1. The Most Commonly-used Data Structures
Terence Parr
USF

2. Introduction Abstract data types Data structures (implementations)
Combinations

3. Abstract Data Types List Set (unordered, unique)
stack, queue, prioritized queue, …
Set (unordered, unique)
Dictionary (also called Map)
Graph (directed or undirected)
Tree
Binary tree
Choose via:
access pattern, properties, element relationships
Implementations chosen to optimize time/space

4. List Operations: add, get, insert, delete, find
queue: add to tail, get only from head
stack: add to “top”, delete from “top”
Typical implementation: array or linked list
Examples: list of users, list of books

5. Set Operations: add, delete, contains
Typical implementation: bit vector (if elements are integers) or hash table
Examples: set of universities, set of students

6. Dictionary Operations: map x->y, get x, delete x
Typical implementation: hash table
Examples:
student -> userid
student -> list of classes

7. Graph
Collection of nodes connected by directed or undirected edges with or without labels
Path==sequence of edges
Operations: add node, add edge x->y, delete node, delete edge
Typical implementation: node has list of edges that point to other nodes
Examples: network simulation, trail between employees (social network), finite automata

8. Tree A kind of directed graph with unique edge path from node x to y
Children: emanating edges, Root: topmost node, Leaves: nodes w/o children
Operations: add child, delete a child
Typical implementation: node has list of children (again, a restricted graph)
Examples: organization chart, class hierarchy, expression tree
Binary tree: at most 2 children per node

9. Implementations

10. Linked list head, tail pointers wrapper to hold value and “next”
Operations:
get O(1)
others O(n)

11. Hash table
Fast implementation of a dictionary; like an associative memory; maps key to value
Idea: break up large search space into many smaller spaces
“hash function” tells you which of the smaller spaces (“buckets”) has element of interest
search linearly within smaller space
simple hash of int: hash(x)=x; hash of string: sum of char values
Array of lists; each list is a bucket of key/value pairs
bucket index(key) = hash(key) % num_buckets

12. Hash table 2

13. Tree Node has list of children; need root ptr CEO + 3 * President 4 5
VP Sales
VP Eng.

14. Graph States or nodes have list of edges to other states Mary Jim
Chris
Tim
Jen