Contiguous data structures often run faster when code processes neighboring elements because those elements sit next to one another in memory. A cache fetch can bring several nearby values into the processor at once, making later accesses more likely to be fast cache hits. Linked structures may require following pointers to nodes stored in different places, which can cause extra memory stalls. That advantage depends on what the program does: no layout is fastest for every workload.
Why memory layout affects speed
Big-O complexity describes how work grows with input size, but it does not capture every cost of that work. For example, both an array scan and a linked-list traversal take O(n) time. They can still take different amounts of time because their elements are arranged differently in memory.
Array elements occupy consecutive memory locations. Linked structures connect separately stored nodes using pointers. The distinction matters because processors transfer memory in blocks, rather than fetching only the exact word a program requested. A block can include nearby values that the program will use next.
Why arrays are faster than linked lists for many scans
Sequential reads take advantage of spatial locality
When code reads an array from one index to the next, a cache block fetched for one element often also contains subsequent elements. Those later reads may be served from cache instead of requiring another trip to main memory. This reuse of nearby data is called spatial locality. OpenStax explains how cache blocks contain consecutive bytes and how sequential array access can reuse data already fetched: OpenStax’s explanation of arrays and cache behavior.
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Linked traversal depends on pointer chasing
To visit the next linked-list node, the program must read the current node’s link and then access the address it names. If nodes are spread across memory, each step may touch another cache line or memory page. The processor cannot know the next node’s address until it has read the pointer, so the traversal can be harder to accelerate with prefetching. Some of each node’s storage is also used for links rather than payload.
Microsoft Learn notes that cache misses and page faults can slow programs and that arrays can outperform dynamically allocated lists because of caching and page-fault effects: Microsoft Learn on data structure performance. Cornell’s notes likewise describe arrays as consecutive locations and explain their advantage when successive indices have locality: Cornell notes on data structures and locality.
How the tradeoffs differ by operation
| Concern | Contiguous structure | Linked structure |
|---|---|---|
| Sequential scan | Often benefits from nearby elements sharing cache blocks. | May incur less predictable access when nodes are spread out. |
| Indexed access | Array elements can be reached in constant time by index. | Finding a position generally requires following links from a node. |
| Insertion and deletion | May require shifting later elements, depending on location and representation. | Can avoid shifting elements when the node and its predecessor are already known, though locating the position and managing nodes still has costs. |
| Growth | A fixed-size array cannot grow in place; a dynamic array may need to allocate a larger block and copy elements when capacity is exhausted. | Nodes can be allocated as needed, but each allocation and link adds overhead. |
| Storage overhead | Does not need a pointer field for every element. | Uses link fields, which take space and occupy part of the fetched node. |
The Stony Brook lecture identifies arrays and matrices as contiguous structures, and lists, trees, and graph adjacency lists as linked structures; it also notes arrays’ constant-time indexed access and locality advantages: Stony Brook lecture on data structures.
When locality helps—and when it may not
Contiguous storage is a strong fit for sequential scans and workloads that repeatedly use nearby indices. Pointer-heavy traversal has less predictable locality, but that does not mean every linked structure is slow or scattered. Small lists may fit in cache; allocators may place nodes near one another; and trees can have useful locality for related keys. Chunking several values into each node can also use cache lines more efficiently than storing one value per node.
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Observed performance depends on the working-set size, access order, element size, allocator, language runtime, hardware, and operation mix. Arrays also do not guarantee a cache hit: random accesses across a large array can still miss cache. Likewise, the label “linked” alone does not determine how its nodes are physically laid out.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose for a real program
- List the operations that matter. Separate scans, indexed reads, insertion, deletion, and growth instead of choosing by a general reputation.
- Check the access pattern. Ask whether accesses tend to move through neighboring elements or jump among unrelated locations.
- Include capacity and allocation costs. Account for possible array resizing and copying as well as linked-node allocation and pointer overhead.
- Measure representative workloads. Use realistic data sizes and operation mixes on the runtime and hardware that matter to the application; compare alternatives rather than assuming a universal winner.
Microsoft’s guidance is to test alternatives because no approach works in every case. The mechanism explains why contiguous layouts often win scans; only measurements for the target workload establish whether that advantage matters in a particular program.
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