# Hot partitions and hot keys

> When one key gets most of the traffic: detection, key salting, splitting and caching.

Concept · Replication & partitioning · https://levelcode.org/concepts/hot-partitions

Partitioning spreads load only if load is spread across keys. When one key — a celebrity's profile, a viral post, a flash-sale product, one enormous tenant — gets a large share of the traffic, its partition (or cache node) saturates while others idle. Adding machines doesn't help, because every request for that key goes to the same place.

## Detecting them

- Track request rate and latency **per partition**, and alert when one is far above the median.
- Sample keys and count the most frequent ones (a [count-min sketch](https://levelcode.org/concepts/count-min-sketch) finds heavy hitters in fixed memory).
- Watch for throttling errors concentrated on one partition (DynamoDB reports these explicitly).

## Fixes for hot reads

- **Cache** the hot key, including a short-lived in-process cache on each server — even one second of local caching turns 100,000 reads per second into one per server per second.
- **Replicate** the key under several names (`item:42#0` … `item:42#7`) and read one at random.
- **Serve from a CDN** when the response is public.

## Fixes for hot writes

- **Salt the key:** write to `counter:post:42#n` with a random `n` from 0–9, spreading writes over ten partitions; reads sum all ten. Works well for counters and append-only data, poorly for read-modify-write.
- **Buffer and batch:** aggregate increments in memory or a write-behind cache and flush periodically ([caching patterns](https://levelcode.org/concepts/caching-patterns)).
- **Split the entity:** partition a huge conversation or tenant by a second key, such as `(conversation_id, month)`.
- **Isolate big tenants** on dedicated partitions or clusters.

## Prevent them with key design

- Avoid keys whose values are naturally skewed (country, status, "today's date").
- Avoid monotonically increasing keys in range-partitioned stores — every write hits the newest range ([range vs hash partitioning](https://levelcode.org/concepts/sharding)).

The news-feed lesson's "celebrity problem" is the same issue at the application level, solved by pulling instead of pushing for the hottest accounts. See also [cache stampedes](https://levelcode.org/concepts/cache-stampede).