This guide explains key GNU C extensions—including zero‑length and variable‑length arrays, case ranges, statement expressions, typeof, variadic macros, designated initializers, built‑in functions, and attribute syntax—showing how they differ from standard ANSI C and how to use them safely in Linux kernel development.
Cython 3.0, announced by co‑developer Dr. Stefan Behnel after nearly five years, drops Python 2 support, aligns fully with Python 3 semantics, improves exception handling and C/C++ interoperability, and resolves compatibility issues that affected tools like the AWS CLI.
This article explains essential GNU C extensions used in the Linux kernel—including typeof, zero‑length (flexible) arrays, case range labels, designated initializers, variadic macros, function and variable attributes, built‑in functions, asmlinkage, and UL suffixes—showing why they matter and how to apply them safely.
This article explains why and when to use table partitioning in PostgreSQL, describes inheritance‑based and declarative partitioning methods (list, range, hash), provides complete SQL and C code examples for creating and maintaining partitions, compares version differences, and shows a real‑world performance improvement case from Qunar.
This article presents 20 practical Python performance tips—from choosing O(1) data structures and reducing redundant data to leveraging generators, C extensions, multiprocessing, and profiling tools—complete with benchmark code snippets that demonstrate measurable speed gains across common coding patterns.
Learn how to dramatically improve Python performance by choosing optimal data structures, minimizing redundant data, using copy wisely, leveraging dict/set lookups, generators, efficient loops, string joining, proper formatting, fast variable swapping, concise comparisons, C extensions, multiprocessing, PyPy, and profiling tools, all backed by real benchmarks.
This article walks through turning a pure‑Python great‑circle distance function into a Cython‑accelerated version, compares timings, shows how to replace Python's math calls with C library functions, and demonstrates how pure C code can achieve up to a five‑fold performance boost.
