At the Python Language Summit 2026, core developer Mark Shannon presented a proposal for a hybrid garbage collection system that combines generational and incremental collection strategies. The talk followed the reversion of Python 3.14's experimental incremental collector back to Python 3.13's generational collector after production environments reported significant memory pressure.
Balancing Pause Times and Memory Footprint
Python relies primarily on reference counting for memory management, utilizing its cycle-detecting garbage collector as a backup that accounts for approximately 11.67% of execution time. While the generational collector from Python 3.13 maintains lower overall memory usage, it can suffer from peak garbage collection pause times of around three seconds. The incremental collector introduced for Python 3.14 successfully reduced maximum pause times to tens of milliseconds, but its non-generational design caused increased memory consumption across large heaps.
Shannon reported that both existing collection approaches exhibit low scavenge effectiveness—the ratio of objects collected to objects visited—with the generational collector sitting at 0.3% and the incremental collector at roughly 1%.
Proposed Hybrid Collector Strategy
To resolve the trade-off between pause duration and memory overhead, Shannon outlined a unified architecture featuring two generations: a 20MB young generation and an old generation. The proposal alternates incremental scavenges between the two tiers and scavenges the old generation at twice the survivor rate of the young generation.
Developer Concerns Around Tuning and Maintenance
The summit discussion highlighted divergent views on garbage collector configuration and maintainability. Core developers Donghee Na and Pablo Galindo Salgado raised questions about public configuration APIs and runtime tuning to prevent upgrade blockers and manage edge cases. In contrast, Shannon and Gregory P. Smith expressed caution regarding Java-style configuration options or supporting multiple collector implementations, noting that public tuning APIs can limit future internal runtime optimizations and increase maintenance overhead.
Addressing queries regarding potential concurrent or lock-free garbage collectors, Shannon noted that C extensions present structural challenges for lock-less designs because they can access memory directly without runtime tracking.
AI-generated editorial illustration; not a documentary photograph.
Source: Garbage Collection: Generational? Incremental? Both! (Python Language Summit 2026)
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