This article delves into common patterns and pitfalls for efficiently storing and sorting data when building systems. Starting from low-level data representation, the author provides a detailed analysis of sorting challenges for integers, signed integers, floating-point numbers, strings, and composite data, along with practical solutions. The article focuses on byte order issues, variable-length integer encoding, length-prefixing techniques, remapping methods for signed integers, and how to use null terminators to solve sorting problems for composite data. These techniques are crucial for building high-performance AI systems, autonomous driving platforms, and other systems that need to process large amounts of data. By understanding these underlying optimization techniques, developers can avoid common pitfalls and design more efficient and reliable data processing systems. The article not only provides theoretical explanations but also includes specific coding examples, enabling readers to directly apply these techniques to solve real-world problems.
