Python And Native Modules

When we think about running Python code, particularly in the CPython interpreter, we normally think about interpreted code (a bytecode interpreter), but we also know that we can write native modules (extension modules) normally in C or C++. but also using cython, nuitka or even rust. These extension modules are not used just by some third party libraries, but the python runtime/environment/standard library also makes good use of them. In this sense, as explained in this article we have 2 types of these modules:

• A built-in extension module is a module built and shipped with the Python interpreter. A built-in module is statically linked into the interpreter, thereby lacking a __file__ attribute.
• A shared (or dynamic) extension module is built as a shared library (.so or .dll file) and is dynamically linked into the interpreter. In particular, the module’s __file__ attribute contains the path to the .so or .dll file.

Normally the Python interpreter is contained in the python3.XX binary (.exe in Windows), that is like 30 MBs in size, but in some builds (those done with the --enable-shared flag), most of the code is put in a libpython3.14.so (.dll in Windows) dynamic libray, and the python binary just bootstraps it.

built-in extension modules (like sys, builtins, _thread, gc...) are obviously part of the Python distribution (they are inside the interpreter as we've seen), while for shared extension modules, we have those that are part of the python distribution (like math, array, _ssl, _socket) and those that are developed by third parties.

You can check all the built-in extension modules like this:

>>> sys.builtin_module_names
('_abc', '_ast', '_codecs', '_collections', '_contextvars', '_datetime', '_functools', '_imp', '_io', '_locale', '_opcode', '_operator', '_signal', '_sre', '_stat', '_string', '_suggestions', '_symtable', '_sysconfig', '_thread', '_tokenize', '_tracemalloc', '_types', '_typing', '_warnings', '_weakref', 'atexit', 'builtins', 'errno', 'faulthandler', 'gc', 'itertools', 'marshal', 'posix', 'pwd', 'sys', 'time')

While for the shared extension modules that are part of your distribution, just check the lib-dynload folder in your installation, e.g.:

ls -la /usr/local/lib/python3.14/lib-dynload/
total 27032
drwxr-xr-x  2 root root    4096 mar 14 13:26 .
drwxr-xr-x 43 root root    4096 mar 14 13:26 ..
-rwxr-xr-x  1 root root  298704 mar 14 13:26 array.cpython-314-x86_64-linux-gnu.so
-rwxr-xr-x  1 root root  399192 mar 14 13:26 _asyncio.cpython-314-x86_64-linux-gnu.so
-rwxr-xr-x  1 root root  201048 mar 14 13:26 binascii.cpython-314-x86_64-linux-gnu.so
-rwxr-xr-x  1 root root   97240 mar 14 13:26 _bisect.cpython-314-x86_64-linux-gnu.so
-rwxr-xr-x  1 root root 1705384 mar 14 13:26 _blake2.cpython-314-x86_64-linux-gnu.so
-rwxr-xr-x  1 root root  105048 mar 14 13:26 _bz2.cpython-314-x86_64-linux-gnu.so
-rwxr-xr-x  1 root root  166800 mar 14 13:26 cmath.cpython-314-x86_64-linux-gnu.so
...

So when running a Python application you have normal Python functions (that as first step of the execution have been compiled to Python bytecodes) that have to be interpreted, and other functions, living in those extension modules that are already native code and have to be executed as such, without interpretation. How does Python manage that?

The essential part in most interpreters is the interpreter loop. This is the code that loops through the bytecode instructions to be interpreted (or traverses the tree of nodes in Tree-parsing interpreters). I say "most" rather than "all" because in tree parsing interpreters we can have things like Truffle, where each node execute() method takes care of moving to the next node (and also manages its own specialization). In CPython, this interpreter loop lives in the _PyEval_EvalFrameDefault function. It's nicely explained here

When a Python function is invoked we have a Function object and a CALL bytecode instruction. If the function being called is not a native one (so a normal function that was written in pure Python and compiled to bytecodes to be interpreted) the interpreter handles this CALL by creating a frame object (the optimized _PyInterpreterFrame that I discussed here), that contains all the information needed for the function execution: local variables (including arguments and closure cells), the code object... and invokes _PyEval_EvalFrameDefault() with that frame. Indeed, there's a very interesting performance optimization added in Python3.11, _PyEval_EvalFrameDefault no longer recursively calls itself when it finds a new CALL, so we keep a flat C stack. From a GPT:

