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The Comprehensive Set of 90 DSA Patterns That Cover Almost All Coding Interviews
Even after solving hundreds of LeetCode questions, do you still struggle when faced with real coding interviews?
Here’s the secret: most coding interviews don’t test unique problems — they reuse established logical templates.
Major companies prefer problem templates that measure reasoning, not rote memory.
Understanding these 90 DSA blueprints equips you to decode almost any interview challenge with ease.
What You’ll Learn
Inside this guide, we organize 90 DSA templates into 15 essential families used by elite programmers.
You’ll be guided on how to practice these patterns dynamically through AI-driven hints and feedback on Thita.ai.
Why Random LeetCode Grinding Doesn’t Work
Random problem-solving builds quantity, not recognition — and interviews reward recognition.
Each DSA pattern functions as a reusable design you can apply to multiple situations.
Sample applications:
– Target sum in sorted list ? Two Pointer technique
– Substring without duplicates ? Sliding Window
– Cycle detection ? Slow & Fast Pointers.
Top performers in FAANG interviews don’t memorize — they recognize recurring logic patterns.
The 15 Core DSA Pattern Families
These pattern families cover the foundational structures behind most coding interview challenges.
1. Two Pointer Patterns (7 Patterns)
Applied in problems where two indices move strategically across data structures.
Core templates: Converging, Slow/Fast, Expansion, and In-place transformations.
? Pro Tip: Check if the data is sorted System design interviews or relationships exist between index pairs.
2. Sliding Window Patterns (4 Patterns)
Use Case: Optimize subarray or substring challenges dynamically.
Key Patterns: Fixed-size window, Variable-size window, Monotonic queue, Character frequency matching.
? Pro Tip: Learn when to expand or contract your window efficiently.
3. Tree Traversal Patterns (7 Patterns)
Use Case: Binary and N-ary tree exploration including BFS, DFS, LCA, and serialization.
4. Graph Traversal Patterns (8 Patterns)
Use Case: Connectivity, pathfinding, and topology analysis.
5. Dynamic Programming Patterns (11 Patterns)
Use Case: Optimization with overlapping subproblems and state transitions.
6. Heap (Priority Queue) Patterns (4 Patterns)
Use Case: Ranking, median finding, and scheduling challenges.
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)
Enables structured data management through stack logic.
11. Bit Manipulation Patterns (5 Patterns)
Used for detecting duplicates, toggling bits, and subset enumeration.
12. Linked List Patterns (5 Patterns)
Includes reversal, merging, and cycle detection problems.
13. Array & Matrix Patterns (8 Patterns)
Covers spiral traversals, rotations, and prefix/suffix computations.
14. String Manipulation Patterns (7 Patterns)
Use Case: Parsing, validation, 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. Report this wiki page