# Cache Avalanche and Cache Penetration — Caching Strategies & CDN Design

Source: https://www.skillbyai.com/en/caching-strategies/x-avalanche

> Protect the origin from mass expiries and lookups for missing data.

## Two more ways to overwhelm the database

A **cache avalanche** happens when a large share of keys become unavailable at once: many keys created together (after a deploy or a bulk warm-up) expire together, or the whole cache node restarts empty. Suddenly the database receives the full read load. Defences: **TTL jitter** so expiries spread out, **staggered warm-up**, cache **replication and persistence** so a node failure does not empty the cache, and **circuit breakers or rate limits** in front of the database so overload degrades service rather than causing collapse. **Cache penetration** happens when requests ask for keys that **do not exist** in the database, for example random IDs from a bot or a bug; every request misses and hits the database. Defences: **negative caching** with short TTLs, **input validation** (reject malformed IDs early), and a **Bloom filter** of existing keys that answers "definitely not present" in memory, with a small false-positive rate.

## A Bloom filter guard

Requests for IDs that certainly do not exist never reach the cache or database.

```python
from pybloom_live import ScalableBloomFilter   # any Bloom filter library works

known_ids = ScalableBloomFilter(error_rate=0.001)
for product_id in db.all_product_ids():           # rebuild periodically, add on create
    known_ids.add(product_id)

def get_product_guarded(product_id):
    if product_id not in known_ids:
        return None          # definitely absent: no cache or DB lookup
    return get_product(product_id)   # may still be absent (false positive), handled normally
```

## A guard with a guest list

A doorman with a guest list turns away strangers instantly instead of sending each one inside to check every room. Occasionally the list has a similar name and someone gets in to check, but most pointless trips are avoided.

**Quiz:** Bots request millions of random, non-existent product IDs, and each one hits the database. What is this called and how can it be mitigated?

- [ ] Cache avalanche; add more replicas
- [x] Cache penetration; use negative caching, validation and a Bloom filter
- [ ] Cache stampede; increase TTL
- [ ] Write-behind; flush faster

*Answer:* Cache penetration; use negative caching, validation and a Bloom filter. Lookups for missing keys bypass the cache; negative caching and Bloom filters stop them early.