EJB timer performance

I am trying to decide if use a java-ee timer in my application or not. The server I am using is Weblogic 10.3.2

The need is: After one hour of a call to an async webservice from an EJB, if the async callback method has not been called it is needed to execute some actions. The information regarding if the callback method has been called and the date of the execution of the call is stored in database.

The two possibilities I see are:

1. Using a batch process that every half hour looks for all the calls that have been more than one hour开发者_Go百科 without response and execute the needed actions.
2. Create a timer of one hour after every single call to the ws and in the @Timeout method check if the answer has come and if it has not, execute the required actions.

From a pure programming point of view, it looks easier and cleaner the second one, but I am worry of the performance issues I could have if let's say there are 100.000 Timer created at a single moment.

Any thoughts?

You would be better off having a more specialized process. The real problem is the 100,000 issue. It would depend on how long your actions take.

Because its easy to see that each second, the EJB timer would fire up 30 threads to process all of the current pending jobs, since that's how it works.

Also timers are persistent, so your EJB managed timer table will be saving and deleting 30 rows per second (60 total), this is assuming 100K transactions/hour.

So, that's an lot of work happening very quickly. I can easily see the system simply "falling behind" and never catching up.

A specialized process would be much lighter weight, could perhaps batch the action calls (call 5 actions per thread instead of one per thread), etc. It would be nice if you didn't have to persist the timer events, but that is what it is. You could almost easily simply append the timer events to a file for safety, and keep them in memory. On system restart, you can reload that file, and then roll the file (every hour create a new file, delete the older file after it's all been consumed, etc.). That would save a lot of DB traffic, but you could lose the transactional nature of the DB.

Anyway, I don't think you want to use the EJB Timer for this, I don't think it's really designed for this amount of traffic. But you can always test it and see. Make sure you test restarting your container see how well it works with 100K pending timer jobs in its table.

All depends of what is used by the container. e.g. JBoss uses Quartz Scheduler to implement EJB timer functionality. Quartz is pretty good when you have around 100 000 timer instances.

@Pau: why u need to create a timer for every call made...instead u can have a single timer thread created at start up of application which runs after every half-hour(configurable) period of time and looks in your Database for all web services calls whose response have not been received and whose requested time is past 1 hour. And for selected records, in for loop, it can execute required action.

Well above design may not be useful if you have time critical activity to be performed.

If you have spring framework in your application, you may also look up its timer services.http://static.springsource.org/spring/docs/1.2.9/reference/scheduling.html

Maybe you could use some of these ideas:
Where I'm at, we've built a cron-like scheduler which is powered by a single timer. When the timer fires the system checks which crons need to run using a Quartz CronTrigger. Generally these crons have a lot of work to do, and the way we handle that is each cron spins its individual tasks off as JMS messages, then MDBs handle the messages. Currently this runs on a single Glassfish instance and as our task load increases, we should be able to scale this up with a cluster so multiple nodes are processing the jms messages. We balance the jms message processing load for each type of task by setting the max-pool-size in glassfish-ejb-jar.xml (also known as sun-ejb-jar.xml).

Building a system like this and getting all the details right isn't trivial, but it's proving really effective.