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I have 5 Scenarios in total, and 70 Users segregated to different Scenarios which runs for around 15 Minutes only with 1 Loop configuration.
Is it ideal test duration to evaluate the realistic Performance results?
Or do I need to adjust with the test duration?
Any suggestion on this is highly appreciated.
Thanks
It depends on what you're trying to achieve. 70 concurrent users doesn't look like a real "load" to me, moreover given you have only one loop you may run into the situation when some users have already finished executing their scenarios and were shut down and some are still running or even have not yet been started. So I would recommend monitoring the real concurrency using i.e. Active Threads Over Time listener to see how many users were online at the given stage of the test.
Normally the following testing types are conducted:
Load testing - putting the system under anticipated load and ensuring that main metrics (i.e. response time and throughput) are matching NFRs or SLAs
Soak testing - basically the same as load testing, but it assumes prolonged duration (several hours, overnight or over the weekend). This testing type allows to discover obvious and non-obvious memory leaks
Stress testing - starting with anticipated number of users and gradually increasing the load until response time starts exceeding acceptable threshold or errors start occurring (whatever comes the first). If will shed some light on the slowest or most fragile component, to wit the first performance bottleneck
Check out Why ‘Normal’ Load Testing Isn’t Enough article for more information on the aforementioned performance testing types.
No matter which test you're conducting consider increasing (and decreasing) the load gradually, i.e. come up with proper ramp-up (and ramp-down) strategies, this way you will be able to correlate increasing load with i.e. increasing response time
Performance Tests in java is a bit tricky, it can vary wildly depending on what other programs are running on the system and what its load is.
In an ideal world, you need to use a dedicated system, if you can't make sure to quit all programs you're running (including the IDE), The Java HotSpot compiler kicks in when it sees a ‘hot spot’ in your code. It is therefore quite common that your code will run faster over time! So, you should adapt and repeat your testing methods, investigate memory and CPU usage.
or even better you can use a profiler. There are plenty around, both free profilers and demos / time-locked trials of commercials strength ones.
I know the title of my question is rather vague, so I'll try to clarify as much as I can. Please feel free to moderate this question to make it more useful for the community.
Given a standard LAMP stack with more or less default settings (a bit of tuning is allowed, client-side and server-side caching turned on), running on modern hardware (16Gb RAM, 8-core CPU, unlimited disk space, etc), deploying a reasonably complicated CMS service (a Drupal or Wordpress project for arguments sake) - what amounts of traffic, SQL queries, user requests can I resonably expect to accommodate before I have to start thinking about performance?
NOTE: I know that specifics will greatly depend on the details of the project, i.e. optimizing MySQL queries, indexing stuff, minimizing filesystem hits - assuming web developers did a professional job - I'm really looking for a very rough figure in terms of visits per day, traffic during peak visiting times, how many records before (transactional) MySQL fumbles, so on.
I know the only way to really answer my question is to run load testing on a real project, and I'm concerned that my question may be treated as partly off-top.
I would like to get a set of figures from people with first-hand experience, e.g. "we ran such and such set-up and it handled at least this much load [problems started surfacing after such and such]". I'm also greatly interested in any condenced (I'm short on time atm) reading I can do to get a better understanding of the matter.
P.S. I'm meeting a client tomorrow to talk about his project, and I want to be prepared to reason about performance if his project turns out to be akin FourSquare.
Very tricky to answer without specifics as you have noted. If I was tasked with what you have to do, I would take each component in turn ( network interface, CPU/memory, physical IO load, SMP locking etc) and get the maximum capacity available, divide by rough estimate of use per request.
For example, network io. You might have 1x 1Gb card, which might achieve maybe 100Mbytes/sec. ( I tend to use 80% of theoretical max). How big will a typical 'hit' be? Perhaps 3kbytes average, for HTML, images etc. that means you can achieve 33k requests per second before you bottleneck at the physical level. These numbers are absolute maximums, depending on tools and skills you might not get anywhere near them, but nobody can exceed these maximums.
Repeat the above for every component, perhaps varying your numbers a little, and you will build a quick picture of what is likely to be a concern. Then, consider how you can quickly get more capacity in each component, can you just chuck $$ and gain more performance (eg use SSD drives instead of HD)? Or will you hit a limit that cannot be moved without rearchitecting? Also take into account what resources you have available, do you have lots of skilled programmer time, DBAs, or wads of cash? If you have lots of a resource, you can tend to reduce those constraints easier and quicker as you move along the experience curve.
