit testing aren't always intended for this purpose, so you may need different tools, or just write a custom script to execute the tests. When creating the tests, though, there are four key principles we need to remember: -\begin{enumerate} - \item 0.00 means no traffic (and in fact anything between 0.00 and 0.99) means we're under capacity and there will be no delay. - \item 1.00 means we are exactly at capacity. Everything is okay, but if one more car shows up, there will be a delay. - \item Anything above 1.00 means there's a backup (delay). If we have 2.00 load, then the bridge is full and there's an equal number of cars waiting to get on the bridge. -\end{enumerate} -Or, visually, also from~\cite{scout}: +\paragraph{Hardware Principle.} +Scalability testing is very different from QA testing (you test your code, right?) in that you will do development and QA on your local computer and all you really care about is whether the program produces the correct output ``fast enough''. That's fine, but it's no way to test if it scales. If you actually want to test for scalability and real world performance, you should be doing it on the machines that are going to run the program in the live/production environment. Why? Well, low-end systems have very different limiting factors. You might be limited by the 16GB of RAM in your laptop and that would go away in the 64GB of RAM server you're using. So you might spend a great deal of time worrying about RAM usage when it turns out it doesn't matter. -\begin{center} - \includegraphics[width=0.55\textwidth]{images/car-analogy.png} -\end{center} +\paragraph{Reality Principle.} +It would be a good idea to use a ``real'' workload, as much as one can possibly simulate. Legal reasons might prevent you from using actual customer data, but you should be trying to use the best approximation of it that you have. -Being at or above 1.00 isn't necessarily bad, but you should be concerned if there is consistent load of 1.00 or above. And if you are below 1.00 but getting close to it, you know how much room you have to scale things up -- if load is 0.4 you can increase handily. If load is 0.9 you're pushing the limit already. If load is above 0.70 then it's probably time to investigate. If it's at 1.00 consistently we have a serious problem. If it's up to 5.00 then this is a red alert situation. +If you only generate some test data, it might not be representative of the actual data: you might say 1\% of your customers are on the annual subscription but if it's really 10\% that might make a difference in how long it takes to run an analysis. On the other hand, your test data might be much larger than in production because your tests create (and don't delete) entities in the test system database every time you run. -Now this is for a single CPU -- if you have a load of 3.00 and a quad core CPU, this is okay. You have, in the traffic analogy, four lanes of traffic, of which 3 are being used to capacity. So we have a fourth lane free and it's as if we're at 75\% utilization on a single CPU. +This isn't theoretical: I [JZ] have been the incident responder on an incident where an app doesn't start up because the database migration takes too long and gets killed. This issue wasn't caught in the test environment because the size of data there was much smaller than the live database. So of course the migration was finished well under the time limit during testing... and failed when going live. Oops. +Related to that, user behaviour is hard to simulate, too. If you are making threads that pretend to be users to simulate concurrent usage of the system, the scripts those threads run may not accurately represent the way the users really behave. Your test example scenario may assume that company managers want to run the report once a month... your users might run them every hour. That one really happened to me [JZ] as well. The manager was using the report to keep an eye on what his team members were doing all day! -\paragraph{Memory and Disk.} Next on the list is memory. One way to tell if memory is the limiting factor is actually to look at disk utilization. If there is not enough RAM in the box, there will be swapping and then performance goes out the window and scalability goes with. That is of course, the worst case. You can ask via \texttt{top} about how much swap is being used, but that's probably not the interesting value. +\paragraph{Volume Principle.} +``More is the new more.'' It's okay to use lighter workloads for regression testing and things like that, but if you actually want to see how your system performs under pressure, you actually need to put it under pressure! You can simulate pressure by limiting RAM or running something very CPU-intensive concurrently, but it's not quite the same as having an actually-high workload. -\begin{verbatim} -KiB Mem: 8167736 total, 6754408 used, 1413328 free, 172256 buffers -KiB Swap: 8378364 total, 1313972 used, 7064392 free. 2084336 cached Mem -\end{verbatim} +Why not? The issue isn't so much the test as it is what you're not seeing. If I want to test the app on my laptop under CPU pressure I can just run the app while I repeatedly convert a lecture video just to consume CPU cycles. That does have the effect of limited CPU time available and we can see how the app performs when CPU time is maxed out. But that's not the same as what happens if you have 500 users concurrently, because you might not get to full CPU usage with 500 users due to other reasons, such as lock contention. -This can be misleading though, because memory being ``full'' does not necessarily mean anything