Multithreading in Python and How to Achieve it?
Time is the most critical factor in life. Owing to its importance, the world of programming provides various tricks and techniques that significantly help you reduce the time consumption, thereby increasing performance. One such approach is Multithreading in Python.
Here is a quick summary of all the majors covered in this article:
- What is multitasking in Python?
- What is a thread?
- When to use multithreading in Python?
- How to achieve Multithreading in Python?
- How to create threads in Python?
- without creating a class
- by extending thread class
- without extending thread class
6. Advantages of using multithreading in Python
To begin with, let us first try to understand multitasking before we start learning about Multithreading in Python.
What is Multitasking in Python?
Multitasking, in general, is the capability of performing multiple tasks simultaneously. In technical terms, multitasking refers to the ability of an operating system to perform different tasks at the same time. For instance, you are downloading something on your PC as well as listening to songs and concurrently playing a game, etc. All these tasks are performed by the same OS an in sync. This is nothing but multitasking which not just helps you save time but also increases productivity.
There are two types of multitasking in an OS:
- Process-based
- Thread-based
In this article, you will be learning about Thread-based multitasking or Multithreading.
What is a thread?
A thread is basically an independent flow of execution. A single process can consist of multiple threads. Each thread in a program performs a particular task. For Example, when you are playing a game say FIFA on your PC, the game as a whole is a single process, but it consists of several threads responsible for playing the music, taking input from the user, running the opponent synchronously, etc. All these are separate threads responsible for carrying out these different tasks in the same program.
Every process has one thread that is always running. This is the main thread. This main thread actually creates the child thread objects. The child thread is also initiated by the main thread. I will show you all further in this article how to check the current running thread.
So with this, I hope you have clearly understood what is a thread. Moving on, let’s see what is Multithreading in Python.
When to use Multithreading in Python?
Multithreading is very useful for saving time and improving performance, but it cannot be applied everywhere.
In the previous FIFA example, the music thread is independent of the thread that takes your input and the thread that takes your input is independent of the thread that runs your opponent. These threads run independently because they are not inter-dependent.
Therefore, multithreading can be used only when the dependency between individual threads does not exist.
This article further shows how you can achieve Multithreading in Python.
How to achieve Multithreading in Python?
Multithreading in Python can be achieved by importing the threading module.
Before importing this module, you will have to install this it. To install this on your anaconda environment, execute the following command on your anaconda prompt:
conda install -c conda-forge tbb
After its successfully installed, you can use any of the following commands to import the threading module:
Now that you have threading module installed, let us move ahead and do Multithreading in Python.
How to create threads in Python?
Threads in Python can be created in three ways:
- Without creating a class
- By extending Thread class
- Without extending Thread class
Without creating a class
Multithreading in Python can be accomplished without creating a class as well. Here is an example to demonstrate the same:
Example:
Output:
The above output shows that the first thread that is present is, the main thread. This main thread then creates a child thread that is executing the function and then the final print statement is executed again by the main thread.
Now let us move ahead and see how to do Multithreading in python by extending the Thread class.
By extending the Thread class:
When a child class is created by extending the Thread class, the child class represents that a new thread is executing some task. When extending the Thread class, the child class can override only two methods i.e. the __init__() method and the run() method. No other method can be overridden other than these two methods.
Here is an example of how to extend the Thread class to create a thread:
Example:
Output:
The above example shows that class myclass is inheriting the Thread class and the child class i.e myclass is overriding the run method. By default, the first parameter of any class function needs to be self which is the pointer to the current object. The output shows that the child thread executes the run() method and the main thread waits for the childs execution to complete. This is because of the join() function, which makes the main thread wait for the child to finish.
This method of creating threads is the most preferred method because its the standard method. But in case you want to create threads without inheriting or extending the Thread class, you can do it in the following manner.
Without Extending Thread class
To create a thread without extending the Thread class, you can do as follows:
Example:
Output:
The child thread executes myfunc after which the main thread executes the last print statement.
Advantages of using threading
Multithreading has many advantages some of which are as follows:
- Better utilization of resources
- Simplifies the code
- Allows concurrent and parallel occurrence of various tasks
- Reduces the time consumption or response time, thereby, increasing the performance.
Here is an example to check how long it takes for a code to execute with and without multithreading in python:
Example:
Output:
The above is the output time taken to execute the program without using threads. Now let us use threads and see what happens to the same program:
Example:
Output:
The above output clearly shows that the time taken when we use threads is much less compared to the time taken for the same program to execute without using threads.
I hope you are clear with the concepts covered under this article related to Multithreading in Python. Make sure to practice as much as possible as this is one of the most important concepts used in programming.
