01. NoSQL vs SQL: CAP Theorem & Document-Oriented Architecture • MongoDB & NoSQL Architecture: CRUD to Sharding & WiredTiger • Rishabh Ki Kaksha

MongoDB & NoSQL Architecture: CRUD to Sharding & WiredTiger › 01. NoSQL vs SQL: CAP Theorem & Document-Oriented Architecture

35 mins

Course Syllabus

Module 1: MongoDB Foundations, BSON & CRUD Mechanics

01. NoSQL vs SQL: CAP Theorem & Document-Oriented Architecture 02. BSON Data Types & Document Schema Design (Embedding vs Referencing) 03. High-Performance CRUD Operations, BulkWrite & Write Concerns 04. Schema Validation with JSON Schema ($jsonSchema)

Module 2: Advanced Aggregation Framework & Data Pipelines

05. Aggregation Pipeline Core: $match, $project, $group, $sort & $limit 06. Complex Transformations: $unwind, $lookup (Joins) & $facet (Multi-Facet Analytics) 07. Window Operators ($setWindowFields) & Accumulator Functions

Module 3: Indexing Strategies & Query Optimization

08. B-Tree Indexes in MongoDB: Single Field, Compound & Partial Indexes 09. Special Indexes: TTL (Time-To-Live), Geospatial (2dsphere) & Text Indexes 10. Query Profiling, explain("executionStats") & Index Covering Analysis

Module 4: Storage Engine Internals (WiredTiger) & ACID Transactions

11. WiredTiger Storage Engine Architecture: Cache, Compression & Checkpoints 12. Read Concerns (local, majority, linearizable) & Write Concerns (w:majority, j:true) 13. Multi-Document Distributed ACID Transactions with Sessions

Module 5: High Availability, Replica Sets, Sharding & Backups

14. Replica Sets Architecture: Primary Election, Oplog & Automatic Failover 15. Sharding Architecture: mongos Router, Config Servers & Shard Key Selection 16. Range vs Hash Sharding, Chunk Balancing & Jumbo Chunks Mitigation 17. Change Streams: Building Real-Time Event-Driven Pipelines with MongoDB 18. Production Backup Strategies: mongodump/mongorestore, Point-in-Time & Atlas CLI

Interactive Lesson Studio

# 01. NoSQL vs SQL: CAP Theorem & Document-Oriented Architecture

> MongoDB Notes by Rishabh - Topic 01

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### 1. NoSQL vs SQL (The Relational vs Document Paradigm 📜)

*   **RDBMS / SQL (PostgreSQL, MySQL):** Strict tabular rows & columns, rigid foreign keys, complex Multi-Table JOINs, ACID compliant.
*   **NoSQL / MongoDB:** Flexible JSON/BSON documents, dynamic schema, horizontal scaling (Sharding), embedded data models.

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### 2. The CAP Theorem & MongoDB's Position 🔺

Distributed systems me 3 properties me se sirf 2 achieve ki ja sakti hain:
1. **C (Consistency):** Har node par exact same latest data read hoga.
2. **A (Availability):** Har non-failing node se response aayega (chahe slightly stale ho).
3. **P (Partition Tolerance):** Network cable disconnect hone par bhi cluster zinda rahega.

```
       [ Consistency (C) ]
             /\
            /  \
           /    \
 [ MongoDB (CP) ]\ [ PostgreSQL (CA) ]
         /        \
        /          \
[ Partition (P) ]---[ Availability (A) ]
         \          /
          [ Cassandra / DynamoDB (AP) ]
```

> **MongoDB is a CP System (Consistency + Partition Tolerance):** Network partition ke waqt agar primary down ho jaye, toh cluster naya primary elect hone tak writes block kar deta hai taaki data consistency compromise na ho!

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### 3. Master Comparison Matrix

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### 4. Interview Ke 3 Sawal Is Topic Se

1. **MongoDB ko CP system kyu classify kiya jata hai?** -> Network split hone par MongoDB consistency ko priority deta hai; minority partition me reads/writes restrict ho jaati hain jab tak majority consensus reach na ho.
2. **Document-oriented database graph ya key-value database se kaise alag hai?** -> Document databases rich hierarchical objects (nested arrays/sub-documents) ko store aur index karne ki capability dete hain.
3. **Polyglot Persistence kya hoti hai?** -> Ek hi application me transactional ledger ke liye PostgreSQL, product catalog ke liye MongoDB aur caching ke liye Redis ko sath me use karna.

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**Next Topic:** 02. BSON Data Types & Document Schema Design (Embedding vs Referencing)

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