2023
~11399 words, 57 min read
This guide serves as a comprehensive exploration of Python's internal workings, focusing on memory management, threading, processes, CPU utilization, and asynchronous programming in Python 3.9.
While it uncovers several core concepts such as Python's memory architecture, primitive types and objects, multithreading, multiprocessing, and the Global Interpreter Lock (GIL), it doesn't pretend to cover all the intricate details of Python's vast universe. Rather, it's aimed at providing experienced Python developers with a clearer understanding of Python's interaction with underlying hardware, and offering insights into designing Python programs for more efficient memory and CPU usage.
Throughout this guide, you'll find practical tools and techniques for profiling memory and CPU usage, which can aid you in optimizing your existing codebase. Nevertheless, the intent is to encourage curiosity and in-depth exploration of Python's internals, rather than serving as an all-encompassing manual.
Remember, the landscape of Python is vast and continually evolving. So while this guide aims to provide a solid foundation, it is crucial to frequently refer to Python's official documentation and other reputable resources for the most accurate and up-to-date information.
This guide was compiled and prepared with the help of ChatGPT, a large language model developed by OpenAI. Although the AI has significant knowledge up until 2021, it was trained on a diverse range of data sources and should not be considered an authoritative source on its own. The information provided in this guide should be cross-referenced with other reputable resources for the most accurate and up-to-date information. The examples and explanations provided throughout are intended to enhance understanding of the core concepts and should not be considered the only way of doing things.
This guide is meant to be used as a deep-dive into Python's memory management and CPU usage. Each section progressively builds upon the previous ones, starting with an overview of Python's general architecture and then delving into specifics such as the memory and CPU costs of different data types and constructs, threading and process management, and how to use these insights for optimization.
Feel free to skip sections that are not of interest or dive deeper into topics that you find particularly relevant. Enjoy your journey in deepening your Python knowledge!
Python is an interpreted language. This means that unlike languages like C or C++, which are compiled directly into machine code that the processor can execute, Python code is first converted by the Python interpreter into something called bytecode. This is a lower-level, platform-independent representation of your source code.
Python bytecode is a set of instructions understandable by Python’s interpreter. When a Python script is executed, it is first compiled to bytecode (with the .pyc file extension), which is then executed by the Python interpreter.
Python's bytecode is platform-independent, allowing you to compile your Python program on one machine and run it on another with a compatible Python interpreter. However, it's important to note that bytecode is specific to each major version of Python.
Python bytecode is an intermediate language for the Python interpreter, generated from the compilation of your Python source code. Understanding Python bytecode can help us better grasp how Python works internally, especially in terms of performance. Here's what you need to know:
When a Python program is run, each of its modules is compiled into a .pyc file, which contains the Python bytecode. If you want to view the bytecode for a module, you can use the built-in dis module. Here's an example:
import dis
def hello_world():
print("Hello, World!")
dis.dis(hello_world)
Running this code will print the bytecode instructions for the hello_world function.
Each line of Python bytecode represents a simple operation that the Python interpreter can perform. These operations are more primitive than Python commands, but higher-level than machine code. Here are some common bytecode instructions:
LOAD_FAST: Loads a local variable onto the top of the stack.LOAD_CONST: Loads a constant onto the top of the stack.CALL_FUNCTION: Calls a function with a number of arguments.RETURN_VALUE: Returns a value from a function.Understanding Python bytecode can help you identify performance bottlenecks. For example, you might find that a piece of code is slow because it's using a large number of bytecode instructions. By optimizing your Python code, you can reduce the number of bytecode instructions, and potentially improve the performance of your code.
Python is an object-oriented programming language and treats everything, even primitives like integers and booleans, as objects. This characteristic has implications for memory management and performance.
In Python, each object has a block of memory which consists of more than just its value. The memory block also contains metadata: information Python uses to carry out tasks.
Here's a brief overview of what this metadata might include:
Type: Python needs to keep track of the type of every object in the system. This information is crucial when operations are performed on the object.
Reference Count: Python uses reference counting as a way of keeping track of memory. If an object's reference count drops to zero, the object can be removed from memory.
Value: Finally, the object's actual value.
When you create an int or a float, you are not just storing a simple integer or floating-point value in memory. You're creating a full Python object, with all the additional metadata.
The term "overhead" in computing generally refers to the additional resources or extra space required for certain operations or data structures.
Every object in Python incurs some memory overhead. This overhead comes from the object metadata mentioned above. Therefore, the size of an object in Python memory is more than just the size needed to store its value; it also includes the space for the object's metadata.
Understanding this overhead is essential for making decisions about data structures, especially when working with large data sets where memory usage is a concern.
In the following sections, we'll dive into more details about Python's specific data types and their memory/CPU costs. In each case, we'll see how this memory overhead comes into play.
Python provides automatic memory management, which means that the memory is automatically cleared when it's no longer needed. This feature is implemented using a technique known as garbage collection.
The Python memory manager allocates memory blocks for objects and deallocates them when they are no longer in use. To determine when an object is no longer in use, Python uses a technique called reference counting.
When the reference count of an object reaches zero (meaning there are no references to the object), the object is considered garbage and can be removed from memory. We'll discuss garbage collection in more depth in a subsequent section.
With these foundational concepts clear, we can better understand the memory and CPU costs of Python's primitive data types.
This section dives into Python's primitive data types, their memory footprint, and their CPU costs. Remember, Python is an object-oriented language, so even primitive data types have an overhead associated with being objects in Python.
An integer in Python is represented as an int object. Even though it's a primitive data type, it is still an object with the associated object overhead.
In Python 3.9, the base size for an int object is 28 bytes. This includes the overhead from the object metadata (16 bytes), plus the integer value (another 4 bytes), plus extra padding bytes to align the data in memory.
In terms of CPU usage, basic arithmetic operations (like addition, subtraction, multiplication) with integers are quite efficient. These operations usually take a constant amount of time, regardless of the size of the integers involved.
The garbage collector in Python deallocates integers (and every other primitive data type) when their reference count drops to zero. That means an integer is removed from memory when there are no references pointing to it.
The float data type is used to represent floating point numbers in Python. The memory footprint of a float is similar to an integer, with a base size of 24 bytes in Python 3.9.
