Computer Science › Data Structures
Data Structure
A way of organizing data so certain operations are efficient.
Also known as: data structures, DS, ADT, abstract data type
A data structure is a way of organizing data in memory so that certain operations on it are efficient. Choosing the right one is often the difference between a program that’s instant and one that crawls.
| Structure | Good at | Weak at |
|---|---|---|
| Array / dynamic array | access by position, appending | inserting in the middle, searching by value |
| Linked list | cheap inserts and removals when you hold the node | access by position |
| Hash table (dictionary, set) | lookup, insert and delete by key, on average constant time | ordered traversal |
| Stack | last-in, first-out (undo, call stack) | anything else |
| Queue | first-in, first-out (task lines) | |
| Tree, binary search tree | ordered data, hierarchy, searching in log time | |
| Heap / priority queue | quickly getting the smallest or largest | arbitrary lookup |
| Graph | relationships and networks |
How to choose
Ask what you’ll do most:
- Look things up by key? A dictionary.
- Keep things ordered, or find ranges? A sorted structure or a tree.
- Process in arrival order? A queue.
- Check “have I seen this?” A set.
- Always grab the next-most-urgent item? A priority queue.
seen = set() # O(1) membership instead of scanning a list
for item in items:
if item in seen: ...This is what Big O notation helps you compare.
Abstract vs concrete
A stack or queue is an abstract idea (what operations it supports). It can be built on top of an array or a linked list. Languages give you ready-made versions, so you rarely implement them yourself, but you must know their costs.
Practical advice
- Learn what your language’s built-in types are good at.
- Measure before worrying about tiny differences (premature optimization).
- Interviews love these, and real code uses them constantly, mostly lists, dictionaries and sets.