# SQL vs NoSQL

Quick Reference: Which is Better? | Data Partitioning | Sharding

# Quick Reference

# Clear Definition

SQL (Structured Query Language) databases are relational databases that store data in tables with predefined schemas. They use SQL for querying and maintain ACID properties for transaction integrity.

NoSQL (Not Only SQL) databases are non-relational databases that store data in various formats (documents, key-value pairs, graphs, etc.) without requiring a fixed schema. They prioritize scalability and flexibility over strict consistency.

💡 Key Insight: The choice between SQL and NoSQL isn't binary—many systems use both (polyglot persistence) for different use cases.

# Core Concepts

# SQL Databases (Relational)

# ACID Properties

• Atomicity: All operations in a transaction succeed or fail together
• Consistency: Data remains valid according to defined rules
• Isolation: Concurrent transactions don't interfere with each other
• Durability: Committed transactions persist even after system failure

# Schema and Relationships

• Fixed Schema: Structure defined before data insertion
• Normalization: Data organized to reduce redundancy
• Foreign Keys: Enforce referential integrity
• Joins: Combine data from multiple tables

# Examples

• PostgreSQL: Advanced features, JSON support, full-text search
• MySQL: Popular, widely used, good performance
• Oracle: Enterprise-grade, high performance, expensive
• SQL Server: Microsoft ecosystem integration

# NoSQL Databases

# Types of NoSQL Databases

1. Document Stores (MongoDB, CouchDB)

   • Store data as JSON-like documents
   • Flexible schema within documents
   • Good for content management, catalogs

2. Key-Value Stores (Redis, DynamoDB, Riak)

   • Simple key-value pairs
   • Extremely fast lookups
   • Good for caching, session storage

3. Column-Family Stores (Cassandra, HBase)

   • Store data in columns grouped by families
   • Optimized for read-heavy workloads
   • Good for time-series data, analytics

4. Graph Databases (Neo4j, Amazon Neptune)

   • Store nodes and relationships
   • Optimized for relationship queries
   • Good for social networks, recommendation engines

# BASE Properties

• Basically Available: System remains available even during failures
• Soft State: System state may change over time
• Eventually Consistent: System will become consistent over time

# Use Cases

# When to Use SQL

1. Complex Queries

   • Multi-table joins
   • Aggregations and analytics
   • Example: Financial reporting systems

2. ACID Transactions

   • Banking systems
   • E-commerce order processing
   • Inventory management

3. Structured Data

   • Well-defined schema
   • Relationships between entities
   • Example: ERP systems

4. Data Integrity

   • Referential integrity required
   • Validation rules critical
   • Example: Healthcare records

# When to Use NoSQL

1. High Volume, Low Latency

   • Real-time analytics
   • IoT data ingestion
   • Example: Twitter feeds, sensor data

2. Flexible Schema

   • Rapidly changing requirements
   • User-generated content
   • Example: Social media platforms

3. Horizontal Scaling

   • Need to scale across multiple servers
   • Large datasets
   • Example: Content delivery, gaming

4. Simple Queries

   • Key-based lookups
   • Document retrieval
   • Example: User profiles, product catalogs

# Advantages & Disadvantages

# SQL Advantages

✅ ACID Compliance: Ensures data integrity and consistency
✅ Mature Technology: Well-established, extensive tooling
✅ Standardized: SQL is standardized across vendors
✅ Complex Queries: Powerful query capabilities with joins
✅ Data Integrity: Foreign keys and constraints enforce rules
✅ Strong Consistency: Immediate consistency guarantees

# SQL Disadvantages

❌ Scalability: Vertical scaling limits (hardware constraints)
❌ Schema Rigidity: Schema changes require migrations
❌ Performance: Joins can be expensive at scale
❌ Cost: Enterprise solutions can be expensive
❌ Complexity: Normalization can lead to complex schemas

# NoSQL Advantages

✅ Horizontal Scalability: Designed to scale across servers
✅ Flexible Schema: Easy to adapt to changing requirements
✅ Performance: Optimized for specific access patterns
✅ Cost: Often open-source and cost-effective
✅ High Throughput: Can handle millions of operations/second

# NoSQL Disadvantages

❌ Consistency: Often eventually consistent (BASE)
❌ Limited Querying: Less powerful query languages
❌ No Standard: Each database has different APIs
❌ Maturity: Newer technology, less tooling
❌ Complex Relationships: Harder to model relationships

# Best Practices

# SQL Best Practices

1. Normalize Appropriately

   • Balance normalization with query performance
   • Denormalize for read-heavy workloads

2. Index Strategically

   • Index foreign keys and frequently queried columns
   • Monitor index usage and performance

