So I am wondering if someone can help me please, I am trying to load test a java rest application with thousands of requests per minute but something is a miss and I can't quite figure out what's happening.
I am executing the load test from my laptop (via terminal) and it's hitting a number of servers in AWS.
I am using the infinite loop function to fire in a lot of requests repeatedly. The 3 thread groups have the same config.
The problem I am having is the CPU is rising very high and the numbers do not match on what I have in production with the same enviornment with regards to CPU etc, the JMeter load test seems to be making the CPU work harder on my test enviorment.
Question 1 - Is my load test too fast for the server?
Question 2 - Is there a way to space out the load test so that I can say 10k rpm exactly?
Question 1 - Is my load test too fast for the server? - we don't know, if you see high CPU usage using 60 threads only and application responds slowly due to high CPU usage it looks like a bottleneck. Another question is the size of the machine, the number of processors and their frequency. So you need to find which function is consuming the CPU cycles using a profiler tool and look for the way to optimize the function
Question 2 - Is there a way to space out the load test so that I can say 10k rpm exactly? - it is, check out Constant Throughput Timer, but be aware of the next 2 facts:
Constant Throughput Timer can only pause the threads to limit JMeter's requests execution speed to the specified number of requests per minute. So you need to make sure to create sufficient number of threads in Thread Group(s) to produce the desired load, 60 might be not enough.
Application needs to be able to respond fast enough, i.e. 10000 requests per minute is approx 166 requests per second, with 60 threads it means that each thread needs to execute 2.7 requests per second which means that response time needs to be 370 ms or less
There are different aspects before we got for 10k requests.
Configure the tests for one user(/thread) and execute. Check for all
the request we are getting a proper response.
Incrementally increase the number of threads from 1 user, 5 users, 10 users, 20
users, 50 users etc.
Try for different duration scenarios like 10mins, 20 mins, 30 mins, 1 hour etc.
Collect metrics like error %, response time, number of request etc..
You can check probable breakpoints like:
CPU utilisaztion of machine getting high(100 %) from where you are executing the tests. in this case, you can setup few machines in master-slave configuration
error % getting high. Server may not be able to respond, so it might have crashed.
response time getting high. server may be getting busy due to the load.
Also, make sure, you have a reliable connectivity and bandwidth. Just imagine, you want to give a huge load, but the connection you have in few kbps. your tests will fail due to this.
I am trying to use JMeter to test an ActiveMQ cluster. As per requirements, I need to get at least 2k messages per second as a test. The issue is that I can't get to the required number of messages.
I am trying to test it against a local queue before going into the cluster, and the results are not good. In a PC (quite beefy) with Windows 10 installed, the best I can do is a few hundred messages per second. In a Mac (Macbook Pro) with OSX 10, I can pump it up to around 1.5k.
I have tried different configurations in JMeter: varying the number of threads, size of messages, Request&Response mode vs Request only... But nothing does the trick.
When I run custom code, I can push around 10k messages into the queue in a second. Are there any particular configurations that I might be missing? I have been through the tutorials online, but I can't find anything that fixes the issue.
JMeter default configuration is good for tests development and debugging, but when it comes to conducting the high load you need to remember several important points:
Don't use GUI for tests execution, you are supposed to be running tests using non-GUI mode
Default JVM Heap allocation is 512 Mb only, you will definitely need to raise this setting in JMeter startup script. Same applies to stack size and garbage collector settings. See JVM Tuning: Heapsize, Stacksize and Garbage Collection Fundamental article to learn more about JVM internals.
Don't use Listeners during the load test, they cause huge overhead in terms of resources utilization and don't add any value.
Reduce usage of Pre/Post Processors and Assertions to the absolute minimum.
See 9 Easy Solutions for a JMeter Load Test “Out of Memory” Failure for above points explained and few more tips.
As a last resort in case you hit the hardware limits of a single load generator machine you can always consider running JMeter in distributed mode and add more JMeter engines.
