Cache Strategies in Distributed Systems
Cache Strategies in Distributed Systems: How Big Systems Avoid Traffic Spikes
One of the most critical methods of ensuring fastness and scalability of systems is caching. Rather than making continuous accesses to a database, we store commonly used information on a cache so as to be able to use it at a high rate.
In distributed systems however, caching is not that straightforward as using a TTL (Time To Live).
When caching is poorly applied to your system; it may crash.
Most large streams such as Netflix, Amazon and Flipkart have well-developed caching policies to avoid traffic spikes and overloading.
In this article we will learn:
Why simple TTL caching fails
Advanced cache strategies used in real systems
When to use each strategy
Why Basic TTL Caching Is Not Enough
A simple cache usually works like this:
Application checks cache
If data exists → return it
If cache expired → fetch from database
Store new value in cache with TTL
Example:
Product Price Cache
TTL = 10 minutes
After 10 minutes the cache expires.
Now imagine 1 million users request the same product price at the same moment.
All of them will hit the database.
This causes a massive traffic spike.
This situation is called the Thundering Herd Problem.
The Real Problem: Many Keys Expiring Together
Imagine thousands of cache keys that all expire at the same time.
Example:
product:101 TTL = 600s
product:102 TTL = 600s
product:103 TTL = 600s
product:104 TTL = 600s
At exactly 600 seconds, all of them expire.
Suddenly:
Millions of requests → Database
The database becomes overloaded.
Real-world examples:
A new season released on Netflix
A flash sale on Flipkart
Millions of users watching Indian Premier League matches
If caching is not handled correctly, the system will experience:
CPU spikes
Database overload
High latency
Possible outages
TTL Jitter (Random Expiration)
Instead of giving every cache key the same expiration time, we add randomness.
Example:
Base TTL = 600 seconds
Actual TTL = 600 ± random(0–120)
Now cache keys expire at different times.
Instead of a spike, requests are spread over time.
Benefits:
Prevents synchronized expiration
Reduces traffic spikes
Very easy to implement
Most large distributed systems apply TTL jitter by default.
Mutex / Cache Locking
Another problem occurs when many requests try to rebuild the same cache at once.
Example:
Cache expired for product:101
1,000 users request the same product.
Without protection:
1000 requests → 1000 database queries
Solution: Mutex Lock
How it works:
First request acquires a lock
Only that request recomputes the value
Other requests wait or return stale data
Cache is updated
Lock is released
Now:
1000 requests → 1 database query
This dramatically reduces database load.
Stale-While-Revalidate (SWR)
This strategy is widely used by CDNs and web platforms.
Example behavior:
Cache expires
Instead of blocking users, system serves stale data
In the background the system refreshes the cache
User never experiences delay.
Flow:
User Request
↓
Cache Expired
↓
Return Stale Data
↓
Background Cache Refresh
Benefits:
Very low latency
Smooth user experience
Prevents request spikes
Many CDNs like Cloudflare and Fastly use this strategy.
Probability-Based Early Expiration
Another advanced technique is probabilistic early recomputation.
Instead of waiting until TTL reaches zero, the system sometimes refreshes cache earlier.
Example idea:
TTL = 600 seconds
When TTL gets close to expiry,
some requests randomly trigger refresh.
This ensures that one request refreshes the cache before it expires.
Result:
The cache never fully expires under heavy load.
This technique is used in large scale distributed caches.
Cache Warming / Pre-Warming
Cache warming means preloading cache before traffic arrives.
Example scenarios:
Netflix Release
Before releasing a new show on Netflix, popular content metadata is cached.
E-commerce Sale
Before a Flipkart sale, product pages are cached.
IPL Streaming
Before a Indian Premier League match begins, video metadata and APIs are cached.
Benefits:
Prevents cold cache
Reduces initial database load
Improves latency
Trade-Offs: Freshness vs Latency vs Consistency
Caching always involves trade-offs.
| Strategy | Freshness | Latency | Consistency |
|---|---|---|---|
| TTL Jitter | Good | Good | Medium |
| Mutex Lock | Very Good | Medium | Strong |
| Stale-While-Revalidate | Medium | Very Low | Weak |
| Probabilistic Expiration | Good | Good | Medium |
| Cache Warming | Good | Very Low | Medium |
System designers choose based on business requirements.
When Should You Use Each Strategy?
Use TTL Jitter
When you want to avoid mass cache expiration spikes.
Use Mutex Lock
When cache recomputation is expensive.
Example:
heavy DB queries
complex computations
Use Stale-While-Revalidate
When low latency is more important than perfect freshness.
Example:
news feeds
product pages
recommendation systems
Use Probabilistic Expiration
When system load is extremely high and cache expiry must be smoothly distributed.
Use Cache Warming
Before predictable traffic spikes like:
product launches
sports events
flash sales
Final Thoughts
Caching is not just about speed.
In distributed systems it is also about protecting your database and infrastructure.
Advanced caching strategies help prevent problems like:
Thundering herd
Traffic spikes
Database overload
Modern platforms combine multiple strategies such as:
TTL Jitter
Mutex locking
Stale-While-Revalidate
Cache warming
to keep their systems stable even under massive traffic.