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DSA SUB PATTERNS

Everyone says, solve 28 DSA Patterns and you will understand everything. But I have a simple question: If it was this easy to master DSA, then everyone should be in Google, Facebook , Meta. And yet, most of them aren’t. Its because . even with this patterns, its still not easy to solve all the problems. And I faced a similar issue . Infact, even knowing and practicing the patterns, I was still not able to solve the problems. But in that dark time, I still persisted and noticed some sub patterns which actually helped me even more. And here are those sub patterns :

SUB PATTERNS :

Pointer & Window Techniques

1. Two Pointers

   • Sorted array + target sum/diff → use inward-moving pointers

   • Removing duplicates in-place → fast/slow pointer

   • Comparing ends → often sorting + two pointers

2. Fast & Slow Pointers

   • Cycle detection (linked list, number transformation) → Floyd’s cycle

   • Middle of linked list → slow.next jumps ahead

3. Sliding Window

   • Fixed length window → maintain sum/count (e.g., max sum subarray of size k)

   • Variable window → shrinking window based on condition (e.g., smallest subarray > sum)

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Grouping & Sorting

4. Merge Intervals

   • If overlap/merge is mentioned → sort by start, then scan with a merged list

   • Calendar/bookings → check for conflicts using sorted intervals

5. Cyclic Sort

   • "Find missing/duplicate number from 1 to N" → place elements at correct index

6. In-place Linked List Reversal

   • Reverse in groups → modify pointers mid-traversal

   • Palindrome check / reorder list → split + reverse + merge

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Data Structure Triggers

7. Stack

   • Next Greater/Smaller Element, Undo, Valid Parentheses → classic stack

   • Histograms, Stock Span → Monotonic stack variant

8. Monotonic Stack

   • Increasing/Decreasing trends → maintain stack in order (e.g., daily temperatures)

9. Hash Maps

   • "Count of..." → freq map

   • Subarrays with given sum/condition → prefix sum + hash map

   • Detect duplicates / indices → store index mapping

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Tree/Graph Traversals

10. Tree BFS

• Level order traversal, min/max depth, zigzag level → use queue

• Right view, left view → enqueue with level tracking

11. Tree DFS

• Path sum, serialize/deserialize → post-order often needed

• Recursion with depth or value constraints → classic DFS recursion

12. Graphs

• Nodes, edges, connections → DFS/BFS/Union Find

• Weighted graph + shortest path → Dijkstra or Bellman-Ford

• Cycles in directed/undirected → DFS with visited/recStack

13. Islands/Matrix traversal

• "Number of regions/clusters" → DFS/BFS flood-fill

• Directions → use a dirs = [(0,1),(1,0),...] style loop

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Heap & Binary Search Utilities

14. Two Heaps

• Median of stream → max heap + min heap balance

15. Subsets

• "All combinations/subsets/permutations" → backtracking or bitmask

16. Modified Binary Search

• Sorted or rotated arrays → customized mid conditions

• First/last occurrence → bounds handling

17. Bitwise XOR

• "Find single non-duplicate" → XOR properties

• "Swap without temp" / parity → XOR tricks

18. Top K Elements

• "K largest/smallest/frequent" → Min or Max heap

19. K-way Merge

• Merge sorted arrays/lists → min heap of next elements

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Problem-Solving Strategies

20. Greedy

• "Minimum X to achieve Y" → sort + greedy choice

• Intervals / activity selection / jumps → greedy on end/start points

21. 0/1 Knapsack (DP)

• "Choose items with weight/value limits" → classic knapsack DP

• Use tabulation or space optimization for constraints

22. Backtracking

• "All combinations with constraints" → recursive DFS + undo

• Sudoku, N-Queens → build → check → backtrack

23. Trie

• Prefix, autocomplete, word search → Trie insert/search

• Replace words in sentence → Trie prefix matching

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Graph Variants & Advanced Structures

24. Topological Sort

• "Order of tasks" or "prerequisites" → DAG + indegree tracking

25. Union Find

• Connected components, cycle detection, Kruskal’s MST → union by rank + path compression

26. Ordered Set / TreeMap

• Find floor/ceiling, sliding window max/min → TreeMap or Balanced BST

• Often replaceable with bisect in Python or TreeSet in Java

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Concurrency & Misc

27. Multi-thread

• Synchronization, shared resource access → locks, semaphores

• Producer-consumer, deadlock scenarios

28. Miscellaneous

• LRU Cache → HashMap + Doubly Linked List

• Least recent/frequent → Priority queue + HashMap