How should a Python service manage redis-py clients and connection pools?

Reuses bounded redis-py pools for the application lifetime and closes async resources deliberately.

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A redis-py client uses a connection pool, so I normally create one configured client per application or event-loop lifetime and reuse it. Creating a client for every request wastes connections and can exhaust Redis under load. I configure connection and socket timeouts, health behavior, and a bounded maximum that fits both the service and Redis capacity. Pipelines and PubSub objects have their own lifecycle and are not shared as general clients.

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Reuses bounded redis-py pools for the application lifetime and closes async resources deliberately.

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Reuses bounded redis-py pools for the application lifetime and closes async resources deliberately.
A redis-py client uses a connection pool, so I normally create one configured client per application or event-loop lifetime and reuse it. Creating a client for every request wastes connections and can exhaust Redis under load. I configure connection and socket timeouts, health behavior, and a bounded maximum that fits both the service and Redis capacity. Pipelines and PubSub objects have their own lifecycle and are not shared as general clients.
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For an async service, I create the async client during application startup and close it during shutdown, including its owned pool. The client must belong to the event loop that uses it; test suites and reloaders can expose accidental cross-loop reuse. Pool exhaustion should fail in a controlled way rather than creating unbounded connections. Retries stay limited because reconnect storms can amplify an outage.

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Creating a Redis client per request
Leaving async pools to garbage collection
Sizing each process without the total deployment

How should a Python service manage redis-py clients and connection pools?

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