If you wish to check out more articles on the market’s most trending technologies like Artificial Intelligence, DevOps, Ethical Hacking, then you can refer to Edureka’s official site.
Do look out for other articles in this series which will explain the various other aspects of Python and Data Science.
threading — Thread-based parallelism¶
This module constructs higher-level threading interfaces on top of the lower level _thread module.
Changed in version 3.7: This module used to be optional, it is now always available.
concurrent.futures.ThreadPoolExecutor offers a higher level interface to push tasks to a background thread without blocking execution of the calling thread, while still being able to retrieve their results when needed.
queue provides a thread-safe interface for exchanging data between running threads.
asyncio offers an alternative approach to achieving task level concurrency without requiring the use of multiple operating system threads.
In the Python 2.x series, this module contained camelCase names for some methods and functions. These are deprecated as of Python 3.10, but they are still supported for compatibility with Python 2.5 and lower.
CPython implementation detail: In CPython, due to the Global Interpreter Lock , only one thread can execute Python code at once (even though certain performance-oriented libraries might overcome this limitation). If you want your application to make better use of the computational resources of multi-core machines, you are advised to use multiprocessing or concurrent.futures.ProcessPoolExecutor . However, threading is still an appropriate model if you want to run multiple I/O-bound tasks simultaneously.
Availability : not Emscripten, not WASI.
This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi . See WebAssembly platforms for more information.
This module defines the following functions:
Return the number of Thread objects currently alive. The returned count is equal to the length of the list returned by enumerate() .
The function activeCount is a deprecated alias for this function.
Return the current Thread object, corresponding to the caller’s thread of control. If the caller’s thread of control was not created through the threading module, a dummy thread object with limited functionality is returned.
The function currentThread is a deprecated alias for this function.
Handle uncaught exception raised by Thread.run() .
The args argument has the following attributes:
exc_type: Exception type.
exc_value: Exception value, can be None .
exc_traceback: Exception traceback, can be None .
thread: Thread which raised the exception, can be None .
If exc_type is SystemExit , the exception is silently ignored. Otherwise, the exception is printed out on sys.stderr .
If this function raises an exception, sys.excepthook() is called to handle it.
threading.excepthook() can be overridden to control how uncaught exceptions raised by Thread.run() are handled.
Storing exc_value using a custom hook can create a reference cycle. It should be cleared explicitly to break the reference cycle when the exception is no longer needed.
Storing thread using a custom hook can resurrect it if it is set to an object which is being finalized. Avoid storing thread after the custom hook completes to avoid resurrecting objects.
sys.excepthook() handles uncaught exceptions.
New in version 3.8.
Holds the original value of threading.excepthook() . It is saved so that the original value can be restored in case they happen to get replaced with broken or alternative objects.
New in version 3.10.
Return the ‘thread identifier’ of the current thread. This is a nonzero integer. Its value has no direct meaning; it is intended as a magic cookie to be used e.g. to index a dictionary of thread-specific data. Thread identifiers may be recycled when a thread exits and another thread is created.
New in version 3.3.
Return the native integral Thread ID of the current thread assigned by the kernel. This is a non-negative integer. Its 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).
Availability : Windows, FreeBSD, Linux, macOS, OpenBSD, NetBSD, AIX.
New in version 3.8.
Return a list of all Thread objects currently active. The list includes daemonic threads and dummy thread objects created by current_thread() . It excludes terminated threads and threads that have not yet been started. However, the main thread is always part of the result, even when terminated.
Return the main Thread object. In normal conditions, the main thread is the thread from which the Python interpreter was started.
New in version 3.4.
Set a trace function for all threads started from the threading module. The func will be passed to sys.settrace() for each thread, before its run() method is called.
Get the trace function as set by settrace() .
New in version 3.10.
Set a profile function for all threads started from the threading module. The func will be passed to sys.setprofile() for each thread, before its run() method is called.
Get the profiler function as set by setprofile() .
New in version 3.10.
Return the thread stack size used when creating new threads. The optional size argument specifies the stack size to be used for subsequently created threads, and must be 0 (use platform or configured default) or a positive integer value of at least 32,768 (32 KiB). If size is not specified, 0 is used. If changing the thread stack size is unsupported, a RuntimeError is raised. If the specified stack size is invalid, a ValueError is raised and the stack size is unmodified. 32 KiB is currently the minimum supported stack size value to guarantee sufficient stack space for the interpreter itself. Note that some platforms may have particular restrictions on values for the stack size, such as requiring a minimum stack size > 32 KiB or requiring allocation in multiples of the system memory page size — platform documentation should be referred to for more information (4 KiB pages are common; using multiples of 4096 for the stack size is the suggested approach in the absence of more specific information).
