Asynchronous programming with asyncio
Asynchronous programming in the Python programming language is a programming technique, method, or concept that allows starting a task in the current Python program, continuing to run other programming tasks while waiting for that task to complete, and then returning to the previous programming task when the system is ready.
In the Python programming language, asynchronous programming is primarily supported by the asyncio module.
What is asynchronous programming in Python?
In a normal synchronous programming task process in Python, multiple system tasks defined by the user programmer are normally run or executed one after the other.
Example of asynchronous programming task.
Programming Task 1 → Wait Process → Complete
Programming Task 2 → Wait Process → Complete
Programming Task 3 → Wait Process → Complete
Programming Task 4 → Wait Process → Complete
Programming Task 5 → Wait Process → Complete
In this asynchronous programming task, task 1 is waiting for a network system response, so the current program may have to wait for the task process to complete before proceeding. With Python asynchronous programming, while task 1 is waiting on the current system process, the program can simultaneously process other system tasks.
Programming Task 1 → Wait ─────────→ Complete
Programming Task 2 → Work → Complete
Programming Task 3 → Work → Complete
Programming Task 4 → Work → Complete
Programming Task 5 → Work → Complete
Asynchronous programming tasks can make your applications more efficient when they wait for a long time for external system operations.
What is asynchronous in Python?
Asynchronous is a standard Python plugin system library in the Python programming language, which allows Python users to create program source code simultaneously by using the asynchronous/await syntax in a program.
Asynchronous programming tasks are particularly useful for I/O-bound operations. Such as,
- Network request processing
- Client-server web server
- Large database operation management
- Reading and writing data from a database
- Communicating with system APIs
- Handling multiple network system database connections
Instead of creating a separate thread for each awaiting system operation, an asynchronous program can manage or handle multiple tasks simultaneously using an event loop.
async keyword in Python.
The async module keyword in the Python programming language is used to create or define an asynchronous function, known as a coroutine in Python.
Example of the async keyword.
import asyncio
async def message():
print(“Welcome, to Python”)
asyncio.run(message())
Output of the async keyword.
Welcome, to Python.
The function message() in this program is defined as a coroutine function.
The await keyword in Python.
The await keyword in the Python programming language is used to await the completion of another asynchronous function within an already asynchronous function.
Example of the await keyword.
import asyncio
async def task():
print(“Task is running”)
await asyncio.sleep(4)
print(“Task done”)
asyncio.run(task())
The await keyword here.
await asyncio.sleep(4)
This means that the coroutine in the current programming task is paused for 4 seconds. During this time, the program’s event loop can run other asynchronous tasks.
What is the event loop in Python programming?
In Python programming, the event loop is a central part of asynchronous programming tasks.
It handles or manages asynchronous tasks in the current program system process and determines which tasks should run first. A simple Python event loop task process looks something like this.
System Programming Event Loop.
↓
┌────────┼───────┐
↓ ↓ ↓
Programming Task 1 Programming Task 2 Programming Task 3
↓ ↓ ↓
Process Waiting Process Running Process Waiting
↓ ↓ ↓
└───────┼───────┘
↓
Task Continue.
When a task in the current system process is waiting for an I/O operation, the event loop can allow other system tasks to run.
Running Multiple Tasks in Python Programming.
One of the best advantages of the asyncio keyword in Python programming is that Python users can run multiple asynchronous tasks simultaneously within a single program system.
Example of Running Multiple Tasks.
import asyncio
async def process(empname, seconds):
print(f”{empname} running”)
await asyncio.sleep(seconds)
print(f”{empname} done”)
async def main():
await asyncio.gather(
process(“Process 1”, 2),
process(“Process 2”, 1),
process(“Process 3”, 3)
)
asyncio.run(main()).
In Python programming, user-defined tasks can proceed simultaneously, instead of waiting for each system programming task to terminate before starting the next task.
One possible output of asyncio.run(main()) is.
Programming process task 1 started
Programming process task 2 started
Programming process task 3 started
Programming process task 2 finished
Programming process task 1 finished
Programming process task 3 finished
Remember that a system programming task 2 terminates before programming task 1, even if programming task 1 started first.
asyncio.create_task() in Python.
In Python programming, the asyncio.create_task() function method can be used to schedule a coroutine as a system programming task.
Example of asyncio.create_task().
import asyncio
async def download():
print(“Data Downloading…”)
await asyncio.sleep(5)
print(“Data Download Process Is Done”)
async def main():
task = asyncio.create_task(download())
print(“You want another task…”)
await task
asyncio.run(main()).
Here, the download task can be scheduled in this task process while the program continues to work on other asynchronous tasks.
Synchronous vs. Asynchronous Programming in Python.
Synchronous Python Programming.
Programming Task X → Complete
↓
Programming Task Y → Complete
↓
Programming Task Z → Complete
Here, each synchronous task waits on the previous task in the system.
Asynchronous Python Programming.
