Stop the Thundering Herd! Explore Jitter, Probabilistic Re-computation, SWR, and Cache Warming to keep your high-traffic systems stable during Netflix-scale spikes.

Why Basic TTL caching Fails?

Basic TTL (Time to Live) is like a ticking time bomb. If you set 1 million keys to expire in exactly 3600 seconds, you are essentially scheduling a system crash for 1 hour from now. Imagine a "Flash Sale" on an e-commerce site:

You cache the "Product Price" for 60 seconds.
50,000 people are looking at that price.
At the 60th second, the cache disappears.
All 50,000 users see a "Cache Miss" and hit the Database at the exact same millisecond.

The Roadmap to a Bulletproof Cache:

Step 1: Advanced Expiry (Jitter & PER)

TTL Jitter: The Power of Randomness

Instead of TTL = 60s , we use TTL = 60s + random(0,10s). Now, some user's cache expire at 60s, some at 63s, some at 70s. This turns a "Thundering Herd" into a "Predictable Stream". It's like a staggered release times for a crowded theatre.

Probabilistic Early Re-computation (PER)

This sounds fancy, but the logic is simple: The close we get to the expiration time, the higher the "chance" that a request will decide to refresh the cache early.

At 10 seconds left, there is a 1% chance a request will refresh it.
At 1 second left, there's an 80% chance.

This ensures that one lucky user refreshes the data before it every actually hits zero and disappears

Step 2: The "Wait or Serve Old" Strategies

1. Mutex / Cache Locking (The "Old Producer" Rule)

Imagine the cache for "Current Score" expires, 100 requests arrive.

Without a lock: All 100 go to the DB.
With a Mutex: The request "locks" the key. It says, "I'm, going to the DB to get the new score. Everyone else, stay here and wait".
The other 99 requests, "wait" for a few milliseconds, until the first one returns and updates the cache.

2. Stale-While-Revalidate (SWR)

This is the "Netflix/CDN favourite." Instead of making users wait for the new data, you give them the old (stale) data one last time while you fetch the new data in the background.

Request arrives: "Is the data expired?"
System: "Yes, it expired 2 seconds ago. But here is the old version so you don't have to wait. I'll go grab the new one now for the next person."
Result: Zero latency for the user!

Step 3: Proactive Measures ("The Chef's Preparation")

Cache Warming / Pre-Warming

Think of an IPL final or a Netflix release. You know a million people are going to ask for the "Match Summary" or "Stranger Things Episode 1" at exactly 8:00 PM. Don't wait for them to ask! Your system should "warm up" the cache by fetching that data and putting it in Redis at 7:55 PM. By the time the crowd arrives, the "oven" is already hot.

The Big Tradeoffs: Freshness vs. Latency

In engineering, there is no free lunch.

SWR: Amazing speed (low latency), but some users see "stale" (old) data for a few seconds.
Mutex: Perfect data (high consistency), but some users have to "wait" (higher latency) while the lock is held.

When to use which Strategy ?

<table style="min-width: 100px;"><colgroup><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"></colgroup><tbody><tr><th colspan="1" rowspan="1">Strategy</th><th colspan="1" rowspan="1">Best For</th><th colspan="1" rowspan="1">Real-World Example</th><th colspan="1" rowspan="1">Tradeoff</th></tr><tr><td colspan="1" rowspan="1">TTL Jitter</td><td colspan="1" rowspan="1">General-purpose stability.</td><td colspan="1" rowspan="1">Any standard web app.</td><td colspan="1" rowspan="1">None! Should be your default.</td></tr><tr><td colspan="1" rowspan="1">Mutex / Locking</td><td colspan="1" rowspan="1">High consistency (data MUST be right).</td><td colspan="1" rowspan="1">Stock prices, election results, bank balances.</td><td colspan="1" rowspan="1">Increased latency for some users.</td></tr><tr><td colspan="1" rowspan="1">SWR (Stale-While-Revalidate)</td><td colspan="1" rowspan="1">High availability (speed is everything).</td><td colspan="1" rowspan="1">Social media feeds, Netflix movie lists.</td><td colspan="1" rowspan="1">Users see slightly old data.</td></tr><tr><td colspan="1" rowspan="1">Probabilistic (PER)</td><td colspan="1" rowspan="1">Extremely hot keys that never want to "miss."</td><td colspan="1" rowspan="1">The "Home Page" of a giant site like Amazon.</td><td colspan="1" rowspan="1">Slightly more CPU used to calculate probability.</td></tr><tr><td colspan="1" rowspan="1">Cache Warming</td><td colspan="1" rowspan="1">Known events with massive predictable starts.</td><td colspan="1" rowspan="1">IPL match start, Black Friday, Netflix series drop.</td><td colspan="1" rowspan="1">Requires manual setup or scripts.</td></tr></tbody></table>

Choosing Your Weapon

Use Jitter ALWAYS: It's the "low-hanging fruit." There is almost no reason not to add a little randomness to your expiries to prevent synchronized spikes.
Use SWR for UX: If your app feels "snappy," users stay longer. If a user sees a post from 10 seconds ago instead of 1 second ago, they usually won't notice. This is why CDNs (like Cloudflare) love SWR.
Use Cache Warming for "The Big Event": If you know 10 million people are coming at 8:00 PM, waiting for the first user to "trigger" the cache is a mistake. You want that data waiting for them.

Cache Strategies in Distributed Systems