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Hectal
Phase 2Intermediate3 of 17 in Redis

Expiration, Eviction & Memory

How keys die: TTLs, lazy and active expiry, maxmemory and eviction policies, and what Redis memory is actually made of.

Redis lives in RAM, so memory is the resource you run out of first. This phase covers the two ways keys leave Redis (they expire, or they're evicted), how each works internally, and how to read, reduce and plan memory.

These topics explain a surprising number of production incidents: sessions that never expire, caches that evict the wrong data, OOM errors on writes, and a server reporting 8 GB of data while using 14 GB of RAM.

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4 topics ~59 min 10 code blocks & diagrams
Start with the first topic
1
2.1

TTL and Expiration: Lazy, Active, and on Replicas

A TTL makes a key disappear after a set time. Redis deletes expired keys lazily (when you touch them) and actively (a background sampler), replicas wait for the primary's DEL, and sliding vs absolute expiry is a design decision you make per use case.

17 min 1 diagram 2 code practice

2
2.2

maxmemory and Eviction Policies

When Redis reaches maxmemory, the eviction policy decides what happens: refuse writes (noeviction), or evict keys chosen by approximated LRU, LFU, TTL or randomness, from all keys or only those with a TTL. Choosing wrong either breaks writes or silently deletes important data.

16 min 1 diagram 1 code practice

3
2.3

Memory Internals: Encodings, Overhead, and Fragmentation

Redis memory is your data plus per-key overhead, encoding choices, allocator fragmentation, buffers and the fork's copy-on-write. Knowing these explains why used_memory and RSS differ, and how to shrink a dataset by 2–10×.

16 min 1 diagram 2 code practice

4
2.4

Memory Optimisation and Capacity Planning

Estimate Redis memory before you build, then shrink it with shorter keys, bucketed hashes, compact encodings, compression and TTLs, and size clusters with headroom for replication, fork and growth.

10 min 2 code practice