https://github.com/shobhakartiwari/database_learning_strategy-

https://github.com/shobhakartiwari/database_learning_strategy-

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• Host: GitHub
• URL: https://github.com/shobhakartiwari/database_learning_strategy-
• Owner: shobhakartiwari
• Created: 2025-02-26T05:25:19.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2025-02-26T05:25:45.000Z (2 months ago)
• Last Synced: 2025-02-26T06:26:14.499Z (2 months ago)
• Size: 2.93 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# The 3 Layers of Databases

I was obsessed. I dove deep into 4 different databases—SQL and NoSQL—to understand:

- How they’re created  

- How they work internally  

- What they’re actually made of  

The deeper I went, the more complex it seemed. But then I saw the pattern.  

No matter how fancy or complex a database looks, they all boil down to **just 3 simple layers**. Yes, just 3.  

If you understand these layers, you can:  

- Grasp any database  

- Build deep intuition about its architecture  

- Answer any interview question confidently  

Here they are:

## 1. User Access Layer

This layer defines how users connect with the database.  

It includes:  

- **SQL** (Structured Query Language)  

- **HTTP APIs** (for NoSQL databases)  

- **Specific Protocols** (database-specific query languages)  

**Examples:**  

- RDBMS: SQL for user access  

- Cassandra: CQL (Cassandra Query Language)  

- NoSQL (DynamoDB, MongoDB): HTTP APIs  

---

## 2. Routing Layer

This layer routes read and write requests to the correct server or node.  

- Optional for single-node databases (e.g., traditional RDBMS)  

- Critical for distributed databases (NoSQL)  

**Examples:**  

- **DynamoDB**: Request router uses partition metadata to route reads/writes  

- **MongoDB**: Mongos connects to config servers for routing  

- **Cassandra**: Snitch finds the node where data resides  

**Pro Tip:** Almost all use **Consistent Hashing**—an efficient algorithm for distributing data across nodes for high availability and scalability.

---

## 3. Storage Layer

This layer defines how data is stored on disk. Two common data structures dominate:  

- **B/B+ Tree**:  

  - Used in 80% of databases (SQL and NoSQL)  

  - Offers consistent O(log n) read/write access  

  - Perfect for range queries  

  - Examples: All SQL DBs, DynamoDB, MongoDB  

- **LSM Tree**:  

  - Optimized for high write throughput  

  - Uses SSTable, compaction, and tombstones for efficient data management  

  - Examples: Cassandra, ScyllaDB  

---

## That’s It

All databases—SQL or NoSQL—are just a combination of these 3 layers.  

What surprised me most? Despite the marketing hype, SQL and NoSQL are fundamentally the same. They just assemble these layers differently.  

Next time you see a “new, fancy” database:  

- Don’t fall for buzzwords or social media hype  

- Apply this 3-layer framework  

- Break it down in 2 minutes flat  

This mindset shift will make you immune to hype and unmatched in system design interviews.