When Should You Use `await` in Python 3.5 Asynchronous Programming?

When and When Not to Employ await in Python 3.5

Asynchronous programming using asyncio in Python 3.5 requires careful consideration regarding the use of await. Here's a detailed guide on when to utilize await and when to refrain:

When to Use await

• I/O Operations: Generally, await should be used for any I/O operation, such as network requests, file reads/writes, or database interactions. These operations typically take a significant amount of time to complete, and using await allows your code to yield control and perform other tasks while waiting for the I/O operation to finish.

When Not to Use await

• Pure Python Calculations: Operations that perform purely computational tasks within Python, such as mathematical calculations or string manipulations, do not require the use of await. Asynchronous programming is designed for I/O-bound operations, where waiting for results is the limiting factor.

Additional Considerations

• Avoid Long Synchronous Operations: It's essential to avoid lengthy synchronous operations within asynchronous code. Any operation that takes longer than 50 ms (approximately) can block other asynchronous tasks in your program.
• Use Multiprocessing for Long Synchronous Operations: If you have long-running synchronous operations that cannot be parallelized, consider running them in a separate process using ProcessPoolExecutor. This will prevent them from blocking asynchronous operations in the main process.
• Use Threading for I/O-Bound Synchronous Operations: For synchronous operations that are I/O-bound, such as HTTP requests, ThreadPoolExecutor can be used to avoid multiprocessing overhead and improve performance.

Remember, the key is to use await where it benefits your program by reducing blocking operations and enhancing concurrency. By carefully considering the guidelines outlined above, you can effectively harness the power of asynchronous programming in Python 3.5.

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