redis

# Redis — cache, lock, rate limiter, queue done correctly Redis is four primitives wearing one server: a **cache**, a **distributed lock**, a **rate limiter**, and a **job queue**. Each has a correctness contract that has nothing to do with whether `SET` returns `OK`. You almost certainly already "have Redis working" — a value goes in, a value comes out. What you do not yet have is a cache that won't stampede your database, a lock that won't release someone else's hold, a rate limiter that survives concurrent requests, and a queue whose jobs aren't silently lost when a worker dies. This skill is engine- and pattern-level, **client-SDK-agnostic**: examples are `redis-cli` and Lua, so they hold across node-redis, ioredis, redis-py, go-redis, and Lettuce. Every pattern applies unchanged to Valkey and any Redis-protocol store, since Valkey is a BSD fork of Redis 7.2 and stays protocol-compatible. ## When to use / When NOT **Use when you are:** - Adding or reviewing a **cache**: TTL strategy, cache-aside vs write-through, key naming, stampede/thundering-herd prevention, negative caching, invalidation. - Building a **distributed lock**: "only one worker does X", cron de-duplication, leader-ish mutual exclusion — the `SET NX PX` + Lua-release + fencing-token contract, and the Redlock call. - Building a **rate limiter**: per-user/IP/API-key throttling (fixed window, sliding-window log, sliding-window counter, token bucket) and why naive `INCR`+`EXPIRE` is a race. - Running a **queue / background jobs on Redis**: Lists vs Streams vs a library (BullMQ, Sidekiq, RQ, Celery), at-least-once delivery, acks, stalled-job recovery, idempotency. - Deciding **eviction & persistence**: `maxmemory-policy`, RDB vs AOF, cache mode vs database mode. - Reviewing Redis code for foot-guns: `KEYS *`, unbounded keys, non-atomic read-modify-write, missing TTL, a lock released by the wrong owner. **NOT for (route instead):** - Durable SQL queue (`SELECT … FOR UPDATE SKIP LOCKED`), engine-level SQL caching → [`../postgresdb/SKILL.md`](../postgresdb/SKILL.md). Boundary: if the *durable store* is the queue, that's postgresdb; if *Redis* is the queue, you're here. - Vector similarity / semantic search as the primary job → [`../vector-db/SKILL.md`](../vector-db/SKILL.md). Redis 8 vector sets are only *noted* here. - Analytical / clickstream event store → [`../clickhouse-analytics/SKILL.md`](../clickhouse-analytics/SKILL.md). - System-level capacity / load strategy, and app/CDN/HTTP-layer caching (Cache-Control, ISR, edge) → [`../scaling/SKILL.md`](../scaling/SKILL.md). This skill owns the Redis-specific data contract underneath. - Provisioning a managed Redis (cluster sizing, dashboards) → [`../fly-io/SKILL.md`](../fly-io/SKILL.md) / [`../aws-essentials/SKILL.md`](../aws-essentials/SKILL.md). This skill gives the client-side contract, not console clicks. - Language-runtime async/job concepts unrelated to Redis → [`../nodejs/SKILL.md`](../nodejs/SKILL.md) or your language skill. ## Non-negotiables 1. **Every cache key has a TTL.** No TTL in a cache = a memory leak that eventually evicts or 500s. 2. **Never `KEYS *` in production — use `SCAN`.** `KEYS` is O(N) and blocks the single thread; one bad pattern freezes every client. 3. **Read-modify-write must be atomic.** App-side `GET` then `SET`/`INCR` races under concurrency. Use one Lua `EVAL`, a single atomic command, or `WATCH`/`MULTI`. 4. **A lock = random token + `PX` + Lua compare-and-delete.** Plain `DEL` releases whoever holds it now (maybe not you); a lock with no `PX` is held forever if the owner crashes. 5. **Pick `maxmemory-policy` deliberately.** The default `noeviction` makes a full cache fail every write — correct for a database, fatal for a cache. 6. **A queue must ack and recover.