FastAPI State Management: Simple Implementation of Global Variables and Caching
In FastAPI, two common approaches for state management are global variables and caching. Global variables are the simplest way to share state, usable directly in single-process environments but requiring asyncio.Lock to prevent race conditions in multi-request scenarios. Their limitations include process isolation issues, memory dependency, and data loss risks. Caching is more efficient and categorized into three types: in-memory caching (using dictionaries or the cachetools library, supporting LRU/TTL strategies), and distributed caching (e.g., Redis, suitable for cross-service sharing and persistence). Comparison: Global variables suit single-process, simple scenarios, while caching is ideal for high-frequency access, distributed systems, or data requiring persistence. Practical recommendations: Use global variables or cachetools in development, and distributed caches like Redis in production to avoid cross-process issues with global variables.
Read MoreFlask Context Processors: Global Variables and Template Reusability
Flask context processors address the repetition of manual parameter passing when multiple templates need to share information (such as navigation menus and current user details). By using the `@app.context_processor` decorator on a function that returns a dictionary, the key-value pairs automatically become available variables in all templates. **Core Usage**: Define a function that returns a dictionary containing shared variables, where keys are the template variable names and values are the variable contents. Examples include displaying the current time, a list of navigation menu items, and dynamic user information (which changes with the login status). **Advantages**: It avoids redundant variable passing in view functions, resulting in cleaner code. Variables are dynamically updated (e.g., user login status). Modifying shared content only requires changes in the context processor, ensuring all templates take effect simultaneously, thus enhancing maintainability. **Comparison**: Without a context processor, each view must manually pass variables, leading to verbose code. With a context processor, views only return the template name, and variables are automatically injected, allowing templates to directly use these variables. **Value**: It simplifies template sharing logic, enables template reuse, and efficiently shares dynamic data across all templates.
Read MoreC++ Variable Scope: Differences Between Local and Global Variables
This article analyzes the scope of C++ variables and the core differences between local and global variables. The scope of a variable determines its access range, which is divided into two categories: local and global. Local variables are defined within a function or code block and are limited to that scope. They are created when the function is called and destroyed when the function execution ends. Local variables have a random default value (unsafe). They are suitable for small - range independent data and are safe because they are only visible locally. Global variables are defined outside all functions and have a scope that covers the entire program. Their lifecycle spans the entire program execution. For basic data types, global variables have a default value of 0. They are easily modified by multiple functions. They are suitable for sharing data but require careful use. The core differences are as follows: local variables have a small scope, a short lifecycle, and a random default value; global variables have a large scope, a long lifecycle, and a default value of 0. It is recommended to prioritize using local variables. If global variables are used, they should be set as const to prevent modification, which can improve code stability. Understanding variable scope helps in writing robust code.
Read MoreScope Mini Lesson: Local and Global Scopes of Python Variables
In Python, scoping determines the access range of variables, mainly divided into two types: local and global. **Local Scope**: Variables defined inside a function are only valid within that function (e.g., `age = 18`). If a variable with the same name as a global variable is defined inside a function, it will be treated as a local variable first (e.g., `x = 200` overwrites the global `x=100`, but the global `x` remains 100 externally). **Global Scope**: Variables defined outside a function are accessible throughout the entire program (e.g., `name = "Xiao Ming"`). There is no issue with direct access. However, if a function intends to modify a global variable, it must be declared with `global` (e.g., `global score`); otherwise, Python will mistakenly treat it as a local variable (e.g., `score=90` does not modify the original global value of 80). **Nested Functions**: The inner function can access the local variables of the outer function. When modifying these variables, the `nonlocal` declaration is required (e.g., `nonlocal outer_var`). Summary of Rules: Local scope is limited to the function, global scope spans the entire program; use `global` to modify global variables and `nonlocal` to modify outer local variables. Proper use of scoping can avoid variable conflicts and enhance code readability.
Read More