bad. It means the resource is being used to its maximum potential, yes, but there is no benefit to keeping a block of memory open for no reason. Things will move into and out of memory as they need to, and nobody hands out medals to indicate that you did an awesome job of keeping free memory. It's not like going under budget in your department for the year. Also, memory is not like the CPU; if there's nothing for the CPU to do, it will just idle (or go to a low power state, which is nice for saving the planet). But memory won't ``forget'' data if it doesn't happen to be needed right now - data will hang around in memory until there is a reason to move or change it. So freaking out about memory appearing as full is kind of like getting all in a knot about how ``System Idle Process'' is hammering the CPU\footnote{Yes, a tech journalist named John Dvorak really wrote an article about this, and I will never, ever forgive him for it.}. +These tests, incidentally, are of great interest to the customers and C-Levels, who would like to know that you can deliver promised levels of performance or throughput, or what the current maximum is. -You can also ask about page faults, with the command \texttt{ps -eo min\_flt,maj\_flt,cmd} which will give you the major page faults (had to fetch from disk) and minor page faults (had to copy a page from another process). The output of this is too big even for the notes, but try it yourself (or I might be able to do a demo of it in class). But this is lifetime and you could have a trillion page faults at the beginning of your program and then after that everything is fine. What you really want is to ask Linux for a report on swapping: +\paragraph{Reproducibility and Regression Testing.} +Your results will need to be reproducible. Remember that good programming practice says that tests should be run, and re-run to make sure it all works. The same is true in your performance tests. Just as we don't want to see old bugs cropping up again, old (solved) performance issues are not nice to see either. Or a new change that slows the whole program down is not a success either. So please, have regression testing and performance regression testing. Real-life example: \url{https://arewefastyet.com} tracks regressions/improvements in Firefox performance. -\begin{verbatim} -jz@Loki:~$ vmstat 5 -procs -----------memory---------- ---swap-- -----io---- -system-- ------cpu----- - r b swpd free buff cache si so bi bo in cs us sy id wa st - 1 0 1313972 1414600 172232 2084296 0 0 3 39 1 1 27 1 72 0 0 - 0 0 1313972 1414476 172232 2084296 0 0 0 21 359 735 19 0 80 0 0 - 0 0 1313972 1414656 172236 2084228 0 0 0 102 388 758 22 0 78 0 0 - 4 0 1313972 1414592 172240 2084292 0 0 0 16 501 847 33 0 67 0 0 - 0 0 1313972 1412028 172240 2084296 0 0 0 0 459 814 29 0 71 0 0 -\end{verbatim} +\subsection*{Endurance Tests: How Long?} +Chances are you're familiar with the idea of endurance being different from peak workload. Can I [JZ] run at 10 km/h for 1 minute? Sure -- I can run much faster than that for 1 minute! Can I run 30 minutes (5 km) at 10 km/h? Yes. 60 minutes (10 km)? Yes, but with difficulty. Four hours (40 km)? No, I'll get tired and slow down, stop, or maybe hurt myself. Okay, so we've found a limit here -- I can endure an hour at this workload but things break down after that. If we only studied my running for a time period that was less than half an hour, we might conclude that I could run at 10 km/h forever (or at least indefinitely), even though that's absolutely not true\footnote{That's a common fallacy in the media, too -- you may notice, if you're looking for it, a plethora of journalists writing opinion pieces that say ``the current situation is the new normal and will go on forever'' -- even though that's almost certainly not true. Things change all the time -- political parties that win an election are often voted out again after a few years; rough job markets improve and good ones get tighter; high interest rates come down or low rates increase, etc. New and unprecedented things happen a lot, and change is constant.}. The problem is that if our sample period is 15 minutes, that is not long enough to reflect the cumulative negative effects that contribute to my eventual slowing down and being forced to stop. -In particular, the columns ``si'' (swap in) and ``so'' (swap out) are the ones to pay attention to. In the above example, they are all zero. That is excellent and tends to indicate that we are not swapping to disk and that's not the performance limiting factor. Sometimes we don't get that situation. A little bit of swapping may be inevitable, but if we have lots of swapping, we have a very big problem. Here's a not-so-nice example, from~\cite{vmstat}: +Is this analogy suitable for software, though? CPUs don't get ``tired'', nor do their parts accumulate fatigue at anywhere near the same rate as a runner's muscles. Yes, it's still valid! A process that has a data hoarding problem is slowly accumulating memory usage and when its memory space is exhausted, we might encounter a hard stop (e.g., encountering the error \texttt{java.lang.OutOfMemoryError: Java heap space}) or just a degradation of performance based on the increasing amount of swapping memory to disk that is required. Same for filling up disk, exhausting file handles, log length, accumulated errors, whatever it is. That's a little bit like fatigue for the executing application, whether it's building in the program or its environment: it builds over time, and eventually the effects of it force a slowdown or stoppage of execution. -\begin{verbatim} - procs