I found the answer after fiddling with it for hours. Turns out there is a checkbox that is unticked by default which makes all messages persistent. When I ticked it, I got the throughput that I was looking for.
I'm using Jmeter to inject workload to an application deployed on an AWS EC2 instance. The test has to be very huge: it lasts for 10 hours and the workload profile has a bimodal shapes with a pitch of about 2600 requests in 5 minutes. Actually I have one m3.xlarge instance in which the application is deployed and 8 m3.xlarge instances each one running a jmeter instance. With a python script the workload to inject is splitted among the 8 client instances so in example if the original workload as to inject 800 requests, each jmeter instance will inject 100 requests. The full test as I said lasts for 10 hours and is divided into timesteps of 5 min each. Every 5 min a little workload variation is applied. Actually I get from each jmeter instance the java.lang.OutOfMemoryError: GC overhead limit exceeded error immediatly after the test is started and no request arrive to the application. I read a lot online and on stackoverflow, and I concluded the possible mistake could be:
JMV heap size too low-> I solved setting the following in the jmeter.bat files in each jmeter instance:
set HEAP=-Xms4g -Xmx4g
set NEW=-XX:NewSize=4g -XX:MaxNewSize=4g
some mistakes in the code that results in a continue unuseful usage of the garbage collector. So I remove from my test all the jmeter listeners. In particular I was using TableVisualizer, ViewResultsFullVisualizer, StatVisualizer, and GraphVisualizer.
Anyway the problem persists. I really have no idea about how to solve it. I know 10 hours of test with 2600 pitch request could be a very heavy test, but I think there should be a way to perform this. I'm using EC2 m3.xlarge instance so I could even raise the heap size to 8G if it could be useful, or splitting the workload among even more clients since I'm using spot instances so I will not pay so much more, but since I have already doubled the number of client instance from 4 to 8 in order to solve the problem and is doesn't work I'm a little bit confused and I want to know r suggestions before continue to get more and more resources.
Thank you a lot in advance.
Your heap settings look wrong:
set HEAP=-Xms4g -Xmx4g
set NEW=-XX:NewSize=4g -XX:MaxNewSize=4g
Your new is equal to Heap size, this is wrong. Comment NEW part first.
Can you do a ps -eaf|grep java and show the output ?
And also check you respect these recommendations:
http://jmeter.apache.org/usermanual/best-practices.html
http://www.ubik-ingenierie.com/blog/jmeter_performance_tuning_tips/
Finally, show an overview of your Test plan and , number of threads that you start.
I'm experiencing a strange but severe problem running several (about 15) instances of a Java EE-ish web applications (Hibernate 4+Spring+Quartz+JSF+Facelets+Richfaces) on Tomcat 7/Java 7.
The system runs just fine, but after a greatly variyng amount of time all instances of the application at the same time suddenly suffer from rising response times. Basically the application still works, but the response times are about three times higher.
This are two diagrams displaying the response time of two certain short workflows/actions (log in, access list of seminars, ajax-refresh this list, log out; the lower line is just the request time for the ajax refresh) of two example instances of the application:
As you can see both instances of the application "explode" at the exact same time and stay slow. After restarting the server everything's back to normal. All the instances of the application "explode" simultaneously.
We're storing the session data to a database and use this for clustering. We checked session size and number and both are rather low (meaning that on other servers with other applications we sometimes have larger and more sessions). The other Tomcat in the cluster usually stays fast for some more hours and after this random-ish amount of time it also "dies". We checked the heap sizes with jconsole and the main heap stays between 2.5 and 1 GB size, db connection pool is basically full of free connections, as well as the thread pools. Max heap size is 5 GB, there's also plenty of perm gen space available. The load is not especially high; there's just about 5% load on the main CPU. The server does not swap. It's also no hardware issue as we additionally deployed the applications to a VM where the problems remain the same.
I don't know where to look anymore, I am out of ideas. Has someone an idea where to look?
2013-02-21 Update: New Data!
