10 Everyday Data Structures

Most software problems are not solved by clever algorithms alone. They are solved by choosing a data structure whose access pattern matches the work being done. A good structure makes the code simpler, the performance more predictable, and the failure modes easier to reason about.

1. Array. Arrays are the default structure for ordered data because indexed access is fast and memory layout is compact. They work well for scans, batching, and fixed-position access, but inserting in the middle can be expensive.

2. Linked list. Linked lists trade memory locality for easy insertion and removal around known nodes. They are less common in business applications, but they teach an important systems lesson: pointer chasing is flexible and often cache-unfriendly.

3. Hash table. Maps and dictionaries usually sit on top of hash tables. They give near-constant lookup on average, but collisions, resizing, and poor key design still affect performance and memory use.

4. Stack. A stack models last in, first out behaviour. It appears in call frames, parsers, undo systems, expression evaluation, and depth-first traversal because the most recent state is the next one needed.

5. Queue. Queues model first in, first out flow. They are central to buffering, work scheduling, background jobs, and message handling. In production systems, growing queue depth is often an early sign that consumers are falling behind.

6. Set. A set answers a membership question without duplicates. Use it when uniqueness is part of the rule itself, such as feature flags enabled for a user, visited nodes during traversal, or ids already processed in a batch.

7. Tree. Trees represent ordered hierarchy. File systems, abstract syntax trees, B-trees, DOM structures, and many configuration models use trees because parent-child relationships and bounded search are natural there.

8. Heap. A heap is a partial ordering optimised for retrieving the highest or lowest priority element quickly. It is useful for schedulers, timers, rate-limit windows, top-k queries, and any system that repeatedly asks what should happen next.

9. Graph. Graphs model relationships that are not naturally hierarchical. Dependency analysis, routing, social networks, and service topology all become clearer once nodes and edges are first-class rather than forced into a tree.

10. Trie. A trie stores prefixes efficiently. That makes it useful for autocomplete, routing tables, spell checking, and dictionary-like lookup where shared prefixes matter more than compact memory.

The important habit is to choose for operations, not for familiarity. Ask what happens most often: random lookup, sequential scan, insertion near known positions, uniqueness checks, range queries, or priority retrieval. The wrong answer usually appears first as awkward code, then later as a performance issue.

Workload shape matters as much as asymptotic complexity. An array with a simple scan may outperform a more sophisticated structure because its memory layout is cache-friendly. A hash table may beat repeated linear search, but only if the memory cost and key behaviour are acceptable. A queue can preserve order, yet still hide backpressure if nobody watches depth and age.

The best developers do not memorise structures as isolated interview topics. They recognise them inside real systems: a retry buffer is a queue, a compiler uses trees and graphs, an autocomplete service may rely on a trie, and a cache index often behaves like a hash table plus an eviction structure. Once you see the operational pattern, the right implementation becomes easier to justify.