Floating point operations are also efficient but can be slightly slower than integer operations due to the complexities of floating-point arithmetic.
Booleans in Python (bool data type) are a subtype of integers. They only have two values: True (equivalent to integer 1) and False (equivalent to integer 0). Their memory and CPU costs are the same as those of integers.
Strings (str data type) in Python are immutable sequences of characters. Each character in a string takes up 1 byte (for ASCII strings) or 4 bytes (for Unicode strings). There's also an overhead of 49 bytes for string objects in Python 3.9.
In terms of CPU usage, accessing a character by index is fast (constant time), while operations that need to traverse the whole string, like finding a substring, take more time (linear time).
The list data type in Python is a mutable ordered sequence of items. The memory footprint for a list includes 40 bytes for the list object itself, plus the memory for each item. The total memory cost can vary depending on the size and type of the items in the list.
In terms of CPU cost, accessing an element by index is fast (constant time), but operations that modify the list (like appending or deleting an item) can take more time, especially if the list is large.
Tuples (tuple data type) are similar to lists, but they are immutable. The memory footprint for a tuple is smaller than that of a list, about 8 bytes less per tuple in Python 3.9.
CPU costs for tuples are similar to those for lists, but since tuples are immutable, there are no CPU costs for modifying a tuple (because it can't be modified).
Dictionaries (dict data type) are mutable collections of key-value pairs. The memory footprint of a dictionary is significant due to the underlying hash table implementation. The base size of a dictionary in Python 3.9 is 216 bytes.
In terms of CPU cost, getting or setting a value by key is fast (approximately constant time), but operations that need to traverse the whole dictionary, like iterating over all keys or values, take more time (linear time).
Sets (set data type) are mutable collections of unique items. The memory footprint of a set is similar to that of a dictionary. The base size of a set in Python 3.9 is 216 bytes.
In terms of CPU cost, checking whether an item is in a set or adding an item to a set is fast (approximately constant time), thanks to the underlying hash table implementation.
Python lists are one of the most used data types. They are dynamic arrays of pointers, meaning they can grow or shrink as required. A list can store elements of different data types, which is part of what makes them versatile and popular. However, this versatility comes with trade-offs in memory and performance.
A Python list is essentially an over-allocated array. This means that the Python memory manager reserves more space than needed when creating a list to accommodate future growth. When you append an item to the list, Python uses this extra space, making the operation relatively quick. If a list grows beyond this allocated space, Python creates a new, larger array and moves all the elements to it.
This strategy helps optimize for the common case where lists are grown by appending elements, but it comes with a memory cost. A Python list can use more memory than it actually needs at any given moment.
In Python 3.9, an empty list takes up 40 bytes of memory. Each additional item in the list adds a certain amount of memory, which varies based on the type of the item. For instance, adding an integer to the list would increase the memory usage by 28 bytes (the memory size of an integer in Python 3.9).
When a Python list needs more space to accommodate additional elements, it doesn't simply increase its size by one element. That's because frequently resizing the array would lead to a significant performance hit. Instead, Python uses an overallocation strategy to minimize the number of resizes.
When the list grows, Python allocates additional memory at the end of the list more than what's currently needed. This extra space allows the list to accommodate future growth without having to resize the array each time an element is appended.
The specific resizing algorithm has changed over various Python versions. In Python 2.4 and later, the growth factor is approximately 1.125. The new size is calculated based on the formula:
new_size = old_size + (old_size >> 3) + (old_size < 9 ? 3 : 6)
Here, >> is a right bit shift, which effectively divides old_size by 8.
This strategy provides a good balance, allowing Python lists to grow without frequently resizing, which can be an expensive operation. However, it does mean that Python lists can sometimes use more memory than just what is required to store their elements, particularly if the list has grown by a large number of small increments.
In Python 3.8 and earlier, the list resizing strategy was different. The memory allocation was approximately 12.5% (1/8th) of the current size plus some additional padding to account for smaller lists. However, in Python 3.9 and later versions, the resizing strategy was slightly modified to accommodate edge cases better.
Keep in mind that these are implementation details of Python's CPython interpreter, and they could potentially change in future versions or be different in other Python interpreters. But understanding these details can help you better grasp the memory behavior of Python lists.
Let's explore the time complexity of some common list operations.
Indexing (list[i]) and Assigning (list[i] = 0): These are constant-time (O(1)) operations. This means that it takes the same amount of time to perform these operations, regardless of the size of the list.
Appending (list.append(x)): This is typically a constant-time operation (O(1)). However, when the underlying array is full and needs to be resized, the operation becomes linear (O(n)), as Python needs to copy all elements to a new array. This case is rare, though, and the operation can still be considered effectively constant time on average due to an optimization known as amortized analysis.
Popping from the end (list.pop()): Popping an element from the end of the list is a constant-time (O(1)) operation.
Inserting (list.insert(i, x)) and Popping from the beginning or middle (list.pop(i)): These are linear-time (O(n)) operations. This is because Python has to shift all the elements after the inserted or removed item, which takes time proportional to the length of the list.
Searching (x in/not in list): Searching for an item in the list is a linear-time (O(n)) operation in the worst case, as Python might need to check every item in the list.
Copying (list.copy()) and Resizing (list.resize()): These are linear-time (O(n)) operations, as Python needs to create a new array and copy all elements.
Concatenating (list1 + list2): This is a linear-time (O(n)) operation, as Python needs to create a new list and copy all elements from the two lists.
Keep in mind that while some operations like searching and inserting may seem costly, they are often acceptable in practice, unless you're dealing with large lists and performance is a critical concern.
Dictionaries are one of the most powerful built-in data types in Python. They store key-value pairs and allow you to quickly retrieve a value given its key. Internally, Python implements dictionaries as hash tables, which offer efficient key-based lookup.
The memory consumption of a dictionary in Python is relatively high due to its underlying hash table structure. The hash table must be large enough to avoid collisions (where two keys hash to the same bucket), which leads to extra memory overhead.
In Python 3.9, an empty dictionary uses 216 bytes of memory. Each key-value pair added to the dictionary will increase the memory usage. The exact amount depends on the types and sizes of the key and value.
