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Concurrency and race condition [duplicate]

Does the presence of python GIL imply that in python multi threading the same operation is not so different from repeating it in a single thread?.
For example, If I need to upload two files, what is the advantage of doing them in two threads instead of uploading them one after another?.
I tried a big math operation in both ways. But they seem to take almost equal time to complete.
This seems to be unclear to me. Can someone help me on this?.
Thanks.

Python's threads get a slightly worse rap than they deserve. There are three (well, 2.5) cases where they actually get you benefits:
If non-Python code (e.g. a C library, the kernel, etc.) is running, other Python threads can continue executing. It's only pure Python code that can't run in two threads at once. So if you're doing disk or network I/O, threads can indeed buy you something, as most of the time is spent outside of Python itself.
The GIL is not actually part of Python, it's an implementation detail of CPython (the "reference" implementation that the core Python devs work on, and that you usually get if you just run "python" on your Linux box or something.
Jython, IronPython, and any other reimplementations of Python generally do not have a GIL, and multiple pure-Python threads can execute simultaneously.
The 0.5 case: Even if you're entirely pure-Python and see little or no performance benefit from threading, some problems are really convenient in terms of developer time and difficulty to solve with threads. This depends in part on the developer, too, of course.

It really depends on the library you're using. The GIL is meant to prevent Python objects and its internal data structures to be changed at the same time. If you're doing an upload, the library you use to do the actual upload might release the GIL while it's waiting for the actual HTTP request to complete (I would assume that is the case with the HTTP modules in the standard library, but I didn't check).
As a side note, if you really want to have things running in parallel, just use multiple processes. It will save you a lot of trouble and you'll end up with better code (more robust, more scalable, and most probably better structured).

It depends on the native code module that's executing. Native modules can release the GIL and then go off and do their own thing allowing another thread to lock the GIL. The GIL is normally held while code, both python and native, are operating on python objects. If you want more detail you'll probably need to go and read quite a bit about it. :)
See:
What is a global interpreter lock (GIL)? and Thread State and the Global Interpreter Lock

Multithreading is a concept where two are more tasks need be completed simultaneously, for example, I have word processor in this application there are N numbers of a parallel task have to work. Like listening to keyboard, formatting input text, sending a formatted text to display unit. In this context with sequential processing, it is time-consuming and one task has to wait till the next task completion. So we put these tasks in threads and simultaneously complete the task. Three threads are always up and waiting for the inputs to arrive, then take that input and produce the output simultaneously.
So multi-threading works faster if we have multi-core and processors. But in reality with single processors, threads will work one after the other, but we feel it's executing with greater speed, Actually, one instruction executes at a time and a processor can execute billions of instructions at a time. So the computer creates illusion that multi-task or thread working parallel. It just an illusion.

Python GIL and multithreading

I would like to separate my sigle-thread application to number of working threads. Just 1 question - what about performance of this action? If GIL prevents python from executing more than 1 thread at the time will I have any profit?
Another point (from c/c++ point of view) - as I know each thread, anyway, can be only executed exclusively, so in the lower level than python interpreter I have the same limitation.
Summary: Will the the python threads have lesser efficiency that 'native' thread in part of task switching?

Don't worry about the GIL. Depending on the kinds of things your program does (calculation vs. I/O) you will have different performance characteristics. If your program is I/O bound then you probably won't notice the GIL at all.
Another approach is to use the multiprocessing module where each process runs in its own OS process with its own Python runtime. You can take full advantage of multiple cores with this approach, and it's usually safer because you don't have to worry about synchronising access to shared memory.

Python threads all executing on a single core

I have a Python program that spawns many threads, runs 4 at a time, and each performs an expensive operation. Pseudocode:
for object in list:
t = Thread(target=process, args=(object))
# if fewer than 4 threads are currently running, t.start(). Otherwise, add t to queue
But when the program is run, Activity Monitor in OS X shows that 1 of the 4 logical cores is at 100% and the others are at nearly 0. Obviously I can't force the OS to do anything but I've never had to pay attention to performance in multi-threaded code like this before so I was wondering if I'm just missing or misunderstanding something.
Thanks.