Do not forget external components too, firewalls may have limits that are lower than expected for sustained traffic.
Sorry I cannot give you real numbers, our workloads are using custom servers, high memory caching and other tricks, and not using all the products you list. However, I would concentrate most on IO/SQL queries and possibly network IO, as these tend to be more hard limits, than CPU/memory, although I'm sure others will have a different opinion.
Obviously, the question is such that does not have a "proper" answer, but I'd like to close it and give some feedback. The client meeting has taken place, performance was indeed a biggie, their hosting platform turned out to be on the Amazon cloud :)
From research I've done independently:
Memcache is a must;
MySQL (or whatever persistent storage instance you're running) is usually the first to go. Solutions include running multiple virtual instances and replicate data between them, distributing the load;
http://highscalability.com/ is a good read :)
Any idea how to do performance and scalability testing if no clear performance requirements have been defined?
More information about my application.
The application has 3 components. One component can only run on Linux, the other two components are Java programs so they can run on Linux/Windows/Mac... The 3 components can be deployed to one box or each component can be deployed to one box. Deployment is very flexible. The Linux-only component will capture raw TCP/IP packages over the network, then one Java component will get those raw data from it and assemble them into the data end users will need and output them to hard disk as data files. The last Java component will upload data from data files to my database in batch.
In the absence of 'must be able to perform X iterations within Y seconds...' type requirements, how about these kinds of things:
Does it take twice as long for twice the size of dataset? (yes = good)
Does it take 10x as long for twice the size of dataset? (yes = bad)
Is it CPU bound?
Is it RAM bound (eg lots of swapping to virtual memory)?
Is it IO / Disk bound?
Is there a certain data-set size at which performance suddenly falls off a cliff?
Surprisingly this is how most perf and scalability tests start.
You can clearly do the testing without criteria, you just define the tests and measure the results. I think your question is more in the lines 'how can I establish test passing criteria without performance requirements'. Actually this is not at all uncommon. Many new projects have no clear criteria established. Informally it would be something like 'if it cannot do X per second we failed'. But once you passed X per second (and you better do!) is X the 'pass' criteria? Usually not, what happens is that you establish a new baseline and your performance tests guard against regression: you compare your current numbers with the best you got, and decide if the new build is 'acceptable' as build validation pass (usually orgs will settle here at something like 70-80% as acceptable, open perf bugs, and make sure that by ship time you get back to 90-95% or 100%+. So basically the performance test themselves become their own requirement.
Scalability is a bit more complicated, because there there is no limit. The scope of your test should be to find out where does the product break. Throw enough load at anything and eventually it will break. You need to know where that limit is and, very importantly, find out how does your product break. Does it give a nice error message and revert or does it spills its guts on the floor?
Define your own. Take the initiative and describe the performance goals yourself.
To answer any better, we'd have to know more about your project.
If there has been 'no performance requirement defined', then why are you even testing this?
If there is a performance requirement defined, but it is 'vague', can you indicate in what way it is vague, so that we can better help you?
Short of that, start from the 'vague' requirement, and pick a reasonable target that at least in your opinion meets or exceeds the vague requirement, then go back to the customer and get them to confirm that your clarification meets their requirements and ideally get formal sign-off on that.
Some definitions / assumptions:
Performance = how quickly the application responds to user input, e.g. web page load times
Scalability = how many peak concurrent users the applicaiton can handle.
Firstly perfomance. Performance testing can be quite simple, such as measuring and recording page load times in a development environment and using techniques like applicaiton profiling to identify and fix bottlenecks.
Load. To execute a load test there are four key factors, you will need to get all of these in place to be successfull.
1. Good usage models of how users will use your site and/or application. This can be easy of the application is already in use, but it can be extermely difficult if you are launching a something new, e.g. a Facebook application.
If you can't get targets as requirements, do some research and make some educated assumptions, document and circulate them for feedback.
2. Tools. You need to have performance testing scripts and tools that can excute the scenarios defined in step 1, with the number of expected users in step 1. (This can be quite expensive)
3. Environment. You will need a production like environment that is isolated so your tests can produce repoducible results. (This can also be very expensive.)
4. Technical experts. Once the applicaiton and environment starts breaking you will need to be able to identify the faults and re-configure the environment and or re-code the application once faults are found.
Generally most projects have a "performance testing" box that they need to tick because of some past failure, however they never plan or budget to do it properley. I normally recommend to do budget for and do scalability testing properley or save your money and don't do it at all. Trying to half do it on the cheap is a waste of time.
