Data Structures and Algorithms in Android
Android Development Through the Lens of Data Structures
Many Android developers ask me a very common question:
“Do we really use Data Structures and Algorithms in day-to-day Android development?”
I’m Amit Shekhar from Outcome School, where I teach AI and Machine Learning, and Android.
Let’s get started.
As we already know, ArrayList and HashMap are among the most frequently used, so I’m sharing some different examples, most of the time, we use them implicitly without naming them.
findViewById
findViewById traverses the view hierarchy, which is a tree structure. Finding a view is essentially a Depth-First Search (DFS).
To locate a specific view ID, Android starts from the root view and explores each child depth first, going down one branch until it finds the target or reaches a leaf, then backtracking to try other branches. This traversal pattern is essentially Depth-First Search (DFS).
Understanding this explains why deeply nested layouts can hurt performance.
LruCache
LruCache combines two data structures to perform operations in O(1) time complexity:
A HashMap provides O(1) access to cached items by key.
A Doubly Linked List keeps track of the usage order.
Whenever an item is accessed, it’s moved to the front of the list (most recently used), depending on the implementation.
When the cache reaches its limit, the item at the end of the list (least recently used) is removed, again based on how the list is maintained.
This combination ensures constant-time lookup, update, and eviction, which is why LruCache works so well in Android apps.
Spell-checker
It uses Trie and Dynamic Programming.
A Trie is used to store a dictionary of valid words efficiently. Since many words share common prefixes, a Trie allows fast character-by-character lookup. This makes it easy to:
Check whether a word exists
Detect where a word goes wrong
Narrow down possible correction candidates
Once an invalid word is detected, the next step is to suggest the closest correct word. This is where Dynamic Programming comes in. Spell-checkers commonly use edit distance to measure how many operations (insert, delete, replace) are needed to convert one word into another.
Dynamic Programming avoids brute-force comparisons by breaking the problem into smaller subproblems and reusing computed results, making the process efficient even with large dictionaries.
The combination of both helps us achieve this.
Trie helps find valid candidate words quickly.
Dynamic Programming helps rank those candidates by similarity.
If we learn how to implement the examples mentioned above, we will master patterns that are useful in day-to-day development.
I teach these types of concepts at Outcome School and help you become a better software engineer.
Thanks
Amit Shekhar
Founder, Outcome School