Note that in many cases (and virtually all cases where your "expensive operation" is a calculation implemented in Python), multiple threads will not actually run concurrently due to Python's Global Interpreter Lock (GIL).
The GIL is an interpreter-level lock.
This lock prevents execution of
multiple threads at once in the Python
interpreter. Each thread that wants to
run must wait for the GIL to be
released by the other thread, which
means your multi-threaded Python
application is essentially single
threaded, right? Yes. Not exactly.
Sort of.
CPython uses what’s called “operating
system” threads under the covers,
which is to say each time a request to
make a new thread is made, the
interpreter actually calls into the
operating system’s libraries and
kernel to generate a new thread. This
is the same as Java, for example. So
in memory you really do have multiple
threads and normally the operating
system controls which thread is
scheduled to run. On a multiple
processor machine, this means you
could have many threads spread across
multiple processors, all happily
chugging away doing work.
However, while CPython does use
operating system threads (in theory
allowing multiple threads to execute
within the interpreter
simultaneously), the interpreter also
forces the GIL to be acquired by a
thread before it can access the
interpreter and stack and can modify
Python objects in memory all
willy-nilly. The latter point is why
the GIL exists: The GIL prevents
simultaneous access to Python objects
by multiple threads. But this does not
save you (as illustrated by the Bank
example) from being a lock-sensitive
creature; you don’t get a free ride.
The GIL is there to protect the
interpreters memory, not your sanity.
See the Global Interpreter Lock section of Jesse Noller's post for more details.
To get around this problem, check out Python's multiprocessing module.
multiple processes (with judicious use
of IPC) are[...] a much better
approach to writing apps for multi-CPU
boxes than threads.
-- Guido van Rossum (creator of Python)
Edit based on a comment from #spinkus:
If Python can't run multiple threads simultaneously, then why have threading at all?
Threads can still be very useful in Python when doing simultaneous operations that do not need to modify the interpreter's state. This includes many (most?) long-running function calls that are not in-Python calculations, such as I/O (file access or network requests)) and [calculations on Numpy arrays][6]. These operations release the GIL while waiting for a result, allowing the program to continue executing. Then, once the result is received, the thread must re-acquire the GIL in order to use that result in "Python-land"

Python has a Global Interpreter Lock, which can prevent threads of interpreted code from being processed concurrently.
http://en.wikipedia.org/wiki/Global_Interpreter_Lock
http://wiki.python.org/moin/GlobalInterpreterLock
For ways to get around this, try the multiprocessing module, as advised here:
Does running separate python processes avoid the GIL?

AFAIK, in CPython the Global Interpreter Lock means that there can't be more than one block of Python code being run at any one time. Although this does not really affect anything in a single processor/single-core machine, on a mulitcore machine it means you have effectively only one thread running at any one time - causing all the other core to be idle.

What is the global interpreter lock (GIL) in CPython?

What is a global interpreter lock and why is it an issue?
A lot of noise has been made around removing the GIL from Python, and I'd like to understand why that is so important. I have never written a compiler nor an interpreter myself, so don't be frugal with details, I'll probably need them to understand.

Python's GIL is intended to serialize access to interpreter internals from different threads. On multi-core systems, it means that multiple threads can't effectively make use of multiple cores. (If the GIL didn't lead to this problem, most people wouldn't care about the GIL - it's only being raised as an issue because of the increasing prevalence of multi-core systems.) If you want to understand it in detail, you can view this video or look at this set of slides. It might be too much information, but then you did ask for details :-)
Note that Python's GIL is only really an issue for CPython, the reference implementation. Jython and IronPython don't have a GIL. As a Python developer, you don't generally come across the GIL unless you're writing a C extension. C extension writers need to release the GIL when their extensions do blocking I/O, so that other threads in the Python process get a chance to run.

Suppose you have multiple threads which don't really touch each other's data. Those should execute as independently as possible. If you have a "global lock" which you need to acquire in order to (say) call a function, that can end up as a bottleneck. You can wind up not getting much benefit from having multiple threads in the first place.
To put it into a real world analogy: imagine 100 developers working at a company with only a single coffee mug. Most of the developers would spend their time waiting for coffee instead of coding.
None of this is Python-specific - I don't know the details of what Python needed a GIL for in the first place. However, hopefully it's given you a better idea of the general concept.

