The physics of Docker build caching

Docker build performance depends on three distinct caches—layer caches, persistent mount caches for package managers and compilers, and the Docker image store—while changes trigger an invalidation wave that reruns affected layers and those after them. The discussion argues that teams should optimize for common source and dependency changes rather than only cold or unchanged rebuilds, particularly by ordering Dockerfiles so stable, expensive dependency-install steps precede frequently changing source files. Benchmark results across several languages indicate that mount caches can be crucial for incremental compiled builds but are generally not preserved by portable layer-cache exporters such as GitHub Actions or registry caches, whose import and export overhead can sometimes make changed builds slower than no caching. Persistent builder state can retain both layers and mounts between jobs, though it must be scoped by shared image lineage to avoid eviction among unrelated images or duplicated shared bases in monorepos. Cache size is driven chiefly by distinct dependency versions, with cleanup behavior varying by toolchain, while separate organization-wide image-store caching can reduce repeated pulls of common large images and improve fan-out workloads after an image has entered the shared store. The central recommendations are to match caching mechanisms to where reuse occurs, weigh transfer costs against cache hits, share state only among builds with common layers, and avoid unnecessary image serialization during distribution.

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