Implementing and Overcoming Challenges in Efficient eBPF File Path Caching
Problem
Frequent parsing of file paths in eBPF programs incurs significant CPU overhead; caching can reduce CPU usage by about 90%, but it introduces extra memory consumption, requires handling cache invalidation, and must support diverse filesystem semantics such as hard links, directory renames, and bind mounts.
Pain points
- Extra memory consumption caused by caching
- Cache invalidation handling when permissions change, directories move, hard links are created or deleted
- Cache size limits and potential OOM risks
- Compatibility with multiple path representations, such as bind mounts
Questions to verify
- What are typical cache hit rates and their corresponding memory footprints?
- What is the optimal cache invalidation strategy for production environments?
- How should cache size and eviction thresholds be tuned for different workloads?
Evidence
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Memoization trades compute time for memory: you reduce CPU cost by 90% _at the cost_ of some memory. The author measured one, but it doesn't look like they measured the other. It's an important detail to take into account.
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How is the cache invalidated when: - The permissions change? - Directories are moved? - Hard-links are added? - Things are deleted? Also: Is the cache limited in size?
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How do your path-only rules handle the many approaches for loading a file but making it appear to have a different path, such as bind mounts for one example? Simple memoization yields massive wins. Had similar experience with a hot loop, sometimes the basics are just magic.
Disclaimer: this card is distilled by AI from community discussions as “pain point evidence”. It is a starting point for need analysis and validation, not a “validated market need”. AI has analytical limits; please open the original discussion above to manually verify before key decisions.