Understand why cache data needs an expiration date and how it saves your application.
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The Lazy Approach: Let Requests Fill Your Cache
Learn why being 'lazy' is actually the smartest way to populate your cache.
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The Smart Waiter: Understanding Cache Placement
Discover where cache lives in your application and why it's like having a super-smart waiter.
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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:
Why is this crucial?
Without Expiry:
Cache fills up with old, unused data
Memory runs out (like phone storage)
Application crashes 💥
With Expiry:
Old data gets cleaned automatically
Memory stays healthy
Only frequently-used data survives
Real-World Example:
Blog post cached for 15 minutes
Popular posts get re-cached frequently
Unpopular posts disappear naturally
Perfect self-cleaning system!
Key Insight: Expiration isn't just cleanup - it's automatic garbage collection that keeps your cache lean and mean!
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:
Real Example: You're building a blog platform. To show one blog post, you need to:
Join 4 different database tables
Fetch author info, tags, comments
Create a JSON response
This is expensive! But with lazy :
First request: Slow (database work)
All following requests: Lightning fast (cache hit)
Key Insight: Lazy population is like learning - you only remember what you actually use!
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!
Your cache sits between your API and database - like that smart waiter between you and the kitchen. When someone asks for data:
API checks the cache first (the waiter's memory)
If found → instant delivery! 🚀
If not found → API goes to database, gets data, saves copy in cache for next time
Key Insight: Cache is your application's short-term memory - it remembers frequently asked questions so it can answer them lightning fast!
Cache Entry: Blog Post #123
Expiry: 10 minutes
Status: After 10 minutes → AUTOMATICALLY DELETED
Cache Entry: Blog Post #123
Expiry: 10 minutes
Status: After 10 minutes → AUTOMATICALLY DELETED
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!
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!
User Request → API Server → Cache → <TopicPreview slug="database">Database</TopicPreview>
↑ ↓
Fast Lane Slow Lane
User Request → API Server → Cache → <TopicPreview slug="database">Database</TopicPreview>
↑ ↓
Fast Lane Slow Lane
The YouTube Strategy: Predicting What People Want
Learn how giants like YouTube use smart prediction to cache content before anyone asks for it.
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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:
Real Scenario:
Recommendation algorithm picks an old video
Video gets pushed to 1 million users' feeds
YouTube predicts: "This will get lots of clicks"
They proactively cache the video data
When users click, everything loads instantly
The Beauty:
It's not a new video (published 2 years ago)
But it's about to become popular
Smart prevents the traffic storm
Other Examples:
Twitter: Popular person tweets → cache immediately
Netflix: Show trending in your region → cache episodes
Key Insight: The smartest systems don't react to demand - they predict it and prepare!
The Eager Approach: Being Proactive with Cache
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:
Method 1: Double Writing
Cricket Score Example:
Score changes from 150 to 155
System updates BOTH database AND cache immediately
10,000 users refreshing page get instant new score
No waiting for cache to expire!
Method 2: Predictive
YouTube knows a video will trend, so they cache it before it goes viral.
When to use Eager Population:
Live scores/real-time data
Trending content
Popular user posts
Anything you KNOW will be requested heavily
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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Growing Your Cache: From Small to Massive
Discover how to scale your cache just like scaling a database - it's easier than you think!
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Your cache is growing popular, and one server isn't enough anymore. Sound familiar? It's exactly like scaling a !
Scaling Strategy 1: Vertical Scaling (Make it Bigger)
Scaling Strategy 2: Read Replicas (Clone for Reads)