It's important to note that Python over-allocates memory for dictionaries to optimize for speed of operations. This means that a dictionary will often use more memory than just the memory required for its key-value pairs, leading to higher memory usage but faster performance.
Here are the time complexities of some common dictionary operations.
Access (dict[key]) and Update (dict[key] = value): These operations are generally constant time, O(1), thanks to the hash table implementation. However, in the worst-case scenario where many keys hash to the same bucket (a hash collision), these operations can potentially become linear time, O(n). In practice, though, hash collisions are rare, and Python's hash function does a good job of distributing keys evenly across the hash table.
Insertion (dict[key] = value): This operation is typically constant time, O(1), for the same reasons as access and update. However, if the dictionary needs to resize its underlying hash table, this operation becomes linear time, O(n), as all key-value pairs must be rehashed and moved to the new, larger table. Similar to list.append, this case is rare and can be considered effectively constant time on average due to amortized analysis.
Deletion (del dict[key]): Like access, update, and insertion, deletion is typically constant time, O(1), but can potentially be linear time, O(n), in the worst case of many hash collisions.
Search (key in/not in dict): Searching for a key in a dictionary is typically constant time, O(1), due to the hash table implementation.
Copying (dict.copy()): Copying a dictionary is a linear time, O(n), operation as all key-value pairs must be copied to the new dictionary.
Iteration (for key in dict): Iterating over a dictionary is a linear time, O(n), operation as you need to visit every key-value pair once.
The performance characteristics of dictionaries make them ideal for tasks that require efficient lookups, insertions, and deletions. However, they do use more memory than other data types, which is a trade-off to consider when dealing with large amounts of data.
Python's set data type is an unordered collection of unique elements. Like dictionaries, sets are implemented as hash tables, which make them excellent for membership tests.
In terms of memory, a set is similar to a dictionary. It's essentially a hash table, but only stores keys (the set elements) with no associated values. Therefore, a set will typically use less memory than a dictionary with the same number of elements.
In Python 3.9, an empty set uses 200 bytes of memory. The memory usage will increase with each additional unique element added to the set. The actual increase depends on the type and size of the element.
As with dictionaries, Python over-allocates memory for sets to optimize for speed, leading to extra memory usage.
The time complexities of common set operations are as follows:
Insertion (set.add(element)): Insertion in a set is typically a constant-time (O(1)) operation. However, when the set needs to resize its hash table (when it's full), this operation becomes linear time (O(n)), as all elements must be rehashed and moved to the new, larger table. Like with lists and dictionaries, this case is rare and can be considered effectively constant time on average due to amortized analysis.
Deletion (set.remove(element)): Deletion is also typically constant time (O(1)), but can potentially be linear time (O(n)) in the worst case of many hash collisions.
Search (element in/not in set): Searching in a set is typically constant time (O(1)), thanks to the hash table implementation. This makes sets especially useful for membership tests.
Intersection (set1 & set2), Union (set1 | set2), Difference (set1 - set2): These operations are typically linear time (O(n)), as they involve iterating over elements. However, their performance can be better if one set is significantly smaller than the other.
Copying (set.copy()): Copying a set is a linear time (O(n)) operation as all elements must be copied to the new set.
The efficient membership tests provided by sets make them ideal for certain tasks, such as removing duplicates from a list or checking if any element of a list is part of another list. However, the memory overhead can be a concern when dealing with large sets.
About Python others built-in types like tuple, string, etc, their underlying implementation and behavior in terms of memory and CPU usage are quite similar to the types we discussed above. Strings, for example, behave similarly to lists in terms of memory and CPU usage.
Each Python data type comes with its own strengths and weaknesses, and understanding these trade-offs is key to writing efficient Python programs. The important thing to remember is that Python's simplicity and ease of use often come with overheads, both in memory and CPU usage. Understanding these overheads will help you make informed decisions when writing Python code.
Python, being an object-oriented language, treats everything as objects. The way Python manages memory for objects and classes plays a crucial role in the overall memory usage of Python programs.
When an object is created in Python, memory is allocated to store the object's data and other necessary information. This memory is allocated from the Python heap, which is a pool of memory reserved for Python to use.
Every object has a header, which contains metadata about the object, such as its type and reference count. The rest of the allocated memory is used to store the object's data.
The size of an object in memory includes the size of the header and the size of the data. The size of the data depends on the type of the object. For example, an integer object has a certain size to store the integer value, while a list object has a different size depending on the number of elements and their types.
The process of creating an object and allocating memory for it is generally fast, but it does contribute to the CPU usage of the Python program. Python creates objects on the heap, a portion of memory specifically allocated for dynamic memory allocation. When you create an object, Python's memory manager is responsible for providing the space needed to store the object data.
Creating an object involves several steps:
Memory Allocation: First, Python needs to allocate a block of memory large enough to store the object data. This allocation process involves a request to the Python memory manager, which will search the heap for a sufficiently large, continuous block of memory. This search operation can require a non-trivial amount of CPU time, especially if the heap is fragmented (i.e., the free memory is not all in one block, but scattered in smaller pieces throughout the heap).
Object Initialization: Once the memory is allocated, Python initializes the object by setting its initial values, including type information and the reference count. If the object is an instance of a user-defined class, Python also needs to run the class's __init__ method, if it exists. This initialization process uses CPU time, as Python needs to execute the necessary instructions to set up the object.
Variable Assignment: If the new object is being assigned to a variable, Python also needs to set the value of the variable to the memory address of the new object. This involves updating the symbol table, a data structure that Python uses to keep track of all variables.
Reference Counting: Python uses a system of reference counting to keep track of the number of references to each object. When an object is created and assigned to a variable, its reference count is incremented. This increment operation is fast but does consume some CPU time.
Overall, while the process of creating an object is designed to be efficient, it does involve several steps and requires a certain amount of CPU time. The exact amount of time depends on various factors, including the size and type of the object, the state of the heap, and the complexity of the object's initialization process.
It's also worth noting that the overhead of object creation can become more significant if you're creating many objects in a loop, for instance. In such cases, optimizing object creation can have a noticeable impact on the performance of your Python program.