Ordinary Python→Python calls are frameless on the C stack. A chain of plain function calls runs inside a single _PyEval_EvalFrameDefault C invocation. The CALL opcode pushes a lightweight _PyInterpreterFrame onto a per thread data stack (chunked heap) and dispatches within the same C frame. Plain recursion is bounded by sys.getrecursionlimit() (a logical / data-stack counter), not the C stack.

And from this very in depth article

In CPython 3.10 and earlier, the CALL instruction used to create a new interpreter stackframe for the function being called and then it used to recursively reenter the interpreter by calling its entry point _PyEval_EvalFrameDefault.

This was bad for performance from many angles at the hardware level. The recursive call into the interpreter required saving the registers for the current function, and pushing a new C stackframe. It would lead to increased memory usage because each recursive interpreter call would allocate its own local variables on the stack, and other heap allocations. Apart from that it would also lead to poor instruction cache locality due to the constant jumps in and out of the bytecode evaluation loop.

In the 3.11 release this was fixed by eliminating the recursive call to the interpreter. Now the CALL instruction simply creates the stackframe for the called function, after that it immediately starts evaluating the new function’s bytecode without ever leaving the loop.

I've explained so far how the interpreter manages "normal" functions, but what about native functions? The Function object for a native function does not have a code object (as obviously they don't have associated bytecodes), but a function pointer to the native code. From a GPT:

Native functions: Stored as PyCFunctionObject with a function pointer
No __code__ attribute
Instead, has a function pointer (ml_meth) stored in PyMethodDef
CPython calls the C function pointer directly, bypassing the interpreter loop
The C function executes natively and returns a PyObject*

How does the interpreter check if this 'CALL function_object' should be treated one way or another (_PyInterpreterFrame + _PyEval_EvalFrameDefault() vs native call)? Basically at a low level different objects are used for a "normal" function and a native function. But the whole story goes like this (GPT explanation)

At the C level, all Python objects are represented by the PyObject structure, which points to a PyTypeObject (its type). The type object defines how instances of that type behave.

To make calls fast and avoid creating temporary tuple/dict arguments, CPython uses the vectorcall protocol (Py_TPFLAGS_HAVE_VECTORCALL).
When the interpreter encounters a CALL opcode, it ultimately looks at the target object's type to find its vectorcall entry point:

- Pure Python Functions (PyFunction_Type): The vectorcall pointer points to _PyFunction_Vectorcall. This function extracts the function's PyCodeObject (func->func_code), allocates a new _PyInterpreterFrame on the evaluation stack, and pushes it to _PyEval_EvalFrameDefault.

- Built-in/Native Functions (PyCFunction_Type): Native functions (like math.sqrt or print) are wrapped in a PyCFunctionObject. Their vectorcall pointer points to _PyCFunction_Vectorcall. This function extracts the underlying C function pointer (func->m_ml->ml_meth) and invokes the compiled C code directly.

But things are even more interesting. I already talked about how in Python3.11 the Python interpreter turned into a Python Adaptive Specializing Interpreter. Thanks to that, Function Calls are optimized like this (as explained by a GPT)

If the interpreter had to look up the type and vectorcall pointer from scratch on every single loop iteration, it would be slow. To solve this, CPython uses Opcodes Specialization (PEP 659 Adaptive Interpreter). When a generic CALL opcode executes, it starts in an "adaptive" state. It looks at the object being called and patches itself in memory to a specialized version based on what it sees:

- Python-to-Python Calls
If the target is a pure Python function, the CALL opcode specializes itself into CALL_PY_EXACT_ARGS (or CALL_PY_BOUND_METHOD).

The Check: It performs a strict pointer comparison on the target's type to ensure it is exactly PyFunction_Type.
The Action: It skips the generic lookup, directly grabs the code object, creates the _PyInterpreterFrame, and increments the frame depth.

