How do worker processes, copy-on-write, and preloading affect Python service memory?

Explains per-worker memory and copy-on-write savings without preloading process-unsafe resources.

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Each Python web worker is a separate process with its own heap, so worker count can multiply application memory, caches, and connection pools. On systems that fork, preloading the application before workers are created can let unchanged memory pages be shared through copy-on-write. Once a worker modifies a page, it gets a private copy. The apparent saving depends on the workload and operating system; it is not shared mutable Python state.

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Explains per-worker memory and copy-on-write savings without preloading process-unsafe resources.

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Explains per-worker memory and copy-on-write savings without preloading process-unsafe resources.
Each Python web worker is a separate process with its own heap, so worker count can multiply application memory, caches, and connection pools. On systems that fork, preloading the application before workers are created can let unchanged memory pages be shared through copy-on-write. Once a worker modifies a page, it gets a private copy. The apparent saving depends on the workload and operating system; it is not shared mutable Python state.
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I preload only fork-safe initialization. Open database connections, network clients, locks, threads, event loops, random state, and telemetry exporters may not remain valid after a fork and should usually be created in the worker lifecycle. Large read-mostly model data may benefit, but later mutation can erase the saving. Spawn-based worker models do not inherit memory this way, so the deployment process model matters.

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Adding worker RSS values as if every page were private
Preloading open connections or threads
Assuming copy-on-write shares later mutations

How do worker processes, copy-on-write, and preloading affect Python service memory?

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