If you find yourself creating a large number of objects, especially in a loop, there are a few optimization strategies you can consider:
Object Pooling: Object pooling is a design pattern where a set of initialized objects are kept ready to use, rather than allocating and destroying them on the fly. When an object is taken from the pool, it is not available in the pool until it is returned. Objects in the pool have a lifecycle: creation, validation, and destruction. This pattern can offer significant performance benefits when the cost of initializing a class instance is high.
Lazy Initialization: With lazy initialization, objects are not created until they are actually needed. This can often save memory and CPU time by avoiding unnecessary object creation.
Flyweight Pattern: The flyweight pattern is a design pattern used when dealing with large numbers of objects that share many common properties. Rather than storing these properties in each object, resulting in substantial memory overhead, the flyweight pattern stores them in external data structures and makes them accessible through a 'flyweight' object. It's a way of using objects in large numbers when a simple repeated representation would use an unacceptable amount of memory.
Using Tuples or Namedtuples instead of Classes: If your class primarily exists to bundle together a few pieces of data, you might save memory by using a tuple or namedtuple instead. This can be more memory-efficient because instances of classes like tuple and collections.namedtuple take up less memory than a typical user-defined class.
Avoid Temporary Objects: In some cases, you might be creating temporary objects without realizing it. For instance, operations like string concatenation can create many temporary strings. In such cases, you can often optimize your code by using different techniques (like "".join(my_list) for string concatenation) to avoid creating temporary objects.
Remember, however, that these optimizations can make your code more complex, and it's often better to prioritize readability over optimization. Always profile your code before and after optimization to ensure that the changes are actually improving performance. And avoid premature optimization: it's usually better to write simple, clear code first, then optimize if and only if you find that performance is a problem.
In Python, a class is also an object. When a class is defined, Python creates a class object, which has its own memory allocation. This class object stores information about the class, such as its name, base classes, and methods.
When an instance of a class is created, Python allocates memory for the instance object. The instance object contains a pointer to the class object, as well as memory for any instance variables.
Class definition and instance creation are more expensive in terms of memory and CPU usage compared to basic data types, due to the additional information that needs to be stored and managed.
When you define a class in Python, Python creates a class object, which itself is an instance of a metaclass, typically type. The class object contains several pieces of information, including:
Name: The name of the class is stored as a string.
Bases: This is a tuple of base classes. If you define a class with no explicit base class, Python automatically uses object as the base class.
Dictionary: This is a dictionary that contains the class's namespace, where all the class attributes are stored. It includes:
Methods: All the function objects created by def statements inside the class.
Class Variables: Any variables that are assigned values in the class body, outside of any method, are class variables.
When an instance of a class is created, Python creates an instance object. The instance object contains:
Type: A reference to the class object, which defines the type of the instance.
Dictionary: This is a dictionary that stores the instance's attributes, including instance variables and any methods or variables that are dynamically added to the instance.
Both the class object and the instance object have an overhead in terms of memory. They require more memory than a basic data type because they need to store the additional information described above.
They also have a CPU cost. When a class is defined, Python needs to execute the class body to create the function objects for the methods, populate the class dictionary, and set up the inheritance from the base classes. When an instance is created, Python needs to allocate memory for the instance, set the type to the class object, and run the __init__ method if it exists.
Understanding these details can help you optimize your Python programs. For example, if you have a class with many methods but only a few instances, the overhead of the class object is relatively small. But if you have a class with few methods and many instances, each instance adds to the memory usage, so the overhead of the instance objects can be significant.
Instance variables in Python are stored in the instance object's memory allocation. Each instance variable adds to the memory usage of the instance object.
Methods, on the other hand, are stored in the class object. When a method is called on an instance, Python uses the instance's pointer to the class object to access the method. This means that methods do not add to the memory usage of each instance. Instead, the memory for all methods is shared among all instances of the class.
The way Python manages memory for objects and classes has implications for the design of Python programs. By understanding how memory is used, you can make better decisions about how to structure your programs and use Python's features.
Understanding Python's memory management can indeed guide your design decisions for more memory-efficient programs. Here are a few considerations:
Reuse objects where possible: As creating a new object incurs a memory and CPU cost, it can be beneficial to reuse existing objects when possible, especially for large data structures.
Leverage immutability: Immutable objects like tuples and strings are safer to reuse than mutable objects because they can't be accidentally changed.
Avoid unnecessary class attributes: Each instance variable consumes additional memory for every instance of a class. If an attribute's value is shared among all instances or doesn't change, it could be more memory-efficient to make it a class variable.
Lazy initialization: If an object's construction is costly (in terms of memory or CPU), consider creating it only when it's actually needed.
Methods in Python are stored at the class level. When a method is defined inside a class, Python creates a function object for the method and stores it in the class's dictionary. This function object is shared by all instances of the class.
When you call a method on an instance, Python first looks for the method in the instance's dictionary. If it's not found, Python looks in the class's dictionary (and continues looking up the inheritance hierarchy if necessary). This way, all instances of a class can access the same method, but no additional memory is used to store the method in each instance.
However, it's important to note that methods do not have their own state that persists across calls or across instances. If a method needs to store some data that persists across calls, it typically does so by setting an instance variable or a class variable.
When a method is called, Python creates a new frame to hold the method's local variables and parameters. This frame is discarded when the method returns. This means that local variables in a method do not persist across calls, and each call to the method has its own separate set of local variables.
So to summarize, methods in Python are stored once at the class level and shared among all instances, but do not have persistent state across calls or across instances. Any persistent state needs to be stored in instance variables or class variables.
The Python interpreter uses a variety of structures to manage memory, some of which are:
The Stack: This stores temporary data like function call information, local variables, and return addresses. It grows and shrinks as functions are called and return, and each thread has its own stack.
The Heap: This is a region of memory used for dynamic memory allocation. All Python objects and data structures are located in a private heap. The programmer does not have access to this private heap. The heap is used by Python’s memory manager for the allocation of Python objects and data structures.
Python Memory Manager: It is responsible for managing Python's memory allocation. It does the job of handling and interfacing with the lower-level system memory through a system of private heap space dedicated for Python objects.
Object-Specific Memory Allocators: Python employs a variety of object-specific allocators, including freelists for integer and float objects, to improve memory allocation performance.
