Developers 2017 vs 2026

Incident Response Planning and Execution Cheatsheet.

CONSISTENCY IN SYSTEM DESIGN Consistency means every user sees the correct and up-to-date data across the entire system. In simple terms: Consistency = Everyone sees the same truth. In distributed systems, maintaining consistency becomes challenging because data is often stored across multiple servers or regions. TYPES OF CONSISTENCY STRONG CONSISTENCY Every read returns the most recent write. Example: You update your bank balance. Every device immediately shows the updated balance. Pros: → Always accurate → No stale data Cons: → Higher latency → Reduced availability during failures EVENTUAL CONSISTENCY Updates take time to propagate. Eventually, every copy of the data becomes the same. Example: You change your profile picture. Some users may still see the old picture for a few seconds until all servers synchronize. Pros: → Better availability → Lower latency → Improved scalability Cons: → Temporary stale data HOW SYSTEMS ACHIEVE CONSISTENCY DATA REPLICATION Synchronize data across multiple replicas. DISTRIBUTED CONSENSUS Ensure all nodes agree before committing changes. Examples: → Raft → Paxos LEADER-BASED REPLICATION One primary node accepts writes. Replicas receive updates from the primary. QUORUM READS AND WRITES Require a majority of replicas to participate before confirming operations. REAL-WORLD EXAMPLES → Banking systems prioritize strong consistency for financial transactions. → Social media platforms often use eventual consistency for posts and likes. → DNS systems use eventual consistency as updates propagate across the internet. BENEFITS OF CONSISTENCY → Accurate data → Predictable application behavior → Improved user trust → Reliable transactions → Reduced data conflicts CHALLENGES → Network partitions → Replication delays → Higher latency → Increased system complexity RULE OF THUMB Choose strong consistency when correctness is critical. Choose eventual consistency when availability, scalability, and performance matter more than immediate synchronization. Grab System Design Ebook: codewithdhanian.gumroad.com/…

As a DSA Engineer, It will be good if you have an understanding of the below 40 topics👇 1. Time and Space Complexity (Big O, Big Θ, Big Ω) 2. Arrays and Dynamic Arrays 3. Strings and String Manipulation 4. Recursion and Backtracking 5. Hash Tables (Hash Maps and Hash Sets) 6. Linked Lists (Singly, Doubly, Circular) 7. Stacks 8. Queues and Deques 9. Prefix Sums and Difference Arrays 10. Sliding Window Technique 11. Two Pointers Technique 12. Binary Search 13. Sorting Algorithms (Bubble, Selection, Insertion, Merge, Quick, Heap) 14. Divide and Conquer 15. Greedy Algorithms 16. Bit Manipulation 17. Matrix Algorithms 18. Trees and Binary Trees 19. Binary Search Trees (BST) 20. Tree Traversals (DFS, BFS, Preorder, Inorder, Postorder) 21. Heaps and Priority Queues 22. Tries (Prefix Trees) 23. Graph Representation (Adjacency List and Matrix) 24. Graph Traversal (DFS and BFS) 25. Shortest Path Algorithms (Dijkstra, Bellman-Ford, Floyd-Warshall) 26. Minimum Spanning Tree (Kruskal and Prim) 27. Topological Sorting 28. Union Find (Disjoint Set Union - DSU) 29. Dynamic Programming (DP) 30. Memoization and Tabulation 31. Segment Trees 32. Fenwick Trees (Binary Indexed Trees) 33. Monotonic Stack and Queue 34. Advanced Graph Algorithms (Tarjan, Kosaraju) 35. Network Flow Algorithms 36. Suffix Arrays and Suffix Trees 37. String Matching Algorithms (KMP, Rabin-Karp, Z Algorithm) 38. Computational Geometry 39. Randomized Algorithms 40. Problem-Solving Patterns and Competitive Programming Techniques 📘 Grab DSA Handbook: codewithdhanian.gumroad.com/… These topics form a strong foundation for becoming a proficient software engineer, competitive programmer, coding interview candidate, or algorithm specialist.