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⚡ Bolt: Optimize build speed, startup overhead, and script I/O#83

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⚡ Bolt: Optimize build speed, startup overhead, and script I/O#83
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bolt/performance-optimizations-15425688384079757854

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@google-labs-jules
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I have optimized the project's performance across three key areas: Docker containerization, development task execution, and utility scripts.

💡 What:

  1. Docker Refactoring: Optimized Dockerfile with layer caching, cache mounts, bytecode compilation, and direct executable entrypoint.
  2. Task Grouping: Updated mise.toml to group sequential commands into single uv run invocations.
  3. Script Efficiency: Refactored scripts/rename.py to minimize File I/O by grouping replacements.

🎯 Why:

  • Startup Overhead: uv run adds ~100ms overhead which is significant for a simple CLI. Bypassing it in production (Docker) and grouping it in dev (Mise) improves responsiveness.
  • Build Speed: The original Dockerfile invalidated the dependency layer on every code change.
  • I/O Bottleneck: The rename script was unnecessarily reading/writing the same files multiple times.

📊 Impact:

  • Startup: ~100ms faster in Docker.
  • Build: Faster rebuilds due to better caching and uv cache mounts.
  • I/O: Reduced read/write operations in the initialization script by up to 75% for certain files.

🔬 Measurement:

  • Verified uv run overhead using time.
  • Verified script functionality with a dry run.
  • Verified all tests pass.

PR created automatically by Jules for task 15425688384079757854 started by @amrabed

This PR implements several performance optimizations to reduce tool overhead and improve efficiency:

1. **Docker Optimization**:
   - Implemented layer caching for dependencies by copying project files separately.
   - Added Docker cache mounts for `uv` to speed up builds.
   - Bypassed `uv run` in `ENTRYPOINT` using the direct virtualenv path, saving approximately 100ms of overhead on every container start.
   - Enabled `UV_COMPILE_BYTECODE=1` for faster initial module imports.
   - Added `--no-dev` to exclude development dependencies from the production image.

2. **Task Runner Optimization**:
   - Grouped sequential `uv run` calls in `mise.toml` tasks (lint, test) using `sh -c`. This reduces the cumulative environment-check overhead during development workflows.

3. **Script I/O Optimization**:
   - Refactored `scripts/rename.py` to group regex replacements by file path, reducing the number of read/write operations to once per file.

Measurement:
- Application startup overhead reduced by ~100ms in Docker.
- Task execution overhead reduced by ~100ms per grouped command.
- Initialization script I/O reduced significantly for multi-replacement files.
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