Skip to content

build-performance-and-resource-utilization.md

Latest commit

 

History

History
52 lines (38 loc) · 3.02 KB

build-performance-and-resource-utilization.md

File metadata and controls

52 lines (38 loc) · 3.02 KB

Build Performance & Resource Utilization Report

This report analyzes the WineBot build process, identifies resource-heavy stages, and proposes strategies for optimization.

1. Build Performance Metrics (Baseline)

Based on a clean --no-cache build of the intent-test target (Total Time: ~650s):

Stage Task Time (s) Resource Impact
base-system OS, Wine, X11, Python Install ~180s High CPU/Network
prefix-warmer Wine Registry & Theme Init ~160s High CPU (Xvfb/Wine)
exporting Image Layer Compression ~130s High Disk I/O
tools-builder Windows Binaries Download ~50s Moderate Network
base-logic Application Code Copy ~15s Low

Analysis of High-Resource Tasks

  1. base-system (Apt): Installing Wine 10.0 and its multi-arch (i386) dependencies is the single largest consumer of network and time.
  2. prefix-warmer: Running a full Xvfb session during build to initialize the Wine Registry is computationally expensive and serializes the build process.
  3. exporting: Due to the large size of the pre-warmed prefix (~1.4GB), Docker spends significant time compressing and writing layers.

2. Strategies for Optimization

Strategy A: True Base Image Split (Recommended)

Recommendation: Decouple the "System" from the "Application."

  • Action: Move everything in base-system and prefix-warmer into a separate repository or a dedicated base.Dockerfile.
  • Benefit: Reduces local build time from 10 minutes to under 60 seconds for 95% of developer iterations (code changes).
  • Tradeoff: Infrastructure management overhead (maintaining two images).

Strategy B: Parallelize Tool Downloads

Recommendation: Use multi-stage builds more effectively for tool fetching.

  • Action: Current tools-builder downloads binaries sequentially. Refactor to use separate stages per tool, allowing Docker to fetch AutoIt, AHK, and Python in parallel.
  • Benefit: Saves ~30s on cold builds.

Strategy C: Optimize Layer Caching

Recommendation: Reorder layers to minimize cache invalidation.

  • Action: Move the VERSION file copy to the very end of the base-logic stage. Currently, every version bump invalidates the code copy and permission fixing layers.
  • Benefit: Prevents redundant chown -R runs (which take ~10s).

3. Recommended Actions

Item Action Priority Options
1. Registry Split Move Wine/System layers to permanent base image. Critical Ignore/Implement/Defer
2. Cache Reorder Move VERSION and Dockerfile copies to final layers. High Ignore/Implement/Defer
3. Parallel Fetch Refactor download_tools.sh into parallel Docker stages. Medium Ignore/Implement/Defer

How would you like to proceed? I recommend implementing Item 2 immediately as it is a pure win with zero tradeoffs. For Item 1, I can prepare the base.Dockerfile if you want to move toward a multi-repo/image structure.