memory swap io system cpu -r b w swpd free buff cache si so bi bo in cs us sy id -. . . -1 0 0 13344 1444 1308 19692 0 168 129 42 1505 713 20 11 69 -1 0 0 13856 1640 1308 18524 64 516 379 129 4341 646 24 34 42 -3 0 0 13856 1084 1308 18316 56 64 14 0 320 1022 84 9 8 -\end{verbatim} +Accumulated ``fatigue'' for an application is not just a hypothetical, either. I [JZ] have personally seen services that encountered a problem due to running out of some internal resources as a result of a code freeze over the holiday season. That wasn't a load test in the sense of applying a higher load to validate execution rate -- it was just an endurance test, even though it was not planned as one. The solution to getting the error rate down involved restarting the instances one-by-one and things got back on track. This example calls back what I said about how endurance tests are not exactly the same as load tests, in that we can have an endurance test with low load and it's still valid. -If we're not doing significant swapping, then memory isn't holding us back, so we can conclude it is not the limiting factor in scaling the application up. On to disk. +With that said, how do we identify what is the relevant period for the endurance test -- or in the running analogy, how do I know if 30 minutes is the right running length to get a real idea? Should it be 3 hours? Once again, our first guide might be the product requirements for what we're building (testing) might give an idea. If it's an e-commerce platform then the endurance test might be something like five days to cover the period of US Thanksgiving, Black Friday, the weekend, and then Cyber Monday. -Looking at disk might seem slightly redundant if memory is not the limiting factor. After all, if the data were in memory it would be unnecessary to go to disk in the first place. Still, sometimes we can take a look at the disk and see if that is our bottleneck. +Other ideas for choosing endurance targets might consider the maintenance windows for the platform. Suppose you have a scheduled maintenance window that takes place on Sundays from 02:00 -- 03:00 and there can be downtime during that period, according to the contracts (service level agreements) the company has signed. In that case, you want to validate that the system will work correctly and consistently long enough that it can be restarted (or updated) only during that maintenance window. -{\scriptsize -\begin{verbatim} -jz@Loki:~$ iostat -dx /dev/sda 5 -Linux 3.13.0-24-generic (Loki) 16-02-13 _x86_64_ (4 CPU) +\subsection*{How to Evaluate Success} +There are two kinds of answers that load testing can give you (and they're related). The first is "Yes or no, can the system handle a load of $X$?"; the second is "What is the maximum load $Y$ our current system can handle?". If we know $Y$ then we can easily answer whether $X$ is higher or lower. Between the two of them, this suggests we might prefer to find $Y$ rather than answer the first question. But it might be hard to find the maximum limit. The difficulty of generating test data or load might increase much faster than the rate of load added to the system, and we might be crossing over into stress testing. Sometimes answering the first question is all that is necessary. -Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await r_await w_await svctm %util -sda 0.24 2.78 0.45 2.40 11.60 154.98 116.91 0.17 61.07 11.57 70.27 4.70 1.34 -\end{verbatim} -} +The value of $Y$ may have some nuance above. The maximum rate we can handle may imply a hard stop -- if the load exceeds $Y$ then we crash, run out of memory, reject requests, or something else. It may also be a degradation of service: this is the point at which performance degrades below our target or minimum. -It's that last column, \%util that tells us what we want to know. The device bandwidth here is barely being used at all. If you saw it up at 100\% then you would know that the disk was being maxed out and that would be a pretty obvious indicator that it is the limiting factor. This does not tell you much about what is using the CPU, of course, and you can look at what processes are using the I/O subsystems with \texttt{iotop} which requires root privileges\footnote{https://xkcd.com/149/}. +Observability has come up previously and it might have been present to help decide what's important. We might also need to add some monitoring or logging that tracks when events start and end, so we can gather that data needed to make the overall evaluation. Examples that we are looking for are things like the following. Is the total work completed within the total time limit? Did individual items get completed within the item time limit 99\% of the time or more? -\paragraph{Network.} That leaves us with networks. We can ask about the network with \texttt{nload}: which gives the current, average, min, max, and total values. And you get a nice little graph if there is anything to see. It's not so much fun if nothing is happening. But you'll get the summary, at least: +As you might expect, the raw load test results are not always sufficient to make the call as to whether a test has passed or succeeded. Then there is post-processing to aggregate and analyze the data. Some manual work might also be necessary to correlate the data with other known factors, particularly if it had not been possible to test on separate hardware or separate instances~\cite{hitchhiking}. At the end of this process, hopefully you can look at the outcomes and conclude whether a given scenario is passed or failed. -\begin{verbatim} -Curr: 3.32 kBit/s -Avg: 2.95 kBit/s -Min: 1.02 kBit/s -Max: 12.60 kBit/s -Ttl: 39.76 GByte \end{verbatim} +Finally, success in an endurance test is not just answering whether the system was able handle a load of $X$, but is about whether that answer continued to be yes, consistently, across a long period of time. In an endurance test especially, looking at the trend may be more instructive as to how well things are working. -\paragraph{Locks.