• •Weineng Chen’s analysis confirms that O(n) linear search is the theoretical floor for data retrieval efficiency in unordered sets.
• •The analysis confirms that the latency cost scales proportionally with dataset size, failing to optimize for modern memory or storage architectures.
• •The report stops short of addressing how hardware prefetching or non-volatile memory impacts the performance of these slow patterns in real-world production environments.

Software engineer Weineng Chen demonstrates that linear search remains the slowest possible data access pattern, requiring an exhaustive traversal of all elements in an unsorted list. While common indexing techniques like B-trees or hash maps provide logarithmic or constant time access, linear search provides no such advantage, forcing a full scan regardless of the specific item requested. However, even this baseline can vary wildly depending on whether the data is cache-resident or requires slow disk I/O. Understanding these mechanical limits is essential for developers, as choosing the wrong retrieval method during early architectural design can lead to irreversible performance bottlenecks as data scales.