Data Oriented Design
Data Oriented Design (DOD) is a software development methodology that prioritizes data layout and access patterns over object-oriented abstractions to optimize performance, particularly for cache efficiency and parallelism. It focuses on structuring data in contiguous memory arrays (like structs of arrays) to minimize cache misses and improve data locality, making it especially valuable in performance-critical domains such as game development, high-performance computing, and real-time systems. This approach contrasts with traditional object-oriented design by emphasizing how data is processed in bulk rather than encapsulating behavior within individual objects.
Developers should learn Data Oriented Design when working on applications where performance is critical, such as video games, simulations, or data-intensive systems, as it can significantly reduce latency and improve throughput by optimizing memory access patterns. It is particularly useful in scenarios involving large datasets, multithreading, or hardware with limited cache sizes, as it helps avoid bottlenecks caused by poor data locality. By adopting DOD, developers can write more efficient code that scales better on modern processors, leading to faster execution times and better resource utilization.