Binary search is a fundamental search algorithm that allows you to quickly find a value in a sorted array. It works by repeatedly dividing the search space in half until the target value is found. Binary search is exponentially faster than linear search and enables blazing fast lookups even in massive datasets.
In this comprehensive guide, we‘ll cover what binary search is, how it works under the hood, implementations in code, its time and space complexity, when to use it, and so much more. I‘ll provide plenty of examples and visuals to make these concepts crystal clear. Let‘s get started!
What Is Binary Search?
Binary search is an algorithm that searches for an element in a sorted array by cutting the array in half with each iteration. It leverages the sorted order of the array to greatly reduce the number of operations needed to find the target value.
The high level steps for binary search are:
 Set boundary points at the start and end of the array.
 Calculate the middle index between boundaries.
 If middle matches target, return index.
 Otherwise, update left or right boundary based on comparisons.
 Repeat steps 24 recursively until target is found.
Here is a simple example to illustrate:
In this example, the sorted array of integers is searched for the value 31. We start by comparing the middle element, which is 22. Since 31 is greater than 22, we only search the right half of the array next. This process repeats, eliminating half the remaining elements each time, until the target value is eventually found.
Key properties:
 Binary search only works on sorted arrays.
 It divides the search space in half each iteration.
 The time complexity is O(log n).
 It is exponentially faster than linear search.
In summary, binary search allows lightning fast searching by leveraging the sorted order of the data. It is one of the most fundamental and useful algorithms in computer science!
How Binary Search Works
Now that you have some intuition for binary search, let‘s go into more depth on how it works under the hood.
The key mechanics are:
 Preprocess – The array must be sorted first.
 Boundary pointers – Track range of possible values.
 Mid index calculation – Find middle index between pointers.
 Comparison – Check if mid matches target.
 Recursion – Divide in half based on comparison.
Let‘s walk through a stepbystep example:
Given a sorted array of numbers from 1 to 15, we want to search for the value 9 using binary search.

Set the left pointer to the first index 0 and the right pointer to the last 15.

Calculate the mid index halfway between 0 and 15, which is 7.

Compare the value at index 7 which is 8 against our target 9.

Since 8 is less than 9, we only search the right half now.

The new mid index halfway between 7 and 15 is 11.

Compare 11 to our target 9.

9 is less than 11, so we only search left half next.

Mid index is now 9, which matches our target!

Return the index 9.
And that‘s it! By dividing in half each time, we located the target value in only 3 steps rather than checking each element. Much faster!
Now let‘s walk through the algorithm line by line in Python:
def binary_search(arr, target):
left = 0
right = len(arr)  1
while left <= right:
mid = (left + right) // 2
if arr[mid] == target:
return mid
if target < arr[mid]:
right = mid  1
if target > arr[mid]:
left = mid + 1
return 1
Stepping through:

Start with left and right pointers on full array.

Calculate mid index and access that element.

Check if mid matches target and return if so.

If not, update left or right pointer based on comparison.

Repeat until pointers cross or target found.
Implementing binary search from scratch takes some practice. Try it out on your own datasets to get comfortable with the core concepts.
Binary Search Complexity
Now let‘s analyze the time and space complexity of binary search. This will give us quantitative insights into its performance.
Time Complexity
 Binary search divides the search space in half each iteration.
 Therefore, the max number of iterations is log2(N) for array size N.
 Hence, the overall time complexity is O(log n).
Space Complexity
 Binary search only requires a few variables for boundaries.
 So the overall space complexity is O(1) constant space.
The key takeaway is that binary search operates in fast O(log n) time. Compare this to the slow O(n) linear search! It also uses minimal extra memory.
Here is how the runtime scales visually:
As you can see, binary search has exceptional scalability for large N. No wonder it‘s the goto search algorithm!
Optimizing Binary Search
There are also several ways we can optimize binary search further:
 Parameter passing – Pass pointers rather than entire array.
 Loop condition – Use < vs <= for extra efficiency.
 Duplicate handling – Special logic to avoid infinite loops.
 Recursion elimination – Iterate rather than recurse.
 Random pivoting – Pick random pivots rather than middle.
Mastering these optimizations will allow you to squeeze every ounce of performance out of binary search when needed.
When NOT to Use Binary Search
Of course, binary search is not a silver bullet. There are some cases where it is not optimal:
 Data is unsorted or partially sorted. Must have full ordering.
 Searching for multiple values at once.
 Lots of duplicate values in the array.
 Requires random access, not just searching.
For these cases, alternate algorithms like linear search, interpolation search, or exponential search may be better suited. Always analyze the particulars of your use case.
Binary Search Variants
There are also many variants of binary search that give it additional capabilities:
 Ternary search – Uses 3 pointers instead of 2.
 Exponential search – Hybrid between binary and linear.
 Interpolation search – Uses predicted position rather than mid.
 Recursive vs iterative – Recursive divides problems, iterative uses loops.
 Lower bound – Find first match, not any match.
Choosing the right variant for your use case results in even better performance.
Applications of Binary Search
Now let‘s look at some examples of applying binary search in the real world:
Database indexing – Databases use tree structures and binary search to make lookups lightning fast even with huge amounts of records.
Dictionary search – Looking up words in a lexicographically sorted dictionary lends itself perfectly to binary search.
Autocomplete – Finding suggested results from a sorted list of previously entered terms.
Allocation tracking – Quickly check which memory segments are free or allocated.
Integer square root – Use binary search find the floor of a square root.
Rotation counting – Count the rotations needed to sort an array using binary search.
Binary search applies anytime you need fast searching on sorted data!
Conclusion
We covered a ton of ground on the essential binary search algorithm. Here are some key takeaways:

Binary search quickly searches ordered data by dividing the problem space in half each loop.

It operates in ultrafast O(log n) time complexity.

Binary search only works on sorted arrays.

There are many optimizations and variants available.

It has widespread realworld applications from databases to autocomplete.
I hope you now feel empowered to leverage binary search in your own projects! Adding this tool to your programming toolkit pays dividends. Look out for more articles on foundational algorithms and data structures soon.