What is Caching?
💡 Concept Name
Caching – The process of storing copies of data temporarily in a faster storage layer to speed up future data retrieval.
📘 Quick Intro
Caching improves application performance by reducing latency and load on databases or APIs by serving frequently requested data from fast storage like memory.
🧠 Analogy / Short Story
Think of caching as keeping your frequently used tools in a desk drawer rather than the garage — much faster to access when needed.
🔧 Technical Explanation
- ⚡ Reduces latency and load on backend data stores like databases and APIs.
- 🧠 Uses in-memory or fast-access storage for quick data retrieval.
- ♻️ Can be local (in-memory), distributed (across servers), or persistent.
- 🛠️ Commonly implemented with key-value data structures like dictionaries or Redis.
- 🕒 Requires cache invalidation policies such as TTL (Time-To-Live) or LRU (Least Recently Used) to manage freshness.
🎯 Purpose & Use Case
- ✅ Speeding up web API responses and database queries.
- ✅ Storing configuration data or frequently accessed information.
- ✅ Reducing server load in high-traffic systems.
💻 Real Code Example
// Using IMemoryCache in .NET
public class DataService
{
private readonly IMemoryCache _cache;
public DataService(IMemoryCache cache)
{
_cache = cache;
}
public string GetUserData(int userId)
{
return _cache.GetOrCreate("User_" + userId, entry =>
{
entry.AbsoluteExpirationRelativeToNow = TimeSpan.FromMinutes(10);
return GetFromDatabase(userId);
});
}
private string GetFromDatabase(int id) => $"User Data for {id}";
}
🔄 Cache Replacement vs Cache Invalidation
Cache Replacement and Cache Invalidation are two key strategies for managing cache content, each serving different goals:
-
🧽 Cache Invalidation: Explicitly removing or updating cache entries when data changes or expires.
- Triggered by updates, TTL expiry, or external signals.
- Example: Updating product price invalidates old cached value.
- Common techniques: TTL, manual invalidation, versioning.
-
♻️ Cache Replacement: Evicting cache entries to free space when cache reaches capacity.
- Driven by cache size limits and eviction policies like LRU or FIFO.
- Does not necessarily mean data is stale, just less used.
- Example: LRU removes least recently accessed items first.
🧠 Key Differences
| Aspect | Cache Invalidation | Cache Replacement |
|---|---|---|
| Purpose | Remove stale data | Free up space for new data |
| Trigger | Data updates or expiry | Cache size limits |
| Strategy | TTL, manual invalidation | LRU, FIFO, LFU |
| Focus | Freshness and accuracy | Efficiency and space optimization |
✅ Both are essential to maintain cache effectiveness: invalidation ensures correctness, replacement manages capacity.
❓ Interview Q&A
Q1: What is caching?
A: Temporarily storing data for faster access to reduce repeated fetching.
Q2: Why is caching important?
A: It reduces latency and improves application performance.
Q3: Name some caching tools.
A: Redis, MemoryCache, NCache, Memcached.
Q4: What does cache invalidation mean?
A: Removing or updating outdated cached data.
Q5: What are TTL and LRU?
A: TTL (Time-To-Live) and LRU (Least Recently Used) are cache eviction strategies.
📝 MCQs
Q1. What is caching?
- Permanent storage
- Temporary data storage for quick access
- Encryption method
- Backup system
Q2. Where is cache commonly stored?
- On disk
- In memory
- In database
- In cloud
Q3. Which is a popular distributed cache?
- SQL Server
- Redis
- MongoDB
- RabbitMQ
Q4. What happens when cached data expires?
- App crashes
- Old data served
- New data is fetched
- Nothing happens
Q5. What is a downside of caching?
- Slower response
- More CPU usage
- Can serve stale data
- Security risk
Q6. What is cache invalidation?
- Adding logs
- Restarting server
- Removing outdated cache entries
- Clearing UI
Q7. Which data structure fits caching?
- Relational tables
- Key-value store
- Graphs
- Queues
Q8. Which .NET interface supports caching?
- IDatabase
- IMemoryCache
- IEnumerable
- IQueryable
Q9. Which policy removes least recently used items?
- FIFO
- LRU
- TTL
- Random
Q10. What does TTL mean in caching?
- Total Table Load
- Time To Live
- Token To Lock
- Temporary Table Length
💡 Bonus Insight
Caching enhances speed but requires balancing between data freshness and quick access through proper eviction and invalidation policies.
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