Unix platforms with POSIX threads support.
This module also defines the following constant:
The maximum value allowed for the timeout parameter of blocking functions ( Lock.acquire() , RLock.acquire() , Condition.wait() , etc.). Specifying a timeout greater than this value will raise an OverflowError .
New in version 3.2.
This module defines a number of classes, which are detailed in the sections below.
The design of this module is loosely based on Java’s threading model. However, where Java makes locks and condition variables basic behavior of every object, they are separate objects in Python. Python’s Thread class supports a subset of the behavior of Java’s Thread class; currently, there are no priorities, no thread groups, and threads cannot be destroyed, stopped, suspended, resumed, or interrupted. The static methods of Java’s Thread class, when implemented, are mapped to module-level functions.
All of the methods described below are executed atomically.
Thread-Local Data¶
Thread-local data is data whose values are thread specific. To manage thread-local data, just create an instance of local (or a subclass) and store attributes on it:
The instance’s values will be different for separate threads.
class threading. local ¶
A class that represents thread-local data.
For more details and extensive examples, see the documentation string of the _threading_local module: Lib/_threading_local.py.
Thread Objects¶
The Thread class represents an activity that is run in a separate thread of control. There are two ways to specify the activity: by passing a callable object to the constructor, or by overriding the run() method in a subclass. No other methods (except for the constructor) should be overridden in a subclass. In other words, only override the __init__() and run() methods of this class.
Once a thread object is created, its activity must be started by calling the thread’s start() method. This invokes the run() method in a separate thread of control.
Once the thread’s activity is started, the thread is considered ‘alive’. It stops being alive when its run() method terminates – either normally, or by raising an unhandled exception. The is_alive() method tests whether the thread is alive.
Other threads can call a thread’s join() method. This blocks the calling thread until the thread whose join() method is called is terminated.
A thread has a name. The name can be passed to the constructor, and read or changed through the name attribute.
If the run() method raises an exception, threading.excepthook() is called to handle it. By default, threading.excepthook() ignores silently SystemExit .
A thread can be flagged as a “daemon thread”. 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.) may not be released properly. If you want your threads to stop gracefully, make them non-daemonic and use a suitable signalling mechanism such as an Event .
There is a “main thread” object; this corresponds to the initial thread of control in the Python program. It is not a daemon thread.
There is the possibility that “dummy thread objects” are created. These are thread objects corresponding to “alien threads”, which are threads of control started outside the threading module, such as directly from C code. Dummy thread objects have limited functionality; they are always considered alive and daemonic, and cannot be joined . They are never deleted, since it is impossible to detect the termination of alien threads.
class threading. Thread ( group = None , target = None , name = None , args = () , kwargs = <> , * , daemon = None ) ¶
This constructor should always be called with keyword arguments. Arguments are:
group should be None ; reserved for future extension when a ThreadGroup class is implemented.
target is the callable object to be invoked by the run() method. Defaults to None , meaning nothing is called.
name is the thread name. By default, a unique name is constructed of the form “Thread-N” where N is a small decimal number, or “Thread-N (target)” where “target” is target.__name__ if the target argument is specified.
args is a list or tuple of arguments for the target invocation. Defaults to () .
kwargs is a dictionary of keyword arguments for the target invocation. 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.__init__() ) before doing anything else to the thread.
Changed in version 3.10: Use the target name if name argument is omitted.
Changed in version 3.3: Added the daemon argument.
Start the thread’s activity.
It must be called at most once per thread object. It arranges for the object’s run() method to be invoked in a separate thread of control.
This method will raise a RuntimeError if called more than once on the same thread object.
Method representing the thread’s activity.
You may override this method in a subclass. The standard run() method invokes the callable object passed to the object’s constructor as the target argument, if any, with positional and keyword arguments taken from the args and kwargs arguments, respectively.
Using list or tuple as the args argument which passed to the Thread could achieve the same effect.
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). As join() always returns None , you must call is_alive() after join() to decide whether a timeout happened – if the thread is still alive, the join() call timed out.
When the timeout argument is not present or None , the operation will block until the thread terminates.
A thread can be joined many times.
join() raises a RuntimeError if an attempt is made to join the current thread as that would cause a deadlock. 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.
Deprecated getter/setter API for name ; use it directly as a property instead.
Deprecated since version 3.10.
The ‘thread identifier’ of this thread or None if the thread has not been started. This is a nonzero integer. See the get_ident() function. Thread identifiers may be recycled when a thread exits and another thread is created. The identifier is available even after the thread has exited.
The Thread ID ( TID ) of this thread, as assigned by the OS (kernel). This is a non-negative integer, or None if the thread has not been started. See the get_native_id() function. 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.
