Data Partitioning

🎯 Data Partitioning: Splitting the Giant Pizza!

Imagine you have the world’s BIGGEST pizza. It’s so huge that one person can’t possibly eat it alone, and it won’t fit on one table. What do you do? You slice it into pieces and share! That’s exactly what Data Partitioning does with your data.

🍕 What is Partitioning?

Partitioning is like cutting a giant pizza into slices so different people can eat at different tables.

The Simple Story

Think of a library with 10 million books. One building can’t hold them all! So what do you do?

• Building A: Books by authors A-F
• Building B: Books by authors G-M
• Building C: Books by authors N-S
• Building D: Books by authors T-Z

Now when someone wants a book by “Shakespeare”, they go straight to Building C. No need to search all buildings!

┌─────────────────────────────────────────┐
│         ALL YOUR DATA (Too Big!)        │
└─────────────────────────────────────────┘
                    ↓
        🔪 PARTITION (Split it!)
                    ↓
┌──────────┐ ┌──────────┐ ┌──────────┐
│ Slice 1  │ │ Slice 2  │ │ Slice 3  │
│ Server A │ │ Server B │ │ Server C │
└──────────┘ └──────────┘ └──────────┘

Why Do We Need It?

Real Example: Netflix has data about 200+ million users. One computer can’t handle it! So they partition:

• Users 1-10M → Server Group A
• Users 10M-20M → Server Group B
• And so on…

🔑 What is a Partition Key?

The Partition Key is the rule you use to decide which slice each piece of data goes to. It’s like the address on an envelope!

The Mail Carrier Story

Imagine you’re a mail carrier. How do you decide which truck carries which letters?

• By ZIP code! Letters with ZIP 10001 go in Truck A
• Letters with ZIP 20002 go in Truck B

The ZIP code is your Partition Key - it tells you exactly where each letter belongs.

    📝 Data Record
    ┌─────────────────────┐
    │ user_id: 12345      │ ← This is the
    │ name: "Alice"       │   Partition Key!
    │ city: "New York"    │
    └─────────────────────┘
            ↓
    Hash(12345) = Partition 3
            ↓
    📦 Goes to Server 3!

Choosing the Right Key

Why does it matter?

Bad key → Some slices get HUGE, others stay tiny

Bad: Partition by "country"
┌──────────────┐ ┌──┐ ┌──┐
│ USA: 100M    │ │10K│ │5K│
│ users!!      │ │   │ │  │
│ OVERLOADED!  │ │   │ │  │
└──────────────┘ └──┘ └──┘

Good key → Nice, even slices

Good: Partition by "user_id"
┌──────────┐ ┌──────────┐ ┌──────────┐
│ 33M users│ │ 33M users│ │ 33M users│
│ BALANCED!│ │ BALANCED!│ │ BALANCED!│
└──────────┘ └──────────┘ └──────────┘

🎲 Data Distribution: How to Spread Data Evenly

Data Distribution is about making sure every server gets a fair share of the work. Like dealing cards - everyone should get the same number!

The Card Dealer

Imagine dealing 52 cards to 4 players:

• Player 1: 13 cards
• Player 2: 13 cards
• Player 3: 13 cards
• Player 4: 13 cards

Perfect! That’s good distribution.

But what if you gave Player 1 all the Aces, Kings, and Queens? They’d have all the powerful cards! That’s bad distribution - it’s called data skew.

Distribution Methods

1. Range-Based Distribution

Split data by ranges (like the library example):

Server A: IDs 1 - 1,000,000
Server B: IDs 1,000,001 - 2,000,000
Server C: IDs 2,000,001 - 3,000,000

Pros: Easy to understand, range queries work great Cons: Can become uneven over time

2. Hash-Based Distribution

Use math to scramble and distribute:

Hash(user_id) % number_of_servers = target_server

Example:
Hash("alice123") = 7493847
7493847 % 3 = 1  → Goes to Server 1!

Pros: Very even distribution Cons: Range queries are harder

The Ice Cream Shop Example

graph TD
    A["🍦 1000 Orders Coming In!"] --> B{Hash Each Order ID}
    B --> C["Server 1: ~333 orders"]
    B --> D["Server 2: ~333 orders"]
    B --> E["Server 3: ~334 orders"]
    style C fill:#90EE90
    style D fill:#90EE90
    style E fill:#90EE90

Each server handles roughly the same work. No one is overwhelmed!

🎡 Consistent Hashing: The Magic Ring

Consistent Hashing is a clever way to distribute data that makes adding or removing servers SUPER easy. Think of it as a magic ring!

The Clock Problem

Imagine you have 3 friends sitting around a round table (like a clock):

• Friend A sits at 12 o’clock
• Friend B sits at 4 o’clock
• Friend C sits at 8 o’clock

When someone brings food, you spin a pointer. Wherever it lands, the next friend clockwise gets the food!

