The Most Commonlyused Data Structures

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The Most Commonly-usedData Structures

• Terence Parr
• USF

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Introduction

• Abstract data types
• Data structures (implementations)
• Combinations

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Abstract Data Types

• List
• 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

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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

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Set

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

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Dictionary

• Operations map x-gty, get x, delete x
• Typical implementation hash table
• Examples
• student -gt userid
• student -gt list of classes

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Graph

• Collection of nodes connected by directed or
  undirected edges with or without labels
• Pathsequence of edges
• Operations add node, add edge x-gty, delete node,
  delete edge
• Typical implementation node has list of edges
  that point to other nodes
• Examples network simulation, email trail between
  employees (social network), finite automata

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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

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Implementations
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Linked list

• head, tail pointers
• wrapper to hold value and next
• Operations
• get O(1)
• others O(n)

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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

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Hash table 2
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Tree

• Node has list of children need root ptr

CEO

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President
4
5
VP Sales
VP Eng.
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Graph

• States or nodes have list of edges to other states

Mary
Jim
Chris
Tim
Jen