- Python-to-Native Calls

If the target is a native C function, the CALL opcode specializes into CALL_BUILTIN_FAST or CALL_BUILTIN_CLASS.

The Check: It verifies that the object's type is PyCFunction_Type and often checks if the specific function handler matches what was cached.
The Action: It bypasses the interpreter frame creation entirely, sets up the C arguments, and jumps straight into the native C function.

That's fascinating!

If out of curiosity we want to know if a function is pure Python or native code we can check its type. For normal functions its type is FunctionType, for native functions it's BuiltinFunctionType

# Normal functions:
import types
import math

def f1(): pass
l1 = lambda x: x

isinstance(f1, types.FunctionType)
True
isinstance(l1, types.FunctionType)
True

isinstance(len, types.FunctionType)
False
isinstance(len, types.BuiltinFunctionType)
True
isinstance(math.sqrt, types.BuiltinFunctionType)

Additionally, as I've mentioned, native functions lack a __code__ attribute.

f1.__code__
code object f1 at 0x76be17bfcac0, file "", line 1

len.__code__
Traceback (most recent call last):
  File "", line 1, in 
    len.__code__
AttributeError: 'builtin_function_or_method' object has no attribute '__code__'. 

Python unpacking and multiple assignment (again)

When I talked about Python destructuring assignment I also mentioned the * and ** syntax, and referred to it as "operators" which indeed is not right (though it's common to name them like that). They should be better referred as unpacking/packing syntax or star expressions. For the different use cases of this syntax:

• function invokation: f1(*args, **kwargs) or function definition: def f2(*args, **kwargs) we should talk about arguments unpacking or packing
• When used in collection literals we should talk about iterator unpacking: [*items1, *items2] and dictionary unpacking: {**dict1, **dict2}, with the expressions being called "starred expressions"
• When used on the left side of an assignment: first, *reminder = my_list, we talk of a "starred target".

As we know the unpacking (destructuring) happens automatically when performing a multiple assignment, with no need of using * at all

x, y = [1, 2]
print(f"x: {x}, y: {y}")
# x: 1, y: 2

There are some advanced uses that I tend to forget. We can use multiple assignment with object attributes or dictionary keys:

@dataclass
class Person:
    name: str
    age: int
    country: str

# multiple assignment to attributes of an object
p1 = Person("Antoine", 47, "France")
p1.name, p1.age = ["Francois", 48]
print(f"p1.name: {p1.name}, p1.age: {p1.age}")

# multiple assignment to dictionary keys
d1 = {}
d1["name"], d1["age"] = ["Francois", 48]
print(f"d1['name']: {d1['name']}, d1['age']: {d1['age']}")

As I explained in this post we can unpack nested structures:

x, [a, b], y = [1, [2, 3], 4]
print(f"x: {x}, a: {a}, b: {b}, y: {y}")
# x: 1, a: 2, b: 3, y: 4

But notice that nested unpacking works for assignment, but not for function parameters. Surprisingly this is something that worked in Python2 but was lost in Python3.

>>> def f(a, (b, c)):
...     return c

SyntaxError: Function parameters cannot be parenthesized


>>> def f(a, [b, c]):
...     return c
...     
            
SyntaxError: invalid syntax

Python is missing the object destructuring assignment feature present JavaScript, I mean:

const user = {
  id: 42,
  isVerified: true,
};

const { id, isVerified } = user;

So we have to use this more verbose approach (notice that itemgetter and attrgetter are a nice option for dynamic scenarios)

p1 = Person("Antoine", 47, "France")

name, country = p1.name, p1.country
name, country = attrgetter("name", "country")(p1)

name, age = d1["name"], d1["age"]
name, age = itemgetter("name", "age")(d1)

Python Class Body to the Limit

In my 2 previous posts [1] and [2] we've seen how the class body of a Python class statement is just executable code that is put in a code object around which a synthetic function is created. This code is executed during the class creation (receiving a namespace object, a dictionary, created by the __prepare__ method of the metaclass). This is pretty powerful, as you can put complex initialization code there, not just a normal assignment). We can apply a decorator conditionally, create multiple function alias, define functions conditionally... Let's see some examples.

