In my project, I am using JAX-RS(websphere), EJB and OpenJPA as major technologies.
Even in a very simple scenario of simple GET calls, service takes quite long. Major tasks I can see involved are DB call using JPA,converting entity object to a transfer object using Dozer mapper, and finally underlying websphere implementation converting transfer object to JSON. Data is just few rows in table with no eager loading(thus no data from table other than target table).
I don't think huge computation involved here. Still it is taking around 10-12 seconds. Also server is powerful enough.
What should be my approach to find out the root cause? I plan to find out time consumed by each major component involved using System.nanoTime(). But are there any other better approaches?
This is what we had done when we had such issues earlier.
We analyzed and found the major bottlenecks, most of the times few incorrect parameters/ method choices result in such bottlenecks.
So try using alternatives for the methods that are consuming most of your time.
You don't need to use system.nanotime. You can use any profiler tool like virtualVM, that would give you a detailed analysis of the time taken every single unit.
This answer may not answer your question directly. But you can use jvisualVM to find the root cause. Below blog will help you to configure it on your eclipse quickly
https://blog.idrsolutions.com/2013/05/setting-up-visualvm-in-under-5-minutes/
jvisualVM is the tool comes free with jdk , you can spot it in the bin folder of the jdk with the name "jvisualvm.exe" you can just double click on it to start
Once you configured run your program then let VisualVM capture your processing. then you could interpret which class spend more CPU or time consumption by clicking "Sampler -> CPU " button, That will give you which method takes more time and CPU.Below blog will also help you to analyze the data.
http://scn.sap.com/community/java/blog/2015/09/18/step-by-step-to-use-visualvm-to-do-performance-measurement
If your program spends most of the time in networking related packages, then either your program is waiting for the response from the server or your program is calling the server more often in side a loop (In this case you have to find a way to aggregate the data request so that call to the external server will be reduced)
Please keep us posted about your findings!
I am using Spring batch to process huge data (150 GB) to produce 60 GB output file. I am using Vertical Scaling approach and with 15 threads (Step partitioning approach).
The Job execution details are stored in the in-memory database. The CPU Utilization is more because its running on single machine and the file size is huge. But the Server is having a good configuration like 32 core processor and i am using 10 GB memory for this process.
My question is, if i move this to separate database will it reduce some CPU Utilization? Also, Using In-Memory database for Production is a bad choice /decision?
Regards,
Shankar
When you are talking about moving from in-memory db to a separate db, you are just talking about the batch runtime tables (job_instance, job_execution, step_execution, ...), right?
If so, I wouldn't expect that the CPU usage will drop a lot. Depending on your chunksize, a lot more CPU usage will be needed for your data processing, than for your updating the batch runtime tables.
If using an in-memory db for production is a good decision or not, depends on your needs. Two points to consider:
You lose any-information which was written into the batch-runtime tables. This could be helpful for debug sessions or simply to have a kind of history. But you can "persist" such information also in logfiles.
You will not be able to implement a restartable job. This could be an issue, if your job takes hours to complete. But for job, that only reads from a file, writes to a file, and is completed within a couple of minutes, this is not really a problem.
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 am providing a RESTful service that is being served by a servlet (running inside Tomcat 7.0.X and Ubuntu Linux). I'm already getting about 20 thousand queries per hour and it will grow much higher. The servlet receives the requests, prepares the response, inserts a records in a MySQL database table and delivers the response. The log in the database is absolutely mandatory. Untily recently, all this happened in a syncronous way. I mean, before the Tomcat thread delivered the response, it had to create the records in the database table. The problem is that this log used to take more than 90% of the total time, and even worse: when the database got slower then the service took about 10-15 seconds instead of just 20 miliseconds.
I recetly made an improvement: Each Tomcat thread creates an extra thread doing a "(new Thread(new certain Object)).start();" that takes care of the SQL insertion in an asyncronous way, so the response gets to the clients faster. But these threads take too much RAM when MySQL runs slower and threads multiply, and with a few thousands of them the JVM Tomcat runs of the memory.
What I need is to be able to accept as much HTTP requests as possible, to log every one of them as fast as possible (not syncronously), and to make everything fast and with a very low usage of RAM when MySQL gets slow and inserts need to queue. I think I need some kind of queue to buffer the entries when the speed of http request is higher than the speed of insertions in the database log.
I'm thinking about these ideas:
1- Creating some kind of FIFO queue myself, maybe using some of those Apache commons collections, and the some kind of thread that polls the collection and creates the database records. But what collection should I use? And how should I program the thread that polls it, so it won't monopolize the CPU? I think that a "Do while (true)...." would eat the CPU cycles. And that about making it thread safe? How to do it? I think doing it myself is too much effort and most likely I will reinvent the wheel.
2- log4J? I have never used it directly, but it seems that this framework is algo designed to creat "appenders" that talk to the database. Would that be the way to do it?
3- Using some kind of any other framework that specializes in this?
What would you suggest?
Thanks in advance!
What comes to mind right away is a queue like you said. You can use things like ActiveMQ http://activemq.apache.org/ or RabbitMQ http://www.rabbitmq.com/.
