Big O

Big O

• Big O shows the time and space complexity of an algorithm. (Performance efficiency)
• Doesn't care about precision, just general trend
• Relationship between input size and time
  • O(1) - constant (input doesn't affect)
  • O(n) - linear (grows propotrionally)
  • O(n^2) - quadratic
  • O(log n)
  • O(n log n)
• 

Big O of objects #

• Insertion - O(1)
• Removal - O(1)
• Searching - O(n)
• Access - O(1)

• Object.keys - O(n)
• Object.values - O(n)
• Object.entries - O(n)
• hasOwnProperty - O(1)

Big O of arrays #

• Insertion - O(1) to the end; O(n) to the beginning(unshift); it needs to reindex each element
• Removal - O(1) from the end; O(n) from the beginning (shift)
• Searching - O(n)
• Access - O(1)

• push - O(1)
• pop - O(1)
• shift - O(n)
• unshift - O(n)
• concat - O(n)
• slice - O(n)
• splice - O(n)
• sort - O(n log n)