Let's first understand what the python GIL provides:
Any operation/instruction is executed in the interpreter. GIL ensures that interpreter is held by a single thread at a particular instant of time. And your python program with multiple threads works in a single interpreter. At any particular instant of time, this interpreter is held by a single thread. It means that only the thread which is holding the interpreter is running at any instant of time.
Now why is that an issue:
Your machine could be having multiple cores/processors. And multiple cores allow multiple threads to execute simultaneously i.e multiple threads could execute at any particular instant of time..
But since the interpreter is held by a single thread, other threads are not doing anything even though they have access to a core. So, you are not getting any advantage provided by multiple cores because at any instant only a single core, which is the core being used by the thread currently holding the interpreter, is being used. So, your program will take as long to execute as if it were a single threaded program.
However, potentially blocking or long-running operations, such as I/O, image processing, and NumPy number crunching, happen outside the GIL. Taken from here. So for such operations, a multithreaded operation will still be faster than a single threaded operation despite the presence of GIL. So, GIL is not always a bottleneck.
Edit: GIL is an implementation detail of CPython. IronPython and Jython don't have GIL, so a truly multithreaded program should be possible in them, thought I have never used PyPy and Jython and not sure of this.

Python 3.7 documentation
I would also like to highlight the following quote from the Python threading documentation:
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.
This links to the Glossary entry for global interpreter lock which explains that the GIL implies that threaded parallelism in Python is unsuitable for CPU bound tasks:
The mechanism used by the CPython interpreter to assure that only one thread executes Python bytecode at a time. This simplifies the CPython implementation by making the object model (including critical built-in types such as dict) implicitly safe against concurrent access. Locking the entire interpreter makes it easier for the interpreter to be multi-threaded, at the expense of much of the parallelism afforded by multi-processor machines.
However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally-intensive tasks such as compression or hashing. Also, the GIL is always released when doing I/O.
Past efforts to create a “free-threaded” interpreter (one which locks shared data at a much finer granularity) have not been successful because performance suffered in the common single-processor case. It is believed that overcoming this performance issue would make the implementation much more complicated and therefore costlier to maintain.
This quote also implies that dicts and thus variable assignment are also thread safe as a CPython implementation detail:
Is Python variable assignment atomic?
Thread Safety in Python's dictionary
Next, the docs for the multiprocessing package explain how it overcomes the GIL by spawning process while exposing an interface similar to that of threading:
multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows.
And the docs for concurrent.futures.ProcessPoolExecutor explain that it uses multiprocessing as a backend:
The ProcessPoolExecutor class is an Executor subclass that uses a pool of processes to execute calls asynchronously. ProcessPoolExecutor uses the multiprocessing module, which allows it to side-step the Global Interpreter Lock but also means that only picklable objects can be executed and returned.
which should be contrasted to the other base class ThreadPoolExecutor that uses threads instead of processes
ThreadPoolExecutor is an Executor subclass that uses a pool of threads to execute calls asynchronously.
from which we conclude that ThreadPoolExecutor is only suitable for I/O bound tasks, while ProcessPoolExecutor can also handle CPU bound tasks.
Process vs thread experiments
At Multiprocessing vs Threading Python I've done an experimental analysis of process vs threads in Python.
Quick preview of the results:
In other languages
The concept seems to exist outside of Python as well, applying just as well to Ruby for example: https://en.wikipedia.org/wiki/Global_interpreter_lock
It mentions the advantages:
increased speed of single-threaded programs (no necessity to acquire or release locks on all data structures separately),
easy integration of C libraries that usually are not thread-safe,
ease of implementation (having a single GIL is much simpler to implement than a lock-free interpreter or one using fine-grained locks).
but the JVM seems to do just fine without the GIL, so I wonder if it is worth it. The following question asks why the GIL exists in the first place: Why the Global Interpreter Lock?