} -The last possibility we'll consider is that your code is slow because we're waiting for locks, either frequently or for lengthy periods of time. We've already discussed appropriate use of locks, so we won't repeat that. The discussion here is about how to tell if there is a locking problem in the first place. -We'll exclude the discussion of detecting deadlock, because we'll say that deadlock is a correctness problem more than a performance problem. In any case, a previous course (ECE 252, SE 350, MTE 241) very likely covered the idea of deadlock and how to avoid it. The Helgrind tool (Valgrind suite) is a good way to identify things like lock ordering problems that cause a deadlock. Onwards then. +\subsection*{So You Failed the Load Test...} +Like a software program, when it comes to running, I [JZ] can get better -- I just need to make some changes. To get better at endurance running, chances are I mostly just need to increase (slowly) the running distances and practice more and I'll get better. Or as the internet might say, get good. -Unexpectedly low CPU usage, that's not explained by I/O-waits, may be a good indicator of lock contention. If that's the case, when CPU usage is low we would see many threads are blocked. +The idea works for the programs too -- if the load test has failed, we'll have one or more specific scenarios to improve and we can apply techniques from this course (or elsewhere) to make that part better. Then re-run the test and re-evaluate. If all is well, done; otherwise, repeat as necessary until we've passed the scenario. -Unlike some of the other things we've discussed, there's no magical \texttt{locktrace} tool that would tell us about critical section locks, and the POSIX pthread library does not have any locks tracing in its specification~\cite{usd}. One possibility is to introduce some logging or tracing ourselves, e.g., recording in the log that we want to enter a critical section $A$ and then another entry once we're in it and a third entry when we leave $A$. That's not great, but it is something! +And also like a software program, there is a point at which I can make no more improvements. At the time of writing, Google says that the world record for marathon running is 2:01:09, held by Eliud Kipchoge, or an average speed of 21.02 km/h. There is absolutely no chance I can get to that level, no matter how hard I train. So I am not getting selected for the Canadian Olympic team. -I did some reading about \texttt{perf lock} but the problem is, as above, that it doesn't really find user-space lock contention. You can ask tools to tell you about thread switches but that's not quite the same. Other commercial tools like the Intel VTune claim that they can find these sorts of problems. But those cost money and may be CPU-vendor-specific. +If we've reached the limits of what we can do in the software, it might not be the right tool for your needs and a major redesign or replacement is needed. Designing the system with higher load in mind is sometimes possible, though even in situations where it's possible the cost might be prohibitive. Are we stuck? +No! You can have different expectations. A four-hour marathon seems achievable for me if I worked on it. If it's unrealistic to bill all your customers on the same day, why not convince the company to let billing be spread across the whole month? Think about the problem outside the constraints that are currently given. -\subsection*{But it's probably CPU...} -Most profiling tools, and most of our discussion, will be about CPU profiling. It's the most likely problem we'll face and something that we are hopefully able to do something about. +\subsection*{Constant Vigilance} +You may have heard the saying ``constant vigilance'' around the topic of defending against the dark arts. Load testing at a given moment in time captures the state of things at that time only. The load testing procedure needs to be repeated regularly to catch performance degradations that would make your software fail to meet its targets or design load. These are rarely intentional, but the tendency of software is to grow in complexity and in functionality, both of which are likely to make it slower. Improved hardware does, over time, offset some of the slowdown of added complexity. However, at the time of writing, this is an era of small, incremental improvements year-to-year, not big leaps and bounds forward \footnote{Considering the previous footnote, it would be thoroughly foolish to now fall into the trap of saying ``the current situation will go on forever'' and say that there will never be revolutionary change in execution hardware that offsets the increases in complexity. But that's not where we are right now.}. So, for now, it is still a priority to repeat the load tests often enough to catch major regressions before they become a problem. \input{bibliography.tex} diff --git a/lectures/L26.tex b/lectures/L26.tex index e1aca2dc..b3ddb099 100644 --- a/lectures/L26.tex +++ b/lectures/L26.tex @@ -2,67 +2,126 @@ \begin{document} -\lecture{26 --- Load Testing}{\term}{Jeff Zarnett} +\lecture{26 --- Finding Bottleneck Devices}{\term}{Jeff Zarnett} -\section*{Load Testing} +\section*{Characterizing Performance \& Scalability Problems} -So far, we have discussed scalability at a high level. We talked about some principles (hardware, reality, and volume), and how we might narrow down which of the components is a bottleneck for the execution of your code. That's a good start, but we are going to take some time to examine more closely the idea of how to design and implement a proper load test. +\begin{quote} +\textit{It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.