Why is creating a Thread said to be expensive?

Question

Why is creating a Thread said to be expensive?

Because it >>is<< expensive.

Java thread creation is expensive because there is a fair bit of work involved:

• A large block of memory has to be allocated and initialized for the thread stack.
• System calls need to be made to create / register the native thread with the host OS.
• Descriptors need to be created, initialized and added to JVM-internal data structures.

It is also expensive in the sense that the thread ties down resources as long as it is alive; e.g. the thread stack, any objects reachable from the stack, the JVM thread descriptors, the OS native thread descriptors.

The costs of all of these things are platform specific, but they are not cheap on any Java platform I've ever come across.

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A Google search found me an old benchmark that reports a thread creation rate of ~4000 per second on a Sun Java 1.4.1 on a 2002 vintage dual processor Xeon running 2002 vintage Linux. A more modern platform will give better numbers ... and I can't comment on the methodology ... but at least it gives a ballpark for how expensive thread creation is likely to be.

Peter Lawrey's benchmarking indicates that thread creation is significantly faster these days in absolute terms, but it is unclear how much of this is due improvements in Java and/or the OS ... or higher processor speeds. But his numbers still indicate a 150+ fold improvement if you use a thread pool versus creating/starting a new thread each time. (And he makes the point that this is all relative ...)

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The above assumes native threads rather than green threads, but modern JVMs all use native threads for performance reasons. Green threads are possibly cheaper to create, but you pay for it in other areas.

Update: The OpenJDK Loom project aims to provide a light-weight alternative to standard Java threads, among other things. The are proposing virtual threads which are a hybrid of native threads and green threads. In simple terms, a virtual thread is rather like a green thread implementation that uses native threads underneath when parallel execution is required.

As of now (Jan 2021) the Project Loom work is still at the prototyping stage, with (AFAIK) no Java version targeted for the release.

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I've done a bit of digging to see how a Java thread's stack really gets allocated. In the case of OpenJDK 6 on Linux, the thread stack is allocated by the call to pthread_create that creates the native thread. (The JVM does not pass pthread_create a preallocated stack.)

Then, within pthread_create the stack is allocated by a call to mmap as follows:

mmap(0, attr.__stacksize, 
     PROT_READ|PROT_WRITE|PROT_EXEC, 
     MAP_PRIVATE|MAP_ANONYMOUS, -1, 0)

According to man mmap, the MAP_ANONYMOUS flag causes the memory to be initialized to zero.

Thus, even though it might not be essential that new Java thread stacks are zeroed (per the JVM spec), in practice (at least with OpenJDK 6 on Linux) they are zeroed.

Others have discussed where the costs of threading come from. This answer covers why creating a thread is not that expensive compared to many operations, but relatively expensive compared to task execution alternatives, which are relatively less expensive.

The most obvious alternative to running a task in another thread is to run the task in the same thread. This is difficult to grasp for those assuming that more threads are always better. The logic is that if the overhead of adding the task to another thread is greater than the time you save, it can be faster to perform the task in the current thread.

Another alternative is to use a thread pool. A thread pool can be more efficient for two reasons. 1) it reuses threads already created. 2) you can tune/control the number of threads to ensure you have optimal performance.

The following program prints....

Time for a task to complete in a new Thread 71.3 us
Time for a task to complete in a thread pool 0.39 us
Time for a task to complete in the same thread 0.08 us
Time for a task to complete in a new Thread 65.4 us
Time for a task to complete in a thread pool 0.37 us
Time for a task to complete in the same thread 0.08 us
Time for a task to complete in a new Thread 61.4 us
Time for a task to complete in a thread pool 0.38 us
Time for a task to complete in the same thread 0.08 us

This is a test for a trivial task which exposes the overhead of each threading option. (This test task is the sort of task that is actually best performed in the current thread.)

final BlockingQueue<Integer> queue = new LinkedBlockingQueue<Integer>();
Runnable task = new Runnable() {
    @Override
    public void run() {
        queue.add(1);
    }
};

for (int t = 0; t < 3; t++) {
    {
        long start = System.nanoTime();
        int runs = 20000;
        for (int i = 0; i < runs; i++)
            new Thread(task).start();
        for (int i = 0; i < runs; i++)
            queue.take();
        long time = System.nanoTime() - start;
        System.out.printf("Time for a task to complete in a new Thread %.1f us%n", time / runs / 1000.0);
    }
    {
        int threads = Runtime.getRuntime().availableProcessors();
        ExecutorService es = Executors.newFixedThreadPool(threads);
        long start = System.nanoTime();
        int runs = 200000;
        for (int i = 0; i < runs; i++)
            es.execute(task);
        for (int i = 0; i < runs; i++)
            queue.take();
        long time = System.nanoTime() - start;
        System.out.printf("Time for a task to complete in a thread pool %.2f us%n", time / runs / 1000.0);
        es.shutdown();
    }
    {
        long start = System.nanoTime();
        int runs = 200000;
        for (int i = 0; i < runs; i++)
            task.run();
        for (int i = 0; i < runs; i++)
            queue.take();
        long time = System.nanoTime() - start;
        System.out.printf("Time for a task to complete in the same thread %.2f us%n", time / runs / 1000.0);
    }
}
}

As you can see, creating a new thread only costs ~70 µs. This could be considered trivial in many, if not most, use cases. Relatively speaking it is more expensive than the alternatives and for some situations a thread pool or not using threads at all is a better solution.

In theory, this depends on the JVM. In practice, every thread has a relatively large amount of stack memory (256 KB per default, I think). Additionally, threads are implemented as OS threads, so creating them involves an OS call, i.e. a context switch.

Do realize that "expensive" in computing is always very relative. Thread creation is very expensive relative to the creation of most objects, but not very expensive relative to a random harddisk seek. You don't have to avoid creating threads at all costs, but creating hundreds of them per second is not a smart move. In most cases, if your design calls for lots of threads, you should use a limited-size thread pool.

There are two kinds of threads:

1. Proper threads: these are abstractions around the underlying operating system's threading facilities. Thread creation is, therefore, as expensive as the system's -- there's always an overhead.

2. "Green" threads: created and scheduled by the JVM, these are cheaper, but no proper paralellism occurs. These behave like threads, but are executed within the JVM thread in the OS. They are not often used, to my knowledge.

The biggest factor I can think of in the thread-creation overhead, is the stack-size you have defined for your threads. Thread stack-size can be passed as a parameter when running the VM.

Other than that, thread creation is mostly OS-dependent, and even VM-implementation-dependent.

Now, let me point something out: creating threads is expensive if you're planning on firing 2000 threads per second, every second of your runtime. The JVM is not designed to handle that. If you'll have a couple of stable workers that won't be fired and killed over and over, relax.

Creating Threads requires allocating a fair amount of memory since it has to make not one, but two new stacks (one for java code, one for native code). Use of Executors/Thread Pools can avoid the overhead, by reusing threads for multiple tasks for Executor.