Optimizing Python Memory Usage and Garbage Collection // NodeFlux Docs

Reduce Python RAM footprints on shared host containers by optimizing data structures and garbage collection. ## Steps 1. Use __slots__ in custom class definitions to eliminate per-instance __dict__ memory overhead. 2. Pr NodeFlux documentation for free Minecraft, Node.js and application hosting.

Reduce Python RAM footprints on shared host containers by optimizing data structures and garbage collection. ## Steps 1. Use __slots__ in custom class definitions to eliminate per-instance __dict__ memory overhead. 2. Pr

Reduce Python RAM footprints on shared host containers by optimizing data structures and garbage collection. ## Steps 1. Use __slots__ in custom class definitions to eliminate per-instance __dict__ memory overhead. 2. Process large datasets using Python generators (yield) instead of loading full lists into RAM. 3. Call gc.collect() explicitly after completing large temporary memory-intensive operations. 4. Avoid keeping large global dictionaries or un-bounded object caches in memory. 5. Monitor resident set size (RSS) memory using psutil library during load testing. ## Notes - Using __slots__ on classes instantiated thousands of times can reduce object memory consumption by up to 60%. - Generators evaluate items lazily on demand, keeping memory footprints constant regardless of dataset size.