Commonly asked System Design CONCEPT BASED interview topics -

1. Horizontal vs Vertical Partitioning:
Vertical partitioning splits tables by columns, often separating different features. Horizontal partitioning splits tables by rows, distributing data across multiple servers. Vertical organizes data logically, while horizontal improves scalability + performance.

2. Apache Kafka:
Kafka is a distributed streaming platform using a publish-subscribe model. It's fast due to the sequential disk I/O, zero-copy principle, and efficient batching of messages.

3. Rate Limiter:
A rate limiter controls the rate of requests a client can make to a service. It prevents overload and ensures fair resource usage.

4. JWT vs OAuth vs SAML:
JWT is a compact, self-contained token for secure information transmission. OAuth is an authorization framework for delegated access. SAML is an XML-based standard for exchanging authentication and authorization data.

5. Single Sign-On (SSO):
SSO allows users to access multiple applications with one set of credentials. It typically uses a central authentication server and protocols like SAML/OAuth.

6. Microservices vs Monolithic Architecture:
Microservices architecture breaks an application into small, independent services. Monolithic architecture is a single, tightly-coupled unit. Microservices offer scalability while monoliths are simpler to develop + deploy.

7. Reverse Proxy vs Forward Proxy:
A reverse proxy sits in front of web servers, forwarding client requests to backend servers. A forward proxy sits in front of clients, forwarding their requests to the internet. Reverse proxies are used for load balancing and security, while forward proxies are used for anonymity and filtering.

8. CAP Theorem:
The CAP theorem states that a distributed system can only provide two of three guarantees: Consistency, Availability, and Partition tolerance. In practice, partition tolerance is necessary, so systems must choose between consistency and availability during network partitions.

10. Efficient Caching Strategy:
Implement multi-level caching (browser, CDN, application server, database). Use appropriate cache invalidation strategies (TTL, event-based). Consider cache coherence for distributed systems.

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🔰 JavaScript Roadmap for Beginners 2025

├── 🌐 Introduction to JavaScript
├── ⚙️ Setting Up Environment (Browser, Node.js, VS Code)
├── 🔢 Variables (var, let, const)
├── 📊 Data Types & Type Coercion
├── 🧮 Operators & Expressions
├── 🔁 Conditional Statements (if, else, switch)
├── 🔄 Loops (for, while, do-while, for-in, for-of)
├── 🧵 Functions (Declaration, Expressions, Arrow)
├── 🧰 Arrays & Array Methods
├── 📄 Objects & Object Methods
├── 📦 Modules (ES6 Import/Export)
├── 📜 Scope & Closures
├── 📂 DOM Manipulation
├── 🖱 Events & Event Handling
├── ⚙️ Error Handling (try/catch)
├── 🧪 Debugging & Dev Tools
├── 🌐 Fetch API & Promises
├── 🔄 Async/Await
├── 📈 JSON & APIs Basics


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✌️ 25 Javascript Path Files Used To Store Sensitive Information In Web Application:-

1️⃣ /js/config.js
2️⃣ /js/credentials.js
3️⃣ /js/secrets.js
4️⃣ /js/keys.js
5️⃣ /js/password.js
6️⃣ /js/api_keys.js
7️⃣/js/auth_tokens.js
8️⃣/js/access_tokens.js
9️⃣/js/sessions.js

♾

1️⃣ /js/authorization.js
2️⃣ /js/encryption.js
3️⃣ /js/certificates.js
4️⃣ /js/ssl_keys.js
5️⃣ /js/passphrases.js
6️⃣ /js/policies.js
7️⃣ /js/permissions.js
8️⃣ /js/privileges.js
9️⃣ /js/hashes.js

♾

1️⃣ /js/salts.js
2️⃣ /js/nonces.js
3️⃣ js/signatures.js
4️⃣ js/digests.js
5️⃣ js/tokens.js
6️⃣ js/cookies.js
7️⃣ /js/topsecr3tdonotlook.js

Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months

### Week 1: Introduction to Python

Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions

Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)

Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules

Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode

### Week 2: Advanced Python Concepts

Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions

Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files

Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation

Day 14: Practice Day
- Solve intermediate problems on coding platforms

### Week 3: Introduction to Data Structures

Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists

Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues

Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions

Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues

### Week 4: Fundamental Algorithms

Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort

Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis

Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques

Day 28: Practice Day
- Solve problems on sorting, searching, and hashing

### Week 5: Advanced Data Structures

Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)

Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps

Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)

Day 35: Practice Day
- Solve problems on trees, heaps, and graphs

### Week 6: Advanced Algorithms

Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)

Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms

Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree

Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms

### Week 7: Problem Solving and Optimization

Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems

Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef

Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization

Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them

### Week 8: Final Stretch and Project

Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts

Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project

Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems

Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report

Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)

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