Learn how to put data into cache efficiently and scale it like a pro
Understand why cache data needs an expiration date and how it saves your application.
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Imagine if your phone never deleted old photos - eventually, you'd run out of storage! Cache works the same way.
Every piece of data in cache needs an expiration time:
Cache Entry: Blog Post #123
Expiry: 10 minutes
Status: After 10 minutes → AUTOMATICALLY DELETED
Why is this crucial?
Without Expiry:
With Expiry:
Real-World Example:
Key Insight: Expiration isn't just cleanup - it's automatic garbage collection that keeps your cache lean and mean!
Sometimes you need to be ahead of the game - learn when and how to fill cache before anyone asks.
Imagine you're a news reporter during a cricket match. You KNOW thousands of people will ask for the latest score every second. Do you wait for them to ask, or do you prepare the answer beforehand?
Eager Population means filling cache proactively:
Score Update Happens:
1. Write to <TopicPreview slug="database">Database</TopicPreview> ✓
2. Write to Cache ✓ (simultaneously)
3. Readers get instant updates!
Cricket Score Example:
YouTube knows a video will trend, so they cache it before it goes viral.
When to use Eager Population:
Key Insight: Eager caching is like preparing for a party - you stock up on what you know guests will ask for!
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Discover where cache lives in your application and why it's like having a super-smart waiter.
Imagine you're at a busy restaurant. You place an order, and instead of the waiter running to the kitchen every single time, there's a smart waiter who remembers popular orders and keeps them ready nearby.
This is exactly how cache works in your application!
User Request → API Server → Cache → <TopicPreview slug="database">Database</TopicPreview>
↑ ↓
Fast Lane Slow Lane
Your cache sits between your API and database - like that smart waiter between you and the kitchen. When someone asks for data:
Key Insight: Cache is your application's short-term memory - it remembers frequently asked questions so it can answer them lightning fast!
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Connect all the dots and see cache for what it really is - your application's speed booster.
Let's step back and see the complete picture. Cache is just a faster - that's the secret!
Traditional Thinking: "Cache is some magical speed thing"
Reality: "Cache is a database optimized for speed over persistence"
| Database | Cache |
|---|---|
| Slow but permanent | Fast but temporary |
| Stores everything | Stores popular stuff |
| Complex queries | Simple key-value |
| Hard disk | RAM |
User Request
↓
API Server
↓
Cache (Fast Database) → Hit? Return data
↓ (Miss?)
Main Database (Slow but Complete)
↓
Store in Cache + Return to User
Why This Matters:
Key Insight: Once you understand that cache = fast database, everything else becomes logical extensions of database concepts you already know!
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Learn why being 'lazy' is actually the smartest way to populate your cache.
Meet Sarah, a librarian who only stocks books that people actually ask for. She doesn't waste space storing books nobody wants. This is lazy population - and it's brilliant!
Here's how lazy cache population works:
1. User asks for blog post #123
2. Cache says: "I don't have it" (Cache Miss)
3. API fetches from <TopicPreview slug="database">database</TopicPreview>
4. API stores copy in cache
5. API returns data to user
6. Next person asking for blog #123 gets instant answer!
Real Example: You're building a blog platform. To show one blog post, you need to:
This is expensive! But with lazy :
Key Insight: Lazy population is like learning - you only remember what you actually use!
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Learn how giants like YouTube use smart prediction to cache content before anyone asks for it.
You open YouTube and see recommended videos. Ever wonder how they load so fast? YouTube doesn't wait for you to click - they predict and prepare!
The Prediction Game:
YouTube's AI: "This 2-year-old cat video will trend today"
Action: Cache the video metadata NOW
Result: When millions click, instant loading!
Real Scenario:
The Beauty:
Other Examples:
Key Insight: The smartest systems don't react to demand - they predict it and prepare!
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Discover how to scale your cache just like scaling a database - it's easier than you think!
Your cache is growing popular, and one server isn't enough anymore. Sound familiar? It's exactly like scaling a !
Before: 8GB RAM cache server
After: 32GB RAM cache server
Result: More data, same server
Master Cache ← Writes go here
↓ (replicates to)
Replica 1, Replica 2, Replica 3 ← Reads come from here
Perfect for: High-read applications (like news websites)
Users A-H → Cache Server 1
Users I-P → Cache Server 2
Users Q-Z → Cache Server 3
How API Knows Where to Go:
Real Example:
Key Insight: Cache scaling follows database patterns because cache IS a database - just a faster, temporary one!
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