However any good developer should be able to do performance testing on their local machine and get some good benefits.
rely on tools (fxcop comes to mind)
rely on common sense
If you want to test performance and scalability with no requirements then you should create your own requirements / specs that can be done in the timeline / deadline given to you. After defining the said requirements, you should then tell your supervisor about it if he/she agrees.
To test scalability (assuming you're testing a program/website):
Create lots of users and data and check if your system and database can handle it. MyISAM table type in MySQL can get the job done.
To test performance:
Optimize codes, check it in a slow internet connection, etc.
Short answer: Don't do it!
In order to get a (better) definition write a performance test concept you can discuss with the experts that should define the requirements.
Make assumptions for everything you don't know and document these assumptions explicitly. Assumptions comprise everything that may be relevant to your system's behaviour under load. Correct assumptions will be approved by the experts, incorrect ones will provoke reactions.
For all of those who have read Tom DeMarcos latest book (Adrenaline Junkies ...): This is the strawman pattern. Most people who are not willing to write some specification from scratch will not hesitate to give feedback to your document. Because you need to guess several times when writing your version you need to prepare for being laughed at when being reviewed. But at least you will have better information.
The way I usually approach problems like this is just to get a real or simulated realistic workload and make the program go as fast as possible, within reason. Then if it can't handle the load I need to think about faster hardware, doing parts of the job in parallel, etc.
The performance tuning is in two parts.
Part 1 is the synchronous part, where I tune each "thread", under realistic workload, until it really has little room for improvement.
Part 2 is the asynchronous part, and it is hard work, but needs to be done. For each "thread" I extract a time-stamped log file of when each message sent, each message received, and when each received message is acted upon. I merge these logs into a common timeline of events. Then I go through all of it, or randomly selected parts, and trace the flow of messages between processes. I want to identify, for each message-sequence, what its purpose is (i.e. is it truly necessary), and are there delays between the time of receipt and time of processing, and if so, why.
I've found in this way I can "cut out the fat", and asynchronous processes can run very quickly.
Then if they don't meet requirements, whatever they are, it's not like the software can do any better. It will either take hardware or a fundamental redesign.
Although no clear performance and scalability goals are defined, we can use the high level description of the three components you mention to drive general performance/scalability goals.
Component 1: It seems like a network I/O bound component, so you can use any available network load simulators to generate various work load to saturate the link. Scalability can be measure by varying the workload (10MB, 100MB, 1000MB link ), and measuring the response time , or in a more precise way, the delay associated with receiving the raw data. You can also measure the working set of the links box to drive a realistic idea about your sever requirement ( how much extra memory needed to receive X more workload of packets, ..etc )
Component 2: This component has 2 parts, an I/O bound part ( receiving data from Component 1 ), and a CPU bound part ( assembling the packets ), you can look at the problem as a whole, make sure to saturate your link when you want to measure the CPU bound part, if is is a multi threaded component, you can look for ways to improve look if you don't get 100% CPU utilization, and you can measure time required to assembly X messages, from this you can calculate average wait time to process a message, this can be used later to drive the general performance characteristic of your system and provide and SLA for your users ( you are going to guarantee a response time within X millisecond for example ).
Component 3: Completely I/O bound, and depends on both your hard disk bandwidth, and the back-end database server you use, however you can measure how much do you saturate disk I/O to optimize throughput, how much I/O counts do you require to read X MB of data, and improve around these parameters.
Hope that helps.
Thanks
I am using Spring with Hibernate to create an Enterprise application.
Now, due to the abstractions given by the framework to the underlying J2EE
architecture, there is obviously going to be a runtime performance hit on my app.
What I need to know is a set of factors that I need to consider to make a decision about the minimum specs(Proc speed + RAM etc) that I need for a single host server of the application running RedHat Linux 3+ and devoted to running this application only, that would produce an efficiency score of say 8 out of 10 given a simultaneous-access-userbase increase of 100 per month.
No clustering is to be used.
No offense, but I'd bet that performance issues are more likely to be due to your application code than Spring.
If you look at the way they've written their source code, you'll see that they pay a great deal of attention to quality.
The only way to know is to profile your app, see where the time is being spent, analyze to determine root cause, correct it, rinse, repeat. That's science. Anything else is guessing.
I've used Spring in a production app that's run without a hitch for three years and counting. No memory leaks, no lost connections, no server bounces, no performance issues. It just runs like butter.
I seriously doubt that using Spring will significantly affect your performance.
