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"description":"Minimal APIs is a lightweight approach to building HTTP APIs in .NET with minimal ceremony. It is designed for simplicity and performance, making it ideal for microservices, serverless applications, and small web services. Minimal APIs provide a streamlined way to define routes, handle requests, and return responses without requiring controllers or extensive configuration. They leverage top-level statements, reducing boilerplate code while maintaining flexibility and scalability.\n\nMinimal APIs support dependency injection, middleware, model binding, and validation. They also integrate seamlessly with OpenAPI (Swagger) for API documentation. Their simplicity makes them an excellent choice for building fast and efficient web applications with .NET.\n\nTo learn more, visit the following resources:",
"links":[
{
"title":"Minimal APIs in .NET 8: A Simplified Approach to Build Services",
"description":"In practice: For every 2-4 tree, there are corresponding red–black trees with data elements in the same order. The insertion and deletion operations on 2-4 trees are also equivalent to color-flipping and rotations in red–black trees. This makes 2-4 trees an important tool for understanding the logic behind red–black trees, and this is why many introductory algorithm texts introduce 2-4 trees just before red–black trees, even though 2-4 trees are not often used in practice.\n\nVisit the following resources to learn more:",
"description":"C++ standards are a set of rules and guidelines that define the language's features, syntax, and semantics. The International Organization for Standardization (ISO) is responsible for maintaining and updating the C++ standards. The main purpose of the standards is to ensure consistency, efficiency, and maintainability across multiple platforms and compilers.\n\nHere's a brief summary of the different C++ standards released to date:\n\n* **C++98/C++03**: The first standardized version of C++, which introduced many features like templates, exceptions, and the Standard Template Library (STL). C++03 is a minor update to C++98 with some bug fixes and performance improvements.\n \n* **C++11**: A major upgrade to the language, which introduced features such as:\n \n * Lambda expressions:\n \n auto sum = [](int a, int b) -> int { return a + b; };\n \n \n * Range-based for loops:\n \n std::vector<int> numbers = {1, 2, 3, 4};\n for (int num : numbers) {\n std::cout << num << '\\n';\n }\n \n \n * Smart pointers like `std::shared_ptr` and `std::unique_ptr`.\n* **C++14**: A minor update to C++11, which added features such as:\n \n * Generic lambda expressions:\n \n auto generic_sum = [](auto a, auto b) { return a + b; };\n \n \n * Binary literals:\n \n int binary_number = 0b1010;\n \n \n* **C++17**: Another major update that introduced features such as:\n \n * `if` and `switch` with initializers:\n \n if (auto it = my_map.find(key); it != my_map.end()) {\n // use 'it' here\n }\n \n \n * Structured bindings:\n \n std::map<std::string, int> my_map = {{\"A\", 1}, {\"B\", 2}};\n for (const auto& [key, value] : my_map) {\n // use 'key' and 'value' here\n }\n \n \n* **C++20**: The latest major update to the language, with features such as:\n \n * Concepts:\n \n template<typename T>\n concept Addable = requires(T a, T b) {\n { a + b } -> std::same_as<T>;\n };\n \n \n * Ranges:\n \n std::vector<int> numbers = {1, 2, 3, 4};\n auto doubled = numbers | std::views::transform([](int n) { return n * 2; });\n \n \n * Coroutines and more.\n\nRemember that to use these language features, you might need to configure your compiler to use the specific C++ standard version. For example, with GCC or Clang, you can use the `-std=c++11`, `-std=c++14`, `-std=c++17`, or `-std=c++20` flags.",
"description":"Query optimization in SQL involves refining queries to enhance their execution speed and reduce resource consumption. Key strategies include indexing columns used in `WHERE`, `JOIN`, and `ORDER BY` clauses to accelerate data retrieval, minimizing data processed by limiting the number of columns selected and filtering rows early in the query. Using appropriate join types and arranging joins in the most efficient order are crucial. Avoiding inefficient patterns like `SELECT`, replacing subqueries with joins or common table expressions (CTEs), and leveraging query hints or execution plan analysis can also improve performance. Regularly updating statistics and ensuring that queries are structured to take advantage of database-specific optimizations are essential practices for maintaining optimal performance.\n\nLearn more from the following resources:",
"description":"Query optimization in SQL involves refining queries to enhance their execution speed and reduce resource consumption. Key strategies include indexing columns used in `WHERE`, `JOIN`, and `ORDER BY` clauses to accelerate data retrieval, minimizing data processed by limiting the number of columns selected and filtering rows early in the query. Using appropriate join types and arranging joins in the most efficient order are crucial. Avoiding inefficient patterns like `SELECT *`, replacing subqueries with joins or common table expressions (CTEs), and leveraging query hints or execution plan analysis can also improve performance. Regularly updating statistics and ensuring that queries are structured to take advantage of database-specific optimizations are essential practices for maintaining optimal performance.\n\nLearn more from the following resources:",
"links":[
{
"title":"12 Ways to Optimize SQL Queries",
@ -1495,7 +1495,7 @@
},
"UVTgbZrqpbYl1bQvQejcF":{
"title":"Reducing Subqueries",
"description":"Recursive queries in SQL allow for iterative processing of hierarchical or tree-structured data within a single query. They consist of an anchor member (the base case) and a recursive member that references the query itself, enabling the exploration of parent-child relationships, traversal of graphs, or generation of series data. This powerful feature is particularly useful for tasks like querying organizational hierarchies, bill of materials structures, or navigating complex relationships in data that would otherwise require multiple separate queries or procedural code.\n\nLearn more from the following resources:",
"description":"Reducing subqueries is a common SQL optimization technique, especially when dealing with complex logic or large datasets. Correlated subqueries, which are evaluated once for each row in the outer query, can degrade the performance. Subqueries can often be replaced with JOIN operations. In cases where subqueries are reused, consider replacing them with Common Table Expressions (CTEs), which offer modularity and avoid repeated executions of the same logic. Limiting the result set returned by subqueries and storing the results of expensive subqueries in temporary tables for reuse can also improve performance.\n\nLearn more from the following resources:",
Arnab Sarkar
3 weeks ago
committed by
GitHub