Programming Task X → Waiting ───────→ Complete
Programming Task Y → Work → Complete
Programming Task Z → Waiting ───→ Complete
When an asynchronous task is in program waiting order, it can automatically switch between tasks.
Asynchronous vs. Threading Concepts in Python.
In Python programming, both asynchronous and threading can be very useful for I/O-bound system programming operations, but you can use asynchronous and threading tasks in different ways.
| Each feature | Asyncio module | Threading method |
| Execution model behaviour | Work with event loop concept | Wok like task in multiple threads |
| Main syntax | Declare with async / await keyword | Create with multiple threading task |
| Memory usages | It is shared within the module process | It is shared within the number of thread process |
| Context switching method | It is cooperative for task | It is managed by runtime/os method |
| Best uses for | Asyncio keyword used for large amounts of i/o-bound concurrency operation | Thread is used for i/o-bound tasks and apis, that are easier to use with blocking code method |
| Cpu-bound task | Asyncio module is not a solution by itself | Thread is not generally effective for cpu-bound python code in cpython library |
| Number of concurrent operations | Asyncio can handle many lightweight system program tasks | Many threads can have greater system overhead |
Advantages of Asynchronous in Python Programming.
Efficient I/O Management.
The asynchronous task concept in Python programming is very useful when your application spends a significant amount of time waiting for I/O system programming tasks.
Handles multiple task connections.
This is useful for system programming task applications that need to manage multiple network connections simultaneously within a system.
Reduced system task overhead.
Asynchronous programming tasks are generally more lightweight than creating a large number of threads.
Easier system task coordination.
Functions like asyncio.gather()
This function makes it easy to run multiple asynchronous operations simultaneously.
Useful for system network applications.
Asynchronous tasks are commonly used in these system applications:
- Client-server web servers
- WebSockets processing
- API handling
- Network client request management
- Asynchronous database access tasks
Limitations of Python asyncio programming.
- The asyncio keyword in the Python library does not automatically make processing faster for every program.
- The asyncio keyword library performs best when program system tasks take a significant amount of time waiting for I/O system operations.
Example of the asyncio keyword.
In a CPU-heavy programming system task, an operation such as
for p in range(100000000):
# complex calculation
pass
Here, this process may block the system event loop if it runs within a direct asynchronous program.
For CPU-intensive system programming tasks, alternatives such as multiprocessing or specialized native libraries may be more appropriate and correct.
Another important point is that blocking functions should generally not be called within a direct event loop, as they can prevent other asynchronous programming tasks from running.
Example from the real world of synchronous and asynchronous programming.
Suppose here is a Python program task that collects data information from 70 websites.
Synchronous data collection method.
Website Task Data 1 → Wait → Complete
Website Task Data 2 → Wait → Complete
Website Task Data 3 → Wait → Complete
Website Task Data 4 → Wait → Complete
Website Task Data 5 → Wait → Complete
…
Website Task Data 70 → Complete
This system process can take a long time because each system task request can spend most of its time waiting.
Asynchronous data collection method.
Website Data Request 1 ── Waiting
Website Data Request 2 ── Waiting
Website Data Request 3 ── Waiting
Website Data Request 4 ── Response
Website Data Request 5 ── Waiting
…
While one request is waiting in the system, the event loop can allow other requests to proceed.
For this reason, asynchronous programming is especially useful for network-heavy applications.
Essential asyncio features in Python.
| # | asyncio Module Term | Description in detail |
| 1. | Async keyword | Async keyword is used to defines a coroutine function task |
| 2. | Await keyword | Await keyword is used to Suspends a coroutine until an awaitable completes task |
| 3. | Coroutine keyword | An asynchronous function/object, that can be paused and resumed task |
| 4. | Event loop | It is used to Manages and schedules asynchronous system tasks |
| 5. | Task | User define A scheduled coroutine task or program method |
| 6. | asyncio.run() function | This function used to Runs an asynchronous entry-point coroutine |
| 7. | asyncio.gather() function | This function used to Runs multiple awaitables concurrently and collects their results |
| 8. | asyncio.sleep() function | This function used to Asynchronously waits for a specified amount of time |
Conclusion on asynchronous programming with asyncio.
Asynchronous programming with the asyncio keyword library in the Python programming language helps Python programs efficiently handle and manage multiple I/O-bound program tasks without the need to create separate threads for each individual program task.
The most important concepts of the asyncio keyword library are.
- async → This keyword defines an asynchronous function in a Python program.
- await → This keyword allows a program to wait for a task to process without blocking the event loop.
- event loop → This keyword efficiently manages or handles asynchronous tasks.
- asyncio → This is a library or keyword module that provides Python users with multiple tools for asynchronous programming.
In simple terms, the asyncio library allows Python users to execute tasks in one program while another system program is waiting for the task, making it especially helpful for network applications, system APIs, web servers, and other I/O-heavy CPU-based programs.