Availability : Windows, FreeBSD, Linux, macOS, OpenBSD, NetBSD, AIX, DragonFlyBSD.
New in version 3.8.
Return whether the thread is alive.
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. Its initial value is inherited from the creating thread; the main thread is not a daemon thread and therefore all threads created in the main thread default to daemon = False .
The entire Python program exits when no alive non-daemon threads are left.
Deprecated getter/setter API for daemon ; use it directly as a property instead.
Deprecated since version 3.10.
Lock Objects¶
A primitive lock is a synchronization primitive that is not owned by a particular thread when locked. In Python, it is currently the lowest level synchronization primitive available, implemented directly by the _thread extension module.
A primitive lock is in one of two states, “locked” or “unlocked”. 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.
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.
All methods are executed atomically.
class threading. Lock ¶
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.
acquire ( blocking = True , timeout = — 1 ) ¶
Acquire a lock, blocking or non-blocking.
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).
Changed in version 3.2: The timeout parameter is new.
Changed in version 3.2: Lock acquisition can now be interrupted by signals on POSIX if the underlying threading implementation supports it.
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.
There is no return value.
Return True if the lock is acquired.
RLock Objects¶
A reentrant lock is a synchronization primitive that may be acquired multiple times by the same thread. Internally, it uses the concepts of “owning thread” and “recursion level” in addition to the locked/unlocked state used by primitive locks. 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. acquire() / release() call pairs may be nested; only the final release() (the release() of the outermost pair) resets the lock to unlocked and allows another thread blocked in acquire() to proceed.
Reentrant locks also support the context management protocol .
class threading. RLock ¶
This class implements reentrant lock objects. A reentrant lock must be released by the thread that acquired it. Once a thread has acquired a reentrant lock, the same thread may acquire it again without blocking; the thread must release it once for each time it has acquired it.
Note that RLock is actually a factory function which returns an instance of the most efficient version of the concrete RLock class that is supported by the platform.
acquire ( blocking = True , timeout = — 1 ) ¶
Acquire a lock, blocking or non-blocking.
When invoked without arguments: if this thread already owns the lock, increment the recursion level by one, and return immediately. Otherwise, if another thread owns the lock, block until the lock is unlocked. Once the lock is unlocked (not owned by any thread), then grab ownership, set the recursion level to one, and return. If more than one thread is blocked waiting until the lock is unlocked, only one at a time will be able to grab ownership of the lock. There is no return value in this case.
When invoked with the blocking argument set to True , do the same thing as when called without arguments, and return True .
When invoked with the blocking argument set to False , do not block. If a call without an argument would block, return False immediately; otherwise, do the same thing as when called without arguments, 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. Return True if the lock has been acquired, False if the timeout has elapsed.
Changed in version 3.2: The timeout parameter is new.
Release a lock, decrementing the recursion level. If after the decrement it is zero, reset the lock to unlocked (not owned by any thread), and if any other threads are blocked waiting for the lock to become unlocked, allow exactly one of them to proceed. If after the decrement the recursion level is still nonzero, the lock remains locked and owned by the calling thread.
Only call this method when the calling thread owns the lock. A RuntimeError is raised if this method is called when the lock is unlocked.
There is no return value.
Condition Objects¶
A condition variable is always associated with some kind of lock; this can be passed in or one will be created by default. Passing one in is useful when several condition variables must share the same lock. The lock is part of the condition object: you don’t have to track it separately.
A condition variable obeys the context management protocol : using the with statement acquires the associated lock for the duration of the enclosed block. The acquire() and release() methods also call the corresponding methods of the associated lock.
Other methods must be called with the associated lock held. The wait() method releases the lock, and then blocks until another thread awakens it by calling notify() or notify_all() . Once awakened, wait() re-acquires the lock and returns. It is also possible to specify a timeout.
The notify() method wakes up one of the threads waiting for the condition variable, if any are waiting. The notify_all() method wakes up all threads waiting for the condition variable.
Note: the notify() and notify_all() methods don’t release the lock; this means that the thread or threads awakened will not return from their wait() call immediately, but only when the thread that called notify() or notify_all() finally relinquishes ownership of the lock.
The typical programming style using condition variables uses the lock to synchronize access to some shared state; threads that are interested in a particular change of state call wait() repeatedly until they see the desired state, while threads that modify the state call notify() or notify_all() when they change the state in such a way that it could possibly be a desired state for one of the waiters. For example, the following code is a generic producer-consumer situation with unlimited buffer capacity:
The while loop checking for the application’s condition is necessary because wait() can return after an arbitrary long time, and the condition which prompted the notify() call may no longer hold true. This is inherent to multi-threaded programming. The wait_for() method can be used to automate the condition checking, and eases the computation of timeouts:
To choose between notify() and notify_all() , consider whether one state change can be interesting for only one or several waiting threads. E.g. in a typical producer-consumer situation, adding one item to the buffer only needs to wake up one consumer thread.
class threading. Condition ( lock = None ) ¶
This class implements condition variable objects. A condition variable allows one or more threads to wait until they are notified by another thread.
