SCR

LLD Interview Notes: Day 17/50 Topic: Decorator Pattern How do you add new features to an object without modifying its existing class or creating a huge inheritance hierarchy? That's where the Decorator Pattern comes in. 1. What Is the Decorator Pattern? A structural design pattern that dynamically adds behavior to an object by wrapping it with another object that implements the same interface. It follows the principle of composition over inheritance. 2. How It Works Define a common interface.Create a concrete component with the basic behavior.Create a decorator that implements the same interface and wraps the component.Add extra behavior before or after delegating to the wrapped object. 3. Real-World Example: Coffee Shop Imagine ordering a basic coffee and adding extras. Basic Coffee: ₹100 Milk: ₹20 Caramel: ₹30Total: ₹150 Each decorator adds its own cost and behavior without changing the original coffee class. 4. UML Structure Component: Common interface, such as getCost()ConcreteComponent: The original object.Decorator: Holds a reference to a Component.ConcreteDecorator: Adds behavior, such as milk or caramel. 5. Where Is It Used? Java I/O streamsMiddleware and request processingLogging and monitoring wrappersAdding features to UI componentsDynamically extending object behavior 6. Decorator vs Inheritance Inheritance: Adds behavior through class hierarchies.Decorator: Adds behavior by wrapping objects at runtime.Decorator makes combinations more flexible without creating a separate subclass for every combination. 7. Common Interview Mistakes Confusing Decorator with Proxy.Forgetting that the decorator should preserve the component's interface.Creating unnecessary layers of wrappers.Using decorators when a simple method would be enough. FAANG Takeaway Decorator Pattern answers: How can you extend an object's behavior without modifying its class? Use it when features need to be combined flexibly at runtime. Remember that too many wrappers can make debugging and tracing execution harder. Next: Day 18/50 Adapter Pattern in LLD

35 LPA internship PPO to a 6.5 LPA offer. All in two days. A 2026 graduate spent 6 months interning, received positive feedback from the manager, and expected a full-time offer. Then, just 2 days before the internship ended, all 90 interns received rejection emails. Now they're left with a 6.5 LPA backup offer and struggling to get interview calls. A harsh reality of today's job market: Good performance doesn't guarantee a PPO. Companies can change hiring plans overnight. Even strong candidates are struggling to get interview calls. Never depend on a single company or opportunity. It's not a reflection of your worth. One company's decision shouldn't define your career.

HLD Interview Notes: Day 9/30 Topic: Cache Stampede Prevention: Locks, Request Coalescing and Refresh-Ahead Your cache works perfectly until a popular key expires and thousands of requests hit your database at once. How do you prevent a cache miss from turning into a production incident? 1. Cache Stampede: The Problem A cache stampede happens when many requests try to rebuild the same expired cache entry simultaneously. Example: A shopping website caches popular product details.The cache expires during a big sale.Thousands of requests miss the cache and query the database. Database load spikes, response times increase, and the service may become unavailable. 2. Distributed Locking Allow only one worker to refresh an expired cache entry at a time. First request acquires a lock.It fetches fresh data from the database.It updates the cache and releases the lock.Other requests wait briefly or use a fallback. Best for: Preventing multiple workers from rebuilding the same key. Trade-off: Lock timeouts, failed workers and incorrect lock handling can create problems. 3. Request Coalescing Combine concurrent requests for the same missing key into one database fetch. First request triggers the fetch.Other requests for the same key wait for the result.The fetched value is shared among waiting requests. Best for: Reducing duplicate work for popular keys. Trade-off: Waiting requests may experience additional latency. 4. Refresh-Ahead Refresh frequently accessed cache entries before they expire. Identify popular cache entries.Refresh them in the background before expiration.Keep serving cached data while the refresh happens. Example: A product page refreshes its cached details before a major sale begins. Best for: Predictable, read-heavy workloads. Trade-off: Some refreshed data may never be used, increasing unnecessary database work. 5. Quick Comparison Distributed locking: Controls which worker refreshes a key.Request coalescing: Shares one in-flight fetch among concurrent requests.Refresh-ahead: Refreshes data before it expires. 6. Interview Tip When designing cache stampede protection, ask: Can multiple requests rebuild the same key?Should requests wait, or can they use stale data?What happens if the refresh worker crashes?How do we prevent a lock from becoming a bottleneck? FAANG Takeaway Preventing cache stampedes is about controlling concurrent work, not just increasing cache hit rates. Combine the right strategy with timeouts, TTL jitter and database protection to make caching more resilient under heavy traffic. Next: Day 10/30 Cache Warming and Preloading Strategies

Are vibe coders even aware of these 50 things? Database Indexes Race Conditions API Rate Limiting Database Transactions Cache Stampedes Connection Pooling Dead Letter Queues ACID Properties Webhook Verification Distributed Tracing N+1 Queries Circuit Breakers Database Sharding Optimistic Locking Exponential Backoff Zero-Downtime Migrations Message Queues Isolation Levels Idempotency Load Balancing Query Optimization Graceful Shutdown Redis Eviction Policies Foreign Key Constraints Replication Lag Background Workers Deadlocks Eventual Consistency Read Replicas Retry Mechanisms Database Migrations Backpressure Pessimistic Locking Horizontal vs Vertical Scaling Webhook Retries EXPLAIN ANALYZE Distributed Caching Unique Constraints Outbox Pattern Health Checks Database Partitioning Job Scheduling Reverse Proxies Distributed Transactions Cache Invalidation Event-Driven Architecture Jitter CQRS Event Sourcing Circuit Breaker Recovery Strategies Building a working demo is one thing. Building a system that survives real users, failures, traffic spikes, and messy data is a completely different game.

30-day DSA revision roadmap 📌 5 LeetCode problems a day. 30 days. 150+ problems to revise. Topics covered: Arrays & Hashing Two Pointers & Sliding Window Binary Search & Linked Lists Stacks & Monotonic Stacks Heaps, Intervals & Greedy Trees, BSTs & Graphs Backtracking & Dynamic Programming Tries, Bit Manipulation & Union-Find Advanced Graphs, Strings & Data Structures Follow this approach: Brute Force → Better → Optimal → Pattern → Complexity Don't just memorize solutions. Understand the pattern, revisit your mistakes, and practise explaining your approach. Save this for your next DSA revision.

5 messaging patterns every system design interview candidate should know: 1. Point-to-point One message goes to one worker. 2.Publish/subscribe Subscribers receive messages for the topics they follow. 3. Fan-out One event is copied to multiple queues or consumers. 4. Request-reply A client sends a request and receives a response. 5. Event streaming Events are stored so consumers can process or replay them. Each pattern solves a different communication problem. Know when to use each one for your HLD interviews.