Why Arrays Still Rule: The Sequential Storage Structure Behind Modern Computing

2026-07-19 · Source: Data Science on Medium · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, long

Summary

Sequential storage, exemplified by arrays, is fundamental to modern computing, shaping CPU architecture, database engines, operating systems, and standard libraries like C++'s `std::vector` and Java's `ArrayList`. Its core principle, "logical order equals physical order," enables O(1) random access through simple arithmetic calculation, eliminating search overhead. Critically, contiguous memory layout capitalizes on CPU cache locality, allowing prefetching of data and significantly outperforming structures like `LinkedList` in real-world scenarios due to reduced cache misses. While offering superior read performance, sequential storage incurs O(n) cost for insertions and deletions due to element shifting and can suffer from memory fragmentation. For sparse data, specialized formats like CSR/CSC maintain sequential storage of only non-zero terms, trading direct indexing for space efficiency.

Key takeaway

For software engineers designing data-intensive applications, understanding sequential storage's trade-offs is crucial for performance. You should default to array-backed structures like `ArrayList` for most workloads, especially those with high read-to-write ratios and random access needs, as their cache-friendliness often outweighs theoretical O(n) write costs. Only consider linked structures for specific niches requiring frequent, non-randomized head/tail mutations, and always benchmark your assumptions against real-world access patterns.

Key insights

Contiguous sequential storage provides immense performance advantages by enabling O(1) random access and utilizing CPU cache locality.

Principles

• Logical data order directly maps to physical memory order.
• Random access implies uniform, constant-time element location.
• CPU cache locality often dictates real-world performance more than Big-O.

Method

When choosing list implementations, prioritize `ArrayList` for read-heavy, random-access workloads, reserving `LinkedList` for frequent head/tail mutations without random access needs.

In practice

• Default to array-backed lists like `ArrayList` for most applications.
• Use sparse data structures for large, mostly empty datasets.
• Benchmark memory access patterns to validate performance assumptions.

Topics

• Data Structures
• Sequential Storage
• CPU Cache
• Performance Optimization
• Memory Management
• ArrayList
• Sparse Matrices

Code references

• totalwar/benchmark-lists
• lenskit/csr
• rwl/sparsetools
• david-cortes/MatrixExtra

Best for: Software Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Science on Medium.