} +\end{quote} +\hfill - Sherlock Holmes (\textit{A Scandal in Bohemia}; Sir Arthur Conan Doyle) -\paragraph{Start with why.} The most important question is: why are we doing this? A few possible answers~\cite{hitchhiking}: -\begin{itemize} - \item A new system is being developed, and management wants to understand the limits and the risks. - \item The workload is expected to increase, and we (the CFO) want(s) to be sure the system can handle it. - \item A sudden spike in workload is expected, like tax season, and we want to know we can handle it. - \item The system has high uptime requirements, like 99.99\%, and must work over an extended period of time. - \item The project plan has a checkbox ``performance testing'', even though nobody has a real idea of what it means. -\end{itemize} +Keeping the wisdom of Mr. Holmes in mind, we need to collect evidence before reaching conclusions. At a high level we probably have five potential culprits to start with: +\begin{enumerate} + \item CPU + \item Memory + \item Disk + \item Network + \item Locks +\end{enumerate} -Leaving aside the last ``reason'', each of the actual reasons why implies a different kind of testing is required. If it's a new system, maybe we just need to understand the average workload (plus, perhaps, a buffer for safety) and make a plan for that. If the workload is expected to increase, like having ten times the number of users, we just need to establish our bottlenecks as in the previous topic to identify what we think the limit is. If it's the spike situation, we need to find a way to generate that spike behaviour to see how the system responds, and the hardest part might actually be in how to create the test. If our reason is uptime requirements, then in addition to putting a lot of load on the system, we have to maintain it for an extended period to make sure there's no performance degradation -- a test of endurance. +These are, obviously, more categories than specific causes, but they are starting points for further investigation. They are listed in some numerical order, but there is no reason why one would have to investigate them in the order defined there. -\subsection*{Plans Are Useless, Planning Is Essential} The previously-cited book suggests making a plan that answers the questions of \textit{who, what, where, when, \& why}. We just covered the ``why'' question, and understood how it points us towards the answer of ``what'' -- what kind of load testing are we intending to do here. That leaves ``who'' and ``when''. In a company (or FYDP) situation, there might be reason for debate or discussion around who should do the tests and when they should take place. For our purposes the answers are: we are going to do them, and we're going to do them now. +\paragraph{CPU.} CPU is probably the easiest of these to diagnose. Something like \texttt{top} or Task Manager will tell you pretty quickly if the CPU is busy. You can look at the \%CPU columns and see where all your CPU is going. Still, that tells you about right now; what about the long term average? Checking with my machine ``Loki'', that used to donate its free CPU cycles to world community grid (I'm singlehandedly saving the world, you see.): -How detailed the plan needs to be is going to depend on your organization, and the same applies for how many sign-offs (if any!) you need on the plan. Some companies have a high need for justification of the time invested in this, after all, because time is money (developer salary) and there are opportunity costs (what work are we not doing in favour of this). There's lots of literature out there about how to justify technical investments to senior management and let's not get sidetracked. +\begin{verbatim} +top - 07:28:19 up 151 days, 23:38, 8 users, load average: 0.87, 0.92, 0.91 +\end{verbatim} -The load testing plan needs at least a definition of what will be tested, how it will be tested, and how we'll know if the test passes or fails. A simple scenario could look something like: we will test saving things to the database by generating 10~000 update-account requests, submit them all at once, and the the total time to complete all transactions should not exceed 15 seconds. That's all made up and probably even a little bit silly, but it meets the important criteria of explaining what to test, how to test it, and how to evaluate success. Let's expand on this. +Those last three numbers are the one, five, and fifteen minute averages of CPU load, respectively. Lower numbers mean less CPU usage and a less busy machine. A small guide on how to interpret this, from~\cite{scout}. -\paragraph{What workflows to test?