I added two more timing traces to the application. As for the measurement: the monitoring system calls a servlet that performs two tasks, measures execution time for each on the server and writes the time taken as response. These values are logged by the monitoring system.
I have several interesting new facts: a hot redeployment of the application causes this single instance on the current Tomcat to go nuts. This also seems to affect raw CPU calculation performance (see below). This individual-context-explosion is different from the overall-context-explosion that occurs randomly.
Now for some data:
First the individual lines:
Light blue is total execution time of a small workflow (details see above), measured on the client
Red is "part" of light blue and is the time taken to perform a special step of that workflow, measured on the client
Dark blue is measured in the application and consists of reading a list of entities from the DB through Hibernate and iterating over that list, fetching lazy collections and lazy entities.
Green is a small CPU benchmark using floating point and integer operations. As far as I see no object allocation, so no garbage.
Now for the individual stages of explosion: I marked each image with three black dots. The first one is a "small" explostion in more or less only one application instance - in Inst1 it jumps (especially visible in the red line), while Inst2 below more or less stays calm.
After this small explosion the "big bang" occurs and all application instances on that Tomcat explode (2nd dot). Note that this explosion affects all high level operations (request processing, DB access), but not the CPU benchmark. It stays low in both systems.
After that I hot-redeployed Inst1 by touching the context.xml file. As I said earlier this instance goes from exploded to completely devestated now (the light blue line is out of the chart - it is at about 18 secs). Note how a) this redeployment does not affect Inst2 at all and b) how the raw DB access of Inst1 is also not affected - but how the CPU suddenly seems to have become slower!. This is crazy, I say.
Update of update
The leak prevention listener of Tomcat does not whine about stale ThreadLocals or Threads when the application is undeployed. There obviously seems to be some cleanup problem (which is I assume not directly related to the Big Bang), but Tomcat doesn't have a hint for me.
2013-02-25 Update: Application Environment and Quartz Schedule
The application environment is not very sophisticated. Network components aside (I don't know enough about those) there's basically one application server (Linux) and two database servers (MySQL 5 and MSSQL 2008). The main load is on the MSSQL server, the other one merely serves as a place to store the sessions.
The application server runs an Apache as a load balancer between two Tomcats. So we have two JVMs running on the same hardware (two Tomcat instances). We use this configuration not to actually balance load as the application server is capable of running the application just fine (which it did for years now) but to enable small application updates without downtime. The web application in question is deployed as separate contexts for different customers, about 15 contexts per Tomcat. (I seemm to have mixed up "instances" and "contexts" in my posting - here in the office they're often used synonymously and we usually magically know what the colleague is talking about. My bad, I'm really sorry.)
To clarify the situation with better wording: the diagrams I posted show response times of two different contexts of the same application on the same JVM. The Big Bang affects all contexts on one JVM but doesn't happen on the other one (the order in which the Tomcats explode is random btw). After hot-redeployment one context on one Tomcat instance goes nuts (with all the funny side effects, like seemingly slower CPU for that context).
The overall load on the system is rather low. It's an internal core business related software with about 30 active users simultaneously. Application specific requests (server touches) are currently at about 130 per minute. The number of single requests are low but the requests itself often require several hundred selects to the database, so they're rather expensive. But usually everything's perfectly acceptable. The application also does not create large infinite caches - some lookup data is cached, but only for a short amount of time.
Above I wrote that the servers where capable of running the application just fine for several years. I know that the best way to find the problem would be to find out exactly when things went wrong for the first time and see what has been changed in this timeframe (in the application itself, the associated libraries or infrastructure), however the problem is that we don't know when the problems first occured. Just let's call that suboptimal (in the sense of absent) application monitoring... :-/
We ruled out some aspects, but the application has been updated several times during the last months and thus we e.g. cannot simply deploy an older version. The largest update that wasn't feature change was a switch from JSP to Facelets. But still, "something" must be the cause of all the problems, yet I have no idea why Facelets for instance should influence pure DB query times.
