We are running a web API hosted in IIS 10 on an 8 core machine with 16 GB Memory and running Windows 10, and throwing a load of say 100 to 200 requests per second through JMeter on the server.
Individual transactions are taking less than 500 milliseconds. When we throw the load initially, IIS threads grow up to around 150-160 mark (monitored through resource monitor and Performance monitor) and throughput increases up to 22-24 transactions per second but throughput and number of threads stop to grow beyond this point even though the CPU usage is less than 40 per cent and we have enough physical memory also available at the peak, the resource monitor does not show any choking at the network or IO level.
The web API is making calls to the Oracle database (3-4 select calls and 2-3 inserts/updates).
We fail to understand what is stopping IIS to further grow its thread pool to process more requests in parallel while all the resources including processing power, memory, network etc are available.
We have placed many performance counters as well, there is no queue build-up (that's probably because jmeter works in synchronous mode)
Also, we have tried to set the min and max threads settings through machine.config as well as ThreadPool.SetMin and Max threads APIs but no difference was observed and seems like those setting are not taking any effect.
Important to mention that we are using synchronous calls/operations (no asnch and await). Someone has advised to convert all our blocking IO calls e.g. database calls to asynchronous mode to achieve more throughput but my understanding is that if threads cant be grown beyond this level then making async calls might not help or may indeed negatively impact the throughput. Since our code size is huge, that would be a very costly activity in terms of time and effort and we dont want to invest in it till we are sure that it would really help. If someone has anything to share on these two problems, pls do share.
Below is a screenshot of the permanence monitor.
I am doing performance testing on an app. I found when the number of virtual users increases, the response time increases linearly(should be natural, right?), but the CPU utilization stops increasing when reaches around 60%. Does it mean the CPU is the bottleneck? If not, what could be the bottleneck?
The bottleneck might or might not be CPU, you need to consider monitoring other OS metrics as well, to wit:
Physical RAM
Swap usage
Network IO
Disk IO
Each of them could be the bottleneck.
Also when you increase number of users ideal system should increase the number of TPS (transactions per second) by the same factor. When you increase virtual users and TPS is not getting increased the situation is called saturation point and you need to find out what is slowing your system down.
If resources utilization is far from 95-100% and your system provides large response times the reason can be non-optimal code of your application or slow database query or something like that, in this case you will need to use profiling tools to get to the bottom of the issue.
See How to Monitor Your Server Health & Performance During a JMeter Load Test article for more information on the application under test monitoring concept
I have a web site and I am using iis as my web server. I noticed that on production server, the cpu reaches 95% usage pretty fast with very little users. this behaviour I don't see on my developement server. I am using visual studio to develop and iis as my local web server as well.
How much big traffic you have on production comparing to development server? How their parameters compare? Before starting a deep analysis of the application itself, I would identify all the infrastructure and environmental differences. Sometime such problems happens because of some other software, like antivirus software running in the background...
Nevertheless, because it sounds rather as a application problem, I would first check Event Viewer for errors. Then I would start from monitoring a few Performance Counters to correlate % Processor Time counter with Current Connections, Available Memory, # of Exceps Thrown / sec, % Time in GC and so on. This kind of behavior usually has a reason from the list:
excessive loops usage due to some logic error, like calling the same service again and again, trying to load or parse malfunctioned file etc. This can be analyzed with dump analysis (look below).
high CPU usage due to Garbage Collector - when memory usage is extensive (or there is a memory leak even) GC may start to consume more and more CPU fighting with the memory shortage. You will see this with memory-related performance counters.
a considerable amount of exceptions thrown (for example due to some environmental problems like network unavailability, production data difference) can also consume a lot of CPU. Event Viewer and exception-related performance counters (as they can be handled silently by your application) should be a indicator here.
To further analyze your application, I suggest to make a full memory dump during high CPU usage. You can do that with Debug Diag tool. Please refer this IIS troubleshooting guide for details.
I am running a Finatra server (https://github.com/capotej/finatra) which a sinatra inspired web framework for scala built on top of Finagle (an asynchronous RPC system).
The application should be design to receive something between 10 and 50 requests concurrently. Each request is quite CPU intensive, mostly due to parsing and serializing large JSON's and operation on arrays, like sorting, grouping, etc...
Now I am wondering what is the impact of the following parameters on performance and how to combine them :
RAM of the server
Number of cores of the server
JVM heap size
Number of threads run on parallel in my Future Pool
As a partial response, I would say :
JVM heapsize should me tuned depending on RAM
Having multiple cores improves performance under concurrent workload but does not really speed up processing of a single request.
Having large RAM, on the contrary, can notably speed up execution of a single request
Number of threads in my Future Pool must be tuned according to my number of cores.
EDIT
I want to compare performance regardless of the the code, only focusing on hardware/threading model. Let's assume the code is already optimized. Additional information :
I am building a data reporting API. Processing time of a request largely depends of the dataset I am manipulating. For big datasets, it can hit 10 seconds max.
I retrieve most of the data from third party API but I am also accessing a MySQL database with a c3po connection pooling mechanism. Execution of the request is additionally delegated to a Future Pool to prevent blocking.
No disk IO excluding MySQL
I don't want to cache anything on the server side because I need to
work with fresh data.
Thanks !!!
The performance and overall behaviour will still depend on your own code, outside of the framework you are using. In other words, you have correctly listed the major factors which will influence performance, but your own code will have such a significant impact on it that it's almost impossible to tell in advance.
Offhand, I'd say that you need to characterize some things about your application in more detail:
You say that each request will be CPU intensive, but what do you mean by it? Will each request take 1 ms? 10 ms? 100 ms?
Do you access a database? What are the characteristics of your database?
Either with the database or without it, do you have any disk IO? How significant is it?
... but if your application is really simple, does not hit the disk much (or at all.. your request may be read-only and everything gets cached), and you are CPU-bound, simply sticking enough CPU cores in your server will be the most significant thing you can do.
How does one determine the best number of maxSpare, minSpare and maxThreads, acceptCount etc in Tomcat? Are there existing best practices?
I do understand this needs to be based on hardware (e.g. per core) and can only be a basis for further performance testing and optimization on specific hardware.
the "how many threads problem" is quite a big and complicated issue, and cannot be answered with a simple rule of thumb.
Considering how many cores you have is useful for multi threaded applications that tend to consume a lot of CPU, like number crunching and the like. This is rarely the case for a web-app, which is usually hogged not by CPU but by other factors.
One common limitation is lag between you and other external systems, most notably your DB. Each time a request arrive, it will probably query the database a number of times, which means streaming some bytes over a JDBC connection, then waiting for those bytes to arrive to the database (even is it's on localhost there is still a small lag), then waiting for the DB to consider our request, then wait for the database to process it (the database itself will be waiting for the disk to seek to a certain region) etc...
During all this time, the thread is idle, so another thread could easily use that CPU resources to do something useful. It's quite common to see 40% to 80% of time spent in waiting on DB response.
The same happens also on the other side of the connection. While a thread of yours is writing its output to the browser, the speed of the CLIENT connection may keep your thread idle waiting for the browser to ack that a certain packet has been received. (This was quite an issue some years ago, recent kernels and JVMs use larger buffers to prevent your threads for idling that way, however a reverse proxy in front of you web application server, even simply an httpd, can be really useful to avoid people with bad internet connection to act as DDOS attacks :) )
Considering these factors, the number of threads should be usually much more than the cores you have. Even on a simple dual or quad core server, you should configure a few dozens threads at least.
So, what is limiting the number of threads you can configure?
First of all, each thread (used to) consume a lot of resources. Each thread have a stack, which consumes RAM. Moreover, each Thread will actually allocate stuff on the heap to do its work, consuming again RAM, and the act of switching between threads (context switching) is quite heavy for the JVM/OS kernel.
This makes it hard to run a server with thousands of threads "smoothly".
Given this picture, there are a number of techniques (mostly: try, fail, tune, try again) to determine more or less how many threads you app will need:
1) Try to understand where your threads spend time. There are a number of good tools, but even jvisualvm profiler can be a great tool, or a tracing aspect that produces summary timing stats. The more time they spend waiting for something external, the more you can spawn more threads to use CPU during idle times.
2) Determine your RAM usage. Given that the JVM will use a certain amount of memory (most notably the permgen space, usually up to a hundred megabytes, again jvisualvm will tell) independently of how many threads you use, try running with one thread and then with ten and then with one hundred, while stressing the app with jmeter or whatever, and see how heap usage will grow. That can pose a hard limit.
3) Try to determine a target. Each user request needs a thread to be handled. If your average response time is 200ms per "get" (it would be better not to consider loading of images, CSS and other static resources), then each thread is able to serve 4/5 pages per second. If each user is expected to "click" each 3/4 seconds (depends, is it a browser game or a site with a lot of long texts?), then one thread will "serve 20 concurrent users", whatever it means. If in the peak hour you have 500 single users hitting your site in 1 minute, then you need enough threads to handle that.
4) Crash test the high limit. Use jmeter, configure a server with a lot of threads on a spare virtual machine, and see how response time will get worse when you go over a certain limit. More than hardware, the thread implementation of the underlying OS is important here, but no matter what it will hit a point where the CPU spend more time trying to figure out which thread to run than actually running it, and that numer is not so incredibly high.
5) Consider how threads will impact other components. Each thread will probably use one (or maybe more than one) connection to the database, is the database able to handle 50/100/500 concurrent connections? Even if you are using a sharded cluster of nosql servers, does the server farm offer enough bandwidth between those machines? What else will run on the same machine with the web-app server? Anache httpd? squid? the database itself? a local caching proxy to the database like mongos or memcached?
I've seen systems in production with only 4 threads + 4 spare threads, cause the work done by that server was merely to resize images, so it was nearly 100% CPU intensive, and others configured on more or less the same hardware with a couple of hundreds threads, cause the webapp was doing a lot of SOAP calls to external systems and spending most of its time waiting for answers.
Oce you've determined the approx. minimum and maximum threads optimal for you webapp, then I usually configure it this way :
1) Based on the constraints on RAM, other external resources and experiments on context switching, there is an absolute maximum which must not be reached. So, use maxThreads to limit it to about half or 3/4 of that number.
2) If the application is reasonably fast (for example, it exposes REST web services that usually send a response is a few milliseconds), then you can configure a large acceptCount, up to the same number of maxThreads. If you have a load balancer in front of your web application server, set a small acceptCount, it's better for the load balancer to see unaccepted requests and switch to another server than putting users on hold on an already busy one.
3) Since starting a thread is (still) considered a heavy operation, use minSpareThreads to have a few threads ready when peak hours arrive. This again depends on the kind of load you are expecting. It's even reasonable to have minSpareThreads, maxSpareThreads and maxThreads setup so that an exact number of threads is always ready, never reclaimed, and performances are predictable. If you are running tomcat on a dedicated machine, you can raise minSpareThreads and maxSpareThreads without any danger of hogging other processes, otherwise tune them down cause threads are resources shared with the rest of the processes running on most OS.