Python has a few specialized memory allocators designed to be faster and more efficient than the general-purpose allocator for specific types of objects. These allocators manage pools of free objects, also known as freelists. When an object of the specified type is created, it is drawn from the free list. When the object is destroyed, it is returned to the free list rather than being immediately deallocated. This approach speeds up allocation and deallocation for these types of objects.
Here are a few examples of the object-specific allocators used by Python:
Freelists for integer and float objects: Python maintains a free list for integers between -5 and 256, as these integers are used frequently. Every time a Python program starts, Python pre-allocates a large number of integer objects for every integer in this range. These integers are always available and shared among all the variables that have these values. Similarly, Python maintains a free list for float objects, reusing them whenever possible.
Freelists for list objects: Python also keeps a free list for list objects. Whenever a list is created, Python first checks if there are any lists in the free list. If there are, Python reuses that list object instead of creating a new one.
Freelists for tuple objects: Similar to list objects, Python keeps a free list for tuple objects, which allows for faster tuple creation and destruction.
The primary benefit of object-specific allocators is that they allow for faster object creation and destruction by reusing memory for objects of the same type. However, they also increase the memory footprint of a Python program, as memory for these objects is not deallocated until the program ends. Understanding these memory allocation strategies can help in writing memory-efficient Python code.
Python’s memory allocation involves a mix of an OS-level allocator, Python’s raw memory allocator, Python’s object allocator, and several object-specific allocators. Python uses its private heap space to manage the memory allocated to its objects and data structures.
Allocation: When a new Python object is created, Python allocates a chunk of memory from the heap to hold the object’s data.
De-allocation: The de-allocation of memory happens automatically in Python through a mechanism called Garbage Collection.
Python performs automatic memory management using a system known as Garbage Collection (GC). The purpose of the garbage collector is to track and deallocate memory for objects that are no longer in use by the program.
Reference Counting: The primary garbage collection method Python uses is reference counting. Each Python object keeps count of the number of references to it held by other objects. When an object’s reference count drops to zero, meaning no other object refers to it, Python automatically deallocates the memory used by the object.
Cycle Detection: In addition to reference counting, Python also has a mechanism to detect and collect objects involved in reference cycles – i.e., a group of objects that refer to each other, forming a cycle that would be unreachable but for reference counting alone.
Generational GC: Python's garbage collector divides all Python objects into three generations. New objects are placed in the first generation (generation 0). If an object survives a collection, it is moved into the next older generation. The idea is that older objects are more likely to be long-lived and therefore should be collected less frequently.
Remember, Python's automatic memory management does not eliminate the need for careful coding. Holding references to large data structures, for instance, can prevent Python from freeing up memory and lead to high memory usage. Therefore, it's always beneficial to understand Python's memory model and management system to write more efficient code.
Asynchronous programming is a form of parallel programming that allows a unit of work to run separately from the primary application thread. When the work is complete, it notifies the main thread about its completion or failure, allowing other work to be executed in the meantime.
In synchronous programming, if a function relies on the result of another function, it has to wait for that other function to finish and return, which effectively leads to idle time.
The idea behind asynchronous programming is to have tasks that are able to run independently of the main application thread, freeing it up to do other tasks. This is especially effective when dealing with IO-bound tasks, like making requests to a database or a web server.
Python introduced native support for asynchronous IO in version 3.5, including two important new keywords:
async: Used to declare a function as a "coroutine", which is a special type of function that can be paused and resumed, allowing it to yield control to other coroutines in a non-preemptive multitasking style.
await: Used to wait for the result of a coroutine.
The basic structure of an asynchronous Python program using async/await looks something like this:
async def my_coroutine():
# coroutine body
async def main():
await my_coroutine()
In the example above, main is the coroutine that is scheduled to run: it could be thought of as the entry point of the asynchronous part of your Python program.
The event loop is the core of every asyncio application. It essentially is an infinite loop that waits for events to happen then reacts to these events. Event loops run asynchronous tasks and callbacks, perform network IO operations, and run subprocesses.
A typical Python program using asyncio will have one event loop, and control is passed back to this loop when your code awaits an operation.
This approach is quite different from multithreaded programming or multiprocessing because it is single-threaded, and purely by design allows you to write concurrent code that is safe from race conditions.
A big advantage of using asynchronous programming and asyncio in Python is the ability to write concurrent code that is simple to write, read, and reason about. However, asynchronous programming is not a silver bullet and might not be the best choice for CPU-bound tasks, where threading or multiprocessing might be more efficient.
Asynchronous context management in Python revolves around the concept of coroutines and the event loop. Coroutines are a generalization of subroutines, which are used in non-preemptive multitasking for their ability to pause and resume execution at certain points, allowing other coroutines to run.
Python implements coroutines through the use of generators and the yield keyword. When you see async def in Python code, it denotes a coroutine function, which when called, returns a coroutine object. This object represents a computation or an I/O operation that can be paused and resumed.
Event Loop: The event loop is the core of every asyncio application. It is responsible for executing coroutines and scheduling callbacks. It can handle multiple I/O operations concurrently and switch between tasks at hand, in accordance with their readiness and priority.
The event loop manages the execution context for each coroutine. When a coroutine yields control, for example with await some_async_operation(), the event loop suspends that coroutine and resumes another. Later, when the awaited async operation completes, the event loop resumes the original coroutine right where it left off.
Here's how it works on a lower level:
Context Variables: Python 3.7 introduced contextvars, which is a module for managing context variables. Context variables are designed to keep track of variables for individual tasks. Even though tasks share the same memory space, context variables help maintain separate states for each task. When a context switch happens, the event loop saves the current context and later restores it when the coroutine is resumed.
Task Execution: The event loop runs tasks by repeatedly calling the __next__() method (or send(value)) of the generator object returned by the coroutine function. This advances the execution of the coroutine to the next yield or await statement, where it gives control back to the event loop. This is all managed by the event loop, which maintains the execution context of each coroutine and knows which one is currently active.
Pausing and Resuming: When a coroutine executes an await expression, it's signaling that it's about to do some potentially long-running operation (like an I/O request) and that it's okay for the event loop to pause its execution and do something else in the meantime. The event loop then suspends that coroutine and runs another. Later, when the awaited operation is done, the event loop can resume the coroutine. The coroutine won't notice the pause; from its perspective, it just waited for the result of an operation.
