Super Fast Python

“Super Fast Python” delves into the intricacies of Python programming speed, exploring the measurement metrics, bottlenecks, and the impact on productivity. The discussion revolves around the balance between development time and run time efficiency, shedding light on the critical aspects that influence Python’s speed. By drawing insights from various Python-related sources, the document aims to provide a comprehensive understanding of how speed considerations in Python programming can impact overall productivity and performance.

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Asyncio gather() Return Values

Super Fast Python

We can retrieve return values from coroutines executed concurrently by asyncio.gather(). The asyncio.gather() function returns an asyncio.Future that executes all provided coroutines concurrently. Onc...

Asyncio gather() In The Background

Super Fast Python

We can run asyncio.gather() in the background by not awaiting the call to asyncio.gather(). The asyncio.gather() returns an asyncio.Future that does not have to be awaited. It is scheduled for executi...

Asyncio gather() Limit Concurrency

Super Fast Python

We can limit concurrency when using asyncio.gather() via a semaphore. In this tutorial, you will discover how to limit concurrency with asyncio.gather(). Let’s get started. Need to Limit Concurrency w...

Asyncio gather() Exception in Task Does Not Cancel

Super Fast Python

We can execute coroutines concurrently with asyncio.gather(). By default, an exception in asyncio.gather() will be propagated to the caller immediately. The asyncio.gather() call will not wait for the...

Asyncio gather() Timeout

Super Fast Python

We can add a timeout when using asyncio.gather() via the asyncio.timeout() context manager. A timeout in seconds can be specified to the asyncio.timeout() context manager and the asyncio.gather() func...

Asyncio Shield Main Coroutine From Cancellation

Super Fast Python

We can simulate shielding the main coroutine from cancellation by using a wrapper coroutine that consumes cancellation requests. In this tutorial, you will discover how to shield the main coroutine fr...

Asyncio Run Multiple Concurrent Event Loops

Super Fast Python

We can run multiple concurrent asyncio event loops by starting and running each new event loop in a separate thread. Each thread can host and manage one event loop. This means we can start one thread ...

Asyncio gather() TypeError: unhashable type: ‘list’

Super Fast Python

We can avoid a TypeError exception when using asyncio.gather() by unpacking the collection of awaitables with the star (*) operator. The asyncio.gather() function is used to execute multiple coroutine...

Asyncio Event Loop in Separate Thread

Super Fast Python

We can run an asyncio event loop in a new thread by starting a new thread and configuring it to start or run an event loop. There are many approaches we can use to run an event loop in a new thread. T...

Asyncio gather() Handle Exceptions

Super Fast Python

We can automatically handle exceptions in coroutines executed via asyncio.gather() by setting the “return_exceptions” argument to True. By default, if a coroutine is executed by asyncio.gather() fails...

Asyncio Event Loop Exception Handler

Super Fast Python

We can configure a custom asyncio event loop exception handler via the asyncio.get_running_loop() method. By default, unhandled exceptions in asyncio programs cause the event loop to emit a warning an...

Asyncio Coroutine Object Methods in Python

Super Fast Python

We can define coroutine methods on custom Python objects. This allows methods on custom Python objects to use async/await syntax, such as awaiting other coroutines and tasks and allows the custom coro...