If the lock argument is given and not None , it must be a Lock or RLock object, and it is used as the underlying lock. Otherwise, a new RLock object is created and used as the underlying lock.
Changed in version 3.3: changed from a factory function to a class.
Acquire the underlying lock. This method calls the corresponding method on the underlying lock; the return value is whatever that method returns.
Release the underlying lock. This method calls the corresponding method on the underlying lock; there is no return value.
wait ( timeout = None ) ¶
Wait until notified or until a timeout occurs. If the calling thread has not acquired the lock when this method is called, a RuntimeError is raised.
This method releases the underlying lock, and then blocks until it is awakened by a notify() or notify_all() call for the same condition variable in another thread, or until the optional timeout occurs. Once awakened or timed out, it re-acquires the lock and returns.
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 underlying lock is an RLock , it is not released using its release() method, since this may not actually unlock the lock when it was acquired multiple times recursively. Instead, an internal interface of the RLock class is used, which really unlocks it even when it has been recursively acquired several times. Another internal interface is then used to restore the recursion level when the lock is reacquired.
The return value is True unless a given timeout expired, in which case it is False .
Changed in version 3.2: Previously, the method always returned None .
Wait until a condition evaluates to true. predicate should be a callable which result will be interpreted as a boolean value. A timeout may be provided giving the maximum time to wait.
This utility method may call wait() repeatedly until the predicate is satisfied, or until a timeout occurs. The return value is the last return value of the predicate and will evaluate to False if the method timed out.
Ignoring the timeout feature, calling this method is roughly equivalent to writing:
Therefore, the same rules apply as with wait() : The lock must be held when called and is re-acquired on return. The predicate is evaluated with the lock held.
New in version 3.2.
By default, wake up one thread waiting on this condition, if any. If the calling thread has not acquired the lock when this method is called, a RuntimeError is raised.
This method wakes up at most n of the threads waiting for the condition variable; it is a no-op if no threads are waiting.
The current implementation wakes up exactly n threads, if at least n threads are waiting. However, it’s not safe to rely on this behavior. A future, optimized implementation may occasionally wake up more than n threads.
Note: an awakened thread does not actually return from its wait() call until it can reacquire the lock. Since notify() does not release the lock, its caller should.
Wake up all threads waiting on this condition. This method acts like notify() , but wakes up all waiting threads instead of one. If the calling thread has not acquired the lock when this method is called, a RuntimeError is raised.
The method notifyAll is a deprecated alias for this method.
Semaphore Objects¶
This is one of the oldest synchronization primitives in the history of computer science, invented by the early Dutch computer scientist Edsger W. Dijkstra (he used the names P() and V() instead of acquire() and release() ).
A semaphore manages an internal counter which is decremented by each acquire() call and incremented by each release() call. The counter can never go below zero; when acquire() finds that it is zero, it blocks, waiting until some other thread calls release() .
class threading. Semaphore ( value = 1 ) ¶
This class implements semaphore objects. A semaphore manages an atomic counter representing the number of release() calls minus the number of acquire() calls, plus an initial value. The acquire() method blocks if necessary until it can return without making the counter negative. If not given, value defaults to 1.
The optional argument gives the initial value for the internal counter; it defaults to 1 . If the value given is less than 0, ValueError is raised.
Changed in version 3.3: changed from a factory function to a class.
Acquire a semaphore.
When invoked without arguments:
If the internal counter is larger than zero on entry, decrement it by one and return True immediately.
If the internal counter is zero on entry, block until awoken by a call to release() . Once awoken (and the counter is greater than 0), decrement the counter by 1 and return True . Exactly one thread will be awoken by each call to release() . The order in which threads are awoken should not be relied on.
When invoked with blocking set to False , do not block. If a call without an argument would block, return False immediately; otherwise, do the same thing as when called without arguments, and return True .
When invoked with a timeout other than None , it will block for at most timeout seconds. If acquire does not complete successfully in that interval, return False . Return True otherwise.
Changed in version 3.2: The timeout parameter is new.
Release a semaphore, incrementing the internal counter by n. When it was zero on entry and other threads are waiting for it to become larger than zero again, wake up n of those threads.
Changed in version 3.9: Added the n parameter to release multiple waiting threads at once.