def log_deco(fn):
    def log_wrapper(*args, **kwargs):
        print(f"invoking {fn.__name__}")
        return fn(*args, **kwargs)
    return log_wrapper

def do_nothing_deco(fn):
    return fn

log_mode = True
profile_mode = Trumult

class Person:
    def __init__(self, name: str): 
        self.name = name

    # conditional decorator
    @(log_deco if log_mode else do_nothing_deco)
    def say_hi(self, to_x: str):
        print(f"hi {to_x} I'm {self.name}")

    # declare a function conditionally
    if profile_mode:
        def get_memory_consumption(self):
            print("my memory consumtpion is ...")

    def do_something(self):
        print(f"{self.name} is doing_something")

    # function aliases
    work = do_something
    cook = do_something
    sleep = do_something

p1 = Person("Francois")
p1.say_hi("Iyan")
p1.get_memory_consumption()
p1.sleep()


# invoking say_hi
# hi Iyan I'm Francois
# my memory consumtpion is ...
# Francois is doing_something

That's pretty nice, right? As the class body ends up being executed as a function you can put any code in it, and the compiler will compile any declarations that you put in it as attributes in the namespace object (that then is passed to the __new__ and __init__ of the metaclass to create the class). But there's one limitation. What if I want to create several alias for a same function using a loop? in principle we can't.

class MyGeoManager:
    _not_implemented = lambda self, item:  print(f"Method is not implemented yet")
    for name in ["get_city", "get_country"]:
        # obviously this does not do what we would like
        name = _not_implemented 

Obviously the above is not doing what we want. It's just creating an attribute named "name" and assigning it in a loop. How could we add an attribute get_city and an attribute get_countr?

Well, we can leverage something we've seen in previous posts, our friend frame.f_locals. I mentioned it in my last post and talked about it in depthhere. f_locals gives us a write-through proxy to the locals of a function. When using an optimized scope, adding variables to that proxy has no particular useful effect, as the function has been compiled to acces by index to the variables that were found at compile time, but in the kind of scope used in a class body, that is a real dictionary, not a fast-array, adding new variables via f_locals adds them to the namespace dictionary that then is passed to __new__ and __init__. So we can do this:

class MyGeoManager: 
    local_namespace = inspect.currentframe().f_locals
    not_implemented = lambda self, item:  print(f"Method is not implemented yet")
    for name in ["get_city", "get_country"]:
        local_namespace[name] = not_implemented

print(f"get_country: {MyGeoManager.get_country}")
print(f"get_city: {MyGeoManager.get_city}")

It works like a charm!

There's another way to do this, derived from how class scope differs from optimized scopes. We are used to the builtin functions exec, compile and eval working with a snapshot of the locals namespace (because we are used to work in optimized scopes), but in a class scope, these functions receive the real namespace object. So we can leverage exec() like this:

class MyGeoManager:
    not_implemented = lambda self, item: print("Method is not implemented yet")
    for name in ["get_city", "get_country"]:
        exec(f"{name} = not_implemented")

Python Class Body and Lexical Scope

In my previous post about class creation in Python I mentioned how a code object is created for the code in the class body, and then that code object is executed as a function receiving a namespace object (created by __prepare__) as its locals. The class body will add attributes to that namespace, and can use whatever is already present in that namespace (if __prepare__ has put something there). This has made me wonder if apart from that, the class body can have access to its enclosing scope. Just remember that in this post we saw that methods in a class have access to its enclosing scope (they close over variables defined outside the class).

So the answer is YES, and it's just the closures mechanism in action. Let's see an example:

def create_class(id: str):
    class MyClass:
        # the class initialiation (the class body) has access to "external" variables in the enclosing scope, such as "id"
        # the code in the class body is placed in a codeobject that will run in a function having trapped (closed over) the "id" free variable
        class_id = id
        
        print(f"Free variables: {inspect.currentframe().f_code.co_freevars}")
        # Free variables: ('id',)

        def __init__(self, value):
            self.value = value

        def display(self):
            print(f"MyClass value: {self.value}, Class ID: {self.class_id}")

    return MyClass

cl = create_class("123")
instance = cl("aa")
instance.display()

