Data Structures Review
Data Structures Interview Questions
Linear Data Structures – Lists
Array, Linked List (Single, Double, Circular), Stack, Queue
Hierarchical – Trees
Binary Tree, Binary Search Tree (BST), B-Tree, Red-Black Tree
How Data Structures are used
Storage structures (arrays, linked structures, hash tables)
process-oriented data structures (stacks, queues, priority queues, iterators), and descriptive data structures (collections, sets, linear lists, binary trees, etc.)
Complex Structures
Priority Queues
Hash Tables
Graph
“Nearly all graph problems will somehow use a grid or network in the problem, but sometimes these will be well disguised. Secondly, if you are required to find a path of any sort, it is usually a graph problem as well. Some common keywords associated with graph problems are: vertices, nodes, edges, connections, connectivity, paths, cycles and direction.” -TopCoder
“They can range in difficulty from finding a path on a 2D grid from a start location to an end location, to something as hard as finding the maximum amount of water that you can route through a set of pipes, each of which has a maximum capacity (also known as the maximum-flow minimum-cut problem)” -TopCoder
“An example of one of the simplest types of graphs is a singly linked list!” -TopCoder
A tree only allows a node to have children, and there cannot be any loops in the tree, with a more general graph we can represent many different situations.
TopCoder – Introduction to Graphs and Their Data Structures
Matrix
A multi-dimensional array.
Uses:
– To represent class hierarchy using Boolean square matrix
– For data encryption and decryption
– To represent traffic flow and plumbing in a network
– To implement graph theory of node representation
An adjacency matrix is often used in the representation of graphs as an alternative to adjacency lists
Data Structures Performance
Chart from http://bigocheatsheet.com/
| Data Structure | Time Complexity | Space Complexity | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Average | Worst | Worst | |||||||
| Access | Search | Insertion | Deletion | Access | Search | Insertion | Deletion | ||
| Array | Θ(1) |
Θ(n) |
Θ(n) |
Θ(n) |
O(1) |
O(n) |
O(n) |
O(n) |
O(n) |
| Stack | Θ(n) |
Θ(n) |
Θ(1) |
Θ(1) |
O(n) |
O(n) |
O(1) |
O(1) |
O(n) |
| Queue | Θ(n) |
Θ(n) |
Θ(1) |
Θ(1) |
O(n) |
O(n) |
O(1) |
O(1) |
O(n) |
| Singly-Linked List | Θ(n) |
Θ(n) |
Θ(1) |
Θ(1) |
O(n) |
O(n) |
O(1) |
O(1) |
O(n) |
| Doubly-Linked List | Θ(n) |
Θ(n) |
Θ(1) |
Θ(1) |
O(n) |
O(n) |
O(1) |
O(1) |
O(n) |
| Skip List | Θ(log(n)) |
Θ(log(n)) |
Θ(log(n)) |
Θ(log(n)) |
O(n) |
O(n) |
O(n) |
O(n) |
O(n log(n)) |
| Hash Table | N/A |
Θ(1) |
Θ(1) |
Θ(1) |
N/A |
O(n) |
O(n) |
O(n) |
O(n) |
| Binary Search Tree | Θ(log(n)) |
Θ(log(n)) |
Θ(log(n)) |
Θ(log(n)) |
O(n) |
O(n) |
O(n) |
O(n) |
O(n) |
| Cartesian Tree | N/A |
Θ(log(n)) |
Θ(log(n)) |
Θ(log(n)) |
N/A |
O(n) |
O(n) |
O(n) |
O(n) |
| B-Tree | Θ(log(n)) |
Θ(log(n)) |
Θ(log(n)) |
Θ(log(n)) |
O(log(n)) |
O(log(n)) |
O(log(n)) |
O(log(n)) |
O(n) |
| Red-Black Tree | Θ(log(n)) |
Θ(log(n)) |
Θ(log(n)) |
Θ(log(n)) |
O(log(n)) |
O(log(n)) |
O(log(n)) |
O(log(n)) |
O(n) |
| Splay Tree | N/A |
Θ(log(n)) |
Θ(log(n)) |
Θ(log(n)) |
N/A |
O(log(n)) |
O(log(n)) |
O(log(n)) |
O(n) |
| AVL Tree | Θ(log(n)) |
Θ(log(n)) |
Θ(log(n)) |
Θ(log(n)) |
O(log(n)) |
O(log(n)) |
O(log(n)) |
O(log(n)) |
O(n) |
| KD Tree | Θ(log(n)) |
Θ(log(n)) |
Θ(log(n)) |
Θ(log(n)) |
O(n) |
O(n) |
O(n) |
O(n) |
O(n) |