Memory, Big Keys, and Hot Keys

What you'll walk away with

Explain the core ideas behind Memory, Big Keys, and Hot Keys
Run the sample Redis command or code and verify its output
Apply the technique correctly to the Tutorial Platform and production scenarios

Build the mental model

Redis capacity includes object overhead, allocator fragmentation, and replication or persistence buffers beyond raw payload bytes. Big keys make commands, deletion, and network transfer expensive. Hot keys concentrate traffic on one key and can overload a single shard.

Connect it to a real scenario

Build a baseline with INFO memory, MEMORY STATS, and sampled MEMORY USAGE. Run redis-cli --bigkeys with production-safe scan settings and use hot-key sampling with LFU policies. Split large collections by time or smaller keys and consider asynchronous deletion.

Budget payload, overhead, and buffers
Big keys differ from hot keys
Observe before resizing or sharding

Try the working example

shell

INFO memory
MEMORY STATS
MEMORY USAGE tutorial:42 SAMPLES 5
OBJECT ENCODING tutorial:42
UNLINK obsolete:large:key

# redis-cli --scan --pattern 'tutorial:*'
# redis-cli --bigkeys

You should see

You get a memory baseline and a list of oversized-key candidates.

5-minute try-it

Estimate capacity for five million 2 KB keys with 30% overhead and 20% headroom.

One important caution

Do not aggressively scan the full production keyspace or run KEYS *; use sampling and off-peak scans.

Redis — Memory Optimization — Redis

Easy traps

Do not aggressively scan the full production keyspace or run KEYS *; use sampling and off-peak scans.
Validate sample commands on a local or test instance with recoverable data before applying them to production Redis.

Exercise

Estimate capacity for five million 2 KB keys with 30% overhead and 20% headroom.

You'll know it worked when: You get a memory baseline and a list of oversized-key candidates.