# Free variables: ('id',)
# locals: {'__module__': '__main__', '__qualname__': 'create_class..MyClass', '__firstlineno__': 7, 'class_id': '123'}
# MyClass value: aa, Class ID: 123

As you can see, that class body (my understanding is that Python will execute the code object corresponding to that class body by putting it in a "synthetic function") has access to the id variable in the outer scope and can assign it to one of its attributes. We can see that 'id' in the list of freevars for the code object of the class body (that we get accessing the current frame from the class body itself). However, I came across something that confused me. If I print locals() from the class body, I can't see 'id' there. That's very strange, if I do the same from a normal function, locals shows both "normal" variables and those that the function has trapped in its closure, but, as I've said, for the class body, 'id' is missing in locals:

def create_class(id: str):
    class MyClass:
        # the class initialiation (the class body) has access to "external" variables in the enclosing scope, such as "id"
        # the code in the class body is placed in a codeobject that will run in a function having trapped (closed over) the "id" free variable
        class_id = id
        
        print(f"Free variables: {inspect.currentframe().f_code.co_freevars}")
        # Free variables: ('id',)

        # notice that locals() does not show the id free var
        # that's because we are running in "class scope" and locals() just shows its namespace, not the closure cells. The closure is a separate object that holds references to the free variables, and it is not part of the local namespace of the class body. However, the class body can still access the free variable "id" through the closure.
        print(f"locals: {locals()}")
        # {'__module__': '__main__', '__qualname__': 'create_class..MyClass', '__firstlineno__': 4, 'class_id': '123'}

While for a normal function, it's well there:

def outer(id):
    def inner(value):
        # locals() shows "id" free variable because we are in a function scope (optimized scope)
        print(f"locals: {locals()}")
        # {'value': 'bb', 'id': '456'}
        print(f"Inner function value: {value}, Outer ID: {id}")
    return inner

inner_func = outer("456")
inner_func("bb")

So, why is that? At the time of the Python3.13 release I wrote a post about locals(), f_locals and the "local namespace" (this is related to PEP-667). Well, indeed what I mention on that post (based on other articles) about the "local namespace" as a sort of dictionary is not correct for normal functions in recent Python versions. In "normal" functions we are working in an Optimized Scope. In this optimized scope local variables are not placed in a dictionary and accessed by key, but in a fastarray, and accessed by index (you can see this using dis to check the bytecodes of a function). This locals fastarray, part of the _PyInterpreterFrame for each running function, contains both local variables (including function arguments and those local variables that are cells, cellvars, because they are trapped by inner functions in its closure) and variables trapped by the function itself in its closure:

In CPython's frame object, the `fastlocals` array is laid out as:

[regular locals] [cellvars] [freevars]

At the beginning of a function that has variables in its __closure__ the `COPY_FREE_VARS` bytecode instruction copies cell references from `__closure__` into the frame's fastlocals array for quick access!*
After `COPY_FREE_VARS` executes, all variables (normal locals, cellvars and freevars) are accessed from the fastlocals array during function execution.

By the way, regarding the aforementioned _PyInterpreterFrame, I'll leverage to copy here some GPT wisdom about frames in recent (Python 3.11 and above) Python versions

The _PyInterpreterFrame is an internal C struct introduced in Python 3.11 that represents a stack frame for execution, aiming to improve performance by reducing the overhead of allocating full Python PyFrameObject objects.

Purpose: It holds the execution state for code objects, including local variables, globals, builtins, and the instruction pointer (f_lasti).
Performance: Unlike older Python versions where every frame was a full heap-allocated PyFrameObject, _PyInterpreterFrame is designed to be lightweight and often lives on the C stack, reducing garbage collection pressure.

The traditional PyFrameObject still exists, but it has been relegated to a "shadow" role. It is now treated purely as a compatibility API wrapper.

Python only creates a PyFrameObject on demand when a tool or a piece of code explicitly asks to inspect the call stack. This process is often referred to as materializing a frame.

Class Statement vs Dynamic Class Creation

We know that along with the standard class statement, Python also allows us to create classes dynamically by calling type() (or another metaclass if our class has a metaclass other than type)

I already dedicated a rather thick post to type. Basically we use it for creating a new class like this: type(classname, superclasses, namespace). (the namespace is just a dictionary with the attributes).