Quartz
As for the Quartz schedule: there's a total of 8 jobs. Most of them run only once per day and have to do with large volume data synchronization (absolutely not "large" as in "big data large"; it's just more than the averate user sees through his usual daily work). However, those jobs of course run at night and the problems occur during daytime. I omit a detailled job listing here (if beneficial I can provide more details of course). The jobs' source code has not been altered during the last months. I already checked whether the explosions align with the jobs - yet the results are inconclusive at best. I'd actually say that they don't align, but as there are several jobs that run every minute I can't rule it out just yet. The acutal jobs that run every minute are pretty low-weight in my opinion, they usually check if data is available (in different sources, DB, external systems, email account) and if so write it to the DB or push it to another system.
However I'm currently enabling logging of indivdual job execution so that I can exactly see start and end timestamp of each single job execution. Perhaps this provides more insight.
2013-02-28 Update: JSF Phases and Timing
I manually added a JSF phae listener to the application. I executed a sample call (the ajax refresh) and this is what I've got (left: normal running Tomcat instance, right: Tomcat instance after Big Bang - the numbers have been taken almost simultaneously from both Tomcats and are in milliseconds):
RESTORE_VIEW: 17 vs 46
APPLY_REQUEST_VALUES: 170 vs 486
PROCESS_VALIDATIONS: 78 vs 321
UPDATE_MODEL_VALUES: 75 vs 307
RENDER_RESPONSE: 1059 vs 4162
The ajax refresh itself belongs to a search form and its search result. There's also another delay between the application's outmost request filter and web flow starts its work: there's a FlowExecutionListenerAdapter that measures time taken in certain phases of web flow. This listener reports 1405 ms for "Request submitted" (which is as far as I know the first web flow event) out of a total of 1632 ms for the complete request on an un-exploded Tomcat, thus I estimate about 200ms overhead.
But on the exploded Tomcat it reports 5332 ms for request submitted (meaning all JSF phases happen in those 5 seconds) out of a total request duration of 7105ms, thus we're up to almost 2 seconds overhead for everything outside of web flow's request submitted.
Below my measurement filter the filter chain contains a org.ajax4jsf.webapp.BaseFilter, then the Spring servlet is called.
2013-06-05 Update: All the stuff going on in the last weeks
A small and rather late update... the application performance still sucks after some time and the behaviour remains erratic. Profiling did not help much yet, it just generated an enormous amount of data that's hard to dissect. (Try poking around in performance data on or profile a production system... sigh) We conducted several tests (ripping out certain parts of the software, undeploying other applications etc.) and actually had some improvements that affect the whole application. The default flush mode of our EntityManager is AUTO and during view rendering lots of fetches and selects are issued, always including the check whether flushing is neccesary.
So we built a JSF phase listener that sets the flush mode to COMMIT during RENDER_RESPONSE. This improved overall performance a lot and seems to have mitigated the problems somewhat.
Yet, our application monitoring keeps yielding completely insane results and performance on some contexts on some tomcat instances. Like an action that should finish in under a second (and that actually does it after deployment) and that now takes more than four seconds. (These numbers are supported by manual timing in the browsers, so it's not the monitoring that causes the problems).
See the following picture for example:
This diagram shows two tomcat instances running the same context (meaning same db, same configuration, same jar). Again the blue line is the amount of time taken by pure DB read operations (fetch a list of entities, iterate over them, lazily fetch collections and associated data). The turquoise-ish and red line are measured by rendering several views and doing an ajax refresh, respectively. The data rendered by two of the requests in turquoise-ish and red is mostly the same as is queried for the blue line.
Now around 0700 on instance 1 (right) there's this huge increase in pure DB time which seems to affect actual render response times as well, but only on tomcat 1. Tomcat 0 is largely unaffected by this, so it cannot be caused by the DB server or network with both tomcats running on the same physical hardware. It has to be a software problem in the Java domain.
During my last tests I found out something interesting: All responses contain the header "X-Powered-By: JSF/1.2, JSF/1.2". Some (the redirect responses produced by WebFlow) even have "JSF/1.2" three times in there.