} -While we might have a clear direction about the kind of test we want from why, the question still remains about what workflows are going to be tested. We cannot test everything -- aiming for 100\% coverage is unnecessary, but load testing should be reserved for only where it's truly needed because of its high cost and effort requirements. +Picture a single core of a CPU as a lane of traffic. You are a bridge operator and so you need to monitor how many cars are waiting to cross that bridge. If no cars are waiting, traffic is good and drivers are happy. If there is a backup of cars, then there will be delays. Our numbering scheme corresponds to this: -If we already know which ones are slow (rate-limiting) or on the critical path, then we can certainly start there. Ideally, the observability/monitoring (you have it, right?) gives us the guidance needed here. If monitoring doesn't exist, that might need to get addressed first. +\begin{enumerate} + \item 0.00 means no traffic (and in fact anything between 0.00 and 0.99) means we're under capacity and there will be no delay. + \item 1.00 means we are exactly at capacity. Everything is okay, but if one more car shows up, there will be a delay. + \item Anything above 1.00 means there's a backup (delay). If we have 2.00 load, then the bridge is full and there's an equal number of cars waiting to get on the bridge. +\end{enumerate} -If it's a new (as-yet-unimplemented) system, which are the critical workflows for this application? Critical is determined by the nature of the product and what you want it to do. If something is computationally intensive, like compiling analytical data, then that workflow is important. In other scenarios, user experience determines what's critical for load testing: if we have information that says users quit the signup process if the signup page takes more than two seconds to load, we care about making sure the signup time doesn't exceed that limit even when the system is busy. There can also be external requirements that require load testing: if you are processing a transaction and you must return a yes or no response within 1 second, anything that is part of coming up with this answer is critical and worth testing. +Or, visually, also from~\cite{scout}: -If your current utilization is low, you might not know what the rate-limiting steps are at a glance. You can take a guess, but be prepared to revise those guesses partway through the process as you ramp up the load. Actually, you might need to do that even if utilization isn't low; you may find new things along the way that turn out to be the real limiting factors. +\begin{center} + \includegraphics[width=0.55\textwidth]{images/car-analogy.png} +\end{center} -In the event of the uptime requirements, the tests likely look the same as the increased-workload situation -- we just run them for longer. Endurance tests have significant overlap with load tests, but are not exactly the same. We'll come back to figuring out how long an endurance test should be shortly. +Being at or above 1.00 isn't necessarily bad, but you should be concerned if there is consistent load of 1.00 or above. And if you are below 1.00 but getting close to it, you know how much room you have to scale things up -- if load is 0.4 you can increase handily. If load is 0.9 you're pushing the limit already. If load is above 0.70 then it's probably time to investigate. If it's at 1.00 consistently we have a serious problem. If it's up to 5.00 then this is a red alert situation. -\paragraph{How to test them.} -Given a workflow and the kind of test that we want to do, then we just need to think about how to test it. Increasing the load and execution of the tests appears in this section for completeness, but we already learned the most important things about how to carry out the tests in the introduction to the scalability topic! Remember the hardware, reality, and volume principles. +Now this is for a single CPU -- if you have a load of 3.00 and a quad core CPU, this is okay. You have, in the traffic analogy, four lanes of traffic, of which 3 are being used to capacity. So we have a fourth lane free and it's as if we're at 75\% utilization on a single CPU. -Carrying out these tests might require provisioning additional (virtual) hardware, particularly if they are long-running. The typical test harness tools used for unit testing aren't always intended for this purpose, so you may need different tools, or just write a custom script to execute the tests. -\paragraph{How to evaluate success.} -There are two kinds of answers that load testing can give you (and they're related). The first is "Yes or no, can the system handle a load of $X$?"; the second is "What is the maximum load $Y$ our current system can handle?". If we know $Y$ then we can easily answer whether $X$ is higher or lower. Between the two of them, this suggests we might prefer to find $Y$ rather than answer the first question. But it might be hard to find the maximum limit. The difficulty of generating test data or load might increase much faster than the rate of load added to the system. Sometimes answering the first question is all that is necessary. +\paragraph{Memory and Disk.} Next on the list is memory. One way to tell if memory is the limiting factor is actually to look at disk utilization. If there is not enough RAM in the box, there will be swapping and then performance goes out the window and scalability goes with. That is of course, the worst case. You can ask via \texttt{top} about how much swap is being used, but that's probably not the interesting value. -The value of $Y$ may have some nuance above. The maximum rate we can handle may imply a hard stop -- if the load exceeds $Y$ then we crash, run out of memory, reject requests, or something else. It may also be a degradation of service: this is the point at which performance degrades below our target or minimum. +\begin{verbatim} +KiB Mem: 8167736 total, 6754408 used, 1413328 free, 172256 buffers +KiB Swap: 8378364 total, 1313972 used, 7064392 free. 2084336 cached Mem +\end{verbatim} -Observability has come up previously and