So I was wondering if the compiler translates a class statement into a call to type(), and yes, more or less we can say so, but there are some extras. I've had a really insightful conversation with a GPT about this, and additionally I've found an excellent article that explains it in full detail

The steps that Python follows when it comes across with a class statement (class Foo(Base, metaclass=Meta): x = 1) are these (I'm taking it from a GPT discussion, it's basically the same that is explained in the linked article)

• Step 1 — Determine the metaclass. Python calls __build_class__ (a builtin), which inspects the bases and the explicit metaclass= kwarg to resolve which metaclass to use (with MRO-based metaclass conflict resolution).
• Step 2 — Prepare the namespace. The metaclass's __prepare__ classmethod is called: namespace = Meta.__prepare__('Foo', (Base,), **kwargs). This returns the dict (or dict-like object) that will serve as the class namespace. For type, this is just a plain dict. For enum.EnumMeta, for example, it returns a special _EnumDict.
• Step 3 — Execute the class body. The compiled code object for the class body is executed as a function, with the namespace from step 2 as its locals(). This is the key insight: it's essentially exec(body_code, globals(), namespace). After this, namespace contains {'x': 1, '__module__': ..., '__qualname__': ...}.
• Step 4 — Call the metaclass. Meta('Foo', (Base,), namespace) is called — which, for the default type, invokes type.__call__ → type.__new__ → type.__init__. This is where the actual class object is constructed.

How do the above steps look at the bytecode level? When the Python compiler (yes, in Python, where compilation is like a hidden step that happens the first time our code is run (or has changed), it's sometimes confusing to establish the difference between compilation time and execution time), comes across a class statement, it creates a code object for the code that we've placed inside that statement (the body of the class statement), along with code objects for each function (method) defined in that code, and creates a sequence of bytecode instructions that at runtime will make use of that code object (and many more things) to create a class object (yes, remember that classes are objects).

That sequence of bytecode instructions can vary slightly with Python versions (what I'll show below, that corresponds to python 3.14 is slightly different from what is shown in the aforementioned article), but the intent is the same.

class Person:
	pass

# translates into:

 0           RESUME                   0

  1           LOAD_BUILD_CLASS
              PUSH_NULL
              LOAD_CONST               0 (code object Person at 0x78d07576e730, file "class_creation.py", line 1)
              MAKE_FUNCTION
              LOAD_CONST               1 ('Person')
              CALL                     2
              STORE_NAME               0 (Person)
              LOAD_CONST               2 (None)
              RETURN_VALUE

So those seem like very few instructions for the complex 4 steps that I've just described!. Well, that's because all the magic happens in a builtin function __build_class, that is loaded by the LOAD_BUILD_CLASS bytecode instruction. The article makes a great job explaining these opcodes.

When we create a class dynamically using type() (or any other metaclass), we are directly at step 4, we are skipping the first 3 steps. Obviously it's us who choose the metaclass to use, and there's not class body to execute. And as we create ourselves the namespace object to pass to the metaclass the __prepare__ method that helps prepare that namespace is not executed. That's maybe the main difference then, that in the dynamic class creation the metaclass __prepare__ method does not intervene. That's interesting, cause indeed I was not familiar with that __prepare__ method (also referred as hook).

When talking about metaclasses I always think about __new__ and __init__ (and __call__ that intervenes when instances of a class created by the metaclass are created), I've talked about them in different posts, one of the most interesting being this, but was unfamiliar with __prepare__. We've seen that it allows us to prepare the namespace, OK, but when can we need that? Well, very rarely (this is particularly dark metaclass stuff). That can be material for another post, for now I'll just say that enum.EnumMeta makes use of it.

