Database Selection

Database Selection: Choosing Your Perfect Data Home

The Restaurant Kitchen Analogy

Imagine you’re building a giant restaurant. You need different tools for different jobs:

• A fridge for keeping food cold
• An oven for baking
• A grill for quick cooking
• A freezer for long-term storage

Would you use ONLY a fridge for everything? Of course not! That would be silly.

Databases work the same way. Different data needs different homes.

1. Polyglot Persistence: Using Multiple Databases Together

What Is It?

Polyglot means “speaking many languages.”

Polyglot Persistence means using multiple different databases in one application—each one doing what it does best.

The Toolbox Analogy

Your Dad's Toolbox:
┌─────────────────────────────────────┐
│  🔨 Hammer    → Nails               │
│  🔧 Wrench    → Bolts               │
│  🪛 Screwdriver → Screws            │
│  📏 Ruler     → Measuring           │
└─────────────────────────────────────┘

You don’t use a hammer for screws. Each tool has its job!

Real Example: An Online Store

graph TD
    A["🛒 Online Store"] --> B["📦 Products"]
    A --> C["👤 User Sessions"]
    A --> D["🔍 Search"]
    A --> E["📊 Analytics"]

    B --> F["#40;PostgreSQL<br>Structured Data#41;"]
    C --> G["#40;Redis<br>Fast Cache#41;"]
    D --> H["#40;Elasticsearch<br>Full-Text Search#41;"]
    E --> I["#40;MongoDB<br>Flexible Logs#41;"]

Key Benefits

• Right tool for right job = Better performance
• Each database shines at what it does best
• No compromise on features

Key Challenges

• More databases = More complexity
• Need to learn multiple systems
• Data sync can be tricky

2. Multi-Model Databases: The Swiss Army Knife

What Is It?

A Multi-Model Database is like a Swiss Army knife—one tool that can do many things!

Instead of using 5 different databases, you use ONE database that supports multiple data models.

The Swiss Army Knife Analogy

Single Multi-Model Database:
┌─────────────────────────────────────┐
│  📄 Documents  (like MongoDB)       │
│  🔗 Graphs     (like Neo4j)         │
│  🔑 Key-Value  (like Redis)         │
│  📊 Tables     (like SQL)           │
│  ALL IN ONE!                        │
└─────────────────────────────────────┘

Popular Multi-Model Databases

Real Example: Social Media App

graph TD
    A["Social Media App"] --> B["ArangoDB"]

    B --> C["📄 User Profiles<br>Document Model"]
    B --> D["🔗 Friendships<br>Graph Model"]
    B --> E["🔑 Sessions<br>Key-Value Model"]

One database handles:

• User profiles as documents
• Friend connections as graphs
• Login sessions as key-value pairs

Key Benefits

• Simpler operations (one database to manage)
• No data sync issues between databases
• Learn one query language

Key Challenges

• May not be THE BEST at any single model
• “Jack of all trades, master of none”
• Vendor lock-in risk

3. Database Selection Criteria: How to Choose?

The Decision Framework

Choosing a database is like choosing a car. You ask questions:

🚗 Car Shopping:
- How many people? (Family vs. Sports car)
- City or highway? (Compact vs. SUV)
- Budget? (Economy vs. Luxury)

💾 Database Shopping:
- What's my data shape? (Structured vs. Flexible)
- Read-heavy or write-heavy? (Query vs. Insert)
- Scale needs? (Small vs. Massive)

The 7 Key Questions

graph TD
    A["Choose Database"] --> B{Data Structure?}
    B -->|Fixed Schema| C["SQL/Relational"]
    B -->|Flexible| D["Document Store"]

    A --> E{Relationships?}
    E -->|Complex Networks| F["Graph Database"]
    E -->|Simple Links| G["Any Database"]

    A --> H{Speed Priority?}
    H -->|Super Fast| I["In-Memory/Redis"]
    H -->|Normal| J["Disk-Based"]

Decision Matrix

Real Example: Choosing for a Blog

📝 Blog Application Needs:
- Articles (structured) → PostgreSQL ✅
- Comments (nested) → PostgreSQL JSON ✅
- Tags (simple) → PostgreSQL arrays ✅
- Search → PostgreSQL full-text ✅

Result: ONE database handles everything!
No need for NoSQL complexity.