it might have been present to help decide what's important. We might also need to add some monitoring or logging that tracks when events start and end, so we can gather that data needed to make the overall evaluation. Examples that we are looking for are things like the following. Is the total work completed within the total time limit? Did individual items get completed within the item time limit 99\% of the time or more? +This can be misleading though, because memory being ``full'' does not necessarily mean anything bad. It means the resource is being used to its maximum potential, yes, but there is no benefit to keeping a block of memory open for no reason. Things will move into and out of memory as they need to, and nobody hands out medals to indicate that you did an awesome job of keeping free memory. It's not like going under budget in your department for the year. Also, memory is not like the CPU; if there's nothing for the CPU to do, it will just idle (or go to a low power state, which is nice for saving the planet). But memory won't ``forget'' data if it doesn't happen to be needed right now - data will hang around in memory until there is a reason to move or change it. So freaking out about memory appearing as full is kind of like getting all in a knot about how ``System Idle Process'' is hammering the CPU\footnote{Yes, a tech journalist named John Dvorak really wrote an article about this, and I will never, ever forgive him for it.}. -Finally, success in an endurance test is not just answering whether the system can handle a load of $X$, but is about whether that answer continues to be yes, consistently, across a long period of time. Let's get into that. +You can also ask about page faults, with the command \texttt{ps -eo min\_flt,maj\_flt,cmd} which will give you the major page faults (had to fetch from disk) and minor page faults (had to copy a page from another process). The output of this is too big even for the notes, but try it yourself (or I might be able to do a demo of it in class). But this is lifetime and you could have a trillion page faults at the beginning of your program and then after that everything is fine. What you really want is to ask Linux for a report on swapping: -\paragraph{Endurance Tests: How Long?} -Chances are you're familiar with the idea of endurance being different from peak workload. Can I [JZ] run at 10 km/h for 1 minute? Sure -- I can run much faster than that for 1 minute! Can I run 30 minutes (5 km) at 10 km/h? Yes. 60 minutes (10 km)? Yes, but with difficulty. Four hours (40 km)? No, I'll get tired and slow down, stop, or maybe hurt myself. Okay, so we've found a limit here -- I can endure an hour at this workload but things break down after that. If we only studied my running for a time period that was less than half an hour, we might conclude that I could run at 10 km/h forever (or at least indefinitely), even though that's absolutely not true\footnote{That's a common fallacy in the media, too -- you may notice, if you're looking for it, a plethora of journalists writing opinion pieces that say ``the current situation is the new normal and will go on forever'' -- even though that's almost certainly not true. Things change all the time -- political parties that win an election are often voted out again after a few years; rough job markets improve and good ones get tighter; high interest rates come down or low rates increase, etc.}. +\begin{verbatim} +jz@Loki:~$ vmstat 5 +procs -----------memory---------- ---swap-- -----io---- -system-- ------cpu----- + r b swpd free buff cache si so bi bo in cs us sy id wa st + 1 0 1313972 1414600 172232 2084296 0 0 3 39 1 1 27 1 72 0 0 + 0 0 1313972 1414476 172232 2084296 0 0 0 21 359 735 19 0 80 0 0 + 0 0 1313972 1414656 172236 2084228 0 0 0 102 388 758 22 0 78 0 0 + 4 0 1313972 1414592 172240 2084292 0 0 0 16 501 847 33 0 67 0 0 + 0 0 1313972 1412028 172240 2084296 0 0 0 0 459 814 29 0 71 0 0 +\end{verbatim} -Is this analogy suitable for software, though? CPUs don't get ``tired'' nor do their parts accumulate fatigue at anywhere near the same rate as a runner's muscles. Yes! A process that has a data hoarding problem is slowly accumulating memory usage and when its memory space is exhausted, we might encounter a hard stop (e.g., encountering the error \texttt{java.lang.OutOfMemoryError: Java heap space}) or just a degradation of performance based on the increasing amount of swapping memory to disk that is required. Same for filling up disk, file handles, log length, accumulated errors, whatever it is. That's a little bit like fatigue. +In particular, the columns ``si'' (swap in) and ``so'' (swap out) are the ones to pay attention to. In the above example, they are all zero. That is excellent and tends to indicate that we are not swapping to disk and that's not the performance limiting factor. Sometimes we don't get that situation. A little bit of swapping may be inevitable, but if we have lots of swapping, we have a very big problem. Here's a not-so-nice example, from~\cite{vmstat}: -Accumulated fatigue for an application is not just a hypothetical, either. I [JZ] have personally seen services that encountered a problem due to running out of some internal resources as a result of a code freeze over the holiday season. That wasn't a load test though, just an endurance test, even if not a planned one. The solution just involved restarting the instances one-by-one and things got back on track. This example calls back what I said about how endurance tests are not exactly the same as load tests, in that we can have an endurance test with low