The idea is to just fire and forget. There should be almost no overhead to send the messages.
Then you can connect some "offline" to pick up messages off the queues and write them to the database at the speed you need.
I feel like I plug this all day on Stack Overflow, but we use Mule (http://www.mulesoft.org/) at work to do this. One of the great things about Mule is that you can explicitly set the number of threads that read from the queue and the number of threads that write to the database. It allows you fine grain control over throttling messages.
Definitely take a look at using a ThreadPoolExecutor. You can provide the thread pool size, and it will handle all the concurrency and queuing for you. Only possible issue is that if your JVM crashes for any reason, you'll lose any queued items in your pool.
http://docs.oracle.com/javase/6/docs/api/java/util/concurrent/ThreadPoolExecutor.html
I would also definitely look into optimizing the MySQL database as much as possible. 20k entries per hour can get hairy pretty quickly. The better optimized your hardware, os, and indexes the quicker your inserts and smaller your queue will be.
First of all: Thanks a lot for your valuable suggestions!
So far I have found a partial solution to my need, and I already implemented it succesfully:
http://docs.oracle.com/javase/6/docs/api/java/util/concurrent/LinkedBlockingQueue.html
Now I'm thinking about using also a queue provider if that gets full, as a failover solution. So far I have thought about Amazon's queue service, but it costs money. I will also check the queue solutions that Ryan suggested.
I have this LAMP application with about 900k rows in MySQL and I am having some performance issues.
Background - Apart from the LAMP stack , there's also a Java process (multi-threaded) that runs in its own JVM. So together with LAMP & java, they form the complete solution. The java process is responsible for inserts/updates and few selects as well. These inserts/updates are usually in bulk/batch, anywhere between 5-150 rows. The PHP front-end code only does SELECT's.
Issue - the PHP/SELECT queries become very slow when the java process is running. When the java process is stopped, SELECT's perform alright. I mean the performance difference is huge. When the java process is running, any action performed on the php front-end results in 80% and more CPU usage for mysqld process.
Any help would be appreciated.
MySQL is running with default parameters & settings.
Software stack -
Apache - 2.2.x
MySQL -5.1.37-1ubuntu5
PHP - 5.2.10
Java - 1.6.0_15
OS - Ubuntu 9.10 (karmic)
What engine are you using for MySQL? The thing to note here is if you're using MyISAM, then you're going to have locking issues due to the table locking that engine uses.
From: MySQL Table Locking
Table locking is also disadvantageous
under the following scenario:
* A session issues a SELECT that takes a long time to run.
* Another session then issues an UPDATE on the same table. This session
waits until the SELECT is finished.
* Another session issues another SELECT statement on the same table.
Because UPDATE has higher priority than SELECT, this SELECT waits for the UPDATE to finish,
after waiting for the first SELECT to finish.
I won't repeat them here, but the page has some tips on increasing concurrency on a table within MySQL. Obviously, one option would be to change to an engine like InnoDB which has a more complex row locking mechanism that for high concurrency tables can make a huge difference in performance. For more info on InnoDB go here.
Prior to changing the engine though it would probably be worth looking at the other tips like making sure your table is indexed properly, etc. as this will increase select and update performance regardless of the storage engine.
Edit based on user comment:
I would say it's one possible solution based on the symptoms you've described, but it may not be
the one that will get you where you want to be. It's impossible to say without more information.
You could be doing full table scans due to the lack of indexes. This could be causing I/O contention
on your disk, which just further exasterbates the table locks used by MyISAM. If this is the case then
the root of the cause is the improper indexing and rectifying that would be your best course of action
before changing storage engines.
Also, make sure your tables are normalized. This can have profound implications on performance
especially on updates. Normalized tables can allow you to update a single row instead of hundreds or
thousands in an un-normalized table. This is due to unduplicated values. It can also save huge amounts
of I/O on selects as the db can more efficiently cache data blocks. Without knowing the structure of
the tables you're working with or the indexes you have present it's difficult to provide you with a
more detailed response.
Edit after user attempted using InnoDB:
You mentioned that your Java process is multi-threaded. Have you tried running the process with a single thread? I'm wondering if maybe it's possibly you're sending the same rows to update out to multiple threads and/or the way you're updating across threads is causing locking issues.
Outside of that, I would check the following:
Have you checked your explain plans to verify you have reasonable costs and that the query is actually using the indexes you have?
Are your tables normalized? More specifically, are you updating 100 rows when you could update a single record if the tables were normalized?
Is it possible that you're running out of physical memory when the Java process is running and the machine is busy swapping stuff in and out?
Are you flooding your disk (a single disk?) with more IOPs than it can reasonably handle?
We'd need to know a lot more about the system to say if thats normal or how to solve the problem.
with about 900k rows in MySQL
I would say that makes it very small - so if its performing badly then you're going seriously wrong somewhere.
Enable the query log to see exactly what queries are running, prioritize based on the product of frequency and duration. Have a look at the explain plans, create some indexes. Think about splitting the database across multiple disks.
HTH
C.