Data Structures Easy to Advanced –
By a Google Engineer
In these first few notes, we will lay the foundation for core
concepts needed throughout these tutorials. Let's start with the
basics.
Data Structures
A data structure organizes data for efficient use and is essential
for creating powerful algorithms. Another reason for using data
structures is to manage and organize data naturally. To
understand the performance that data structures provide, we
need to look at the wild world of computational complexity.
The abstract data type defines how a data structure should
behave and what methods it should have. However, it does not
provide the details surrounding how those methods are
implemented. Big O notation only cares about what happens
when input becomes arbitrarily large, ignoring things like
constants and multiplicative factors. Almost any mathematical
expression containing n can be wrapped around a Big O. If the
algorithm takes logarithmic or quadratic/cubic time, then it's
represented as Big O of a log event.
Constant Time Algorithms
Both of the following algorithms run in constant time relative to
the input size because they are independent of n. As the input
size grows infinitely large, the loop still runs for the same amount
of time.
Algorithm 1
Algorithm 2
Binary Search Algorithm
A classic algorithm for binary search has a logarithmic time
complexity. This algorithm starts by creating two pointers: one at
the beginning and one at the end of the array. Then, it selects a
midpoint between the two pointers and checks if the value being
searched for is found at the midpoint. Regardless of whether the
By a Google Engineer
In these first few notes, we will lay the foundation for core
concepts needed throughout these tutorials. Let's start with the
basics.
Data Structures
A data structure organizes data for efficient use and is essential
for creating powerful algorithms. Another reason for using data
structures is to manage and organize data naturally. To
understand the performance that data structures provide, we
need to look at the wild world of computational complexity.
The abstract data type defines how a data structure should
behave and what methods it should have. However, it does not
provide the details surrounding how those methods are
implemented. Big O notation only cares about what happens
when input becomes arbitrarily large, ignoring things like
constants and multiplicative factors. Almost any mathematical
expression containing n can be wrapped around a Big O. If the
algorithm takes logarithmic or quadratic/cubic time, then it's
represented as Big O of a log event.
Constant Time Algorithms
Both of the following algorithms run in constant time relative to
the input size because they are independent of n. As the input
size grows infinitely large, the loop still runs for the same amount
of time.
Algorithm 1
Algorithm 2
Binary Search Algorithm
A classic algorithm for binary search has a logarithmic time
complexity. This algorithm starts by creating two pointers: one at
the beginning and one at the end of the array. Then, it selects a
midpoint between the two pointers and checks if the value being
searched for is found at the midpoint. Regardless of whether the