I traced down the code parts that set those headers and the first time this header is set it's caused by this stack:
... at org.ajax4jsf.webapp.FilterServletResponseWrapper.addHeader(FilterServletResponseWrapper.java:384)
at com.sun.faces.context.ExternalContextImpl.<init>(ExternalContextImpl.java:131)
at com.sun.faces.context.FacesContextFactoryImpl.getFacesContext(FacesContextFactoryImpl.java:108)
at org.springframework.faces.webflow.FlowFacesContext.newInstance(FlowFacesContext.java:81)
at org.springframework.faces.webflow.FlowFacesContextLifecycleListener.requestSubmitted(FlowFacesContextLifecycleListener.java:37)
at org.springframework.webflow.engine.impl.FlowExecutionListeners.fireRequestSubmitted(FlowExecutionListeners.java:89)
at org.springframework.webflow.engine.impl.FlowExecutionImpl.resume(FlowExecutionImpl.java:255)
at org.springframework.webflow.executor.FlowExecutorImpl.resumeExecution(FlowExecutorImpl.java:169)
at org.springframework.webflow.mvc.servlet.FlowHandlerAdapter.handle(FlowHandlerAdapter.java:183)
at org.springframework.webflow.mvc.servlet.FlowController.handleRequest(FlowController.java:174)
at org.springframework.web.servlet.mvc.SimpleControllerHandlerAdapter.handle(SimpleControllerHandlerAdapter.java:48)
at org.springframework.web.servlet.DispatcherServlet.doDispatch(DispatcherServlet.java:925)
at org.springframework.web.servlet.DispatcherServlet.doService(DispatcherServlet.java:856)
at org.springframework.web.servlet.FrameworkServlet.processRequest(FrameworkServlet.java:920)
at org.springframework.web.servlet.FrameworkServlet.doPost(FrameworkServlet.java:827)
at javax.servlet.http.HttpServlet.service(HttpServlet.java:641)
... several thousands ;) more
The second time this header is set by
at org.ajax4jsf.webapp.FilterServletResponseWrapper.addHeader(FilterServletResponseWrapper.java:384)
at com.sun.faces.context.ExternalContextImpl.<init>(ExternalContextImpl.java:131)
at com.sun.faces.context.FacesContextFactoryImpl.getFacesContext(FacesContextFactoryImpl.java:108)
at org.springframework.faces.webflow.FacesContextHelper.getFacesContext(FacesContextHelper.java:46)
at org.springframework.faces.richfaces.RichFacesAjaxHandler.isAjaxRequestInternal(RichFacesAjaxHandler.java:55)
at org.springframework.js.ajax.AbstractAjaxHandler.isAjaxRequest(AbstractAjaxHandler.java:19)
at org.springframework.webflow.mvc.servlet.FlowHandlerAdapter.createServletExternalContext(FlowHandlerAdapter.java:216)
at org.springframework.webflow.mvc.servlet.FlowHandlerAdapter.handle(FlowHandlerAdapter.java:182)
at org.springframework.webflow.mvc.servlet.FlowController.handleRequest(FlowController.java:174)
at org.springframework.web.servlet.mvc.SimpleControllerHandlerAdapter.handle(SimpleControllerHandlerAdapter.java:48)
at org.springframework.web.servlet.DispatcherServlet.doDispatch(DispatcherServlet.java:925)
at org.springframework.web.servlet.DispatcherServlet.doService(DispatcherServlet.java:856)
at org.springframework.web.servlet.FrameworkServlet.processRequest(FrameworkServlet.java:920)
at org.springframework.web.servlet.FrameworkServlet.doPost(FrameworkServlet.java:827)
at javax.servlet.http.HttpServlet.service(HttpServlet.java:641)
I have no idea if this could indicate a problem, but I did not notice this with other applications that are running on any of our servers, so this might as well provide some hints. I really have no idea what that framework code is doing (admittedly I did not dive into it yet)... perhaps someone has an idea? Or am I running into a dead end?