Class implementing bounded semaphore objects. A bounded semaphore checks to make sure its current value doesn’t exceed its initial value. If it does, ValueError is raised. In most situations semaphores are used to guard resources with limited capacity. If the semaphore is released too many times it’s a sign of a bug. If not given, value defaults to 1.
Changed in version 3.3: changed from a factory function to a class.
Semaphore Example¶
Semaphores are often used to guard resources with limited capacity, for example, a database server. In any situation where the size of the resource is fixed, you should use a bounded semaphore. Before spawning any worker threads, your main thread would initialize the semaphore:
Once spawned, worker threads call the semaphore’s acquire and release methods when they need to connect to the server:
The use of a bounded semaphore reduces the chance that a programming error which causes the semaphore to be released more than it’s acquired will go undetected.
Event Objects¶
This is one of the simplest mechanisms for communication between threads: one thread signals an event and other threads wait for it.
An event object manages an internal flag that can be set to true with the set() method and reset to false with the clear() method. The wait() method blocks until the flag is true.
class threading. Event ¶
Class implementing event objects. An event manages a flag that can be set to true with the set() method and reset to false with the clear() method. The wait() method blocks until the flag is true. The flag is initially false.
Changed in version 3.3: changed from a factory function to a class.
Return True if and only if the internal flag is true.
The method isSet is a deprecated alias for this method.
Set the internal flag to true. All threads waiting for it to become true are awakened. Threads that call wait() once the flag is true will not block at all.
Reset the internal flag to false. Subsequently, threads calling wait() will block until set() is called to set the internal flag to true again.
wait ( timeout = None ) ¶
Block until the internal flag is true. If the internal flag is true on entry, return immediately. Otherwise, block until another thread calls set() to set the flag to true, 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).
This method returns True if and only if the internal flag has been set to true, either before the wait call or after the wait starts, so it will always return True except if a timeout is given and the operation times out.
Changed in version 3.1: Previously, the method always returned None .
Timer Objects¶
This class represents an action that should be run only after a certain amount of time has passed — a timer. Timer is a subclass of Thread and as such also functions as an example of creating custom threads.
Timers are started, as with threads, by calling their start() method. The timer can be stopped (before its action has begun) by calling the cancel() method. The interval the timer will wait before executing its action may not be exactly the same as the interval specified by the user.
Create a timer that will run function with arguments args and keyword arguments kwargs, after interval seconds have passed. If args is None (the default) then an empty list will be used. If kwargs is None (the default) then an empty dict will be used.
Changed in version 3.3: changed from a factory function to a class.
Stop the timer, and cancel the execution of the timer’s action. This will only work if the timer is still in its waiting stage.
Barrier Objects¶
New in version 3.2.
This class provides a simple synchronization primitive for use by a fixed number of threads that need to wait for each other. Each of the threads tries to pass the barrier by calling the wait() method and will block until all of the threads have made their wait() calls. At this point, the threads are released simultaneously.
The barrier can be reused any number of times for the same number of threads.
As an example, here is a simple way to synchronize a client and server thread:
Create a barrier object for parties number of threads. An action, when provided, is a callable to be called by one of the threads when they are released. timeout is the default timeout value if none is specified for the wait() method.
wait ( timeout = None ) ¶
Pass the barrier. When all the threads party to the barrier have called this function, they are all released simultaneously. If a timeout is provided, it is used in preference to any that was supplied to the class constructor.
The return value is an integer in the range 0 to parties – 1, different for each thread. This can be used to select a thread to do some special housekeeping, e.g.:
If an action was provided to the constructor, one of the threads will have called it prior to being released. Should this call raise an error, the barrier is put into the broken state.
If the call times out, the barrier is put into the broken state.
This method may raise a BrokenBarrierError exception if the barrier is broken or reset while a thread is waiting.
Return the barrier to the default, empty state. Any threads waiting on it will receive the BrokenBarrierError exception.
Note that using this function may require some external synchronization if there are other threads whose state is unknown. If a barrier is broken it may be better to just leave it and create a new one.
Put the barrier into a broken state. This causes any active or future calls to wait() to fail with the BrokenBarrierError . Use this for example if one of the threads needs to abort, to avoid deadlocking the application.
It may be preferable to simply create the barrier with a sensible timeout value to automatically guard against one of the threads going awry.
The number of threads required to pass the barrier.
The number of threads currently waiting in the barrier.
A boolean that is True if the barrier is in the broken state.
exception threading. BrokenBarrierError ¶
This exception, a subclass of RuntimeError , is raised when the Barrier object is reset or broken.
