redis-connections
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/redis/agent-skills --skill redis-connections- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads redis-connections automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Redis Connections
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.
When to apply
- Creating or reviewing a Redis client setup (redis-py, Jedis, Lettuce, go-redis, NRedisStack).
- Making many small Redis calls and wondering where the latency is going.
- Iterating large keyspaces, sets, hashes, or lists.
- Enabling client-side caching for hot keys.
- Tuning connect / read / write timeouts.
1. Pool or multiplex — never one connection per request
The single biggest mistake in Redis client code is opening a new TCP connection for every operation. Always either:
- Pool — keep N persistent connections that the application leases per call (redis-py
ConnectionPool, JedisJedisPooled, go-redis client). - Multiplex — share a single connection across all requests (Lettuce, NRedisStack).
| 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.
2. Pipeline bulk work
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.
3. Avoid commands that scan everything
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.
4. Client-side caching for hot keys
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.
5. Set explicit timeouts
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.
References
FAQ
What does the redis-connections skill do?
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.
How do I install redis-connections?
Run npx skills add https://github.com/redis/agent-skills --skill redis-connections in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/redis-connections/ (personal) or .claude/skills/redis-connections/ (project).
Which AI agents can use redis-connections?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is redis-connections safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains redis-connections?
It is published by redis in redis/agent-skills, and it is an official skill from the maker of the technology it covers. It has 2,801 recorded installs.
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