Appendix
My CPU benchmark code consists of a loop that calculates Math.tan and uses the result value to modify some fields on the servlet instance (no volatile/synchronized there), and secondly performs several raw integer calcualations. This is not severly sophisticated, I know, but well... it seems to show something in the charts, however I am not sure what it shows. I do the field updates to prevent HotSpot from optimizing away all my precious code ;)
long time2 = System.nanoTime();
for (int i = 0; i < 5000000; i++) {
double tan = Math.tan(i);
if (tan < 0) {
this.l1++;
} else {
this.l2++;
}
}
for (int i = 1; i < 7500; i++) {
int n = i;
while (n != 1) {
this.steps++;
if (n % 2 == 0) {
n /= 2;
} else {
n = n * 3 + 1;
}
}
}
// This execution time is written to the client.
time2 = System.nanoTime() - time2;
Solution
Increase the maximum size of the Code Cache:
-XX:ReservedCodeCacheSize=256m
Background
We are using ColdFusion 10 which runs on Tomcat 7 and Java 1.7.0_15. Our symptoms were similar to yours. Occasionally the response times and the CPU usage on the server would go up by a lot for no apparent reason. It seemed as if the CPU got slower. The only solution was to restart ColdFusion (and Tomcat).
Initial analysis
I started by looking at the memory usage and the garbage collector log. There was nothing there that could explain our problems.
My next step was to schedule a heap dump every hour and to regularly perform sampling using VisualVM. The goal was to get data from before and after a slowdown so that it could be compared. I managed to get accomplish that.
There was one function in the sampling that stood out: get() in coldfusion.runtime.ConcurrentReferenceHashMap. A lot of time was spent in it after the slowdown compared to very little before. I spent some time on understanding how the function worked and developed a theory that maybe there was a problem with the hash function resulting in some huge buckets. Using the heap dumps I was able to see that the largest buckets only contained 6 elements so I discarded that theory.
Code Cache
I finally got on the right track when I read "Java Performance: The Definitive Guide". It has a chapter on the JIT Compiler which talks about the Code Cache which I had not heard of before.
Compiler disabled
When monitoring the number of compilations performed (monitored with jstat) and the size of the Code Cache (monitored with Memory Pools plugin of VisualVM) I saw that the size increased up to the maximum size (which is 48 MB by default in our environment -- the default varies depending on Java version and Java compiler). When the Code Cache became full the JIT Compiler was turned off. I have read that "CodeCache is full. Compiler has been disabled." should be printed when that happens but I did not see that message; maybe the version we are using does not have that message. I know that the compiler was turned off because the number of compilations performed stopped increasing.
Deoptimization continues
The JIT Compiler can deoptimize previously compiled functions which will caues the function to be executed by the interpreter again (unless the function is replaced by an improved compilation). The deoptimized function can be garbage collected to free up space in the Code Cache.
For some reason functions continued to be deoptimized even though nothing was compiled to replace them. More and more memory would become available in the Code Cache but the JIT Compiler was not restarted.
I never had -XX:+PrintCompilation enabled when we experience a slowdown but I am quite sure that I would have seen either ConcurrentReferenceHashMap.get(), or a function that it depends on, be deoptimized at that time.
Result
We have not seen any slowdowns since we increased the maximum size of the Code Cache to 256 MB and we have also seen a general performance improvement. There is currently 110 MB in our Code Cache.
First, let me say that you have done an excellent job grabbing detailed facts about the problem; I really like how you make it clear what you know and what you are speculating - it really helps.
