Avoid N 1 query problems, reduce the number of database queries by loading associated data in advance; 2. Select only the required fields to avoid loading complete entities to save memory and bandwidth; 3. Use cache strategies reasonably, such as Doctrine's secondary cache or Redis cache high-frequency query results; 4. Optimize the entity life cycle and call clear() regularly to free up memory to prevent memory overflow; 5. Ensure that the database index exists and analyze the generated SQL statements to avoid inefficient queries; 6. Disable automatic change tracking in scenarios where changes are not required, and use arrays or lightweight modes to improve performance. Correct use of ORM requires combining SQL monitoring, caching, batch processing and appropriate optimization to ensure application performance while maintaining development efficiency.

The settings.json file is located in the user-level or workspace-level path and is used to customize VSCode settings. 1. User-level path: Windows is C:\Users\\AppData\Roaming\Code\User\settings.json, macOS is /Users//Library/ApplicationSupport/Code/User/settings.json, Linux is /home//.config/Code/User/settings.json; 2. Workspace-level path: .vscode/settings in the project root directory

ReadonlypropertiesinPHP8.2canonlybeassignedonceintheconstructororatdeclarationandcannotbemodifiedafterward,enforcingimmutabilityatthelanguagelevel.2.Toachievedeepimmutability,wrapmutabletypeslikearraysinArrayObjectorusecustomimmutablecollectionssucha

Install pyodbc: Use the pipinstallpyodbc command to install the library; 2. Connect SQLServer: Use the connection string containing DRIVER, SERVER, DATABASE, UID/PWD or Trusted_Connection through the pyodbc.connect() method, and support SQL authentication or Windows authentication respectively; 3. Check the installed driver: Run pyodbc.drivers() and filter the driver name containing 'SQLServer' to ensure that the correct driver name is used such as 'ODBCDriver17 for SQLServer'; 4. Key parameters of the connection string

First, use JavaScript to obtain the user system preferences and locally stored theme settings, and initialize the page theme; 1. The HTML structure contains a button to trigger topic switching; 2. CSS uses: root to define bright theme variables, .dark-mode class defines dark theme variables, and applies these variables through var(); 3. JavaScript detects prefers-color-scheme and reads localStorage to determine the initial theme; 4. Switch the dark-mode class on the html element when clicking the button, and saves the current state to localStorage; 5. All color changes are accompanied by 0.3 seconds transition animation to enhance the user

Introduction to Statistical Arbitrage Statistical Arbitrage is a trading method that captures price mismatch in the financial market based on mathematical models. Its core philosophy stems from mean regression, that is, asset prices may deviate from long-term trends in the short term, but will eventually return to their historical average. Traders use statistical methods to analyze the correlation between assets and look for portfolios that usually change synchronously. When the price relationship of these assets is abnormally deviated, arbitrage opportunities arise. In the cryptocurrency market, statistical arbitrage is particularly prevalent, mainly due to the inefficiency and drastic fluctuations of the market itself. Unlike traditional financial markets, cryptocurrencies operate around the clock and their prices are highly susceptible to breaking news, social media sentiment and technology upgrades. This constant price fluctuation frequently creates pricing bias and provides arbitrageurs with

Use performance analysis tools to locate bottlenecks, use VisualVM or JProfiler in the development and testing stage, and give priority to Async-Profiler in the production environment; 2. Reduce object creation, reuse objects, use StringBuilder to replace string splicing, and select appropriate GC strategies; 3. Optimize collection usage, select and preset initial capacity according to the scene; 4. Optimize concurrency, use concurrent collections, reduce lock granularity, and set thread pool reasonably; 5. Tune JVM parameters, set reasonable heap size and low-latency garbage collector and enable GC logs; 6. Avoid reflection at the code level, replace wrapper classes with basic types, delay initialization, and use final and static; 7. Continuous performance testing and monitoring, combined with JMH

shutil.rmtree() is a function in Python that recursively deletes the entire directory tree. It can delete specified folders and all contents. 1. Basic usage: Use shutil.rmtree(path) to delete the directory, and you need to handle FileNotFoundError, PermissionError and other exceptions. 2. Practical application: You can clear folders containing subdirectories and files in one click, such as temporary data or cached directories. 3. Notes: The deletion operation is not restored; FileNotFoundError is thrown when the path does not exist; it may fail due to permissions or file occupation. 4. Optional parameters: Errors can be ignored by ignore_errors=True