Data Consistency: Since coroutines can be paused at any await, you need to be careful when they interact with shared state. If a coroutine modifies some data and then awaits an operation, another coroutine could run and see the data in a partially modified state. For data consistency, you must ensure that any such "transaction" is completed without awaiting.
Asynchronous context management is a unique feature of Python, providing the ability to write concurrent code using a sequential programming style. The async/await syntax, along with the event loop and context variables, allows you to manage and coordinate multiple tasks, all within a single thread of execution. This paradigm brings about more efficient utilization of system resources, especially in I/O-bound applications.
Asynchronous programming in Python provides efficient memory usage because of its non-blocking nature. However, understanding how memory is managed can help you write more efficient and scalable async code.
Coroutine Memory: Each coroutine in Python has its own execution context, which includes its call stack, local variables, and any other state information needed to resume its execution after being suspended. This information is stored in the coroutine object. While the exact memory usage of a coroutine will depend on the specifics of what it's doing, it is typically quite small, because it only needs to store the state of a single thread of execution.
Event Loop Memory: The event loop has to maintain a schedule of all the coroutines that are currently being managed. This takes up some memory, but again, it's relatively small, because each task in the event loop schedule is just a reference to a coroutine object.
Memory Spikes and Leaks: Memory management in asynchronous code can be challenging, particularly because it's easy to unintentionally hold onto memory longer than necessary. For example, if a coroutine function starts an operation that produces a large amount of data and then awaits another operation, that data will remain in memory until the coroutine is resumed and can release the data. Care should be taken to release large data structures as soon as they are no longer needed.
Garbage Collection: The async nature of coroutines doesn't exempt them from Python's garbage collection. If a coroutine object is no longer accessible, it becomes eligible for garbage collection, which can free up the memory it was using. But as with synchronous code, circular references or other complex object graphs can potentially delay garbage collection.
Sharing Data Between Coroutines: Data sharing in async programming must be done carefully. While coroutines run in the same thread, and hence share the same memory space, the context switching can lead to race conditions if shared data is not properly managed. Solutions like asyncio.Lock can help in synchronizing access to shared resources.
Understanding how memory is managed in asynchronous programming, particularly in the context of coroutines and the event loop, is crucial for writing efficient and scalable async applications. Not only can it help in reducing memory usage, but it can also prevent potential issues like memory leaks and race conditions.
In Python, an asyncio Task is a subclass of Future that wraps a coroutine. A Task is responsible for executing a coroutine object in an event loop. When a Task is created, it schedules the execution of its coroutine and also allows you to check on its status, cancel the operation, or wait for its completion.
Here's a more detailed look at how it works and what it contains:
Task Creation: A Task is created by calling asyncio.create_task(coro) or loop.create_task(coro), where coro is a coroutine object. This wraps the coroutine in a Task object and schedules its execution on the provided event loop (or the current event loop, if none is provided). The Task is then returned, which can be used to monitor or control the execution of the coroutine.
What's Inside a Task: A Task object contains the following information:
Coroutine Object: The coroutine that the Task is responsible for executing.
State: The current state of the Task, which can be one of 'PENDING', 'CANCELLED', 'FINISHED', or 'RUNNING'.
Result: If the coroutine has finished executing and returned a value, this value is stored in the Task.
Exception: If the coroutine raised an exception during its execution, the exception is stored in the Task.
Event Loop Reference: A reference to the event loop that the Task is scheduled on.
Callbacks: A list of callbacks to be executed when the Task is done.
Task Size: The size of a Task in memory primarily depends on the size of its coroutine, the size of the result or exception (if any), and the number of callbacks. However, each Task also has a baseline memory overhead due to its attributes and methods. As of Python 3.8, this overhead is approximately 400 bytes, not including the memory used by the coroutine, result, exception, or callbacks.
Task Execution: When the event loop that the Task is scheduled on is running, the Task will run its coroutine until it is completed or until an await expression is encountered. If an await expression is encountered, the Task will yield control back to the event loop, which can then run other Tasks. Once the awaited operation is completed, the Task will be resumed.
Data Management: The Task holds the local variables of its coroutine, which are stored in the coroutine's frame (part of the coroutine object). Each await statement in the coroutine creates a new frame, adding some memory overhead. These local variables and frames are cleared as soon as the coroutine completes, releasing their memory.
Cancellation and Exception Handling: Tasks can be cancelled by calling their cancel() method. This raises a CancelledError in the Task's coroutine, which can be caught and handled. If it's not caught, it causes the Task to exit and the exception is stored in the Task.
Understanding asyncio Tasks and their memory management is critical for effectively using asyncio and writing efficient, non-blocking Python code. With proper handling of tasks, you can create high-performance applications that can handle many simultaneous I/O-bound tasks.
In Python, threads are lighter than processes and share the same memory space, allowing for easier data sharing. Threads can be used to run multiple operations concurrently, which is especially useful for I/O-bound tasks such as network or disk operations.
Threads in Python can be created using the threading module, which provides a Thread class. Here's an example:
import threading
def function_to_run_in_thread():
print("Thread started")
thread = threading.Thread(target=function_to_run_in_thread)
thread.start()
thread.join()
When a Thread object is created, it takes a function (the target) and its arguments. The start() method on the Thread object is used to begin the execution of the thread, and join() is used to make the main thread wait for the completion of this thread.
A Thread object in Python contains a number of items:
Target Function: The function that this thread is responsible for executing.
Arguments: The positional arguments and keyword arguments to be passed to the target function.
State: The current state of the thread, which can be running, ready (not running, but ready to run), or blocked (waiting for some resource to become available).
Thread ID: A unique identifier for the thread.
Stack: A call stack for the thread, which keeps track of function calls.
Thread-specific data: Data that is local to the thread. This is managed using the threading.local() function, which returns an object that can have different values for each thread.
Each thread in Python has its own stack, which is used for storing function call information, local variables, etc. However, threads share the heap space, which is used for storing objects, and global variables. This shared memory model allows for easy sharing of data between threads, but also necessitates the need for careful synchronization when accessing shared data to avoid race conditions.
