Python threading how many threads

You first enqueue a function and its arguments using the library. This pickles the function call representation, which is then appended to a Redis list. Enqueueing the job is the first step, but will not do anything yet. We also need at least one worker to listen on that job queue. The first step is to install and run a Redis server on your computer, or have access to a running Redis server.

After that, there are only a few small changes made to the existing code. We first create an instance of an RQ Queue and pass it an instance of a Redis server from the redis-py library. The enqueue method takes a function as its first argument, then any other arguments or keyword arguments are passed along to that function when the job is actually executed.

One last step we need to do is to start up some workers. RQ provides a handy script to run workers on the default queue. Just run rqworker in a terminal window and it will start a worker listening on the default queue. Please make sure your current working directory is the same as where the scripts reside in. The great thing about RQ is that as long as you can connect to Redis, you can run as many workers as you like on as many different machines as you like; therefore, it is very easy to scale up as your application grows.

Here is the source for the RQ version:. However, RQ is not the only Python job queue solution. RQ is easy to use and covers simple use cases extremely well, but if more advanced options are required, other Python 3 queue solutions such as Celery can be used. If your code is IO bound, both multiprocessing and multithreading in Python will work for you. Multiprocessing is a easier to just drop in than threading but has a higher memory overhead. If your code is CPU bound, multiprocessing is most likely going to be the better choice—especially if the target machine has multiple cores or CPUs.

For web applications, and when you need to scale the work across multiple machines, RQ is going to be better for you. Something new since Python 3. This package provides yet another way to use concurrency and parallelism with Python. This was because the Python 3 threading module required subclassing the Thread class and also creating a Queue for the threads to monitor for work.

Using a concurrent. ThreadPoolExecutor makes the Python threading example code almost identical to the multiprocessing module. We can create thumbnail versions of all the images in both a single-threaded, single-process script and then test a multiprocessing-based solution.

We are going to use the Pillow library to handle the resizing of the images. Running this script on images totaling 36 million takes 2. Lets see if we can speed this up using a ProcessPoolExecutor. The main difference is the creation of a ProcessPoolExecutor.

Running this script on the same images took 1. Compared to the other examples, there is some new Python syntax that may be new to most people and also some new concepts. We will need to use an async HTTP library to get the full benefits of asyncio. For 2. What kindda kernel scheduler you have. Comparing Linux 2. So also the SMP Capabilities of the kernel schedule also play a good role in max number of sustainable threads in a system.

Jay D Jay D 3, 3 3 gold badges 29 29 silver badges 47 47 bronze badges. Note that these virtual memory limits only apply to bit systems. On 64 bits you won't run out of virtual memory. Add a comment. Andrew Grant Andrew Grant Yes, and it should be used in conjunction with a queue or pool of requests. Andrew: Why? It should add a task to the thread pool each time it receives a request. It is up to the thread pool to allocate a thread for the task when there is one available. So what do you do when you have hundreds of requests coming in and are out of threads?

Create more? Return an error? Place your requests in a pool that can be as large as need be, and then feed these queued requests to your thread pool as threads become free. Typically, there are many more tasks than threads. As soon as a thread completes its task, it will request the next task from the queue until all tasks have been completed. Andrew: I am not sure what python thread pool the OP is using, but if you want a real world example of this functionality I am describing: msdn. Chad Okere Chad Okere 4, 1 1 gold badge 19 19 silver badges 19 19 bronze badges.

After reading this, I tried running sieve of Eratosthenes tasks on three threads. Thanks for the heads up. Next, I'll try a scenario that involves some database calls. There are two at least types of tasks: CPU bound e. If your tasks are CPU bound then you should consider multiprocessing instead of multithreading. Of course, it depends on many things, that's why you must measure yourself. That's a tad higher than I would have expected as well. Still, if that's what you got, then that's what you got, I can't argue with that.

For this specific application, most threads are just waiting a response from the DNS server. So, the more parallelism, the better, in wall-clock time.

I speak from experience here. Matthew Lund Matthew Lund 3, 6 6 gold badges 27 27 silver badges 37 37 bronze badges. Can you mention some of the numbers you've seen for thread count?

It'd be helpful to just get a sense of it. Hot Licks Hot Licks The whole point of threads was before multicore and multiple processors became prevalent is to be able to mimic having multiple processors on a machine that has just one.

That's how you get responsive user interfaces-- a main thread and ancillary threads. The statement I made was that the number of cores on a machine represents a hard limit on the number of threads that can be doing work at a given time, which is a fact. Anyway - you have GIL in Python, which makes threads only theoretically parallel.

No more than 1 thread can run simultaneously, so it's only the responsiveness and blocking operations that matters. Rich B: A thread pool is just one of many ways to handle a collection of threads. It is a good one, but certainly not the only one.

