December 01, 2025
Asynchronous and synchronous programming are two concepts that define how tasks are executed in a program. Synchronous programming waits for tasks to execute one after another, while asynchronous programming sends multiple tasks to be executed concurrently. Synchronous programming is useful for cases in which order matters while asynchronous programming is useful for tasks that can be executed independently and concurrently such as I/O bound tasks. For example, synchronous programming might be useful when processing a series of calculations that depend on each other, while asynchronous programming might be useful when several different users are making requests to a web server simultaneously.
Asynchronous programming in python might be confused with multi-threading at a glance, but they are different concepts. Both synchronous programming and asynchronous programming can be single-thread or multi-thread. The key difference is that an asynchronous system will not block a thread during an input/output operation.
In most cases, asynchronous programming is implemented in a single-threaded environment using an event loop to manage the execution of tasks. This allows for efficient use of resources and can lead to improved performance, especially for I/O-bound tasks. Multi-threading, on the other hand, involves creating multiple threads of execution within a program, which can lead to increased complexity and potential issues with thread safety.
Although both asynchronous programming and multi-threading can be used to achieve concurrency and thus faster execution of tasks, they do so in different ways. Asynchronous programming relies on non-blocking I/O operations and an event loop to manage the execution of tasks, while multi-threading involves creating multiple threads of execution within a program. An easy analogy is to think of asynchronous programming as a single worker who can juggle multiple tasks at once without getting stuck on any one task, while multi-threading is like having multiple workers who can each work on their own tasks simultaneously.
Asynchronous programming is often implemented in a single-threaded environment because it allows for efficient use of resources and can lead to improved performance, especially for I/O-bound tasks. In a single-threaded asynchronous program, the event loop can manage the execution of multiple tasks without the overhead of creating and managing multiple threads. This can lead to lower memory usage and reduced context switching, which can improve overall performance compared to a multi-threaded approach. Additionally, single-threaded asynchronous programming can be easier to implement and debug, as there are fewer potential issues with thread safety and synchronization.
Yes, however, this would only be true in an incredibly specific set of circumstances. For example, if a program is both I/O-bound and CPU-bound, using multi-threading in conjunction with asynchronous programming could potentially lead to improved performance. I/O bound tasks would often have to do with waiting for API responses, file reads/writes, or database queries, while CPU-bound tasks would involve heavy computations such as data processing, mathematical calculations, or image processing. As such, if a program has a mix of both I/O-bound and CPU-bound tasks, using multi-threading to handle the CPU-bound tasks while using asynchronous programming to handle the I/O-bound tasks could lead to improved performance.
One personal analogy could be how I had to send multiple video generation requests to an API while also having to crop and resize the videos once they were received. The video generation requests were I/O-bound tasks since they involved waiting for the API to respond, while the cropping and resizing of the videos were CPU-bound tasks since they involved heavy computations. To optimize the performance of my program, using both asynchronous programming to handle the video generation requests while using multi-threading to handle the cropping and resizing of the videos could lead to improved performance. However, in most cases, single-threaded asynchronous programming is sufficient for handling I/O-bound tasks efficiently since the overhead of managing multiple threads may outweigh the benefits.
Concurrency and parallelism are two related but distinct concepts in computer science that deal with the execution of multiple tasks. Concurrency refers to the ability of a system to handle multiple tasks at the same time, while parallelism refers to the ability of a system to execute multiple tasks simultaneously. In other words, concurrency is about managing multiple tasks, while parallelism is about executing multiple tasks at the same time. Concurrency can be achieved through techniques such as multi-threading, asynchronous programming, and event-driven programming, while parallelism is typically achieved through techniques such as multi-core processing and distributed computing. Both concurrency and parallelism can lead to improved performance and scalability in a system, but they require different approaches and techniques to implement effectively.