Using locks, conditions, and semaphores in the with statement¶
All of the objects provided by this module that have acquire() and release() methods can be used as context managers for a with statement. The acquire() method will be called when the block is entered, and release() will be called when the block is exited. Hence, the following snippet:
is equivalent to:
Currently, Lock , RLock , Condition , Semaphore , and BoundedSemaphore objects may be used as with statement context managers.
Maximum limit on number of threads in python
I am using Threading module in python. How to know how many max threads I can have on my system?
1 Answer 1
I am using Threading module in python. How to know how many max threads I can have on my system?
There doesn’t seem to be a hard-coded or configurable MAX value that I’ve ever found, but there is definitely a limit. Run the following program:
I suppose I should mention that this is using Python 3.
The first function, mythread() , is the function which will be executed as a thread. All it does is sleep for 1000 seconds then terminate.
The main() function is a for-loop which tries to start one million threads. The daemon property is set to True simply so that we don’t have to clean up all the threads manually.
If a thread cannot be created Python throws a RuntimeError . We catch that to break out of the for-loop and display the number of threads which were successfully created.
Because daemon is set True, all threads terminate when the program ends.
If you run it a few times in a row you’re likely to see that a different number of threads will be created each time. On the machine from which I’m posting this reply, I had a minimum 18,835 during one run, and a maximum of 18,863 during another run. And the more you fiddle with the code, as in, the more code you add to this in order to experiment or find more information, you’ll find the fewer threads can/will be created.
So, how to apply this to real world.
Well, a server may need the ability to start a triple-digit number of threads, but in most other cases you should re-evaluate your game plan if you think you’re going to be generating a large number of threads.
One thing you need to consider if you’re using Python: if you’re using a standard distribution of Python, your system will only execute one Python thread at a time, including the main thread of your program, so adding more threads to your program or more cores to your system doesn’t really get you anything when using the threading module in Python. You can research all of the pedantic details and ultracrepidarian opinions regarding the GIL / Global Interpreter Lock for more info on that.
What that means is that cpu-bound (computationally-intensive) code doesn’t benefit greatly from factoring it into threads.
I/O-bound (waiting for file read/write, network read, or user I/O) code, however, benefits greatly from multithreading! So, start a thread for each network connection to your Python-based server.
Threads can also be great for triggering/throwing/raising signals at set periods, or simply to block out the processing sections of your code more logically.
Учимся писать многопоточные и многопроцессные приложения на Python
Эта статья не для матёрых укротителей Python’а, для которых распутать этот клубок змей — детская забава, а скорее поверхностный обзор многопоточных возможностей для недавно подсевших на питон.
К сожалению по теме многопоточности в Python не так уж много материала на русском языке, а питонеры, которые ничего не слышали, например, про GIL, мне стали попадаться с завидной регулярностью. В этой статье я постараюсь описать самые основные возможности многопоточного питона, расскажу что же такое GIL и как с ним (или без него) жить и многое другое.
Python — очаровательный язык программирования. В нем прекрасно сочетается множество парадигм программирования. Большинство задач, с которыми может встретиться программист, решаются здесь легко, элегантно и лаконично. Но для всех этих задач зачастую достаточно однопоточного решения, а однопоточные программы обычно предсказуемы и легко поддаются отладке. Чего не скажешь о многопоточных и многопроцессных программах.
Многопоточные приложения
В Python есть модуль threading, и в нем есть все, что нужно для многопоточного программирования: тут есть и различного вида локи, и семафор, и механизм событий. Один словом — все, что нужно для подавляющего большинства многопоточных программ. Причем пользоваться всем этим инструментарием достаточно просто. Рассмотрим пример программы, которая запускает 2 потока. Один поток пишет десять “0”, другой — десять “1”, причем строго по-очереди.
Никакой магии и voodoo-кода. Код четкий и последовательный. Причем, как можно заметить, мы создали поток из функции. Для небольших задач это очень удобно. Этот код еще и достаточно гибкий. Допустим у нас появился 3-й процесс, который пишет “2”, тогда код будет выглядеть так:
Мы добавили новое событие, новый поток и слегка изменили параметры, с которыми
стартуют потоки (можно конечно написать и более общее решение с использованием, например, MapReduce, но это уже выходит за рамки этой статьи).
Как видим по-прежнему никакой магии. Все просто и понятно. Поехали дальше.
Global Interpreter Lock
Существуют две самые распространенные причины использовать потоки: во-первых, для увеличения эффективности использования многоядерной архитектуры cоврменных процессоров, а значит, и производительности программы;
во-вторых, если нам нужно разделить логику работы программы на параллельные полностью или частично асинхронные секции (например, иметь возможность пинговать несколько серверов одновременно).
В первом случае мы сталкиваемся с таким ограничением Python (а точнее основной его реализации CPython), как Global Interpreter Lock (или сокращенно GIL). Концепция GIL заключается в том, что в каждый момент времени только один поток может исполняться процессором. Это сделано для того, чтобы между потоками не было борьбы за отдельные переменные. Исполняемый поток получает доступ по всему окружению. Такая особенность реализации потоков в Python значительно упрощает работу с потоками и дает определенную потокобезопасность (thread safety).
Но тут есть тонкий момент: может показаться, что многопоточное приложение будет работать ровно столько же времени, сколько и однопоточное, делающее то же самое, или за сумму времени исполнения каждого потока на CPU. Но тут нас поджидает один неприятный эффект. Рассмотрим программу:
Эта программа просто пишет в файл миллион строк “1” и делает это за
0.35 секунды на моем компьютере.
Рассмотрим другую программу:
Эта программа создает 2 потока. В каждом потоке она пишет в отдельный файлик по пол миллиона строк “1”. По-сути объем работы такой же, как и у предыдущей программы. А вот со временем работы тут получается интересный эффект. Программа может работать от 0.7 секунды до аж 7 секунд. Почему же так происходит?
Это происходит из-за того, что когда поток не нуждается в ресурсе CPU — он освобождает GIL, а в этот момент его может попытаться получить и он сам, и другой поток, и еще и главный поток. При этом операционная система, зная, что ядер много, может усугубить все попыткой распределить потоки между ядрами.
UPD: на данный момент в Python 3.2 существует улучшенная реализация GIL, в которой эта проблема частично решается, в частности, за счет того, что каждый поток после потери управления ждет небольшой промежуток времени до того, как сможет опять захватить GIL (на эту тему есть хорошая презентация на английском)
«Выходит на Python нельзя писать эффективные многопоточные программы?», — спросите вы. Нет, конечно, выход есть и даже несколько.
Многопроцессные приложения
Для того, чтобы в некотором смысле решить проблему, описанную в предыдущем параграфе, в Python есть модуль subprocess. Мы можем написать программу, которую хотим исполнять в параллельном потоке (на самом деле уже процессе). И запускать ее в одном или нескольких потоках в другой программе. Такой способ действительно ускорил бы работу нашей программы, потому, что потоки, созданные в запускающей программе GIL не забирают, а только ждут завершения запущенного процесса. Однако, в этом способе есть масса проблем. Основная проблема заключается в том, что передавать данные между процессами становится трудно. Пришлось бы как-то сериализовать объекты, налаживать связь через PIPE или друге инструменты, а ведь все это несет неизбежно накладные расходы и код становится сложным для понимания.
Здесь нам может помочь другой подход. В Python есть модуль multiprocessing. По функциональности этот модуль напоминает threading. Например, процессы можно создавать точно так же из обычных функций. Методы работы с процессами почти все те же самые, что и для потоков из модуля threading. А вот для синхронизации процессов и обмена данными принято использовать другие инструменты. Речь идет об очередях (Queue) и каналах (Pipe). Впрочем, аналоги локов, событий и семафоров, которые были в threading, здесь тоже есть.
Кроме того в модуле multiprocessing есть механизм работы с общей памятью. Для этого в модуле есть классы переменной (Value) и массива (Array), которые можно “обобщать” (share) между процессами. Для удобства работы с общими переменными можно использовать классы-менеджеры (Manager). Они более гибкие и удобные в обращении, однако более медленные. Нельзя не отметить приятную возможность делать общими типы из модуля ctypes с помощью модуля multiprocessing.sharedctypes.
Еще в модуле multiprocessing есть механизм создания пулов процессов. Этот механизм очень удобно использовать для реализации шаблона Master-Worker или для реализации параллельного Map (который в некотором смысле является частным случаем Master-Worker).
Из основных проблем работы с модулем multiprocessing стоит отметить относительную платформозависимость этого модуля. Поскольку в разных ОС работа с процессами организована по-разному, то на код накладываются некоторые ограничения. Например, в ОС Windows нет механизма fork, поэтому точку разделения процессов надо оборачивать в:
Впрочем, эта конструкция и так является хорошим тоном.
Что еще .
Для написания параллельных приложений на Python существуют и другие библиотеки и подходы. Например, можно использовать Hadoop+Python или различные реализации MPI на Python (pyMPI, mpi4py). Можно даже использовать обертки существующих библиотек на С++ или Fortran. Здесь можно было упомянуть про такие фреймфорки/библиотеки, как Pyro, Twisted, Tornado и многие другие. Но это все уже выходит за пределы этой статьи.
Если мой стиль вам понравился, то в следующей статье постараюсь рассказать, как писать простые интерпретаторы на PLY и для чего их можно применять.