Real Example: Choosing for IoT

🌡️ IoT Sensor Platform Needs:
- Millions of readings/second
- Time-series data
- Flexible sensor types
- Fast aggregations

Result: Time-series DB (InfluxDB/TimescaleDB)
SQL can't handle this volume efficiently.

4. Migration from SQL to NoSQL: The Big Move

What Is Migration?

Migration is moving your data from one database to another—like moving houses!

🏠 Moving Houses:
1. Pack boxes (Export data)
2. Load truck (Transform format)
3. Drive to new home (Transfer)
4. Unpack boxes (Import data)
5. Check nothing broke (Validate)

Why Migrate?

The Migration Journey

graph TD
    A["1. ANALYZE"] --> B["2. PLAN"]
    B --> C["3. TRANSFORM"]
    C --> D["4. MIGRATE"]
    D --> E["5. VALIDATE"]
    E --> F["6. SWITCH"]

    A --> G["Map current schema"]
    B --> H["Design new model"]
    C --> I["Convert data format"]
    D --> J["Move data over"]
    E --> K["Test everything"]
    F --> L["Go live!"]

Step-by-Step Example

Scenario: Moving a User table from MySQL to MongoDB

Step 1: Analyze Current Schema

MySQL Users Table:
┌────┬──────────┬─────────────────┬─────┐
│ id │ name     │ email           │ age │
├────┼──────────┼─────────────────┼─────┤
│ 1  │ Alice    │ alice@mail.com  │ 25  │
│ 2  │ Bob      │ bob@mail.com    │ 30  │
└────┴──────────┴─────────────────┴─────┘

Step 2: Design MongoDB Structure

{
  "_id": "ObjectId",
  "name": "Alice",
  "email": "alice@mail.com",
  "age": 25,
  "metadata": {
    "migrated_from": "mysql",
    "original_id": 1
  }
}

Step 3: Transform Data

• Write a script to convert rows to documents
• Handle data type differences
• Add new fields if needed

Step 4: Migrate

• Export from MySQL
• Transform format
• Import to MongoDB

Step 5: Validate

• Count records (same number?)
• Spot check data (correct values?)
• Test queries (same results?)

Migration Strategies

The Strangler Fig Pattern

graph LR
    A["App"] --> B{Router}
    B -->|Old Features| C["#40;MySQL#41;"]
    B -->|New Features| D["#40;MongoDB#41;"]

    E["Over Time"] --> F["More → MongoDB"]
    F --> G["Less → MySQL"]
    G --> H["Eventually: All MongoDB"]

Like a strangler fig tree that slowly wraps around and replaces the host tree!

Common Pitfalls

Rollback Plan

Always have a way back!

Emergency Rollback:
1. Keep old database running (read-only)
2. Maintain sync scripts
3. Test rollback procedure
4. Set clear rollback triggers

Quick Summary

┌─────────────────────────────────────────────┐
│        DATABASE SELECTION GUIDE             │
├─────────────────────────────────────────────┤
│                                             │
│  🎭 POLYGLOT PERSISTENCE                    │
│     Use multiple databases together         │
│     Right tool for each job                 │
│                                             │
│  🔧 MULTI-MODEL DATABASES                   │
│     One database, many models               │
│     Swiss Army knife approach               │
│                                             │
│  🎯 SELECTION CRITERIA                      │
│     Match database to your needs            │
│     Consider: shape, scale, speed, team     │
│                                             │
│  🚚 SQL → NoSQL MIGRATION                   │
│     Plan carefully, test thoroughly         │
│     Always have rollback ready              │
│                                             │
└─────────────────────────────────────────────┘

Remember This!

The best database is the one that:

1. Fits your data naturally
2. Your team can manage
3. Scales with your growth
4. Stays within budget

There’s no “best” database—only the best database FOR YOUR NEEDS!