Asynchronous programming is becoming increasingly prevalent in today’s technologically advanced world, and it’s essential to understand how it differs from synchronous programming. Asynchronous programming is a powerful technique that is commonly used in web development, data processing, and other applications that rely on network resources.
This article aims to provide a comprehensive understanding of asynchronous programming.
Synchronous and Asynchronous Programming
The programming paradigm known as synchronous programming involves making an application pause until the current task is complete before going on to the next one. The development model is based on the single-threaded execution model and processes a task after it has finished executing the previous one.
Synchronous programming is straightforward to understand, but it has some limitations. One of the major limitations of synchronous programming is that it is time-consuming, particularly when tasks are complex.
Due to its inefficiency and limitations, it has been replaced by asynchronous programming. Asynchronous programming, on the other hand, is a more efficient way of handling tasks.
The term “asynchronous” refers to the ability to perform tasks without waiting for a previous task to be completed. It is a paradigm that allows multiple tasks to be performed simultaneously with minimal resource usage.
Numerous programming languages, including Python, support asynchronous programming.
Advantages of Using Asynchronous Programming
The Python programming language is an excellent example of a language that supports asynchronous programming. Python 3.4 and later versions come with significant additions specifically designed to make developing asynchronous programs straightforward.
The async/await syntax can be used to write cleaner and more readable code when performing tasks, and the asyncio library makes it easy to perform tasks asynchronously. There are several advantages of using asynchronous programming, including:
1.
Resource Efficiency: Asynchronous programming uses fewer resources since it does not use an entire thread for each task. Instead, it dispatches tasks to event loops that manage task scheduling and switch between tasks when they are waiting for input/output operations.
2. Faster Task Completion: Asynchronous programming parallelizes tasks and avoids blocking, allowing for faster completion times.
It gives the opportunity for several tasks to be executed at once, enabling faster execution times compared to synchronous programming. 3.
Simplified Code Structure: Asynchronous programming simplifies code structure since it avoids the need for multiple threads, locks, and other threading mechanisms. The code is easier to manage due to better control over the flow of execution.
Example of a Synchronous Web Server
One example of a synchronous application is a typical web server. A web server that uses synchronous programming creates a new thread for each client request to process the request.
As a result, the server can only handle a finite number of requests simultaneously. Any additional request after the server has reached the limit has to wait for a response.
The web server’s primary goal is to process requests and dispatch responses to clients in the shortest possible time frame. A web server that uses synchronous programming achieves this by processing requests one at a time, which makes it relatively slow when there are numerous requests that need to be handled concurrently.
The Importance of Blocking and Non-Blocking Code
When programming in an asynchronous environment, it is essential to understand the difference between blocking and non-blocking code. A blocking call stops the execution of a script until the call is completed, which may take several seconds or minutes, depending on the task being performed.
As a result, the application becomes unresponsive, and the end-user has to wait for the call to end before the application can continue. To overcome this issue, asynchronous programming uses non-blocking code, which allows other tasks to be executed while a call is being processed.
In other words, non-blocking code provides optimal usage of available resources since you can start a task without having to wait for it to finish first. Programming Parents: A Metaphor for Asynchronous Programming
Asynchronous programming can be illustrated by a metaphor of parents trying to cook dinner while keeping an eye on their baby.
In this metaphor, if the parent is performing a task like cooking dinner (which takes some time), they can’t stop looking after the baby. They must keep an eye on the baby every few minutes to ensure their safety.
If the parent’s cooking task isn’t completed in time, they can assign a job of keeping an eye on the baby to someone else while they complete their task. In this case, they are using asynchronous programming since both the tasks (cooking and child care) were progressing simultaneously.
With a multi-tasking environment like this, the parents have the flexibility to move between tasks without having to wait for one task to complete before moving to the next.
Conclusion
Asynchronous programming offers significant benefits over synchronous programming and is becoming increasingly useful in today’s technologically driven world. It provides faster and more efficient completion of tasks, offers greater control over the flow of execution, and is much easier to manage than its synchronous counterpart.
Understanding how asynchronous programming works and its advantages and limitations is essential to delivering successful applications that meet the needs of our increasingly connected world. Asynchronous programming in Python is becoming increasingly popular due to the language’s support for it.
Compared to synchronous programming, asynchronous programming executes tasks with minimal resource usage and faster completion time. Python’s asyncio library is an essential feature that allows developers to take advantage of the advantages of asynchronous programming.
In this article, we’ll explore several practical examples to demonstrate how we can take advantage of Python’s async features to provide a more efficient and effective way to work with multiple tasks.
Synchronous Programming Example
Let’s start with a simple example of synchronous programming. Suppose we have a queue with several tasks waiting to be executed.
Assuming that each task takes a significant amount of time to execute, using synchronous programming with a single threaded application will lead to longer wait times. With synchronous programming, each task needs to be completed before processing the next.