3. Use Transactions Wisely

   • Keep transactions short
   • Avoid long-running transactions

4. Connection Pooling

   • Reuse database connections
   • Configure pool size appropriately

5. Query Optimization

   • Use EXPLAIN to analyze queries
   • Avoid N+1 queries
   • Use appropriate JOIN types

# NoSQL Best Practices

1. Design for Access Patterns

   • Structure data based on how it's accessed
   • Denormalize for read performance

2. Choose Right Type

   • Document store for flexible schema
   • Key-value for simple lookups
   • Graph for relationship-heavy data

3. Handle Consistency

   • Understand consistency guarantees
   • Implement application-level consistency if needed

4. Plan for Scale

   • Design sharding strategy early
   • Consider data distribution

5. Monitor Performance

   • Track query performance
   • Monitor replication lag
   • Watch for hotspots

# Common Pitfalls

⚠️ Common Mistake: Choosing NoSQL just because it's "modern" without understanding requirements.

Solution: Evaluate based on actual needs: query complexity, consistency requirements, scalability needs.

⚠️ Common Mistake: Using SQL joins excessively in high-traffic systems.

Solution: Denormalize data or use NoSQL for read-heavy workloads.

⚠️ Common Mistake: Assuming NoSQL means "no schema."

Solution: Schema exists in application code; changes still require careful migration.

⚠️ Common Mistake: Ignoring consistency guarantees in NoSQL.

Solution: Understand eventual consistency and handle it in application logic.

⚠️ Common Mistake: Not planning for scale from the beginning.

Solution: Design partitioning/sharding strategy early, even if not needed immediately.

# Interview Tips

🎯 Interview Focus: Interviewers often ask "When would you use SQL vs NoSQL?" Expect to:

1. Explain Trade-offs: Discuss ACID vs BASE, consistency vs availability
2. Provide Examples: Give real-world scenarios (e.g., "For a banking system, I'd use SQL for ACID compliance")
3. Discuss Hybrid: Mention polyglot persistence (using both)
4. Scalability: Explain how each handles scaling
5. Query Patterns: Discuss when complex queries are needed

# Common Questions

• "Design a system that handles both SQL and NoSQL. When would you use each?"
• "How would you migrate from SQL to NoSQL?"
• "Explain CAP theorem in the context of SQL vs NoSQL."
• "How does eventual consistency work in NoSQL databases?"

# Related Topics

• Which is Better and Use Cases: Decision-making framework
• Data Partitioning: How SQL and NoSQL handle partitioning
• Sharding: Scaling strategies for both types
• Step 3: Consistency vs Availability: CAP theorem and consistency models
• Step 4: In-Memory Databases: Caching strategies complement both

# Visual Aids

# SQL Database Structure

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Users     │────▶│   Orders    │────▶│ Order Items │
├─────────────┤     ├─────────────┤     ├─────────────┤
│ id (PK)     │     │ id (PK)     │     │ id (PK)     │
│ name        │     │ user_id(FK) │     │ order_id(FK)│
│ email       │     │ total       │     │ product_id  │
│ created_at  │     │ status      │     │ quantity    │
└─────────────┘     └─────────────┘     └─────────────┘

# NoSQL Document Structure (MongoDB)

{
  "_id": "507f1f77bcf86cd799439011",
  "user": {
    "name": "John Doe",
    "email": "john@example.com"
  },
  "orders": [
    {
      "order_id": "12345",
      "items": [
        {"product_id": "p1", "quantity": 2},
        {"product_id": "p2", "quantity": 1}
      ],
      "total": 99.99
    }
  ]
}

# Comparison Flowchart

Start: Need Database?
│
├─ Complex queries & ACID needed?
│  ├─ Yes → SQL (PostgreSQL, MySQL)
│  └─ No ↓
│
├─ High volume & horizontal scale needed?
│  ├─ Yes → NoSQL
│  │   ├─ Document store (MongoDB)
│  │   ├─ Key-value (Redis, DynamoDB)
│  │   ├─ Column-family (Cassandra)
│  │   └─ Graph (Neo4j)
│  └─ No → SQL
│
└─ Consider: Polyglot Persistence (Use Both)

# Quick Reference Summary

SQL: Use when you need ACID transactions, complex queries, strong consistency, and structured data with relationships. Best for financial systems, ERP, and applications requiring data integrity.

NoSQL: Use when you need horizontal scalability, flexible schema, high throughput, and can tolerate eventual consistency. Best for social media, IoT, content management, and real-time analytics.

Hybrid Approach: Many modern systems use both—SQL for transactional data, NoSQL for high-volume, flexible data. This is called polyglot persistence.

Next Topic: Which is Better and Use Cases →

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