load. +\begin{verbatim} + procs memory swap io system cpu +r b w swpd free buff cache si so bi bo in cs us sy id +. . . +1 0 0 13344 1444 1308 19692 0 168 129 42 1505 713 20 11 69 +1 0 0 13856 1640 1308 18524 64 516 379 129 4341 646 24 34 42 +3 0 0 13856 1084 1308 18316 56 64 14 0 320 1022 84 9 8 +\end{verbatim} -With that said, how do we identify what is the relevant period for the endurance test -- or in the running analogy, how do I know if 30 minutes is the right running length to get a real idea? 3 hours? Once again, our first guide might be the product requirements for what we're building (testing) might give an idea. If it's an e-commerce platform then the endurance test might be something like five days to cover the period of US Thanksgiving, Black Friday, the weekend, and then Cyber Monday. +If we're not doing significant swapping, then memory isn't holding us back, so we can conclude it is not the limiting factor in scaling the application up. On to disk. -\paragraph{So you failed the load test...} -Like a software program, though, with some changes, I can get better. To get better at endurance running, chances are I mostly just need to increase (slowly) the running distances and practice more and I'll get better. That's probably true for the program -- if the load test has failed, we'll have a specific scenario to improve and we can apply techniques from this course (or elsewhere) to make that part better, re-run the test, and repeat as necessary until we've passed the scenario. +Looking at disk might seem slightly redundant if memory is not the limiting factor. After all, if the data were in memory it would be unnecessary to go to disk in the first place. Still, sometimes we can take a look at the disk and see if that is our bottleneck. + +{\scriptsize +\begin{verbatim} +jz@Loki:~$ iostat -dx /dev/sda 5 +Linux 3.13.0-24-generic (Loki) 16-02-13 _x86_64_ (4 CPU) + +Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await r_await w_await svctm %util +sda 0.24 2.78 0.45 2.40 11.60 154.98 116.91 0.17 61.07 11.57 70.27 4.70 1.34 +\end{verbatim} +} + +It's that last column, \%util that tells us what we want to know. The device bandwidth here is barely being used at all. If you saw it up at 100\% then you would know that the disk was being maxed out and that would be a pretty obvious indicator that it is the limiting factor. This does not tell you much about what is using the CPU, of course, and you can look at what processes are using the I/O subsystems with \texttt{iotop} which requires root privileges\footnote{https://xkcd.com/149/}. + +\paragraph{Network.} That leaves us with networks. We can ask about the network with \texttt{nload}: which gives the current, average, min, max, and total values. And you get a nice little graph if there is anything to see. It's not so much fun if nothing is happening. But you'll get the summary, at least: + +\begin{verbatim} +Curr: 3.32 kBit/s +Avg: 2.95 kBit/s +Min: 1.02 kBit/s +Max: 12.60 kBit/s +Ttl: 39.76 GByte \end{verbatim} + +\paragraph{Locks.} +The last possibility we'll consider is that your code is slow because we're waiting for locks, either frequently or for lengthy periods of time. We've already discussed appropriate use of locks, so we won't repeat that. The discussion here is about how to tell if there is a locking problem in the first place. + +We'll exclude the discussion of detecting deadlock, because we'll say that deadlock is a correctness problem more than a performance problem. In any case, a previous course (ECE 252, SE 350, MTE 241) very likely covered the idea of deadlock and how to avoid it. The Helgrind tool (Valgrind suite) is a good way to identify things like lock ordering problems that cause a deadlock. Onwards then. + +Unexpectedly low CPU usage, that's not explained by I/O-waits, may be a good indicator of lock contention. If that's the case, when CPU usage is low we would see many threads are blocked. + +Unlike some of the other things we've discussed, there's no magical \texttt{locktrace} tool that would tell us about critical section locks, and the POSIX pthread library does not have any locks tracing in its specification~\cite{usd}. One possibility is to introduce some logging or tracing ourselves, e.g., recording in the log that we want to enter a critical section $A$ and then another entry once we're in it and a third entry when we leave $A$. That's not great, but it is something! + +I did some reading about \texttt{perf lock} but the problem is, as above, that it doesn't really find user-space lock contention. You can ask tools to tell you about thread switches but that's not quite the same. Other commercial tools like the Intel VTune claim that they can find these sorts of problems. But those cost money and may be CPU-vendor-specific. + + +\subsection*{But it's probably CPU...} +Most profiling tools, and most of our discussion, will be about CPU profiling. It's the most likely problem we'll face and something that we are hopefully able to do something about. -And also like a software program, there is a point at which I can make no more improvements. At the time of writing, Google says that the world record for marathon running is 2:01:09, held by Eliud Kipchoge, or an average speed of 21.02 km/h. There is absolutely no chance I can get to that level, no matter how hard I train. If that's the case for your software, it might not be the right software for your needs and a major redesign or replacement is needed. Or you can have different expectations. A four-hour marathon seems achievable for me if I worked on it. 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