The key to dealing with API authentication is to understand and use the authentication method correctly. 1. APIKey is the simplest authentication method, usually placed in the request header or URL parameters; 2. BasicAuth uses username and password for Base64 encoding transmission, which is suitable for internal systems; 3. OAuth2 needs to obtain the token first through client_id and client_secret, and then bring the BearerToken in the request header; 4. In order to deal with the token expiration, the token management class can be encapsulated and automatically refreshed the token; in short, selecting the appropriate method according to the document and safely storing the key information is the key.

There are three common ways to initiate HTTP requests in Node.js: use built-in modules, axios, and node-fetch. 1. Use the built-in http/https module without dependencies, which is suitable for basic scenarios, but requires manual processing of data stitching and error monitoring, such as using https.get() to obtain data or send POST requests through .write(); 2.axios is a third-party library based on Promise. It has concise syntax and powerful functions, supports async/await, automatic JSON conversion, interceptor, etc. It is recommended to simplify asynchronous request operations; 3.node-fetch provides a style similar to browser fetch, based on Promise and simple syntax

To test the API, you need to use Python's Requests library. The steps are to install the library, send requests, verify responses, set timeouts and retry. First, install the library through pipinstallrequests; then use requests.get() or requests.post() and other methods to send GET or POST requests; then check response.status_code and response.json() to ensure that the return result is in compliance with expectations; finally, add timeout parameters to set the timeout time, and combine the retrying library to achieve automatic retry to enhance stability.

MySQL query performance optimization needs to start from the core points, including rational use of indexes, optimization of SQL statements, table structure design and partitioning strategies, and utilization of cache and monitoring tools. 1. Use indexes reasonably: Create indexes on commonly used query fields, avoid full table scanning, pay attention to the combined index order, do not add indexes in low selective fields, and avoid redundant indexes. 2. Optimize SQL queries: Avoid SELECT*, do not use functions in WHERE, reduce subquery nesting, and optimize paging query methods. 3. Table structure design and partitioning: select paradigm or anti-paradigm according to read and write scenarios, select appropriate field types, clean data regularly, and consider horizontal tables to divide tables or partition by time. 4. Utilize cache and monitoring: Use Redis cache to reduce database pressure and enable slow query

In Python, variables defined inside a function are local variables and are only valid within the function; externally defined are global variables that can be read anywhere. 1. Local variables are destroyed as the function is executed; 2. The function can access global variables but cannot be modified directly, so the global keyword is required; 3. If you want to modify outer function variables in nested functions, you need to use the nonlocal keyword; 4. Variables with the same name do not affect each other in different scopes; 5. Global must be declared when modifying global variables, otherwise UnboundLocalError error will be raised. Understanding these rules helps avoid bugs and write more reliable functions.

To create modern and efficient APIs using Python, FastAPI is recommended; it is based on standard Python type prompts and can automatically generate documents, with excellent performance. After installing FastAPI and ASGI server uvicorn, you can write interface code. By defining routes, writing processing functions, and returning data, APIs can be quickly built. FastAPI supports a variety of HTTP methods and provides automatically generated SwaggerUI and ReDoc documentation systems. URL parameters can be captured through path definition, while query parameters can be implemented by setting default values ??for function parameters. The rational use of Pydantic models can help improve development efficiency and accuracy.

How to efficiently handle large JSON files in Python? 1. Use the ijson library to stream and avoid memory overflow through item-by-item parsing; 2. If it is in JSONLines format, you can read it line by line and process it with json.loads(); 3. Or split the large file into small pieces and then process it separately. These methods effectively solve the memory limitation problem and are suitable for different scenarios.

In Python, the method of traversing tuples with for loops includes directly iterating over elements, getting indexes and elements at the same time, and processing nested tuples. 1. Use the for loop directly to access each element in sequence without managing the index; 2. Use enumerate() to get the index and value at the same time. The default index is 0, and the start parameter can also be specified; 3. Nested tuples can be unpacked in the loop, but it is necessary to ensure that the subtuple structure is consistent, otherwise an unpacking error will be raised; in addition, the tuple is immutable and the content cannot be modified in the loop. Unwanted values can be ignored by \_. It is recommended to check whether the tuple is empty before traversing to avoid errors.