        12:00
         (A)
          │
    ┌─────┼─────┐
    │     │     │
8:00(C)───┼───(B)4:00
    │     │     │
    └─────┼─────┘
          │
        6:00

Food lands at 5:00 → Goes to C (next clockwise)
Food lands at 1:00 → Goes to B (next clockwise)

Why Is This Magic?

Old Way (Regular Hashing):

server = hash(data) % 3

What if we add Server 4?
server = hash(data) % 4  ← EVERYTHING CHANGES!
Almost ALL data needs to move! 😱

New Way (Consistent Hashing):

When we add Server D at 6:00...
- Only data between C and D moves to D
- Everything else stays put! 🎉

Visual Example

graph TD
    subgraph Before
    A1["Server A"] --- B1["Server B"]
    B1 --- C1["Server C"]
    C1 --- A1
    end

    subgraph After Adding D
    A2["Server A"] --- B2["Server B"]
    B2 --- C2["Server C"]
    C2 --- D2["Server D"]
    D2 --- A2
    end

Only about 1/4 of data moves when adding a 4th server, not everything!

Real-World Example: Adding a New Server

Your social media app has 3 servers and is getting popular. Time to add Server 4!

That’s 3x less work!

📍 Data Locality: Keep Related Data Together!

Data Locality means storing data that’s often used together in the same place. Like keeping your socks in the sock drawer, not scattered around the house!

The Kitchen Analogy

Imagine cooking breakfast:

• Eggs are in the fridge (kitchen)
• Pan is in the cabinet (kitchen)
• Salt is on the counter (kitchen)

Everything you need is close together. That’s data locality!

Now imagine:

• Eggs in the garage
• Pan in the bedroom
• Salt in the backyard

You’d spend all morning running around! Bad locality = slow performance.

Why Locality Matters

Good Locality (Same Server):
┌─────────────────────────────┐
│ Server A                    │
│ ┌─────────────────────────┐ │
│ │ User "Alice"            │ │
│ │ Alice's Posts           │ │
│ │ Alice's Comments        │ │  ← All together!
│ │ Alice's Likes           │ │     FAST! ⚡
│ └─────────────────────────┘ │
└─────────────────────────────┘

Bad Locality (Different Servers):
┌─────────┐  ┌─────────┐  ┌─────────┐
│Server A │  │Server B │  │Server C │
│ Alice's │→→│ Alice's │→→│ Alice's │
│ Profile │  │ Posts   │  │Comments │
└─────────┘  └─────────┘  └─────────┘
     ↑            ↑            ↑
     └────────────┴────────────┘
        Must talk to ALL THREE!
        SLOW! 🐌

Strategies for Good Locality

1. Composite Partition Keys

Group related data by combining keys:

Partition Key: user_id + data_type

User 123's data:
├── 123_profile → Server A
├── 123_posts   → Server A  (same server!)
├── 123_comments→ Server A  (same server!)
└── 123_likes   → Server A  (same server!)

2. Data Co-location

Design your partition key so related queries hit one server:

graph LR
    Q["Query: Get Alice's Timeline] --> S[Server A]
    S --> P[Alice's Posts"]
    S --> C[Alice's Comments]
    S --> F[Alice's Friends' Posts]
    style S fill:#98FB98

One server, one query, fast response!

The E-commerce Example

Online store with millions of orders:

❌ Bad Design:
Orders → Server A
Order Items → Server B
Payments → Server C

To show one order = 3 server calls! 🐌

✅ Good Design:
Order 12345 (everything) → Server A
Order 12346 (everything) → Server B

To show one order = 1 server call! ⚡

🎮 Putting It All Together

Let’s see how all these concepts work together in a real system!

Twitter-like App Example

Goal: Store tweets for 500 million users

graph TD
    A["500M Users' Tweets] --> B[Choose Partition Key]
    B --> C[user_id - good choice!]
    C --> D[Hash with Consistent Hashing]
    D --> E[Ring of 100 Servers]
    E --> F[Data Locality: User's tweets together"]

    style C fill:#90EE90
    style F fill:#90EE90

Step by step:

1. Partition Key: user_id (every user has unique ID)
2. Distribution: Hash-based (even spread)
3. Consistent Hashing: Easy to add servers as we grow
4. Locality: All tweets from one user on same server

Result: When someone loads @elonmusk’s profile:

• System hashes “elonmusk” → finds Server 47
• Server 47 has ALL his tweets together
• One server call, super fast!

🏆 Quick Summary

💡 Key Takeaways

1. Partition your data when it’s too big for one server
2. Choose partition keys that spread data evenly
3. Use consistent hashing to make scaling smooth
4. Keep related data together for faster queries
5. Think ahead - your choices affect everything!

Remember: Good partitioning is like being a great pizza chef - you want perfect slices that are easy to serve and delicious to consume! 🍕

Next time you use Netflix, Instagram, or any big app - remember there’s partitioning magic happening behind the scenes, making everything fast and reliable!