Python provides the asyncio library to facilitate asynchronous programming. The asyncio library provides a framework for writing single-threaded concurrent code using coroutines, multiplexing I/O access over sockets and other resources, running network clients and servers, and other related primitives. The asyncio library uses the async and await keywords to define and manage asynchronous tasks.
import aiohttp
async with aiohttp.ClientSession() as session:
async with session.get('http://python.org') as response:
html = await response.text()
print(html)
In this example, we use the aiohttp library to make an asynchronous HTTP request to the Python website. The async with statement is used to create an asynchronous context manager for the ClientSession and the get request. The await keyword is used to pause the execution of the coroutine until the response is received and the HTML content is read. In simple terms, while the program is waiting for the HTTP response, it can perform other tasks instead of being blocked. The async with statement starts an asynchronous context, while the await keyword pauses the execution of the coroutine until it receives the response.
Synchronous programming has several advantages and disadvantages compared to synchronous programming. For one, it is preferred by developers since it is much easier to read, understand, learn, and debug. Synchronous programming also ensures that tasks are executed in a specific order, which is important for certain applications. However, synchronous programming can lead to performance issues, especially when dealing with I/O-bound tasks, as it can block the execution of other tasks while waiting for a task to complete. On the other hand, asynchronous programming can improve performance by allowing multiple tasks to be executed concurrently, which is especially useful for I/O-bound tasks. However, asynchronous programming can be more complex to implement and debug, and it may not be suitable for all applications. Bringing into consideration costs, one personal analogy required the careful use of asynchronous programming. I had to create a dataset that would send API requests to a VLM generator. However, given the cost of each API request being about $3 each, I had to ensure that the requests were being sent successfully. One time while debugging, I attempted to send 10 asynchronous requests at once which were sent, but then I failed to download the results properly due to exception handling. This led to a loss of about $30. If I had sent 100 asynchronous requests at once instead of 10, the loss would have been $300. As such, I had to carefully implement asynchronous programming only when I was sure that the requests were being sent and received properly. To avoid large losses when sending the requests, I split the requests into batches of 3, sending 3 requests asynchronously at a time and then synchronously waiting for the results to be downloaded before sending the next batch. This ensured that I could monitor the success of each batch of requests and avoid large losses due to failed requests.
In asynchronous messaging systems like Kafka or Redis, maintaining the order of messages is crucial for ensuring data consistency and integrity. These systems employ various techniques to retain order while still allowing for concurrent processing of messages. One common approach is to use partitions or channels to group related messages together. In Kafka, for example, messages are organized into topics, which are further divided into partitions. Each partition is an ordered sequence of messages, and messages within a partition are guaranteed to be delivered in the order they were produced. Consumers can read messages from a specific partition, ensuring that they receive messages in the correct order. Additionally, these systems often use offsets or sequence numbers to track the position of messages within a partition or channel. This allows consumers to keep track of which messages they have already processed and ensures that they can resume processing from the correct position in case of failures or restarts. By combining these techniques, asynchronous messaging systems can effectively retain the order of messages while still allowing for concurrent processing and high throughput.
Imagine a scenario where a web server receives over 10,000,000 posts/comments/likes/views per day. If the server were to handle each request synchronously, it would have to wait for each request to be fully processed before moving on to the next one. This would lead to significant delays and a poor user experience. However, by using asynchronous programming, the server can handle multiple requests concurrently. When multiple requests are received, the server can initiate the processing of each request and then move on to the next one without waiting for the previous request to complete. This allows the server to handle a much higher volume of requests in a shorter amount of time, leading to improved performance and scalability. Additionally, asynchronous programming can help to reduce resource consumption, as the server can make better use of its available resources by handling multiple requests simultaneously. Overall, asynchronous programming is a powerful tool for building high-throughput systems that can handle large volumes of data and traffic efficiently. One such example of efficient asynchronous handling might be when multiple users send requests to view a popular post simultaneously. The server can process these requests asynchronously, allowing all users to view the post without significant delays.
To ensure order when multiple users send comments to the same post simultaneously, we can implement a few strategies: