An effective indexing strategy needs to be combined with query patterns, data distribution and business needs, rather than blindly added. 1. Understand common query paths, prioritize the establishment of joint indexes for multi-field combination, sorting or grouping operations, and pay attention to index order; 2. Avoid excessive indexing to reduce write overhead, regularly clean redundant indexes, and view unused indexes through the system view; 3. Use overlay indexes to make the index itself contain the fields required for query, reduce table back operations, and improve reading efficiency; 4. Consider partitioning and indexing for super-large tables, select partition keys that are consistent with the query conditions, and establish a reasonable index for each partition, but the complexity and performance improvement are required.

FunctionhidinginC occurswhenaderivedclassdefinesafunctionwiththesamenameasabaseclassfunction,makingthebaseversioninaccessiblethroughthederivedclass.Thishappenswhenthebasefunctionisn’tvirtualorsignaturesdon’tmatchforoverriding,andnousingdeclarationis

There are mainly the following methods to obtain stack traces in C: 1. Use backtrace and backtrace_symbols functions on Linux platform. By including obtaining the call stack and printing symbol information, the -rdynamic parameter needs to be added when compiling; 2. Use CaptureStackBackTrace function on Windows platform, and you need to link DbgHelp.lib and rely on PDB file to parse the function name; 3. Use third-party libraries such as GoogleBreakpad or Boost.Stacktrace to cross-platform and simplify stack capture operations; 4. In exception handling, combine the above methods to automatically output stack information in catch blocks

To call Python code in C, you must first initialize the interpreter, and then you can achieve interaction by executing strings, files, or calling specific functions. 1. Initialize the interpreter with Py_Initialize() and close it with Py_Finalize(); 2. Execute string code or PyRun_SimpleFile with PyRun_SimpleFile; 3. Import modules through PyImport_ImportModule, get the function through PyObject_GetAttrString, construct parameters of Py_BuildValue, call the function and process return

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

When Windows 10 slows down, it does not necessarily require changing the computer, but it can often be improved by optimizing settings and cleaning resources. 1. Check and disable unnecessary boot program, open the task manager and switch to the "Startup" tab for management; 2. Close the program that secretly occupies resources in the background, go to "Settings>System>Battery/Memory/Processor" to view and close infrequently used applications; 3. Use the "Disk Cleanup" tool to delete temporary files, the system updates residual and recycling bin content, and enable "Storage Awareness" automatic cleaning; 4. Regularly optimize the drive for HDD hard disks, and SSD does not require manual operation; 5. Update key hardware drivers, especially graphics card and network card drivers, to ensure full performance; 6. In "System Properties>gt

std::move does not actually move anything, it just converts the object to an rvalue reference, telling the compiler that the object can be used for a move operation. For example, when string assignment, if the class supports moving semantics, the target object can take over the source object resource without copying. Should be used in scenarios where resources need to be transferred and performance-sensitive, such as returning local objects, inserting containers, or exchanging ownership. However, it should not be abused, because it will degenerate into a copy without a moving structure, and the original object status is not specified after the movement. Appropriate use when passing or returning an object can avoid unnecessary copies, but if the function returns a local variable, RVO optimization may already occur, adding std::move may affect the optimization. Prone to errors include misuse on objects that still need to be used, unnecessary movements, and non-movable types

In C, the POD (PlainOldData) type refers to a type with a simple structure and compatible with C language data processing. It needs to meet two conditions: it has ordinary copy semantics, which can be copied by memcpy; it has a standard layout and the memory structure is predictable. Specific requirements include: all non-static members are public, no user-defined constructors or destructors, no virtual functions or base classes, and all non-static members themselves are PODs. For example structPoint{intx;inty;} is POD. Its uses include binary I/O, C interoperability, performance optimization, etc. You can check whether the type is POD through std::is_pod, but it is recommended to use std::is_trivia after C 11.