What particular aspects of Spring are you expecting to cause performance issues?
There are so many variables here that the only answer is to "suck it and see", but, in a scientific manner.
You need to build a server than benchmark this. Start of with some "commodity" setup say 4 core cpu and 2 gig ram, then run a benchmark script to see if it meets your needs. (which most likely it will!).
If it doesnt you should be able to calculate the required server size from the nulbers you get out of the benchmark -- or -- fix the performance problem so it runs on hte hardware youve got.
The important thing is to identiffy what is limmiting your performance. Is you server using all the cores or are your processes stuck on a single core, is your JVM getting enough memory, are you IO bound or database bound.
Once you know the limiting factors its pretty easy to work out the solution -- either improve the efficiency of your programs or buy more of the right hardware.
Two thing to watch out for with J2EE -- most JVMs have default heap sizes from the last decade, make sure your JVM has enough Heap and Stack (at least 1G each!), -- it takes time for all the JIT compiling, object cacheing, module loading etc to settle down -- exercise your system for at least an hour before you start benchmarking.
As toolkit, I don't see Spring itself affecting the performance after initialization, but I think Hibernate will. How big this effect is, depends on a lot of details like the DB-Schema and how much relational layout differs from the OO layer and of course how DB-access is organized and how often DB-access happens etc. So I doubt, there is a rule of thumb to this. Just try out by developing significant prototypes using alternative applications servers or try a own small no-ORM-use-JDBC-version.
I've never heard that Spring creates any type of runtime performance hit. Since it uses mainly POJOs I'd be surprised if there was something wrong with it. Other than parsing a lot of XML on startup maybe, but that's solved by using annotations.
Just write your app first and then tune accordingly.
Spring is typically used to create long-lived objects shortly after the application starts. There is virtually no performance cost over the life of the process.
Which performance setback? In relation to what?
Did you measure the performance before using the framework?
If the Spring framework causes inacceptable performance issues the obvious solution is not to use it.
How credible are the benchmarks carried out within a virtual machine, as opposed to real hardware?
Let's dissect a specific situation. Assume we want to benchmark the performance impact of a recent code change. Assume for simplicity that the workload is fully CPU bound (though IO bound and mixed workloads are also of interest). Assume that the machine is running under VirtualBox because it's the best one ;)
Assume that we measured the original code and the new code, and the new code was 5% faster (when benchmarked in virtual machine). Can we safely claim that it will be at least 5% faster on real hardware too?
And even more important part, assume that the new code is 3% slower. Can we be completely sure that on real hardware it will be 3% or less slower, but definitely not worse than 3%?
UPDATE: what I'm most interested in is your battlefield results. Ie. can you witness a case when code that was 10% slower in VM performed 5% faster on real iron, or vice versa? Or was it always consistent (ie. if it's faster/slower in VM, it's always proportionally faster/slower on real machine)? Mine are more or less consistent so far; at the very least, always going in the same direction.
If you are comparing results on a VM to results not run on a VM, then no, the results are not credible.
On the other hand, if both tests were run in the same environment, they yes, the results are credible. Both tests will be slower inside a VM, but the difference should still be credible.
All things considered, using Fair Witness principals, all you can assert is how well the application performs in a VM, because that is what you are actually measuring.
Now, if you wish to try and extrapolate what you observe based on the environment, then, assuming you're running a native VM (vs an emulated one, PPC on x86 for example), a CPU bound task is a CPU bound task even in a VM because the CPU is doing most of the heavy lifting.
Arguably there may be some memory management issues involved that can distinguish between a VM and a native application, but once the memory is properly mapped, I can't think there would be dramatic differences in CPU bound run times between a VM and a native machine.
So, I think it is fair to intuit that performance change from one instance of the application to another when run on a VM would have a similar performance change, particularly with a CPU heavy application, when run on a native machine.
However, I don't think you can fairly say that "you know" unless you actually test it your self on the correct environment.
I don't think there is anything that special about a VM for this. Even on a 'real' machine, you are still running with virtual memory and sharing the CPU(s) with other processes, so similar considerations apply.
The ONLY way to get credible performance results between a testing and production environment is to run IDENTICAL hardware and software. Right down to hardware version and software patch levels.
Otherwise you are pretty much wasting your time.
As an example, some memory sticks perform better than others which could easily account for a 5% throughput difference on otherwise identical boxes.
With regards to software the VM software will ALWAYS have an impact; and certain operations may be impacted more than others depending on so many different factors that there is no possible way to compare them.