Show 9 more comments. For Python that's especially true, as multiple processes can run in parallel, while multiple threads - don't. The cost is however quite high. By default, threading. The significance of this flag is that the entire Python program exits when only daemon threads are left.

The initial value is inherited from the creating thread. The flag can be set through the daemon property or the daemon constructor argument. Daemon threads are abruptly stopped at shutdown.

Their resources such as open files, database transactions, etc. If you want your threads to stop gracefully, make them non-daemonic and use a suitable signalling mechanism such as an Event.

It is not a daemon thread. Dummy thread objects have limited functionality; they are always considered alive and daemonic, and cannot be join ed. They are never deleted, since it is impossible to detect the termination of alien threads. Defaults to None , meaning nothing is called. Defaults to. If not None , daemon explicitly sets whether the thread is daemonic. If None the default , the daemonic property is inherited from the current thread.

If the subclass overrides the constructor, it must make sure to invoke the base class constructor Thread. It must be called at most once per thread object. This method will raise a RuntimeError if called more than once on the same thread object. You may override this method in a subclass.

Wait until the thread terminates. This blocks the calling thread until the thread whose join method is called terminates — either normally or through an unhandled exception — or until the optional timeout occurs. When the timeout argument is present and not None , it should be a floating point number specifying a timeout for the operation in seconds or fractions thereof. When the timeout argument is not present or None , the operation will block until the thread terminates. A thread can be join ed many times.

It is also an error to join a thread before it has been started and attempts to do so raise the same exception. A string used for identification purposes only.

It has no semantics. Multiple threads may be given the same name. The initial name is set by the constructor. The identifier is available even after the thread has exited. This is a non-negative integer, or None if the thread has not been started.

This value may be used to uniquely identify this particular thread system-wide until the thread terminates, after which the value may be recycled by the OS. Similar to Process IDs, Thread IDs are only valid guaranteed unique system-wide from the time the thread is created until the thread has been terminated. This method returns True just before the run method starts until just after the run method terminates.

The module function enumerate returns a list of all alive threads. A boolean value indicating whether this thread is a daemon thread True or not False. This must be set before start is called, otherwise RuntimeError is raised. A primitive lock is a synchronization primitive that is not owned by a particular thread when locked. It is created in the unlocked state.

It has two basic methods, acquire and release. When the state is unlocked, acquire changes the state to locked and returns immediately. When the state is locked, acquire blocks until a call to release in another thread changes it to unlocked, then the acquire call resets it to locked and returns. The release method should only be called in the locked state; it changes the state to unlocked and returns immediately. If an attempt is made to release an unlocked lock, a RuntimeError will be raised.

Locks also support the context management protocol. When more than one thread is blocked in acquire waiting for the state to turn to unlocked, only one thread proceeds when a release call resets the state to unlocked; which one of the waiting threads proceeds is not defined, and may vary across implementations.

The class implementing primitive lock objects. Once a thread has acquired a lock, subsequent attempts to acquire it block, until it is released; any thread may release it. Note that Lock is actually a factory function which returns an instance of the most efficient version of the concrete Lock class that is supported by the platform.

When invoked with the blocking argument set to True the default , block until the lock is unlocked, then set it to locked and return True. When invoked with the blocking argument set to False , do not block. If a call with blocking set to True would block, return False immediately; otherwise, set the lock to locked and return True.

When invoked with the floating-point timeout argument set to a positive value, block for at most the number of seconds specified by timeout and as long as the lock cannot be acquired. A timeout argument of -1 specifies an unbounded wait. It is forbidden to specify a timeout when blocking is false.

The return value is True if the lock is acquired successfully, False if not for example if the timeout expired. Release a lock. This can be called from any thread, not only the thread which has acquired the lock. When the lock is locked, reset it to unlocked, and return. If any other threads are blocked waiting for the lock to become unlocked, allow exactly one of them to proceed.

When invoked on an unlocked lock, a RuntimeError is raised. A reentrant lock is a synchronization primitive that may be acquired multiple times by the same thread. In the locked state, some thread owns the lock; in the unlocked state, no thread owns it. To lock the lock, a thread calls its acquire method; this returns once the thread owns the lock.

To unlock the lock, a thread calls its release method. Reentrant locks also support the context management protocol. This class implements reentrant lock objects. A reentrant lock must be released by the thread that acquired it. Is multiprocessing faster than multithreading? Can Python run in parallel?

Are Python threads real? Is NumPy thread safe? Is Python set thread safe? How many threads can I run? What does 4 cores and 4 threads mean? How many maximum threads can you create? How many threads can Windows handle? How many threads can Windows 10 handle?

How much RAM should be in a thread? How do I increase thread limit in Windows? What is the maximum number of threads that can be created by a single process?