Redis client and connection guidance covering connection pooling, multiplexing, pipelining, client-side caching with RESP3, avoiding slow commands (KEYS, SMEMBERS, HGETALL), and tuning socket timeouts. Use when configuring a Redis client (redis-py, Jedis, Lettuce, NRedisStack), batching commands for throughput, eliminating per-request connection creation, iterating large keyspaces with SCAN, enabling client-side caching for read-heavy workloads, or setting connect and read timeouts.
Permissions
Just instructions. No commands, network, or file access.
Files
Redis client and connection guidance covering connection pooling, multiplexing, pipelining, client-side caching with RESP3, avoiding slow commands (KEYS, SMEMBERS, HGETALL), and tuning socket timeouts. Use when configuring a Redis client (redis-py, Jedis, Lettuce, NRedisStack), batching commands for throughput, eliminating per-request connection creation, iterating large keyspaces with SCAN, enabling client-side caching for read-heavy workloads, or setting connect and read timeouts.
Client-side guidance for talking to Redis efficiently: how to share connections, how to batch commands, which commands not to call in production, when to turn on client-side caching, and how to set timeouts that fail fast without breaking healthy traffic.
Version history
The single biggest mistake in Redis client code is opening a new TCP connection for every operation. Always either:
ConnectionPool, Jedis JedisPooled, go-redis client).| Style | Used by | Note |
|---|---|---|
| Pool | redis-py, Jedis, go-redis | Each lease blocks if pool exhausted; size the pool to your concurrency |
| Multiplex | Lettuce, NRedisStack | Single connection; cannot carry blocking commands like BLPOP |
# redis-py — connection pool
pool = redis.ConnectionPool(host="localhost", port=6379, max_connections=50)
r = redis.Redis(connection_pool=pool)
See references/pooling.md for Python + Java + Lettuce examples.
For N commands that don't depend on each other's results, send them as a single batch with pipelining. One round-trip instead of N.
pipe = redis.pipeline()
for user_id in user_ids:
pipe.get(f"user:{user_id}")
results = pipe.execute()
Use non-transactional pipelining for performance, and pipeline(transaction=True) only when you actually need atomicity (see redis-core's transactions guidance).
See references/pipelining.md.
Anything that walks the whole keyspace (or a whole large container) blocks the server. Use incremental variants instead.
| Don't | Use |
|---|---|
KEYS pattern | SCAN cursor loop |
SMEMBERS large_set | SSCAN |
HGETALL large_hash | HSCAN |
LRANGE 0 -1 on a huge list | Paginate (LRANGE 0 100) |
cursor = 0
while True:
cursor, keys = redis.scan(cursor, match="user:*", count=100)
for key in keys:
process(key)
if cursor == 0:
break
Blocking commands (BLPOP, BRPOP, BLMOVE) are different — they intentionally wait for data and are fine for queue consumers, but always pass a timeout, and don't issue them on a multiplexed connection (Lettuce, NRedisStack).
See references/blocking.md.
For data that's read often and written rarely (config, feature flags, sessions on every request), enable RESP3 client-side caching. The client keeps a local copy and the server invalidates it on writes — saving the round trip for hot reads.
client = redis.Redis(
host="localhost",
port=6379,
protocol=3, # RESP3 is required
cache_config=redis.CacheConfig(max_size=1000),
)
Skip it for write-heavy workloads or data that changes constantly — the invalidation traffic overruns the savings.
See references/client-cache.md.
Defaults vary by client and may be too generous. Pick values that match the application's failure model:
r = redis.Redis(
host="localhost",
socket_connect_timeout=2.0, # fail fast on dead nodes
socket_timeout=5.0, # tune to expected operation time
retry_on_timeout=True,
)
Rule of thumb: connect timeout shorter than read/write timeout. Tight timeouts + retry-on-timeout for latency-sensitive paths; longer timeouts for batch jobs.
See references/timeouts.md.
In these kits
Works with
Claude, Codex, Cursor & more