The Ninety DSA Patterns That Cover 99% Coding Interviews
You’ve spent hours grinding LeetCode problems — yet still find yourself freezing during live interviews?
Most companies reuse recurring data structure and algorithm (DSA) templates to evaluate problem-solving skills.
Tech giants like Google, Meta, Amazon, and Microsoft repeatedly test the same core ideas.
By learning 90 carefully chosen DSA patterns, you’ll unlock solutions to 99% of interview problems instantly.
What You’ll Learn
The guide maps all 90 DSA patterns into 15 logical families — the same framework elite engineers use to master FAANG interviews.
You’ll also discover how to practice these patterns interactively with AI feedback using Thita.ai.
Why Random LeetCode Grinding Doesn’t Work
Without pattern-based learning, random LeetCode practice fails to build adaptability.
Think of patterns as templates you can reuse for any similar scenario.
Example mappings include:
– Sorted Array + Target Sum ? Two Pointers (Converging)
– Longest Substring Without Repeats ? Sliding Window (Variable Size)
– Cycle in Linked List ? Fast & Slow Pointers.
Success in interviews comes from recognizing underlying DSA themes, not recalling exact problems.
The 15 Core DSA Pattern Families
Let’s dive into the core families that represent nearly every type of DSA problem.
1. Two Pointer Patterns (7 Patterns)
Applied in problems where two indices move strategically across data structures.
Key Patterns: Converging pointers, Fast & Slow pointers, Fixed separation, In-place modification, Expand from center, String reversal, and Backspace comparison.
? Pro Tip: Check if the data is sorted or relationships exist between index pairs.
2. Sliding Window Patterns (4 Patterns)
Used to handle range-based optimizations in arrays and strings.
Examples include fixed or variable windows, character tracking, and monotonic operations.
? Hint: Balance expansion and contraction logic to optimize results.
3. Tree Traversal Patterns (7 Patterns)
Used for recursive and iterative traversals across hierarchical structures.
4. Graph Traversal Patterns (8 Patterns)
Applied in DFS, BFS, shortest paths, and union-find logic.
5. Dynamic Programming Patterns (11 Patterns)
Covers problems like Knapsack, LIS, Edit Distance, and Interval DP.
6. Heap (Priority Queue) Patterns (4 Patterns)
Ideal for top-K computations and real-time priority adjustments.
7. Backtracking Patterns (7 Patterns)
Use Case: Recursive search and exhaustive solution exploration.
8. Greedy Patterns (6 Patterns)
Use Case: Achieving global optima through local choices.
9. Binary Search Patterns (5 Patterns)
Core to logarithmic time optimizations.
10. Stack Patterns (6 Patterns)
Involves handling nested structures and validation problems.
11. Bit Manipulation Patterns (5 Patterns)
Crucial for low-level data operations.
12. Linked List Patterns (5 Patterns)
Use Case: Efficient pointer-based data manipulation.
13. Array & Matrix Patterns (8 Patterns)
Covers spiral Mock interviews traversals, rotations, and prefix/suffix computations.
14. String Manipulation Patterns (7 Patterns)
Includes palindrome checking, encoding/decoding, and pattern validation.
15. Design Patterns (Meta Category)
Use Case: Data structure and system design logic.
How to Practice Effectively on Thita.ai
Knowledge without practice falls short — Thita.ai helps bridge that gap with interactive learning.
Begin by opening the full Thita.ai DSA pattern catalog.
Choose one category (e.g., Sliding Window) to practice related LeetCode-style problems.
Engage Thita.ai’s AI tutor for instant suggestions and solution breakdowns.
Step 4: Track Progress ? Analyze performance and identify weak zones.
The Smart Way to Prepare
Stop random practice; focus on mastering logic templates instead.
Use Thita.ai’s roadmap to learn, practice, and refine through intelligent feedback.
Why Choose Thita.ai?
Thita.ai empowers learners to:
– Master 90 reusable DSA patterns
– Practice interactively with AI feedback
– Experience realistic mock interviews
– Prepare for FAANG and top-tier interviews
– Build a personalized, AI-guided learning path.