EDIT 1 Massive edit after the update on context vs. instance
We can rule out:
GCs (that would affect the CPU benchmark service thread and spike the main CPU)
Quartz jobs (that would either affect both Tomcats or the CPU benchmark)
The database (that would affect both Tomcats)
Network packet storms and similar (that would affect both Tomcats)
I believe that you are suffering from is an increase in latency somewhere in your JVM. Latency is where a thread is waiting (synchronously) for a response from somewhere - it's increased your servlet response time but at no cost to the CPU. Typical latencies are caused by:
Network calls, including
JDBC
EJB or RMI
JNDI
DNS
File shares
Disk reading and writing
Threading
Reading from (and sometimes writing to) queues
synchronized method or block
futures
Thread.join()
Object.wait()
Thread.sleep()
Confirming that the problem is latency
I suggest using a commercial profiling tool. I like [JProfiler](http://www.ej-technologies.com/products/jprofiler/overview.html, 15 day trial version available) but YourKit is also recommended by the StackOverflow community. In this discussion I will use JProfiler terminology.
Attach to the Tomcat process while it is performing fine and get a feel for how it looks under normal conditions. In particular, use the high-level JDBC, JPA, JNDI, JMS, servlet, socket and file probes to see how long the JDBC, JMS, etc operations take (screencast. Run this again when the server is exhibiting problems and compare. Hopefully you will see what precisely has been slowed down. In the product screenshot below, you can see the SQL timings using the JPA Probe:
(source: ej-technologies.com)
However it's possible that the probes did not isolate the issue - for example it might be some threading issue. Go to the Threads view for the application; this displays a running chart of the states of each thread, and whether it is executing on the CPU, in an Object.wait(), is waiting to enter a synchronized block or is waiting on network I/O . When you know which thread or threads is exhibiting the issue, go to the CPU views, select the thread and use the thread states selector to immediately drill down to the expensive methods and their call stacks. [Screencast]((screencast). You will be able to drill up into your application code.
This is a call stack for runnable time:
And this is the same one, but showing network latency:
When you know what is blocking, hopefully the path to resolution will be clearer.
We had the same problem, running on Java 1.7.0_u101 (one of Oracle's supported versions, since the latest public JDK/JRE 7 is 1.7.0_u79), running on G1 garbage collector. I cannot tell if the problem appears in other Java 7 versions or with other GCs.
Our process was Tomcat running Liferay Portal (I believe the exact version of Liferay is of no interest here).
This is the behavior we observed: using a -Xmx of 5GB, the inital Code Cache pool size right after startup ranged at about 40MB. After a while, it dropped to about 30MB (which is kind of normal, since there is a lot of code running during startup which will be never executed again, so it is expected to be evicted from the cache after some time). We observed that there was some JIT activity, so the JIT actually populated the cache (comparing to the sizes I am mentioning later, it seems that the small cache size relative to the overall heap size places stringent requirements on the JIT, and this makes the latter evict the cache rather nervously). However, after a while, no more compilations ever took place, and the JVM got painfully slow. We had to kill our Tomcats every now and then to get back adequate performance, and as we added more code to our portal, the problem got worse and worse (since the Code Cache got saturated more quickly, I guess).
It seems that there are several bugs in JDK 7 JVM that cause it to not restart the JIT (look at this blog post: https://blogs.oracle.com/poonam/entry/why_do_i_get_message), even in JDK 7, after an emergency flush (the blog mentions Java bugs 8006952, 8012547, 8020151 and 8029091).
This is why increasing manually the Code Cache to a level where an emergency flush is unlikely to ever occur "fixes" the issue (I guess this is the case with JDK 7).
In our case, instead of trying to adjust the Code Cache pool size, we chose to upgrade to Java 8. This seems to have fixed the issue. Also, the Code Cache now seems to be quite larger (startup size gets about 200MB, and cruising size gets to about 160MB). As it is expected, after some idling time, the cache pool size drops, to get up again if some user (or robot, or whatever) browses our site, causing more code to be executed.
I hope you find the above data helpful.
Forgot to say: I found the exposition, the supporting data, the infering logic and the conclusion of this post very, very helpful. Thank you, really!
Has someone an idea where to look?
Issue could be out of Tomcat/JVM- do you have some batch job which kicks in and stress the shared resource(s) like a common database?
Take a thread dump and see what the java processes are doing when application response time explodes?
