pyfagorass Redis Clustering
Guidance for designing keys and routing reads in a sharded Redis Cluster (and in standalone primary/replica replication). Covers the two failure modes that bite most new cluster users: CROSSSLOT errors on multi-key operations, and overloading primaries with read traffic.
When to apply
- Designing keys for a Redis Cluster deployment.
- Debugging a
CROSSSLOTerror onMGET,SDIFF, transactions, or pipelines. - Implementing transactions / Lua scripts that touch multiple keys.
- Scaling out read traffic without adding shards.
1. Hash tags for multi-key operations
Redis Cluster distributes keys across 16,384 slots by hashing the key name. Any command that touches multiple keys (MGET, SDIFF, SUNIONSTORE, transactions, pipelines, Lua scripts with multiple KEYS[]) requires all keys to live on the same slot — otherwise the server returns a CROSSSLOT error.
Hash tags force this: the part between { and } is the only thing hashed for slot assignment, so two keys sharing a hash tag always land together.
# Same slot — multi-key ops work
redis.set("{user:1001}:profile", "...")
redis.set("{user:1001}:settings", "...")
redis.lmove("{user:1001}:pending", "{user:1001}:processed", "LEFT", "RIGHT")
# Different keys, no hash tag — CROSSSLOT on multi-key commands in cluster mode
redis.set("user:1001:profile", "...")
redis.set("user:1001:settings", "...")
pipe = redis.pipeline()
pipe.get("user:1001:profile")
pipe.get("user:1001:settings")
pipe.execute() # CROSSSLOT error in cluster
Rules of thumb:
- Use a tag scoped to the meaningful entity, e.g.
{user:1001}. Avoid bare{1001}— unrelated namespaces (purchase:{1001},employee:{1001}) would all collide on the same slot. - Only tag where you actually need multi-key ops. Tagging everything creates hotspots and defeats the point of sharding.
- A single-key command on a hash-tagged key works fine, so adding tags later is incremental — but renaming keys in production is painful, so plan tagging up front for entities you'll group.
See references/hash-tags.md.
2. Read replicas for read-heavy workloads
If reads dominate writes, route them to replicas to free primary capacity. Works both in Redis Cluster (each shard has 1+ replica) and in standalone primary/replica replication.
# Redis Cluster: enable replica reads on the client
from redis.cluster import RedisCluster
rc = RedisCluster(host="localhost", port=6379, read_from_replicas=True)
rc.set("key", "value") # → primary
value = rc.get("key") # → may be served by a replica
For non-cluster setups, point two clients at the right nodes:
primary = Redis(host="primary-host", port=6379)
replica = Redis(host="replica-host", port=6379)
primary.set("key", "value")
value = replica.get("key")
The trade-off is consistency: replicas are eventually consistent. Don't read your own writes from a replica; don't use replica reads for anything that requires strict freshness (financial balances, idempotency state). Good fits: cache layers, analytics, dashboards, recommendation feeds.
See references/read-replicas.md.