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Informational 15 Bitcoin

make: use Docker named volumes for ~21x faster local linting

Public commit record

What the developer wrote

Authored by Olaoluwa Osuntokun

73/100 · Adequate
make: use Docker named volumes for ~21x faster local linting

This commit optimizes Docker cache mounting for the linter with a
CI-aware strategy:

**Local development (macOS/Windows)**: Uses Docker named volumes which
keep data inside Docker's native Linux filesystem, avoiding the slow
host-syncing overhead of bind mounts. This yields ~21x faster linting
on warm cache.

**CI (GitHub Actions)**: Uses bind mounts to host paths (`~/.cache/go-build`,
`~/go/pkg/mod`) that GitHub Actions already caches via the setup-go
action. This ensures CI benefits from cached dependencies across runs.

The Makefile detects CI mode via the `CI` environment variable that
GitHub Actions sets automatically.

Local benchmark results:
- Cold run (empty cache): ~2m 28s
- Warm run (cached): ~11s (~21x faster)

Key improvements in warm runs:
- Go packages loading: 1m 58s → 5.6s
- Linters execution: 20.5s → 2.7s
- Total execution: 2m 20s → 8.6s
✓ Specific, descriptive subject✓ Names a concrete action or component✓ Provides detailed explanatory context
The short version

What changed, and why it matters

This commit is a build-system performance improvement, not a security fix. It changes how the project's linter Docker container stores its cache: on local developer machines it switches from slow folder-sharing (bind mounts) to faster internal Docker storage volumes, while in GitHub Actions CI it keeps using host paths so existing caching still works. There is no change to LND's runtime code, network behavior, or cryptographic handling.

Recommended action

No security action needed. Reviewers may optionally confirm that the CI bind-mount paths match the GOCACHE/GOMODCACHE values used in tools/Dockerfile and that the new clean-docker-volumes target is documented for developers.

Security signals we found

No strong security signals were identified.

Risk score

Why this scored 15/100

Our methodology →
Potential impact 0/30
Exploitability 0/25
Stealth signal 0/15
Affected reach 0/15
Confidence 10/10
Evidence quality 5/5
Human-validated context

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