Problem Solving Techniques for Junior Programmers

In the last 15 years, I have interviewed 800+ Software Engineers across Google, Paytm, Amazon & various startups. Here are the most actionable tips I can give you on how to approach  solving coding problems in Interviews  (My DMs are always flooded with this particular question) 1. Use a Heap for K Elements      - When finding the top K largest or smallest elements, heaps are your best tool.      - They efficiently handle priority-based problems with O(log K) operations.      - Example: Find the 3 largest numbers in an array.   2. Binary Search or Two Pointers for Sorted Inputs      - Sorted arrays often point to Binary Search or Two Pointer techniques.      - These methods drastically reduce time complexity to O(log n) or O(n).      - Example: Find two numbers in a sorted array that add up to a target.   3. Backtracking    - Use Backtracking to explore all combinations or permutations.      - They’re great for generating subsets or solving puzzles.      - Example: Generate all possible subsets of a given set.   4. BFS or DFS for Trees and Graphs      - Trees and graphs are often solved using BFS for shortest paths or DFS for traversals.      - BFS is best for level-order traversal, while DFS is useful for exploring paths.      - Example: Find the shortest path in a graph.   5. Convert Recursion to Iteration with a Stack      - Recursive algorithms can be converted to iterative ones using a stack.      - This approach provides more control over memory and avoids stack overflow.      - Example: Iterative in-order traversal of a binary tree.   6. Optimize Arrays with HashMaps or Sorting      - Replace nested loops with HashMaps for O(n) solutions or sorting for O(n log n).      - HashMaps are perfect for lookups, while sorting simplifies comparisons.      - Example: Find duplicates in an array.   7. Use Dynamic Programming for Optimization Problems      - DP breaks problems into smaller overlapping sub-problems for optimization.      - It's often used for maximization, minimization, or counting paths.      - Example: Solve the 0/1 knapsack problem.   8. HashMap or Trie for Common Substrings      - Use HashMaps or Tries for substring searches and prefix matching.      - They efficiently handle string patterns and reduce redundant checks.      - Example: Find the longest common prefix among multiple strings.   9. Trie for String Search and Manipulation      - Tries store strings in a tree-like structure, enabling fast lookups.      - They’re ideal for autocomplete or spell-check features.      - Example: Implement an autocomplete system.   10. Fast and Slow Pointers for Linked Lists      - Use two pointers moving at different speeds to detect cycles or find midpoints.      - This approach avoids extra memory usage and works in O(n) time.      - Example: Detect if a linked list has a loop.   💡 Save this for your next interview prep!

 The wrong way to approach your work as a junior engineer:   → Read the problem and jump straight into coding.   → Get stuck, then start Googling.   → Try different fixes, tweak some code.   → Realize nothing works, delete everything.   → Reread the problem, now even more confused.   → Finally, ask for help.   The right way to approach your work as a junior engineer:   → Understand the problem first – Break it down, and rephrase it in your own words.   → Research possible solutions – See how similar problems have been solved.   → Plan your approach – Write down the steps before writing code.   → Write code with clarity – Solve the problem systematically.   → Ask for help when needed – But only after making an honest effort.   → Optimize & improve – Refactor, clean up, and make it better.  Code is the last step, not the first. The better your plan, the smoother your execution.

If you’re a junior developer in your 20s, stop measuring your worth by how quickly you can write code. Slow down and focus on these steps instead: 1// Research the problem: → Make sure you actually understand what’s being asked. Read the requirements, look for edge cases, and clarify any doubts up front. 2// Identify and understand each possible solution: → Don’t settle for the first idea that pops into your head. Sketch out a few different approaches, think about time/space complexity, scalability, and maintainability for each one. 3// Decide on the best approach: → Choose the path that not only solves the problem but also makes sense for the project, the team, and the long run. Sometimes “best” means simplest, sometimes it means most robust. Then start coding, but do it deliberately: Write code you can explain. Make it readable and easy to test. Pause and review as you go. Anyone can write code fast. Few take the time to understand the why behind every line. The best developers aren’t the fastest, They’re the ones who solve the right problem, the right way. If you master understanding, speed will follow, without the need to rush.