Threads in Python, unlike processes, do not benefit from multiple CPUs due to the Global Interpreter Lock (GIL). This means that even though you might have multiple threads in your Python program, only one of them can execute Python bytecodes at a time. However, threads can be beneficial for I/O-bound tasks, where the program spends a lot of time waiting for I/O operations (like network or disk operations) to complete. In such cases, using threads can help your program to do other work while waiting for I/O operations to complete.
The Global Interpreter Lock (GIL) prevents Python threads from running in true parallel on multiple processors. This is not typically a problem for I/O-bound programs, but it can significantly slow down CPU-bound programs.
Additionally, because threads share memory, precautions must be taken to prevent race conditions. Python's threading module provides several synchronization primitives including locks, semaphores, and condition variables to help with this.
Finally, creating and managing many threads can add significant overhead, both in terms of CPU usage (due to context switching) and memory usage (due to each thread's stack). Care should be taken to balance the need for concurrent execution with the resources required to manage multiple threads.
We will discuss more about the Global Interpreter Lock (GIL) and its impact on threading in Python in the next section.
The Global Interpreter Lock, or GIL, is a mechanism used in CPython to synchronize access to Python objects, preventing multiple native threads from executing Python bytecodes concurrently. This lock is necessary because CPython's memory management is not thread-safe.
When Python runs, only one thread executes Python bytecode at a time, even on a multi-core processor. This restriction is enforced by the GIL. When a thread wants to execute, it needs to acquire the GIL. Only the thread that has acquired the GIL can execute Python bytecodes, while the others wait their turn.
In CPython, the GIL is a single lock on the interpreter itself which adds a rule that execution of any Python bytecode requires acquiring the interpreter lock. This prevents deadlocks (as there is only one lock) and doesn't introduce much performance overhead. But it effectively makes any CPU-bound Python program single-threaded.
GIL plays a crucial role in managing memory in Python. As we know, Python uses a reference counting mechanism for memory management. This mechanism is not thread-safe, meaning that manipulating these reference counts from multiple threads can lead to memory leaks or memory corruption.
GIL resolves this issue by preventing multiple native threads from executing Python bytecodes at once. Hence, Python can handle memory management in a thread-safe manner without introducing the overhead of locking and unlocking each object accessed.
Although GIL allows for simpler memory management, it also limits the ability of Python programs to effectively utilize multiple CPUs or cores. Since only one thread can execute Python bytecodes at a time due to GIL, even a multi-threaded Python program cannot run threads in parallel on multiple cores.
However, it's important to note that GIL is not much of an issue for I/O-bound programs or programs that spend much of their time waiting for external resources. These programs can use threading to handle concurrent operations efficiently as threads waiting for I/O can release the GIL for other threads.
GIL has significant implications on multi-threaded Python programs. Due to GIL, threads in Python are best suited for I/O-bound tasks and not for CPU-bound tasks. If you have a CPU-bound program where you want to speed up a task by running it on multiple CPUs, Python threads would not be the right choice due to the restrictions imposed by the GIL. In such cases, using processes, which run in separate memory spaces and have their own Python interpreters, would be more appropriate.
To understand more about how processes work in Python and how they compare with threads, we will discuss in the next section.
In the context of computing, a process can be thought of as an instance of a program in execution. Every process has its own isolated memory space and runs independently of other processes. When a Python program is launched, it runs as a single process, but the program can create additional processes as needed.
In Python, the multiprocessing module allows for the creation and management of separate processes. Each of these processes runs its own Python interpreter and maintains its own global interpreter lock (GIL), hence overcoming the limitations of threading caused by GIL in CPU-bound tasks.
Python's multiprocessing module creates separate processes, each with its own Python interpreter. This means that each process has its own GIL, allowing it to run independently of other processes and take full advantage of multiple CPUs or cores.
The multiprocessing module provides a number of ways to create and manage processes. The most straightforward is to create a Process object and call its start method. Here's a simple example:
from multiprocessing import Process
def f(name):
print('hello', name)
if __name__ == '__main__':
p = Process(target=f, args=('world',))
p.start()
p.join()
In this example, the f function runs in its own process, separate from the main process.
In Python, each process runs in its own isolated memory space, which includes its own Python interpreter and its own Global Interpreter Lock (GIL). This isolated memory model has several implications:
Safety: Each process is fully isolated from all other processes. This means that a bug or crash in one process won't affect other processes. The operating system ensures that each process can't access memory from other processes unless explicitly allowed.
Memory Overhead: Each process consumes its own memory, which can be considerable, especially for large Python programs. This includes the memory needed for the Python interpreter, loaded modules, global variables, and so forth. When creating many processes, the memory usage can add up quickly. Because of this, process-based concurrency is often better suited for I/O-bound tasks or CPU-bound tasks that require a small number of long-lived processes.
Data Sharing: Since each process has its own private memory space, sharing data between processes can be more challenging compared to threads, which share the same memory space. Python provides several mechanisms for inter-process communication (IPC), including multiprocessing.Queue, multiprocessing.Pipe, and shared memory objects (e.g., multiprocessing.Value or multiprocessing.Array). However, these IPC mechanisms typically involve serialization and deserialization of data, which can be a performance overhead. Shared memory objects can avoid this overhead, but they require synchronization mechanisms (like multiprocessing.Lock) to prevent race conditions, which can be complex to manage correctly.
Python's multiprocessing module allows Python code to take advantage of multiple CPUs or cores. Because each process runs in its own interpreter with its own GIL, each process can run on a separate CPU core. This is a big advantage for CPU-bound tasks, where Python's threading can be limited by the GIL.
However, process-based concurrency has its own overheads and limitations:
Process Creation and Destruction: Creating a new process involves creating a new memory space, loading a new Python interpreter, and more. This process creation overhead can be considerable, especially if many processes are being created and destroyed. Python provides a multiprocessing.Pool class, which maintains a pool of worker processes. This can minimize the process creation overhead for tasks that can be broken into smaller independent tasks.
Context Switching: Switching between processes (known as a context switch) is more expensive than switching between threads. Each context switch involves saving the state of the current process and loading the state of the new process, which can be a significant overhead for many context switches.