Python doesn't allow multi-threading in the truest sense of the word. It has a multi-threading package but if you want to multi-thread to speed your code up, then it's usually not a good idea to use it. Python has a construct called the Global Interpreter Lock (GIL).
https://www.youtube.com/watch?v=ph374fJqFPE
The GIL makes sure that only one of your 'threads' can execute at any one time. A thread acquires the GIL, does a little work, then passes the GIL onto the next thread. This happens very quickly so to the human eye it may seem like your threads are executing in parallel, but they are really just taking turns using the same CPU core. All this GIL passing adds overhead to execution. This means that if you want to make your code run faster then using the threading package often isn't a good idea.
There are reasons to use Python's threading package. If you want to run some things simultaneously, and efficiency is not a concern, then it's totally fine and convenient. Or if you are running code that needs to wait for something (like some IO) then it could make a lot of sense. But the threading library wont let you use extra CPU cores.
Multi-threading can be outsourced to the operating system (by doing multi-processing), some external application that calls your Python code (eg, Spark or Hadoop), or some code that your Python code calls (eg: you could have your Python code call a C function that does the expensive multi-threaded stuff).

Whenever two threads have access to the same variable you have a problem.
In C++ for instance, the way to avoid the problem is to define some mutex lock to prevent two thread to, let's say, enter the setter of an object at the same time.
Multithreading is possible in python, but two threads cannot be executed at the same time
at a granularity finer than one python instruction.
The running thread is getting a global lock called GIL.
This means if you begin write some multithreaded code in order to take advantage of your multicore processor, your performance won't improve.
The usual workaround consists of going multiprocess.
Note that it is possible to release the GIL if you're inside a method you wrote in C for instance.
The use of a GIL is not inherent to Python but to some of its interpreter, including the most common CPython.
(#edited, see comment)
The GIL issue is still valid in Python 3000.

Why Python (CPython and others) uses the GIL
From http://wiki.python.org/moin/GlobalInterpreterLock
In CPython, the global interpreter lock, or GIL, is a mutex that prevents multiple native threads from executing Python bytecodes at once. This lock is necessary mainly because CPython's memory management is not thread-safe.
How to remove it from Python?
Like Lua, maybe Python could start multiple VM, But python doesn't do that, I guess there should be some other reasons.
In Numpy or some other python extended library, sometimes, releasing the GIL to other threads could boost the efficiency of the whole programme.

I want to share an example from the book multithreading for Visual Effects. So here is a classic dead lock situation
static void MyCallback(const Context &context){
Auto<Lock> lock(GetMyMutexFromContext(context));
...
EvalMyPythonString(str); //A function that takes the GIL
...
}
Now consider the events in the sequence resulting a dead-lock.
╔═══╦════════════════════════════════════════╦══════════════════════════════════════╗
║ ║ Main Thread ║ Other Thread ║
╠═══╬════════════════════════════════════════╬══════════════════════════════════════╣
║ 1 ║ Python Command acquires GIL ║ Work started ║
║ 2 ║ Computation requested ║ MyCallback runs and acquires MyMutex ║
║ 3 ║ ║ MyCallback now waits for GIL ║
║ 4 ║ MyCallback runs and waits for MyMutex ║ waiting for GIL ║
╚═══╩════════════════════════════════════════╩══════════════════════════════════════╝

How do threads work in Python, and what are common Python-threading specific pitfalls?

I've been trying to wrap my head around how threads work in Python, and it's hard to find good information on how they operate. I may just be missing a link or something, but it seems like the official documentation isn't very thorough on the subject, and I haven't been able to find a good write-up.
From what I can tell, only one thread can be running at once, and the active thread switches every 10 instructions or so?
Where is there a good explanation, or can you provide one? It would also be very nice to be aware of common problems that you run into while using threads with Python.

Yes, because of the Global Interpreter Lock (GIL) there can only run one thread at a time. Here are some links with some insights about this:
http://www.artima.com/weblogs/viewpost.jsp?thread=214235
http://smoothspan.wordpress.com/2007/09/14/guido-is-right-to-leave-the-gil-in-python-not-for-multicore-but-for-utility-computing/
From the last link an interesting quote:
Let me explain what all that means.
Threads run inside the same virtual
machine, and hence run on the same
physical machine. Processes can run
on the same physical machine or in
another physical machine. If you
architect your application around
threads, you’ve done nothing to access
multiple machines. So, you can scale
to as many cores are on the single
machine (which will be quite a few
over time), but to really reach web
scales, you’ll need to solve the
multiple machine problem anyway.
If you want to use multi core, pyprocessing defines an process based API to do real parallelization. The PEP also includes some interesting benchmarks.

