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:

• 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!

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:

1. API checks the cache first (the waiter's memory)
2. If found → instant delivery! 🚀
3. 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!

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:

• Join 4 different database tables
• Fetch author info, tags, comments
• Create a JSON response

This is expensive! But with lazy caching:

• 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 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

Score Update Happens:
1. Write to <TopicPreview slug="database">Database</TopicPreview> ✓
2. Write to Cache ✓  (simultaneously)
3. Readers get instant updates!

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 Caching

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!