In Python, asynchronous functions offer a higher degree of parallelism compared to multithreading, especially in situations where the application experiences input/output (I/O-bound) blockages. In such cases, asynchrony facilitates task-switching without impeding the primary execution thread.

As I told you in my previous answer, multithreading in Python makes sense when threads manage external operations that don't consume processor time (such as input-output operations). Asynchronous functions serve similar purposes and aim to avoid blocking the execution thread during waiting operations.

It's really important to understand that each thread has its assigned memory, while asynchronous functions operate within a single thread, and they share a common memory stack. In other words, a thread is a distinct entity, and it takes extra time to create, maintain, and eliminate it. So, if there's a need for frequent execution of many non-blocking operations, asynchronous functions prove to be more efficient. A typical example is handling database queries in a program. We can manage many more queries if multiple asynchronous functions work within one thread instead of creating a new thread for each query.

Actually, in Python, neither multithreading nor asynchrony achieves real parallelism. They can only achieve concurrent execution, but it's a bit different. In scenarios with a large number of input/output operations, asynchrony slightly outperforms threads because threads have extra work when switching.

I've built several Telegram bots in Python, using async functions for information processing and user interaction. Typically, they include the asyncio module and inherit from it. This approach allows:
a) No interference with each other, working within a single thread;
b) Concurrent database handling for multiple users;
c) Loading files from the network without blocking other processes, such as fetching JSON objects.

The problem with this function declaration is that a list is created and stored as an attribute of this function throughout its entire lifespan. We always work with the original object created during the declaration by calling the function with the default argument value. We may face a problem when we do not understand that this default list is always the same object and is not protected from changes. This leads to a typical situation where a developer calls the function with the default parameter, thinking that the list will always be initially empty. But it may have been modified during previous calls.

I should add that, generally, this language behavior is not a problem or deal-breaker. There could be some legit reasons for storing a mutable object in the default argument value. But, since this happens implicitly, you're more likely to stumble upon some unexpected behaviour or memory leaks. And, as they say, these things can be hard to find and impossible to forget.

Initializing the parameter some_arg as an empty list can cause problems when calling the function and working with it.

In the function declaration, we generate a list object. This object is created only once, and each time the function is called without arguments, a reference to it is assigned to the variable some_arg. Since lists are mutable, any modifications made to this object will be retained in memory between function calls, and it may cause some unexpected behavior and potential errors.

The issue with the function declaration "def kek(some_arg = [])" lies in the fact that the list "[]" is a mutable object, and it will be shared among all invocations of this function. Any modification to some_arg in one function call will have a cascading effect on all subsequent calls.

These services are designed for storing user information and recalling past interactions. The choice between stateful and stateless often depends on the project's business logic.
Stateful is more static and more suitable for shopping carts or tracking databases. Stateless is more dynamic, and it fits more for APIs and websites.

Stateless services don't retain client state, and each call to this service operates independently of previous calls. Stateful services, on the contrary, store state information, like the details about prior requests. Typically, these services are databases.

Stateful and stateless services pertain to web services. Stateful services retain the client's state between requests, whereas stateless services abstain from storing state and process each request autonomously. Stateless services are frequently used to enhance scalability and fault tolerance.

These terms describe how the external state is managed during the operation of a service. Specifically whether or not the external state is retained and considered during the service's operation. Picture a standard function that takes parameters, calculates something, and gives you a result.

Stateful – this function processes a request while considering data saved during previous calls. For example, it might recognize that a request with certain values has been executed before or that a specific external function has performed the request N times. The function retains context from the external calls. And it's important that this data is stored locally within the function.

Stateless – means a function processes a request without considering its previous interactions with an external. It doesn’t keep any data about the previous context of calls and relies just on the current parameters it receives.

I think it became popular because it boosts project performance and its business logic.
Also, it lets you handle many users simultaneously without a significant loss in performance. Asynchronous programming is often applied for:
a) Fetching data from API layers or web sockets,
b) Handling databases,
c) Processing events and handlers.

Asynchronous programming gained popularity due to its capacity to effectively handle a multitude of concurrent tasks, particularly in domains where input/output blocking could severely impede application performance, such as in web services, network applications, and large-scale data analysis.

I think people like it because it's a natural way to effectively handle a bunch of tasks pseudo-simultaneously, if possible. When you're doing a few things at once, you usually deal with them one by one, if none of them needs your constant attention. This idea just moved into the tech world. In Python, the implementation of asynchrony comes with a user-friendly and relatively clear syntax.

The systems have become complex, highly loaded, and distributed. In scenarios where numerous network interactions need to be performed, asynchronous execution of code helps maximize CPU resource utilization. But there's another form of asynchrony — asynchronous communication between services. This architectural approach reduces complexity by minimizing the interdependence between services.

Yes, Python is an interpreted language, and it’s noticeably slower than compiled languages.

If you're dealing with complex math equations in Python, it might not be as fast as some other popular languages. However, here's the thing — Python, in its basic form, isn't really designed for those heavy-duty tasks. It makes more sense to evaluate its speed in the scenarios where it's most commonly used – currently, that's web apps.

In these cases, the language mainly serves as a kind of tie layer or a bridge. The effectiveness of these systems relies more on how well the language manages input-output interactions rather than just how fast it operates on its own. What really matters is the language's ability to smoothly carry out operations while communicating with external components, and that's where concurrency comes in. Also, as containers and cloud technology become more popular, vertical scaling is being replaced by horizontal scaling. So, the language's raw performance is not the top priority.

It's a tricky question, but I think it all comes down to business logic. Let me give you some examples:

A) It can be really slow: I have a Python bot on Telegram that loads tests. These tests contain some questions, images, and answer options. And they can take a bit to load, especially with around 20 of them, because there is a lot of info in the JSON object.
B) It can be fast: I also have a survey bot that saves user answers in a database. It works so fast that I actually have to slow it down on purpose.
Also, there’s one more important thing — the hardware it runs on plays a big role.

Python may be considered slower in comparison to certain compiled languages such as C++ or Rust. Nevertheless, this isn't always a critical concern, as Python offers an extensive array of libraries and tools, allowing for high performance in numerous domains, particularly when leveraging JIT-compilers like PyPy.

Python Zen is a set of principles and recommendations for writing clean, readable code. My favorite rule from the Zen of Python is "Explicit is better than implicit," highlighting the importance of straightforward and understandable code.

Python Zen is a set of 19 principles to help you write Python programs. It's basically the common-sense rules for coding, like "Simple is better than complex." Well, what else is there to say… These are good guidelines, but sometimes, real-world business needs don't let us follow them strictly. Some people say Python itself doesn't always follow its own rules, and I kinda agree.

Well, most of the time, I like to name the arguments I pass to functions/methods explicitly. So, the concept of "Explicit is better than implicit" is close to my heart.

Zen of Python is a guide with principles that reflect the philosophy of Python. The one I often remember is "Explicit is better than implicit."