The Comprehensive Set of 90 DSA Patterns That Cover Almost All Coding Interviews
Many candidates solve 200+ LeetCode challenges but still blank out during actual technical rounds.
The truth is, interviewers rarely invent new problems; they adapt known logical structures.
Big tech interviews at companies like Google, Amazon, and Microsoft revolve around consistent logic frameworks.
By learning 90 carefully chosen DSA patterns, you’ll unlock solutions to 99% of interview problems instantly.
What You’ll Learn
This comprehensive guide breaks down 90 DSA patterns grouped into 15 core categories.
On Thita.ai, you can experience pattern-based learning with interactive guidance and feedback.
Why Random LeetCode Grinding Doesn’t Work
Blindly solving hundreds of questions rarely helps you identify underlying algorithmic blueprints.
Patterns act like reusable schematics that instantly reveal how to solve new problems.
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.
Those who excel identify the pattern first and adapt instantly.
The 15 Core DSA Pattern Families
Each category groups related concepts that repeatedly surface in coding interviews.
1. Two Pointer Patterns (7 Patterns)
Ideal for array manipulation and pointer-based optimization problems.
Examples: Converging pointers, expanding from center, and two-pointer string comparison.
? Quick Insight: Two-pointer System design interviews works best when the array is sorted or positional relationships exist.
2. Sliding Window Patterns (4 Patterns)
Use Case: Optimize subarray or substring challenges dynamically.
Common templates: expanding/shrinking windows and character frequency control.
? Insight: Timing your window adjustments correctly boosts performance.
3. Tree Traversal Patterns (7 Patterns)
Applicable in computing paths, depths, and relationships within trees.
4. Graph Traversal Patterns (8 Patterns)
Includes Dijkstra, Bellman-Ford, and disjoint set operations.
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)
Applied in finding thresholds, boundaries, or minimum feasible values.
10. Stack Patterns (6 Patterns)
Use Case: LIFO operations, expression parsing, and monotonic stacks.
11. Bit Manipulation Patterns (5 Patterns)
Use Case: XOR-based logic, bit counting, and power checks.
12. Linked List Patterns (5 Patterns)
Includes reversal, merging, and cycle detection problems.
13. Array & Matrix Patterns (8 Patterns)
Applied in image processing, pathfinding, and transformation tasks.
14. String Manipulation Patterns (7 Patterns)
Essential for problems involving text or symbol processing.
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.
Get personalized progress tracking and adaptive recommendations.
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.