🚀 𝗟𝗲𝗲𝘁𝗖𝗼𝗱𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗜 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗳𝗼𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 Over time, I’ve noticed that most problems follow certain patterns. Identifying the right pattern early makes solving problems much easier. Here are the ones I focus on the most: ✅ 𝗜𝗳 𝘁𝗵𝗲 𝗶𝗻𝗽𝘂𝘁 𝗮𝗿𝗿𝗮𝘆 𝗶𝘀 𝘀𝗼𝗿𝘁𝗲𝗱, 𝘁𝗵𝗲𝗻: Binary search (O(log N) for efficient searching) Two pointers (Optimizing sum/pair problems) ✅ 𝗜𝗳 𝗮𝘀𝗸𝗲𝗱 𝗳𝗼𝗿 𝗮𝗹𝗹 𝗽𝗲𝗿𝗺𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝘀/𝘀𝘂𝗯𝘀𝗲𝘁𝘀, 𝘁𝗵𝗲𝗻: Backtracking (Explore all possible solutions) ✅ 𝗜𝗳 𝗴𝗶𝘃𝗲𝗻 𝗮 𝘁𝗿𝗲𝗲, 𝘁𝗵𝗲𝗻: DFS (Depth-first for deep traversal) BFS (Level-order traversal) ✅ 𝗜𝗳 𝗴𝗶𝘃𝗲𝗻 𝗮 𝗴𝗿𝗮𝗽𝗵, 𝘁𝗵𝗲𝗻: DFS (Finding connected components, cycles) BFS (Shortest path, traversals) ✅ 𝗜𝗳 𝗴𝗶𝘃𝗲𝗻 𝗮 𝗹𝗶𝗻𝗸𝗲𝗱 𝗹𝗶𝘀𝘁, 𝘁𝗵𝗲𝗻: Two pointers (Fast & slow for cycle detection, middle node, etc.) ✅ 𝗜𝗳 𝗿𝗲𝗰𝘂𝗿𝘀𝗶𝗼𝗻 𝗶𝘀 𝗯𝗮𝗻𝗻𝗲𝗱, 𝘁𝗵𝗲𝗻: Stack (Simulate recursion iteratively) ✅ 𝗜𝗳 𝗺𝘂𝘀𝘁 𝘀𝗼𝗹𝘃𝗲 𝗶𝗻-𝗽𝗹𝗮𝗰𝗲, 𝘁𝗵𝗲𝗻: Swap corresponding values (For sorting or reordering) Store multiple values in the same pointer (Memory optimization) ✅ 𝗜𝗳 𝗮𝘀𝗸𝗲𝗱 𝗳𝗼𝗿 𝗺𝗮𝘅𝗶𝗺𝘂𝗺/𝗺𝗶𝗻𝗶𝗺𝘂𝗺 𝘀𝘂𝗯𝗮𝗿𝗿𝗮𝘆/𝘀𝘂𝗯𝘀𝗲𝘁/𝗼𝗽𝘁𝗶𝗼𝗻𝘀, 𝘁𝗵𝗲𝗻: Dynamic Programming (DP) (Break into overlapping subproblems) ✅ 𝗜𝗳 𝗮𝘀𝗸𝗲𝗱 𝗳𝗼𝗿 𝘁𝗼𝗽/𝗹𝗲𝗮𝘀𝘁 𝗞 𝗶𝘁𝗲𝗺𝘀, 𝘁𝗵𝗲𝗻: Heap (Efficient priority-based extraction) QuickSelect (Optimized selection of Kth smallest/largest) ✅ 𝗜𝗳 𝗮𝘀𝗸𝗲𝗱 𝗳𝗼𝗿 𝗰𝗼𝗺𝗺𝗼𝗻 𝘀𝘁𝗿𝗶𝗻𝗴𝘀, 𝘁𝗵𝗲𝗻: Map (HashMap) (Quick lookup & frequency counting) Trie (Efficient prefix-based searching) ✅ 𝗘𝗹𝘀𝗲: Map/Set (O(1) lookup, O(N) space) Sort input (O(N log N) time, O(1) space) These patterns help break down complex problems and quickly guide me toward an optimal solution. 🚀 💬 Which pattern do you find most useful in interviews? Let’s discuss! #LeetCode #DSA #JobSearch #CodingInterview #FAANG #MAANG #Amazon #Meta #Google #Microsoft #Netflix #Apple #Blind75 #Grind75 #TechInterview #SoftwareEngineering #Algorithms #DataStructures #InterviewPreparation #ProblemSolving #CrackingTheCodingInterview #Careers #SystemDesign #CompetitiveProgramming #FullStackDeveloper #DevCommunity #CodeNewbie #TechCareers #JobSearch #Python #Java #CodingLife #TechIndustry