** `RPOP` straight into a worker loses the job if the worker dies mid-task; use a processing list or Streams consumer groups. 7. **Cap unbounded structures.** Lists grow forever without `LTRIM`; streams without `MAXLEN ~`. 8. **One Lua = one short atomic step.** Redis is single-threaded; a long script or huge `MULTI/EXEC` blocks *all* clients, not just yours. 9. **Namespace keys** as `svc:entity:id` (e.g. `cart:user:42`) so scans, eviction, and humans can reason. 10. **Know cache vs database durability before choosing persistence.** Cache = lose-it-and-rebuild; database = RDB/AOF and `noeviction`. Decide which Redis is *before* you tune it. ## Decision table — which primitive, which structure | Need | Structure | Canonical commands | The one gotcha | | --- | --- | --- | --- | | Cache | string / hash + TTL | `SET k v EX 300` / `GETEX` / `HSET`+`EXPIRE` | stampede on expiry | | Lock | string + random token | `SET k tok NX PX 30000` + Lua compare-and-`DEL` | wrong-owner release | | Rate limit | string / zset / hash | one atomic `EVAL` (window/log/bucket) | lost `EXPIRE` → immortal counter | | Queue (simple) | list | `LMOVE src proc LEFT RIGHT` / `BRPOPLPUSH` | job loss on crash without a processing copy | | Queue (reliable) | stream | `XADD` / `XREADGROUP` / `XACK` / `XAUTOCLAIM` | unacked PEL grows unbounded | ## Caching **Namespace + TTL with jitter.** A fixed TTL on a batch of keys written together synchronizes their expiry — they all die in the same second and stampede the origin at once. Add jitter. ```bash # Bad: 1000 keys warmed in a loop with the same TTL all expire together SET product:42 "$json" EX 300 # Good: spread expiry with per-key jitter (300s ± up to 60s) SET product:42 "$json" EX $(( 300 + RANDOM % 60 )) ``` **Cache-aside (read path).** *Why:* it is the default for read-heavy data; the trap is forgetting the TTL and forgetting to cache the miss. ```text # Bad: cache only hits, never the miss → every request for an absent id hits the DB val = GET product:42 if val == nil: val = db.fetch(42); SET product:42 val EX 300 # absent rows still hammer the DB # Good: cache-aside with negative caching val = GET product:42 if val == "__MISS__": return null # cached miss, no DB call if val == nil: val = db.fetch(42) if val == null: SET product:42 "__MISS__" EX 30 # short negative TTL else: SET product:42 val EX 300 ``` **Stampede prevention.** When a hot key expires, N concurrent requests all miss and hammer the origin. Two mitigations: - **Recompute lock** — one request rebuilds, the rest briefly serve stale or wait. *The lock TTL must exceed worst-case origin latency*, or two requests rebuild anyway. ```bash # On miss, try to claim the rebuild; only the winner recomputes SET product:42:lock 1 NX PX 5000 # 5s > slowest DB rebuild # winner: rebuild, SET product:42, then DEL product:42:lock # losers: serve last-known value or retry after a short sleep ``` - **Probabilistic early expiration (XFetch)** — refresh *before* TTL with rising probability so one request renews early while others still hit a warm key. Math + write-through/read-through/ write-behind in [`references/caching.md`](references/caching.md). **Invalidation:** on write, either delete the key (next read rebuilds) or write-through. Never rely on manual invalidation *instead of* a TTL — you will miss a path and serve stale forever. ## Distributed locks ```bash # Bad: GET-then-DEL races — between your GET and DEL the lock can expire and a NEW owner takes it, # and your DEL frees THEIR lock. GET resource:lock # == my token? DEL resource:lock # ← may delete someone else's lock # Good: claim with a random token + PX, release only if the token is still mine (atomic Lua). SET resource:lock "$TOKEN" NX PX 30000 # NX = only if absent; PX = auto-expire so a crash frees it ``` Release **must** be a compare-and-delete in one Lua step: ```lua -- KEYS[1] = lock key, ARGV[1] = my token. Returns 1 if I held and released it, else 0. if redis.call('GET', KEYS[1]) == ARGV[1] then return redis.call('DEL', KEYS[1]) else return 0 end ``` **TTL vs work duration.