For a simple synchronous program, we can do something like this:
“`
import queue
def process_task(task):
# Process a task
pass
def main():
# Receive a queue of tasks
task_queue = queue.Queue()
while not task_queue.empty():
task = task_queue.get()
process_task(task)
“`
This synchronous code works fine if the task’s processing time is relatively short, but if a task takes a long time to complete, the execution will be blocked during that period. The code will wait for the completion of the current task before processing the next.
Simple Cooperative Concurrency
Python async features provide a powerful technique called Cooperative Concurrency. Cooperative concurrency allows us to yield to other active tasks when a particular task is blocked.
The yield allows the event loop to switch to another task. When the task becomes unblocked, the event loop returns to perform the function.
To use the async feature of Python, we can modify the previous example as follows:
“`
import asyncio
async def process_task(task):
# Process a task
pass
async def main():
# Receive a queue of tasks
task_queue = asyncio.Queue()
while not task_queue.empty():
task = await task_queue.get()
await process_task(task)
“`
In this example, the `process_task` function is now an asynchronous function that is processed by the event loop without blocking. The creation of an `asyncio.Queue` instead of the standard `queue.Queue` is used to support cooperative concurrency.
Cooperative Concurrency With Blocking Calls
There are times when we need to call blocking functions like file I/O, database queries, or even sleep in our asynchronous code. Not handling the blocking calls appropriately will entirely negate the advantages of asynchronous programming.
For instance, if we use the following code to perform a blocking task like file I/O:
“`
async def read_file(filename):
with open(filename, “r”) as f:
content = f.read()
return content
“`
The file operation performed in the `read_file` function is blocking and can take a long time to complete, leading to a delay in the execution of the thread. Using await with the previous blocking calls will yield control of the event loop until the file operation is complete.
Still, doing this can defeat the purpose of asynchronous programming. To address this issue, we can use the `run_in_executor` method of the `asyncio` event loop to delegate the blocking call to a separate thread to avoid blocking the main thread:
“`
async def read_file(filename):
loop = asyncio.get_running_loop()
with open(filename, “r”) as f:
content = await loop.run_in_executor(None, f.read)
return content
“`
In the above example, the blocking `f.read` operation is delegated to a separate thread by calling `run_in_executor`, allowing the main thread to continue with other operations.
Cooperative Concurrency With Non-Blocking Calls
Asynchronous programming is ideal for handling requests in web development, where the application needs to handle multiple requests simultaneously. Python’s aiohttp library provides non-blocking HTTP for taking advantage of the asynchronous features of Python.
To make a non-blocking HTTP request using `aiohttp`, we can do something like this:
“`
import aiohttp
async def fetch(session, url):
async with session.get(url) as response:
return await response.text()
async def main():
async with aiohttp.ClientSession() as session:
html = await fetch(session, ‘http://example.com’)
print(html)
“`
In this example, the function “fetch” is an example of an asynchronous function that uses the `aiohttp` library. The `ClientSession` is necessary to make an HTTP request over the Internet.
The `async with` statement is used to instantiate the `session` object, which serves as a `context manager` and automatically closes connections when not active, thereby preventing resource wastage.
Synchronous (Blocking) HTTP Calls
Python requests library provides synchronous HTTP requests. Requests library is an excellent tool when we need to make a few requests at once.
However, it is not efficient when working with numerous requests at once, as the results may take longer to complete. To issue a simple HTTP request using requests, we use the code below:
“`
import requests
def main():
response = requests.get(“http://api.example.com/get”)
content = response.text
print(content)
“`
This code is blocking since the program halts until the response is retrieved before proceeding to the next function.
Asynchronous (Non-Blocking) HTTP Calls
To perform non-blocking HTTP calls in Python, we can use the `aiohttp` library instead of `requests`. Here is an example of how non-blocking HTTP requests can be done:
“`
import aiohttp
async def fetch(url):
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
return await response.text()
async def main():
html = await fetch(‘http://api.example.com/get’)
print(html)
“`
In this example, we’ve used the `aiohttp` library to perform non-blocking HTTP requests. `fetch` is the asynchronous function responsible for making the HTTP requests in an asynchronous environment.
Conclusion
In conclusion, asynchronous programming is a powerful tool for developing efficient applications. Python 3.4 and later versions have several features built into the language that make it easy to use, including the asyncio and aiohttp libraries.
By using cooperative concurrency, we can execute tasks simultaneously without being constrained by resource limitations or task completion time. By understanding these Python async features, you can create faster and more efficient applications with minimal resource usage.
In summary, the article discussed how to use Python async features in practice. It covered synchronous programming and provided examples of how to use Async features effectively.
We discussed cooperative concurrency, which is a powerful technique that allows us to yield to other active tasks when a particular task is blocked. We also looked at using non-blocking HTTP requests, asynchronous I/O operations, and how to handle blocking calls without hurting the performance of the application.
Python’s Async features and libraries like aiohttp and asyncio enable developers to create faster and more efficient applications with minimal resource usage. Understanding these features and leveraging them effectively is an essential skill for any developer in today’s technologically advanced world.