At the center of successfully creating software lies DSA. While many entry-level engineers will often spend time solving many different coding challenges, experienced engineers will see that the majority of interview and real-world issues will actually fall under a common pattern. By knowing how to identify these patterns, you can solve problems much quicker and write more efficient code. These are the 50 DSA patterns that you should be aware of.
Patterns for Arrays & Strings
Two Pointer Pattern
Sliding Window Pattern
Prefix Sum Pattern
Suffix Sum Pattern
Difference Array Pattern
Kadane's Algorithm
Dutch National Flag Problem
Merging Intervals Pattern
Cyclic Sorting Pattern
Monotonic Array Processing
Hashing is a means of performing fast searches through which many algorithms will experience reduced time complexity compared to other types of storage
List Patterns Interview Question Examples
Fast / Slow Pointer Strategy
Algorithm to Reverse a List
How to Combine Two Lists
Cycle Detection Problem
Dummy Node Concept
Use stacks for Nested Structures and Ordering
Stack Patterns
- Monotonic Stack
- Balanced Parentheses
- Next Greater Element
- Area of Histogram
- Evaluation of Expressions
Stacks are used to manage nested data structures and to maintain order.
Queue and Deque Patterns
- BFS Queue Pattern
- Monotonic Queue
- Circular Queue
- Level Order Traversal
- Maximum of Sliding Window
This set of patterns is particularly helpful for traversal and optimization.
Binary Search Patterns
- Basic Binary Search
- Binary search of answer
- Lower Bound Search
- Upper Bound Search
- Rotated Array Search
Binary search can also be applied to many different areas outside of simply finding items in sorted arrays.
Tree Pattern Types
DFS (Depth-First Search)
BFS (Breadth-First Search)
Tree Traversal Pattern
Lowest Common Ancestor (LCA)
Dynamic Programming on Trees
Trees are one of the most important data structures utilized during interviews as well as practical applications.
Graph Pattern Types
Graph Traversal (DFS/BFS)
Topologically Sorted
Union Find (Disjoint Set)
Shortest Path
Minimum Spanning Tree
Patterns of graph structures can be utilized to solve a number of real-world applications such as networking and dependency.
Dynamic Programming Pattern Types
Fibonacci DP
Knapsack
Longest Common Substring
State Transition DP
Dynamic Programming on Trees/Graphs
Dynamic programming is often viewed as the most difficult area of data structure/algorithm (DSA).
The Importance Of Patterns
Rather than attempting to recall and memorize all the solutions to a massive number of problems, good engineers will attempt to recognize the similarities (patterns) between all the different problems being resolved. For instance, when dealing with a substring type of problem, the solution for many of those substring types of problems will use the Sliding Window pattern. Conversely, when attempting to optimize a problem, we will typically use the Binary Search on Answer. The act of identifying the various patterns within your problems will significantly reduce your cognitive load and allow you to resolve coding questions in a more timely manner.
Best Practices To Effectively Study Data Structures And Algorithms Patterns
To properly learn patterns for DSA:
Know the intuition behind each pattern.
Learn how to implement without a template.
Practice a variety of different types of problems that utilize a similar pattern.
Compare the different ways to approach the problem.
Be sure to do a periodic review of the pattern.
You will develop a problem solution approach that will benefit you more than simply memorizing the individual problems by simply getting familiar with each of your various patterns.
Summary
Many of the problems that you will encounter at coding interviews and daily tasks can be solved by simply utilizing the 50-60 repeated patterns in data structures and algorithms. By learning these fundamental types of patterns, you will be able to improve your algorithm skills dramatically as a software engineer to create more optimized code as well as optimizing dealing with very complex types of problems.
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