** If the protected work can outlast `PX`, the lock expires mid-task and a second worker starts. Either size `PX` above the worst case, or run a watchdog that renews the TTL (again via a token-checked Lua `PEXPIRE`) while work continues — never blindly `PEXPIRE`. **Fencing tokens.** A GC pause or a long syscall can freeze the lock holder *past* expiry; another worker acquires, then the paused one wakes and writes — both think they hold the lock. The only fix is a **fencing token**: a monotonically increasing number issued with the lock and checked by the protected resource, which rejects any write carrying a stale token. No Redis lock alone provides this. > **Redlock decision.** Single-instance `SET NX PX` is fine when occasional double-execution is > *tolerable* (idempotent work, best-effort cron de-dup). For *hard* mutual exclusion, a single > Redis lock is not enough and multi-instance Redlock is contested (it assumes bounded clock drift > and no long pauses) — **fence the resource or use a lease/consensus system**. Full Redlock > walk-through and critique in [`references/locks-and-rate-limiting.md`](references/locks-and-rate-limiting.md). ## Rate limiting ```text # Bad: GET/INCR then EXPIRE as two steps. If the process dies (or loses the race) between INCR and # EXPIRE, the key has NO TTL and counts forever — the user is throttled permanently. n = INCR rl:user:42 if n == 1: EXPIRE rl:user:42 60 # ← may never run ``` Fixed window, done atomically in one `EVAL`: ```lua -- KEYS[1] = counter, ARGV[1] = limit, ARGV[2] = window seconds. Returns 1 = allow, 0 = deny. local n = redis.call('INCR', KEYS[1]) if n == 1 then redis.call('EXPIRE', KEYS[1], ARGV[2]) end -- TTL set atomically with the first hit if n > tonumber(ARGV[1]) then return 0 end return 1 ``` Fixed window allows up to 2× the limit across a boundary. For smoother limits: - **Sliding-window log** — a sorted set scored by timestamp, pruned and counted in one Lua: `ZREMRANGEBYSCORE` (drop entries older than the window) → `ZCARD` (count) → `ZADD` (record now) → `EXPIRE`. Exact, but O(requests) memory per key. - **Token bucket** — a hash holding `{tokens, last_refill}`; refill by elapsed time and decrement, all in one Lua. Allows bursts up to bucket size, then a steady rate. Smallest memory. Choose by accuracy-vs-memory; full Lua for all four in [`references/locks-and-rate-limiting.md`](references/locks-and-rate-limiting.md). ## Queues | Option | When | Acks / recovery | | --- | --- | --- | | **List** | simplest at-least-once, low volume | manual: processing list + requeue stalled | | **Stream** | reliable jobs, consumer groups, native acks | `XACK` + `XAUTOCLAIM` for stalled/pending | | **Library** (BullMQ/Sidekiq/RQ/Celery) | you want retries, scheduling, a dashboard | built-in; don't hand-roll | **Reliable list:** never `RPOP` straight into the worker — a crash mid-task loses the job. Move it to a per-worker processing list atomically, then ack by removing it. ```bash job=$(redis-cli LMOVE jobs jobs:proc:w1 LEFT RIGHT) # atomic: pop from jobs, push to processing # ... do work (idempotently) ... redis-cli LREM jobs:proc:w1 1 "$job" # ack: remove from processing # a reaper requeues anything left in jobs:proc:* after a worker dies ``` **Streams (preferred for reliable jobs):** consumer groups give per-message acks and a Pending Entries List for recovery. ```bash redis-cli XGROUP CREATE jobs g1 '$' MKSTREAM redis-cli XREADGROUP GROUP g1 worker1 COUNT 10 BLOCK 5000 STREAMS jobs '>' # ... process ... redis-cli XACK jobs g1 "$id" # ack a done message redis-cli XAUTOCLAIM jobs g1 worker2 60000 0 # reclaim messages idle > 60s (stalled worker) ``` Delivery is **at-least-once**: a message can be reprocessed after a crash, so make handlers idempotent (dedupe on a job id / idempotency key). Reach for **BullMQ** (TypeScript; stalled-job lock renewal via `lockDuration`/`lockRenewTime`), **Sidekiq** (Ruby), or **RQ**/**Celery** (Python) when you want retries, delays, and a dashboard — don't reinvent them. Full implementations, DLQ, and the library comparison in [`references/queues.md`](references/queues.md). ## Eviction & persistence | `maxmemory-policy` | Behavior | Use for | | --- | --- | --- | | `noeviction` (default) | writes fail when full | Redis-as-database (durable data you can't drop) | | `allkeys-lru` / `allkeys-lfu` | evict any key by recency / frequency | a pure cache (every key is disposable) | | `volatile-lru` / `volatile-ttl` / `volatile-lfu` | evict **only keys that have a TTL** | mixed: durable keys without TTL stay | A "cache" running `noeviction` with no TTLs is a memory leak that starts 500ing writes when full — the single most common Redis incident. Set `maxmemory` *and* an `allkeys-*` policy for a cache. **Persistence:** RDB = periodic point-in-time snapshots (fast restart, can lose the last interval); AOF = append every write (durable, slower, larger). A pure cache often needs *neither* — losing it just rebuilds from the origin. Decide cache-mode vs database-mode first, then persistence follows. ## Anti-patterns → STOP | Rationalization | What actually happens | Do instead | | --- | --- | --- | | "`INCR` then `EXPIRE` is fine" | a lost `EXPIRE` leaves a TTL-less counter → immortal throttle | one atomic `EVAL` (set TTL when `n == 1`) | | "I'll `GET` then `DEL` the lock" | races; frees a *different* owner's lock | random token + Lua compare-and-delete | | "`KEYS user:*` to find my keys" | O(N), blocks the single thread, freezes all clients | `SCAN MATCH user:* COUNT 100` | | "No TTL, I'll invalidate manually" | you miss a path → stale forever + unbounded memory | TTL on every cache key, always | | "Redlock means the lock is safe" | a GC pause double-acquires; clock-drift assumptions | fence the resource, or accept double-run on idempotent work | | "`RPOP` the job into the worker" | worker dies mid-task → job vanishes | `LMOVE` to a processing list, or Streams + `XACK` | | "Big `MULTI` / long Lua = throughput" | single thread blocks every client for the duration | keep each atomic step short; chunk the work | | "Fixed-window limiter is exact" | allows ~2× the limit across the window boundary | sliding-window log or token bucket if exactness matters | ## Quick reference ```bash # Cache: SET k v EX 300 | GETEX k EX 300 | DEL k | SCAN 0 MATCH 'product:*' COUNT 100 # Lock: SET k tok NX PX 30000 (release via the Lua compare-and-delete above) # Rate limit: EVAL "<lua>" 1 rl:user:42 100 60 (limit 100 / 60s) # Queue: LMOVE jobs jobs:proc LEFT RIGHT | LREM jobs:proc 1 "$job" # XADD jobs '*' field v | XREADGROUP ... | XACK | XAUTOCLAIM # Diagnostics (read-only): redis-cli INFO memory # used_memory, maxmemory, evicted_keys redis-cli --bigkeys # find the keys eating your memory redis-cli SLOWLOG GET 10 # slowest recent commands redis-cli OBJECT FREQ mykey # access frequency (needs allkeys-lfu) redis-cli MEMORY USAGE mykey # bytes for one key redis-cli SCAN 0 MATCH 'sess:*' COUNT 100 # never KEYS in prod ``` `scripts/verify.sh` scans your project for the high-confidence foot-guns (`KEYS *` in source, lock release without a token compare, `INCR` with no `EXPIRE`, `maxmemory` with no policy). It is read-only, never connects to a server, and exits 0 when no Redis usage is found. ## Project grounding