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.
If you internalize these 90 key templates, recognizing the logic behind any problem becomes second nature.
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.
Learn how to train smarter through real-time AI-assisted exercises on 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.
For instance:
– Sorted array with a target ? Two Pointers (Converging)
– Find longest substring without repeats ? Sliding Window (Variable Size)
– Detect loop 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)
Use Case: Fast array or string traversal through pointer logic.
Key Patterns: Converging pointers, Fast & Slow pointers, Fixed separation, In-place modification, Expand from center, String reversal, and Backspace comparison.
? Hint: Look for sorted input or pairwise relationships between indices.
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)
Includes subsets, permutations, N-Queens, Sudoku, and combination problems.
8. Greedy Patterns (6 Patterns)
Use Case: Achieving global optima through local choices.
9. Binary Search Patterns (5 Patterns)
Use Case: Efficient searching over sorted data or answer ranges.
10. Stack Patterns (6 Patterns)
Enables structured data management through stack logic.
11. Bit Manipulation Patterns (5 Patterns)
Crucial for low-level data operations.
12. Linked List Patterns (5 Patterns)
Focuses on optimizing node traversal and transformation.
13. Array & Matrix Patterns (8 Patterns)
Covers spiral traversals, rotations, and prefix/suffix computations.
14. String Manipulation Patterns (7 Patterns)
Use Case: Parsing, validation, System design interviews and frequency analysis in strings.
15. Design Patterns (Meta Category)
Represents higher-order algorithmic design and data structure construction.
How to Practice Effectively on Thita.ai
The real edge lies in applying these patterns effectively through guided AI coaching.
Access the DSA 90 framework sheet to visualize all pattern families.
Next, select any pattern and explore associated real-world problems.
Step 3: Solve with AI Coaching ? Receive real-time hints, feedback, and explanations.
Track your improvement and focus on weak areas using detailed reports.
The Smart Way to Prepare
Traditional grinding wastes time — pattern-based learning delivers results.
Thita.ai provides the smartest route — combining AI guidance with proven DSA frameworks.
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.