CPU Scheduling: The operating system's CPU scheduler determines when and for how long each process runs. This can lead to scenarios where a process is paused in the middle of a critical operation, leading to unpredictable execution timing. However, for many tasks, especially CPU-bound tasks, this isn't a significant concern.
Overall, while process-based concurrency can effectively use multiple CPU cores for CPU-bound tasks, it comes with its own trade-offs and complexities. Understanding these complexities can help you make better design decisions in your Python programs.
Both processes and threads allow for concurrent execution in Python programs. However, there are important differences in how they work, their memory management, and their CPU usage.
Memory: Threads share the same memory space, while each process has its own isolated memory space. This makes data sharing straightforward with threads, while providing a degree of safety with processes.
CPU Utilization: Threads in Python are limited by the GIL, making them less suited for CPU-bound tasks, but they are efficient for I/O-bound tasks. Processes, on the other hand, can run on separate CPU cores, making them suitable for CPU-bound tasks.
Overhead: Threads are lighter, with less creation and destruction overhead, while processes are heavier and more expensive to create and destroy. Communication and data sharing are also more costly with processes.
It's important to choose the right approach based on the nature of the task. For CPU-bound tasks, multiprocessing may be the better choice, while for I/O-bound tasks, threading or asynchronous programming may be more efficient. Understanding these intricacies helps you design more efficient Python programs.
Understanding how Python utilizes CPU resources can provide insight into how to optimize your code and make efficient use of system resources. Let's dive into how Python manages these resources and the distinction between CPU-bound and I/O-bound tasks.
The Python interpreter is essentially a program that reads and executes Python code. Given its interpretive nature, Python isn't as fast as compiled languages like C or Java. Each line of Python code is read, interpreted, and then executed, which can be CPU-intensive, especially for complex or numerically heavy computations.
The Global Interpreter Lock, or GIL, is a mechanism used in CPython to synchronize access to Python objects, preventing multiple native threads from executing Python bytecodes simultaneously. The GIL is necessary because CPython's memory management is not thread-safe.
While the GIL allows only one thread to execute at a time even on a multi-core processor, it doesn't mean only one thread is active. It just means that only one thread executes Python bytecodes at a time. Other threads may be processing I/O, waiting to acquire the GIL, or performing computation in C-coded extensions.
In terms of CPU utilization, the GIL can be a significant factor. For CPU-bound programs that do not release the GIL, multithreading might not give you the expected performance improvement, as threads would essentially execute sequentially due to the GIL.
Understanding the nature of your task—whether it is CPU-bound or I/O-bound—can help in designing efficient programs and making better use of Python's concurrency features. Let's break down these two categories:
CPU-bound tasks are those where the speed of the CPU is the limiting factor. These tasks involve a lot of calculations, such as numerical computations, image processing, or data analysis.
For CPU-bound tasks, the GIL can become a bottleneck when using threading. Since only one thread can execute Python bytecodes at a time due to the GIL, CPU-bound tasks might not see a speedup from multithreading and might even slow down due to the overhead of managing multiple threads.
In these cases, using multiple processes with the multiprocessing module can be more effective, as each process has its own Python interpreter and its own GIL, thus allowing Python bytecodes to execute in parallel.
I/O-bound tasks are tasks that spend time waiting for Input/Output (I/O) operations to complete, such as network requests, disk reads/writes, or user input.
For I/O-bound tasks, the speed of the CPU is generally not the limiting factor. Instead, the program spends most of its time waiting for I/O operations to complete.
In these scenarios, concurrency can provide significant speedup by allowing the program to continue doing work while waiting for I/O to complete. Both threading and asynchronous I/O can be effective here, as they allow other tasks to proceed while an I/O-bound task is waiting for its I/O operations to complete.
In conclusion, understanding how Python utilizes CPU resources and the nature of your tasks can guide you towards optimizing your code and making more efficient use of your system resources. In the following sections, we'll look at some techniques to profile and optimize your Python programs for both memory and CPU usage.
Once you have a good grasp of Python's memory and CPU management, it's time to dive into optimization techniques. The Python ecosystem offers numerous tools and techniques to help optimize your programs' memory usage and CPU utilization. Let's explore them.
Profiling is a way to measure the memory and performance characteristics of your code. It involves monitoring and recording the resources used by a piece of software, which can help you identify areas that might benefit from optimization.
There are several tools available for memory profiling in Python, such as memory_profiler and objgraph.
memory_profiler: This tool provides line by line memory consumption for Python programs. It's an excellent tool for spotting memory leaks or places where your program is consuming more memory than expected.objgraph: objgraph allows you to create graphs that show the relationships between different objects in your program. It can be beneficial to identify areas where unnecessary references are keeping objects from being garbage collected.To profile your memory usage effectively:
Just as with memory usage, profiling CPU usage can give you insights into the performance characteristics of your code. Tools like cProfile can help you identify the bottlenecks in your program that might be slowing it down.
cProfile: cProfile is a built-in module in Python that can provide detailed profiling information. It can be used to measure the time taken by different parts of your program, helping you identify functions or methods that are consuming more CPU time.To profile your CPU usage effectively:
After profiling your code, you will likely find areas that could benefit from optimization. Here are some general optimization tips:
del to free up memory.By applying these techniques, you can make your Python programs more efficient and resource-friendly.
Understanding the intricacies of Python's memory management and CPU utilization is key to writing efficient Python programs. By exploring Python's memory management and object model, we've seen how Python handles various data types and structures, and how these factors can impact your programs' performance.
In this guide, we've delved deep into the Python interpreter, its memory management, threading model, and the GIL. We've also explored Python's concurrency models, including threads, processes, and async I/O, and how they interact with Python's memory model and the GIL.
We discussed several optimization techniques, from memory and CPU profiling to specific tips for writing efficient Python code. We hope that this guide provides you with a deeper understanding of Python and enables you to write more efficient and performant Python programs.
For further exploration, here are some resources:
Python Speed: A collection of performance-related resources on the official Python wiki.
The Bloatware Debate: How Much Does RAM Consumption Really Matter?: An in-depth article by Real Python on Python memory management.
Python Performance Optimization: Another great article from Real Python focused on performance optimization.
Speed Up Your Python Program With Concurrency: An excellent guide to understanding concurrency in Python from Real Python.