Summary of ⚡Python 3.11 — СКОРОСТНОЙ горячий пирожочек!

00:00:00 - 00:20:00

The video discusses the release of Python 3.11 and focuses on the improvements made to the speed of the language. Python has been criticized for its slower execution compared to other scripting languages, but Python 3.11 aims to address this issue. The release claims to offer an average speed boost of 25%, with some tasks seeing up to a 60% acceleration. The interpreter launches faster, reducing execution time, and optimizations have been made to the bytecode generation process. The new adaptive interpreter can automatically optimize code segments, providing acceleration without the need for changes to the codebase. These improvements offer significant speed enhancements for developers, making Python 3.11 an exciting release for the Python community.

• 00:00:00 In this section, the release of Python 3.11 is discussed, specifically focusing on the improvements made to the speed of the language. Python has often been criticized for its slower execution compared to other scripting languages like PHP and JavaScript. However, a plan to accelerate Python by five times its current speed has been developed. The release of Python 3.11 claims to offer an average speed boost of 25% compared to the previous version, with some tasks seeing up to a 60% acceleration. This increased speed is essential for console applications and scripts, as Python is currently the most popular language for automation tasks. The interpreter now launches 10-15% faster, reducing the time between input and execution. Additionally, optimizations have been made to the bytecode generation process, resulting in more efficient and optimal bytecode. The adaptive interpreter can automatically optimize parts of the code, providing acceleration without requiring any changes to the existing codebase. Overall, the improvements in Python 3.11 offer significant speed enhancements for developers, making it an exciting release for the Python community.
• 00:05:00 In this section, the speaker discusses the optimizations and improvements made in Python 3.11, including an adaptive interpreter that optimizes the execution of the most frequently run code segments. They also mention the enhancements made to the exception mechanism, reducing overhead and making error messages more clear and specific. Additionally, they explain the ability to add notes to exceptions, providing additional information for debugging purposes. The speaker also introduces the concept of exception groups in Python 3.11, which allows for more convenient handling of asynchronous and synchronous tasks.
• 00:10:00 In this section, the speaker discusses exception groups in Python 3.11. They explain that exception groups allow for the handling of multiple exceptions within a single structure, making it easier to process and categorize them. By using the `except *` syntax, all exceptions of a certain type can be caught and processed. The speaker demonstrates how to work with exception groups and highlights the ability to create nested exception groups. They also mention updates in type hints and the use of `typedict` and recommend using data classes instead of typed dictionaries. Additionally, they mention the new `self` type for methods that return an instance of the current class, providing an example of singleton implementation.
• 00:15:00 In this section, the speaker discusses the new features in Python 3.11. They start by mentioning the type hinting feature, which allows methods to specify the type of the instance they will return. The speaker also mentions the Singleton design pattern, where only one instance of a class is created and reused. They then talk about the new data literals introduced in Python 3.11, which distinguish between strings that are hardcoded in the application code and strings that come from external sources, such as user inputs. The speaker explains that hardcoded strings are considered safe, while user inputs can be potentially unsafe. They demonstrate how type hinting can help identify potential vulnerabilities when using user inputs in SQL queries. Additionally, they discuss the new built-in module for working with TOML files, a text-based format commonly used for storing configurations. Finally, the speaker touches on the improvements made to handling asynchronous tasks in Python 3.11, showcasing an example of running multiple tasks concurrently using the asyncio module. They explain how the new Main function simplifies the process of running asynchronous tasks by using a context manager for task grouping.
• 00:20:00 In this section, the speaker talks about the improvements in Python 3.11, specifically in terms of performance. They mention that Python developers are now focusing more on optimizing the interpreter and that the current average speedup is around 25%, with some cases even reaching 60%. They express their excitement about these improvements and look forward to seeing what Python 3.12 will bring. Overall, they emphasize the importance of these performance enhancements and describe them as the most significant changes in Python 3.11.