Threading vs. multiprocessing
Threading and multiprocessing are two popular methods for creating and running multiple individual programming tasks simultaneously in Python. Both threading and multiprocessing can help Python user programmers solve problems by dividing an existing program into multiple tasks. However, threading and multiprocessing work in different ways or orders in Python, and are the best choices for solving different types of Python programming issues.
What is threading in the Python programming language?
Threading in the Python programming language means that multiple threads are created and run simultaneously within a single program task in multiple program processes.
A thread is a small execution unit (a method or task) within a group task in a Python program. Multiple program threads can share the same memory and system resources within a single process.
Python provides a threading module to allow programming users to create and manage a new thread.
Example of Python threading.
import threading
def tasks():
print(“Thread task is active now”)
thrd1 = threading.Thread(target=tasks)
thrd2 = threading.Thread(target=tasks)
thrd1.start()
thrd2.start()
thrd1.join()
thrd2.join()
Here, in this threading example, two threads are created to execute the tasks() function.
Advantages of threading in the Python programming language.
- Threading is a lightweight task compared to a task process.
- Multiple threads used in the system easily group together and share memory and resources.
- Threading is useful for tasks that require the system user to wait for a task process to complete, or for defined tasks.
Threading is an excellent choice for I/O operations in the system.
For example.
- Reading files from system storage locations
- Processing network requests within a task
- Downloading data from a dedicated web server
- Waiting for user input into the system
Disadvantages of threading task processes.
- Sometimes, shared memory and resources within a single threading task group can lead to synchronization issues and problems.
- If threading task data is not handled or managed carefully, threads in the system can interfere with each other’s tasks.
- In CPython, the Global Interpreter Lock (GIL) limits the simultaneous execution of Python bytecode by multiple threads. Because of this, threading does not generally provide speedups for CPU-bound Python program source code.
What is multiprocessing in the Python programming language?
Multiprocessing in the Python programming language means that a Python user can run or process multiple individual task processes simultaneously. In the Python programming language, each task process has its own separate memory space and is defined by the Python interpreter.
Python provides the multiprocessing module to allow programmers to create and manage a new process.
Example of Python multiprocessing.
import multiprocessing
def tasks():
print(“Multiprocessing Task is active now”)
prs1 = multiprocessing.Process(target=tasks)
prs1.start()
prs2.start()
prs1.join()
prs2.join()
Here, two separate individual processes run the tasks() function in this program.
Advantages of multiprocessing in Python programming.
- In Python programming, multiprocessing task processes can run on different CPU cores.
- Multiprocessing Python programs are the best choice for CPU-intensive tasks.
- Python multiprocessing features avoid the major drawback of the CPython GIL for CPU-bound tasks.
- In multiprocessing, each task process has its own memory, thereby avoiding some of the shared-memory issues and problems of multiprocessing tasks.
Disadvantages of multiprocessing in Python programming.
- In Python multiprocessing, threading uses more system memory than a task.
- Creating a new task process in multiprocessing is generally more costly than creating a new thread.
- Sharing task data between multiprocessing process tasks becomes more complex.
- Communication between multiprocessing task processes can incur higher overhead.
Threading vs. multiprocessing concepts in Python
| Each feature | Threading concept in python | Multiprocessing concept in python |
| Basic unit method | Thread creates with individual group tasks | Process creates individual separate for all one or more task |
| Memory usages | Multiple thread shared group memory resources | It provides separate memory resources for each process task |
| Resource usage | Thread task usage lower memory resources | Multiprocessing usage higher memory resources |
| Creation method | Thead task generally works faster | Multiprocessing process generally slower |
| Best for use | Thead task best use for i/o-bound tasks | Multiprocessing process best for cpu-bound tasks |
| Communication channel | Thead task relatively easy through shared memory behaviour | Multiprocessing process requires inter-process communication |
| Effect of cpython gil method | Gil thread task important for cpu-bound code | Multiprocessing process each process has its own interpreter/gil method |
| For example, | Thead task usages in client server network requests | Multiprocessing process usages in image/video data processing |
Input/Output: I/O-bound vs. CPU-bound tasks.
Understanding these two types of input/output system tasks in Python programming helps Python users manage whether to use threading or the multiprocessing task process method in a Python program.
I/O-bound Tasks Python Tasks.
In Python programming, an I/O-bound system task spends most of its time waiting for external system task process operations.
Examples of Python I/O-bound Tasks.
- Downloading files and data from a dedicated web server
- Handling or managing API requests
- Reading the desired file from a database
- Reading and writing files in the current system
For these Python system tasks, threading features can be useful because while the system is waiting for one process thread, another thread can perform work in that process.
Python CPU-bound Tasks.
In Python programming, a CPU-bound process task spends most of its time performing task calculations.
Examples of Python CPU-bound tasks.
- It is used to handle complex mathematical process calculations in Python programming.
- In Python graphical image graphics object processing
- In large-volume system data calculations
- In some scientific simulation process task handling
To manage CPU-intensive task processes in standard CPython, a multiprocessing task is often a better choice because it allows separate processes to run Python program source code on multiple CPU cores.
A simple example of threading and multiprocessing in Python programming.
Here, Python users imagine a hotel restaurant.
Threading concept in Python.
A hotel chef may have multiple customer food orders at a time.
A hotel chef starts or processes an order, then works on other client orders while waiting for some food items from other customers to be cooked.
One process → Multiple threads share resources.
Multiprocessing concept in Python.
Now this time, let the Python user imagine multiple chefs working simultaneously in a hotel, each working on a different food customer order.
Multiple processes → Individual workers with their own resources.
When should a Python user use which?
Use threading in Python when.
If your program in Python programming spends a lot of time waiting for input/output system task process operations.
For example.
Multiple clients downloading or uploading files to a dedicated web FTP server location.
↓
Thread Task 1 → File 1
Thread Task 2 → File 2
Thread Task 3 → File 3
Thread Task 4 → File 4
Thread Task 5 → File 5
…
Use multiprocessing in Python when.
In Python programming, user-created program tasks or processes require a lot of CPU computation.
For example.
Process 1 → CPU calculation 1 → CPU core 1
Process 2 → CPU calculation 2 → CPU core 2
Process 3 → CPU calculation 3 → CPU core 3
Process 4 → CPU calculation 4 → CPU core 4
Process 5 → CPU calculation 5 → CPU core 5
Important Note in Python → GIL.
When analyzing concurrency features in Python, the Global Interpreter Lock (GIL) is an important concept.
In the standard CPython implementation concept, the GIL method allows only one system user-created thread to run Python bytecode within a specific interpreter at a time. Because of this, simply adding multiple threads does not typically speed up the execution of CPU-bound Python program source code.
Threads in Python can still be a useful feature for I/O-bound programs, as threads can proceed even while other threads are waiting for their turn to access I/O system resources.
Multiprocessing in Python tasks utilizes multiple individual system task processes, so each system task process defines its own Python interpreter and GIL.
Conclusion of Python Threading vs. Multiprocessing.
In the Python programming language, both threading and multiprocessing are popular techniques or methods for processing multiple tasks.
- Threading tasks → is the best choice for many I/O-bound tasks.
- Multiprocessing → is the best choice for CPU-bound tasks in most cases.
- Threading tasks → in a Python program, create threads that share a single memory in a group of tasks.
- Multiprocessing tasks → each user-defined Python process task has its own individual memory.
There’s an easy rule to remember for both threading and multiprocessing in the Python programming language.
- Waiting → means threading tasks.
- Computing → means each task has its own individual task (multiprocessing).
