eBPF security agent reduces kernel CPU cost by 90% using inode-based memoization cache

The optimization targets a common bottleneck in eBPF-based security agents: repeatedly resolving file paths to enforce policies. By caching the policy result for each inode, the agent avoids walking the dentry tree for every file access. This is especially impactful for workloads like databases, where the same files or directories are accessed in tight loops. The cache key combines the inode number with mount and namespace IDs to ensure correctness across containerized or multi-tenant environments.

The performance improvement is dramatic but comes with trade-offs. The cache reduces kernel CPU cycles by 90% in benchmarks, but it introduces memory overhead for the LRU hash map. The 10,000-entry limit is a practical compromise, but engineers must validate whether this size is sufficient for their workloads. The cache also adds complexity to the eBPF code, requiring careful handling of edge cases like mount namespace changes or inode reuse.

The approach is narrowly scoped to path-based policies, which may not cover all security use cases. For example, policies based on file attributes or process context would require different caching strategies. The memoization technique is also less effective for workloads with low file access locality, where cache misses dominate. Engineers adopting this pattern should profile their specific workloads to confirm the cache hit rate justifies the memory cost.

The open-source release of the agent provides a reference implementation for the caching logic, but the solution is not a drop-in fix. Integrating it into existing eBPF tools would require adapting the cache key structure and policy bitmasking to match the tool’s specific requirements. The blog post’s focus on Postgres as a benchmark case suggests the optimization is most valuable for database-like workloads, but other high-throughput systems could see similar gains.