Python's a fairly easy language to thread in, but there are caveats. The biggest thing you need to know about is the Global Interpreter Lock. This allows only one thread to access the interpreter. This means two things: 1) you rarely ever find yourself using a lock statement in python and 2) if you want to take advantage of multi-processor systems, you have to use separate processes. EDIT: I should also point out that you can put some of the code in C/C++ if you want to get around the GIL as well.
Thus, you need to re-consider why you want to use threads. If you want to parallelize your app to take advantage of dual-core architecture, you need to consider breaking your app up into multiple processes.
If you want to improve responsiveness, you should CONSIDER using threads. There are other alternatives though, namely microthreading. There are also some frameworks that you should look into:
stackless python
greenlets
gevent
monocle

Below is a basic threading sample. It will spawn 20 threads; each thread will output its thread number. Run it and observe the order in which they print.
import threading
class Foo (threading.Thread):
def __init__(self,x):
self.__x = x
threading.Thread.__init__(self)
def run (self):
print str(self.__x)
for x in xrange(20):
Foo(x).start()
As you have hinted at Python threads are implemented through time-slicing. This is how they get the "parallel" effect.
In my example my Foo class extends thread, I then implement the run method, which is where the code that you would like to run in a thread goes. To start the thread you call start() on the thread object, which will automatically invoke the run method...
Of course, this is just the very basics. You will eventually want to learn about semaphores, mutexes, and locks for thread synchronization and message passing.

Note: wherever I mention thread i mean specifically threads in python until explicitly stated.
Threads work a little differently in python if you are coming from C/C++ background. In python, Only one thread can be in running state at a given time.This means Threads in python cannot truly leverage the power of multiple processing cores since by design it's not possible for threads to run parallelly on multiple cores.
As the memory management in python is not thread-safe each thread require an exclusive access to data structures in python interpreter.This exclusive access is acquired by a mechanism called GIL ( global interpretr lock ).
Why does python use GIL?
In order to prevent multiple threads from accessing interpreter state simultaneously and corrupting the interpreter state.
The idea is whenever a thread is being executed (even if it's the main thread), a GIL is acquired and after some predefined interval of time the
GIL is released by the current thread and reacquired by some other thread( if any).
Why not simply remove GIL?
It is not that its impossible to remove GIL, its just that in prcoess of doing so we end up putting mutiple locks inside interpreter in order to serialize access, which makes even a single threaded application less performant.
so the cost of removing GIL is paid off by reduced performance of a single threaded application, which is never desired.
So when does thread switching occurs in python?
Thread switch occurs when GIL is released.So when is GIL Released?
There are two scenarios to take into consideration.
If a Thread is doing CPU Bound operations(Ex image processing).
In Older versions of python , Thread switching used to occur after a fixed no of python instructions.It was by default set to 100.It turned out that its not a very good policy to decide when switching should occur since the time spent executing a single instruction can
very wildly from millisecond to even a second.Therefore releasing GIL after every 100 instructions regardless of the time they take to execute is a poor policy.
In new versions instead of using instruction count as a metric to switch thread , a configurable time interval is used.
The default switch interval is 5 milliseconds.you can get the current switch interval using sys.getswitchinterval().
This can be altered using sys.setswitchinterval()
If a Thread is doing some IO Bound Operations(Ex filesystem access or
network IO)
GIL is release whenever the thread is waiting for some for IO operation to get completed.
Which thread to switch to next?
The interpreter doesn’t have its own scheduler.which thread becomes scheduled at the end of the interval is the operating system’s decision. .

Use threads in python if the individual workers are doing I/O bound operations. If you are trying to scale across multiple cores on a machine either find a good IPC framework for python or pick a different language.

One easy solution to the GIL is the multiprocessing module. It can be used as a drop in replacement to the threading module but uses multiple Interpreter processes instead of threads. Because of this there is a little more overhead than plain threading for simple things but it gives you the advantage of real parallelization if you need it.
It also easily scales to multiple physical machines.
If you need truly large scale parallelization than I would look further but if you just want to scale to all the cores of one computer or a few different ones without all the work that would go into implementing a more comprehensive framework, than this is for you.

Try to remember that the GIL is set to poll around every so often in order to do show the appearance of multiple tasks. This setting can be fine tuned, but I offer the suggestion that there should be work that the threads are doing or lots of context switches are going to cause problems.
I would go so far as to suggest multiple parents on processors and try to keep like jobs on the same core(s).