Відео

Thank you for this series its great !

Nice🎉

41:10 revealing moment

Wow, amazing introduction of the python concurrency history!

The Excellent!! What an effort and knowledge content of this video! BTW, what is the status of GIL in 2024. Found a PEP 703, which is making GIL optinal.

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great series. not easy to elaborate on this topic. thanks, teacher

it's rare to find videos like this, detailed and deep. amazing work, thank you

great great tutorial, no one does it better than you. . could you please create a new updated version of this series. If you get time from your busy schedule. Cheers !

only geniuses can understand async, not dumb idiots like me.

we are som much greatful to you for these videos ! best wishes for edge db too ! Cheers

Why are only 3 more videos left 😭😭😭

14:18

How does EdgeDB handle timeseries data? (think real time IOT updates or streaming financial tick data?) SQL is notoriously bad for this as row based rather than column based storage. Are there any metrics to the standard timeseries DBs or literature on it being used in this way?

Excellent video, thank you!

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I wish you had (or would) create more such amazing in-depth playlists. Teachers like you make us fall in love with programming again and again. Thank you so much!!

wow.. awesome content! thank you

This video is nothing short of amazing! Hats off, and thanks for the lecture.

I feel bad for the bartender

So how do I join two tables in a query where there is no predefined link?

So branches can be used as a separation unit in a multitenant system? Currently I'm using schemas of postgres for this purpose.

First. Looks like this has grabbed some views since theos vid. Lol

Thanks so much for your effort! This is by far the best material on this topic.

Do look forward for more content. The idea is intriguing

I’m interested to find out more. Great talk

Nice. Thanks.

This is a super super good video. I didn't expect such a comprehensive take on async await.

Mate thank you for putting these together, you’re a great teacher.

Amazing, thank you so much for this

🎯 Key Takeaways for quick navigation: 00:00 🎬 *Introduction to Async IO in Python* - Overview of the Async IO series by HDB, aimed at introducing Async IO to Python programmers. - Highlights the contributors' expertise in Python's asynchronous stack and its application in the HDB server codebase. - Outlines the structure of the series, starting with the rationale behind using Async IO and progressing through key concepts and practical examples. 05:41 🕰️ *Understanding Latency and Concurrency* - Discussion on the significance of latency in user experience, emphasizing Facebook's approach to optimizing "time to interact." - Differentiates between concurrency and parallelism, stressing the importance of asynchronous execution in enhancing responsiveness. - Explores traditional methods of multitasking, such as threading in Python, as precursors to Async IO. 09:19 🔄 *Synchronous vs. Asynchronous Execution* - Clarifies the fundamental distinction between synchronous and asynchronous execution models, using practical examples and analogies. - Illustrates how synchronous execution can lead to UI lag and unresponsiveness, prompting the need for asynchronous processing. - Introduces the concept of background execution and its benefits in improving user interaction and program responsiveness. 13:29 🔒 *Synchronization and Threading Issues* - Explores the challenges of synchronization in multithreaded environments, focusing on issues like lock contention and starvation. - Discusses common threading problems, including race conditions and deadlocks, with illustrative examples. - Highlights the significance of synchronization primitives like locks in mitigating threading issues and ensuring data integrity. 19:01 ⚙️ *The Global Interpreter Lock (GIL) in Python* - Examines the role of the Global Interpreter Lock (GIL) in Python, addressing its impact on multithreaded performance. - Discusses past attempts to remove the GIL and the challenges associated with its elimination. - Illustrates real-world scenarios where the GIL can affect application performance and debugging efforts. 22:37 🌐 *Overview of Async IO* - Async IO maximizes single-threaded program productivity by handling IO asynchronously. - It avoids blocking functions by using coroutines, enabling concurrent code execution. 25:50 🍹 *Achieving Parallelism in Python* - Parallelism in Python can be achieved through multiple processes or threads. - Various methods include manual process spawning, using Python libraries like multiprocessing, or deploying services with load balancing. 26:01 🛠️ *Evolution of Asynchronous IO* - Asynchronous IO traces back to 1983 with the introduction of the Select system call in BSD. - Python's journey towards asynchronous IO involved frameworks like Medusa, Twisted, and Stackless Python, leading to the inclusion of async IO in the standard library. 29:46 🔄 *Introduction of Event Loop* - The Select system call evolved into the concept of an event loop, facilitating asynchronous IO operations. - Event loops allow programs to handle multiple socket events efficiently, leading to improved performance in IO-heavy applications. 30:12 📜 *Historical Context of Async IO in Python* - Python's history of asynchronous IO development involves contributions from various individuals and projects like Medusa, Twisted, and Stackless Python. - Guido van Rossum's inclusion of asynchronous IO in Python's standard library marked a significant milestone in Python's evolution. 38:20 🔄 *Evolution of Coroutines and Generators* - Python's journey towards asynchronous IO involved the evolution of coroutines and generators, from iterator protocol enhancements to the introduction of yield from expressions. - PEPs like PEP 380 and PEP 492 played crucial roles in formalizing coroutine support and introducing async/await keywords. 43:39 🚀 *Improvements with Async/Await Keywords* - The introduction of async/await keywords in PEP 492 significantly improved the usability and productivity of asynchronous IO in Python. - Async generators, asynchronous iterators, and asynchronous context managers further enhanced Python's asynchronous programming capabilities. 45:01 💡 *Managing Local Storage in Async IO* - Async IO programs may require local storage for computation, which shouldn't be shared across workers. - Thread-local storage is used to store data specific to a single thread, avoiding data corruption in multi-threaded environments. - Passing a context dictionary down to every coroutine was a common practice, but it could lead to cumbersome and error-prone code. 47:48 🚀 *Future Developments in Async IO* - Async IO in Python continues to evolve with future versions likely to introduce new features and refined APIs. - Ongoing research and development within the community contribute to advancements in async IO. - Examples like Curio and Trio showcase innovative approaches to concurrent Python systems programming using native coroutines and async generators.

This code was very impressive and explained lot to me, i will be revisiting it tomorrow as well

Crisp and detailed contents with nice presentation. :) --> and going to try edge DB

It's already 2024, but it's still one of the best videos about async. Thank you so much.

Excellent videos, I'm watching them all. At 25:07 you mentioned that the _step function calls coro.send(None) to advance the coroutine to the next stopping point. But, what is the equivalent for async generators? They suspend both at the lowest level awaits and also at yields. Does asyncio call asend to resume async generators from awaits?

reading off the script

that's the best asyncio series

Instead of using asyncio.wait, couldn't we just add a done callback on each task so that it's automatically removed from the todo set? task.add_done_callback(todo.discard)

Correct me if I'm wrong. I have an example for deadlock, "You are trying to find job to gain experience, but need experiance to get a job"

Thanks a lot EdgeDB Team. I really like the shirt. Is there a place I can get it? I am happy to pay. Thank you!

😂😂😂 black background is better

Are there any articles or blogs that accompany the videos?

Na pewno najlepszy kurs na ten temat na całeym YT. Ogromny szacun za tak wspaniały materiał!

Best tutorial playlist!

21:40 Integrated auth

Great series ! 19:35 Do you know why I am not getting the cancellation error in python 3.10?

Great lecture series. After spending hours online and looking at asyncio implementation, I am finally starting to get it. Thank you so much!

Aaron Paul is amazing in this series

Are you planning to implement web3 authorization support?

it is the future