Not able to recognize patterns even after solving hundreds of Leetcode problems? With this pattern recognition cheatsheet you will be able to solve more than 80% of the problems: ➡️Array / String Inputs 1. Is the array sorted? - Yes → Think: Binary Search, Two Pointers, or Prefix Sums. - No → Move to structural/intent-based decisions. 2. What's the intent of the problem? - Optimization (Max/Min, count of ways, min cost) - Interdependent choices? → Use Dynamic Programming (Top-Down or Bottom-Up). - Independent decisions? → Consider a Greedy approach (with proof of correctness). - Feasibility / Existence Check - Use Backtracking, DFS with pruning, or Binary Search on Answer. - Does it involve String Manipulation? - Prefix/Suffix logic? → Trie, Rolling Hash, or Z-Algorithm. - Sequential manipulation? → Stack, Monotonic Stack, or Deque. - Frequent lookups / duplicates / uniqueness - Use Hash Maps, Hash Sets, or Counting Arrays. - Sliding window behavior - Use Two Pointers, Deque, or Counting Hash Map. - Frequent Min/Max retrieval - Use Heap, Monotonic Queue, or Segment Tree (for range queries). ➡️ Graph Inputs 1. Pathfinding / Traversals Shortest path / fewest steps → BFS (Unweighted), Dijkstra (Weighted). Exhaustive exploration / component discovery → DFS, Union-Find. 2. Cycle Detection / Topological Order Use DFS with visited tracking, Kahn’s Algorithm, or Disjoint Sets. 3. Optimization on graphs Minimum Spanning Tree? → Kruskal, Prim. Connectivity? → Tarjan's Algorithm, Bridge Finding, etc. ➡️ Tree Inputs (Typically Binary Trees) 1. Traversals / Depth Calculations Level-order traversal / Lowest Common Ancestor → Use BFS / DFS with depth tracking. Recursive divide-and-conquer logic → Postorder traversal is your friend. 2. Balancing / Ordering Constraints Use BST properties, AVL / Red-Black trees, or Segment Trees if mutable. ➡️ Linked List Inputs 1. Cycle detection → Fast/Slow Pointers 2. Structural changes Reversal? → Track prev, curr, next pointers. Head/edge cases? → Use a dummy node strategy. Follow me Suresh G. for more content around System Design and Interview Prep

Yesterday I produced a logical argument to reason why an algorithm was correct. How did I do that? Here are some logical thinking tips for algorithmic problem solving: - For each line, reason about the syntax and expected behavior for the calls and expressions. This mostly takes some language knowledge, API usage knowledge, and attention to detail. - If conditional statement occurs, employ the proof by cases strategy. Exhaust each possible case to ensure the code produces a correct output. - If iteration occurs or recursion occurs, employ an inductive proof strategy. Loop invariants can be used as a mechanism for achieving this with iteration. For recursion, raw induction helps. - If you’re dealing with a recursive data structure such as a tree or graph, structural induction helps. Otherwise, use all known facts about your domain to help reason about the correctess. In yesterday’s problem we were dealing with sums, so, as it turns out, facts about sums helped. As you can probably guess by now, if you have the logical thinking tools and knowledge base about your domain, you’ll likely be able to solve the problem. To solve, iterate over what you know about your domain and ask yourself if it can help you link it to what you don’t know and need to solve. Problem solving is about exploring the frontier of your knowledge graph and finding a pathway to a solution. Problem solving is path finding. #softwareengineering