SQL, NULL, Unknown

Lately I've been revisiting the rather particular behaviour of NULL in SQL, and it has led me into a better understanding of how different SQL is from General Programming Languages

- Binary Logic vs Ternary Logic

General Programming Languages (Python, JavaScript, ruby, Java...) use binary logic (Boolean logic in particular, and indeed that's the only logic I was aware of). Conditions are either True or False.
SQL uses a ternary logic (kleene logic), where we have TRUE, FALSE, and UNKNOWN

- The meaning of Missing Data

Both in General Programming Languages and in SQL we use null (None in Python) to represent missing data. There are 2 reasons for missing data, either it does not apply to that object, or we don't know it. Let's say we have an instance of a ShopItem class. Its expirationDate attribute can be null either because this object is a Book, and books do not expire, or because the printed date on this beans can is blurry (or we've had no time to read it yet) and then we don't know it, it's unknown.

In general Programming Languages null is a value (that represents that there's nothing here, there is no value here, for whatever the reason, either because it does not apply or because we don't know it), and with binary logic comparing a value to to another value is either true or false. So "a" == null is false, and null == null is true.

In SQL we have a sort of mismatch. On one hand we have ternary logic with that additional UNKOWN concept, but on the other hand we still have a single value, NULL, to represent both that it does not apply or that we don't know it. So how should NULL behave in comparisons? SQL designers decided to treat NULL as a marker that represents that the value is unknown (so we can not express that the value does not apply).

Once we have understood that, the apparent odd behaviour of NULL in comparisons suddenly makes sense. Any comparison using the standard operators (=, !=, <, >, <>) involving a NULL value will return UNKNOWN, even NULL = NULL or NULL != NULL return UNKNOWN. The negation of UNKNOWN (NOT UNKNWON) is also UNKNOWN.

What is odd is what I've just said, that SQL lacks a way to indicate that the value does not apply. It seems one of the main influences in the design of SQL ended realizing this was a serious problem, but too late:

Codd actually realized this flaw later in his life and proposed that SQL should have two different kinds of NULLs: A-Values (Absence) and I-Values (Information Unknown). Sadly, by then, SQL was already set in stone.

Context Managers Part 1

Context Managers have existed in Python since version 2.5, while Assignment Expressions (walrus operator) were added in version 3.8. Somehow recently I came up to wondering if we can replace the "as" by a ":=" assignment. I mean, can we do this?:

with it := MyContextManager():
	# do whatever with it

rather than this:

with MyContextManager() as it:
	# do whatever with it

The answer is NO, or well, more accurately, sometimes yes, sometimes no, but you should always avoid it. To understand this we have to review what a Context Manager is and how they work. Notice that they're part of a broader concept: Automatic Resource Management that also includes Garbage Collecion and RAII (Resource Acquisition Is Initialization).

A Python Context Manager handles the setup and cleanup of resources in your programs. A context manager is any object that implements:

__enter__(self)
__exit__(self, exc_type, exc_value, traceback)

And it's used like this:

with EXPR as target:
    BODY

And now the important part. Conceptually, Python does something roughly like this (as explained by a GPT):

resource_manager = EXPR
resource = resource_manager.__enter__()
try:
    target = resource
    BODY
finally:
    manager.__exit__(...)

So the key point is: The object used to manage the context and the object bound after as do not have to be the same object. That is exactly why __enter__() is allowed to return anything.

Some Context Managers are implemented so that __enter__ returns the context manager itself, while others return a different object. Basically, in the first case the resource being managed and the Resource Manager (Context Manager) are the same object, in the second case they are different as the management responsability has been moved away from the resource itself, to a different object.

Another interesting topic. We know that in Python a single with block can include multiple context managers. I mean:

with open('a.txt', 'r') as fr, open('b.txt', 'w') as fw:
    do_something(fr, fw)

I was wondering if cases where the second context manager makes use of the first context manager, and that I think is more common to find written like this:

with ContextManager1("aaa") as ctx1:
	with ContextManager2(ctx1) as ctx2:
    		do_something(ctx1, ctx2)

Could be written with a single with (avoiding the additional nesting level):

with ContextManager1("aaa") as ctx1, ContextManager2(ctx1) as ctx2:
	do_something(ctx1, ctx2)

The answer is YES. The first

Python evaluates multiple context managers in a single with statement sequentially from left to right. The moment the first context manager is entered, its return value is bound to the as variable, making it immediately available for the next context manager on the same line.