If you are using Linux, use a tool like strace and check what is java process doing.
Have you checked JVM GC times? Some GC algorithms might 'pause' the application threads and increase the response time.
You can use jstat utility to monitor garbage collection statistics:
jstat -gcutil <pid of tomcat> 1000 100
Above command would print GC statistics on every 1 second for 100 times. Look at the FGC/YGC columns, if the number keeps raising, there is something wrong with your GC options.
You might want to switch to CMS GC if you want to keep response time low:
-XX:+UseConcMarkSweepGC
You can check more GC options here.
What happens after your app is performing slow for a while, does it get back to performing well?
If so then I would check if there is any activity that is not related to your app taking place at this time.
Something like an antivirus scan or a system/db backup.
If not then I would suggest running it with a profiler (JProfiler, yourkit, etc.) this tools can point you to your hotspots very easily.
You are using Quartz, which manages timed processes, and this seems to take place at particular times.
Post your Quartz schedule and let us know if that aligns, and if so, you can determine which internal application process may be kicking off to consume your resources.
Alternately, it is possible a portion of your application code has finally been activated and decides to load data to the memory cache. You're using Hibernate; check the calls to your database and see if anything coincides.
I've got a somewhat dated Java EE application running on Sun Application Server 8.1 (aka SJSAS, precursor to Glassfish). With 500+ simultaneous users the application becomes unacceptably slow and I'm trying to assist in identifying where most of the execution time is spent and what can be done to speed it up. So far, we've been experimenting and measuring with LoadRunner, the app server logs, Oracle statpack, snoop, adjusting the app server acceptor and session (worker) threads, adjusting Hibernate batch size and join fetch use, etc but after some initial gains we're struggling to improve matters more.
Ok, with that introduction to the problem, here's the real question: If you had a slow Java EE application running on a box whose CPU and memory use never went above 20% and while running with 500+ users you showed two things: 1) that requesting even static files within the same app server JVM process was exceedingly slow, and 2) that requesting a static file outside of the app server JVM process but on the same box was fast, what would you investigate?
My thoughts initially jumped to the application server threads, both acceptor and session threads, thinking that even requests for static files were being queued, waiting for an available thread, and if the CPU/memory weren't really taxed then more threads were in order. But then we upped both the acceptor and session threads substantially and there was no improvement.
Clarification Edits:
1) Static files should be served by a web server rather than an app server. I am using the fact that in our case this (unfortunately) is not the configuration so that I can see the app server performance for files that it doesn't execute -- therefore excluding any database performance costs, etc.
2) I don't think there is a proxy between the requesters and the app server but even if there was it doesn't seem to be overloaded because static files requested from the same application server machine but outside of the application's JVM instance return immediately.
3) The JVM heap size (Xmx) is set to 1GB.
Thanks for any help!
SunONE itself is a pain in the ass. I have a very same problem, and you know what? A simple redeploy of the same application to Weblogic reduced the memory consumption and CPU consumption by about 30%.
SunONE is a reference implementation server, and shouldn't be used for production (don't know about Glassfish).
I know, this answer doesn't really helps, but I've noticed considerable pauses even in a very simple operations, such as getting a bean instance from a pool.
May be, trying to deploy JBoss or Weblogic on the same machine would give you a hint?
P.S. You shouldn't serve static content from under application server (though I do it too sometimes, when CPU is abundant).
P.P.S. 500 concurrent users is quite high a load, I'd definetely put SunONE behind a caching proxy or Apache which serves static content.
After using a Sun performance monitoring tool we found that the garbage collector was running every couple seconds and that only about 100MB out of the 1GB heap was being used. So we tried adding the following JVM options and, so far, this new configuration as greatly improved performance.
-XX:+DisableExplicitGC -XX:+AggressiveHeap
See http://java.sun.com/docs/performance/appserver/AppServerPerfFaq.html
Our lesson: don't leave JVM option tuning and garbage collection adjustments to the end. If you're having performance trouble, look at these settings early in your troubleshooting process.