Concurrency within Java EE environment_问答_开发者

Goal

My goal to better understand how concurrency within Java EE environment and how can I better consume it.

General questions

Let's take typical servlet container (tomcat) as example. For each request it uses 1 thread to process it. Thread pool is configured so, that it can have max 80 threads in pool. Let's also take simple webapp - it makes some processing and DB communication during each request.

At peak time I can see 80 parallel running threads (+ several other infrastructure threads). Let's also assume I running it in 'm1.large' EC2 instance.

I don't think that all these threads can really run in parallel on this hardware. So now scheduler should decide how better to split CPU time between them all. So the questions are - how big is scheduler overhead in this case? How can I find right balance between thread amount and processing speed?

Actors comparison

Having 80+ threads on 4 core CPU doesn't sound healthy to me. Especially if most of them are blocked on some kind of IO (DB, Filesystem, Socket) - they just consume precious resources. What if we will detach request from thread and will have only reasonable amount of threads (8 for instance) and will just send processing tasks to them. Of course in this case IO should be also non-blocking, so that I receive events when some data, that I need, is available and I send event, if I have some results.

As far as I understand, Actor model is all about this. Actors are not bound to threads (at least in Akka and Scala). So I have reasonable thread pool and bunch of actors with mailboxes that contain processing tasks.

Now question is - how actor model compares to traditional thread-per-request model in terms of performance, scheduler overhead a开发者_JAVA技巧nd resources (RAM, CPU) consumption?

Custom threads

I have some requests (only several) that take too much time to process. I optimized code and all algorithms, added caches, but it still takes too much time. But I see, that algorithm can be parallelized. It fits naturally in actor model - I just split my big task in several tasks, and then aggregate results somehow (if needed). But in thread-per-request model I need spawn my own threads (or create my small thread pool). As far as I know, it's not recommended practice within Java EE environment. And, from my point of view, it doesn't fits naturally in thread-per-request model. Question arise: how big my thread pool size should be? Even if I will make it reasonable in terms of hardware I still have this bunch of threads managed by servlet container. Thread management becomes decentralized and goes wild.

So my question - what is the best way to deal with these situations in thread-per-request model?

Having 80+ threads on 4 core CPU doesn't sound healthy to me. Especially if most of them are blocked on some kind of IO (DB, Filesystem, Socket) - they just consume precious resourecs.

Wrong. Exactly in this scenario the processors can handle many more threads than the number of individual cores, since most of the threads at any point in time are blocked waiting for I/O. Fair enough, context switching takes time, but that overhead is usually irrelevant compared to file/network/DB latency.

The rule of thumb that the number of threads should be equal - or a little more than - the number of processor cores applies only for computation-intensive tasks when the cores are kept busy most of the time.

I have some requests (only several) that take too much time to process. I optimized code and all algorithms, added caches, but it still takes too much time. But I see, that algorithm can be parallelized. It fits naturally in actor model - I just split my big task in several tasks, and then aggregate results somehow (if needed). But in thread-per-request model I need spawn my own threads (or create my small thread pool). As far as I know, it's not recommended practice within Java EE environment.

Never heard about that (but I don't claim myself to be the ultimate Java EE expert). IMHO there is nothing wrong in executing tasks associated with a single request parallelly using e.g. a ThreadPoolExecutor. Note that these threads are not request handling threads, so they don't directly interfere with the thread pool used by the EJB container. Except that they compete for the same resources of course, so they may slow down or completely stop other request processing threads in a careless setup.

what is the best way to deal with these situations in thread-per-request model?

In the end, you can't escape measuring concurrent performance and fine-tuning the size of your thread pool and other parameters for your own specific environment.

The whole point of Java EE is to put common architectural concerns like security, state, and concurrency into the framework and let you provide the bits of business logic or data mappings along with the wiring to connect them. As such, Java EE intentionally hides the nasty bits of concurrency (locking to read/write mutable state) in the framework.

This approach lets a much broader range of developers successfully write correct applications. A necessary side effect though is that these abstractions create overhead and remove control. That's both good (in making it simple and encoding policies as policies not code) and bad (if you know what you're doing and can make choices impossible in the framework).

It is not inherently bad to have 80 threads on a production box. Most will be blocked or waiting on I/O which is fine. There is a (tunable) pool of threads doing the actual computation and Java EE will give you external hooks to tune those knobs.

Actors are a different model. They also let you write islands of code (the actor body) that (can) avoid locking to modify state. You can write your actors to be stateless (capturing the state in the recursive function call parameters) or hide your state completely in an actor instance so the state is all confined (for react style actors you probably still need to explicitly lock around data access to ensure visibility on the next thread that runs your actor).

I can't say that one or the other is better. I think there is adequate proof that both models can be used to write safe, high-throughput systems. To make either perform well, you need to think hard about your problem and build apps that isolate parts of state and the computations on each kind of state. For code where you understand your data well and have a high potential for parallelism I think models outside Java EE make a lot of sense.

Generally, the rule of thumb in sizing compute-bound thread pools is that they should be approximately equal to N of cores + 2. Many frameworks size to that automatically. You can use Runtime.getRuntime().availableProcessors() to get N. If your problem decomposes in a divide-and-conquer style algorithm and the number of data items is large, I would strongly suggest checking out fork/join which can be used now as a separate library and will be part of Java 7.

As far as how to manage this, you're not supposed to spawn threads as such inside Java EE (they want to control that) but you might investigate sending a request to your data-crunching thread pool via a message queue and handling that request via a return message. That can fit in the Java EE model (a bit clumsily of course).

I have a writeup of actors, fork/join, and some other concurrency models here that you might find interesting: http://tech.puredanger.com/2011/01/14/comparing-concurrent-frameworks/