Path: Data Structures on Tech Interview Prep

Quick Notes

Mon, 01 Jan 0001 00:00:00 +0000

Data Structures: Quick Notes#

Many structures have good average performance but bad worst-case. Interviews often want both.

Data Structure	Access	Search	Insert	Delete	Space	Notes
Array	`O(1)`	`O(n)`	`O(n)`	`O(n)`	`O(n)`	insert/delete in middle shifts
Dynamic Array (vector/list)	`O(1)`	`O(n)`	amort. `O(1)` append	`O(n)`	`O(n)`	occasional resize
Stack	top `O(1)`	`O(n)`	`O(1)`	`O(1)`	`O(n)`	LIFO
Queue / Deque	front/back `O(1)`	`O(n)`	`O(1)`	`O(1)`	`O(n)`	FIFO / double-ended
Singly Linked List	`O(n)`	`O(n)`	`O(1)`*	`O(1)`*	`O(n)`	*if pointer to node/prev is known
Doubly Linked List	`O(n)`	`O(n)`	`O(1)`*	`O(1)`*	`O(n)`	easy delete given node
Hash Table / Hash Map	N/A	avg `O(1)`, worst `O(n)`	avg `O(1)`, worst `O(n)`	avg `O(1)`, worst `O(n)`	`O(n)`	depends on hashing + resizing
BST (unbalanced)	`O(n)`	`O(n)`	`O(n)`	`O(n)`	`O(n)`	can degrade to linked list
AVL / Red-Black Tree	`O(log n)`	`O(log n)`	`O(log n)`	`O(log n)`	`O(n)`	guaranteed height `O(log n)`
B-Tree / B+Tree	`O(log n)`	`O(log n)`	`O(log n)`	`O(log n)`	`O(n)`	great for disks/DBs
Binary Heap	peek `O(1)`	`O(n)`	`O(log n)`	`O(log n)`	`O(n)`	priority queue
Trie	`O(L)`	`O(L)`	`O(L)`	`O(L)`	large	`L` = key length
Fenwick Tree (BIT)	N/A	range sum `O(log n)`	update `O(log n)`	N/A	`O(n)`	prefix sums
Segment Tree	N/A	query `O(log n)`	update `O(log n)`	N/A	`O(n)`	flexible aggregates

Linear collection of nodes; each node points to next (and possibly prev).
Good for:
- frequent inserts/deletes when you already have a node reference,
- building other structures (LRU list).
Bad for:
- random access,
- cache locality.

Variants:

Data Structures: Problems#

Pick a data structure and drill problems tagged with it.