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Find Why a Python Process Keeps Growing in Memory

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Self-Hosting & Infra intermediate 7 min read $0.01 Updated 2026-10-19

Diagnosing Python memory growth: rss vs allocated objects, tracemalloc snapshots and diffs, objgraph for object counts, unbounded caches and lists, reference cycles, lru_cache pitfalls and worker recycling as a safety net.

A Python service uses more memory every hour. Separate real leaks from fragmentation, find the growing allocation with tracemalloc, and fix caches, global lists, reference cycles and long-lived objects.

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#python#memory#tracemalloc#debugging#performance

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