🎄 Advent of Code 2024 - Rust Learning Journey

A portfolio project showcasing systematic learning of Rust, algorithm optimization, and AI-assisted development

This repository documents my approach to mastering Rust through Advent of Code challenges, emphasizing performance optimization, memory efficiency, and modern development practices with AI assistance.

🎯 Learning Objectives

• Rust Mastery: Deep dive into Rust's ownership model, type system, and zero-cost abstractions
• Algorithm Optimization: Focus on computational efficiency and performance benchmarking
• Memory Management: Explore heap allocation patterns and memory optimization techniques
• AI-Enhanced Learning: Use AI as a learning partner for code review, refactoring guidance, and optimization insights after independent problem solving

🚀 Performance Benchmarks

All solutions are optimized for performance with comprehensive benchmarking. Execution times measured on optimized builds:

Benchmarks

Day	Part 1	Part 2
Day 1	4.1µs	40.9µs
Day 2	407.6µs	492.8µs
Day 3	258.7µs	243.7µs
Day 4	72.4µs	48.6µs
Day 5	232.7µs	347.8µs
Day 6	49.1µs	50.5ms
Day 7	1.1ms	24.0ms
Day 8	15.5µs	33.9µs
Day 9	259.0µs	4.6ms
Day 10	304.7µs	29.6µs
Day 11	104.6µs	6.9ms
Day 12	4.5ms	8.2ms
Day 13	37.2µs	36.9µs
Day 14	21.4µs	571.4ms

Total: 674.24ms

🧠 Development Approach

Independent Problem Solving First

• Self-Reliant Analysis: Initially solve each problem independently, focusing on understanding constraints and developing algorithmic solutions
• Original Implementation: Write first-pass solutions using my own reasoning and Rust knowledge
• Personal Learning: Build problem-solving intuition and strengthen core programming skills through independent work

AI-Enhanced Learning & Optimization

• Code Review & Learning: Use AI to analyze my solutions and learn about alternative approaches or Rust idioms I might have missed
• Refactoring Guidance: Leverage AI for suggesting optimizations while maintaining code readability and safety
• Performance Analysis: Apply AI to interpret DHAT profiling results and understand memory allocation patterns
• Knowledge Expansion: Use AI as a learning partner to deepen understanding of algorithms and Rust-specific optimizations

Performance-First Methodology

1. Benchmark-Driven Development: Every solution includes comprehensive performance measurements
2. Memory Profiling: Regular heap allocation analysis using DHAT integration
3. Algorithmic Optimization: Focus on optimal time/space complexity for each problem
4. Iterative Refinement: Multiple optimization passes guided by profiling data

Rust Learning Focus Areas

• Zero-Cost Abstractions: Leveraging Rust's performance guarantees
• Memory Safety: Exploring ownership, borrowing, and lifetime management
• Type System: Utilizing advanced type features for compile-time guarantees

Iterative Development Process

• Version History Documentation: Most solution's evolution is preserved in git history, showing the journey from initial implementation to optimized final version
• Learning Trail: Commit history demonstrates iterative improvements, refactoring decisions, and optimization discoveries
• Transparent Development: Full development process visible through file history - from first working solution through performance optimizations and code quality improvements

🛠️ Technical Implementation

Code Architecture

• Modular Design: Each day's solution is a standalone binary with dedicated input/example files
• Test-Driven Development: Comprehensive unit tests against example inputs for all solutions
• Performance Monitoring: Integrated benchmarking with automatic README updates
• Memory Profiling: DHAT integration for heap allocation analysis

Rust-Specific Techniques

• Iterator Chains: Extensive use of functional programming patterns for clean, efficient code
• Pattern Matching: Leveraging Rust's powerful pattern matching for elegant problem decomposition
• Type Safety: Using strong typing to prevent runtime errors and encode invariants
• Zero-Copy Operations: Minimizing allocations through strategic use of slices and references

Development Tools & Workflow

• Cargo Integration: Custom commands for scaffolding, testing, and benchmarking
• CI/CD: Automated testing, linting, and performance tracking
• Profiling: Integration with DHAT for detailed memory analysis
• Version Control: Systematic commit patterns documenting optimization journey with full development history preserved for learning reference

---

📚 Getting Started

Quick Start

# Clone and setup
git clone <repository-url>
cd advent-of-code-rust

# Run a specific day's solution
cargo solve 01

# Benchmark performance
cargo time 01 --store

# Run all tests
cargo test

# View development history for any solution
git log --oneline src/bin/01.rs

Development Environment Setup

Prerequisites:

1. Install the Rust toolchain
2. (Recommended) Install rust-analyzer for your editor
3. (Optional) Install CodeLLDB for debugging

This project supports all major platforms (macOS, Linux, Windows) and includes comprehensive tooling for performance analysis and development workflow optimization.

---

🌟 Portfolio Highlights

Key Achievements

• Performance Optimization: Solutions consistently achieve microsecond-level execution times
• Memory Efficiency: Zero-allocation solutions where possible, with detailed profiling of necessary allocations
• Code Quality: Comprehensive test coverage, documentation, and adherence to Rust best practices
• AI-Enhanced Learning: Strategic use of AI for post-solution analysis, refactoring guidance, and learning acceleration

Skills Demonstrated

• Systems Programming: Low-level optimization and memory management in Rust
• Algorithm Design: Implementation of efficient algorithms for complex computational problems
• Performance Engineering: Systematic benchmarking and optimization methodology
• Modern Development: Strategic AI integration for learning, automated testing, and continuous performance monitoring

Learning Outcomes

This project demonstrates my commitment to:

• Continuous Learning: Systematic skill development through challenging programming exercises
• Quality Engineering: Focus on performance, safety, and maintainability
• Tool Mastery: Effective use of modern development tools and strategic AI collaboration for learning
• Documentation: Clear communication of technical concepts and methodologies

---

📖 Development Guide

The following sections provide detailed documentation for using this template and development environment.

Usage

➡️ Scaffold a day

# example: `cargo scaffold 1`
cargo scaffold <day>

# output:
# Created module file "src/bin/01.rs"
# Created empty input file "data/inputs/01.txt"
# Created empty example file "data/examples/01.txt"
# ---
# 🎄 Type `cargo solve 01` to run your solution.

Individual solutions live in the ./src/bin/ directory as separate binaries. Inputs and examples live in the the ./data directory.

Every solution has tests referencing its example file in ./data/examples. Use these tests to develop and debug your solutions against the example input. In VS Code, rust-analyzer will display buttons for running / debugging these unit tests above the unit test blocks.

Tip

If a day has multiple example inputs, you can use the read_file_part() helper in your tests instead of read_file(). If this e.g. applies to day 1, you can create a second example file 01-2.txt and invoke the helper like let result = part_two(&advent_of_code::template::read_file_part("examples", DAY, 2));. This supports an arbitrary number of example files.

➡️ Download input for a day

Important

This requires installing the aoc-cli crate.

You can automatically download puzzle input and description by either appending the --download flag to scaffold (e.g. cargo scaffold 4 --download) or with the separate download command:

# example: `cargo download 1`
cargo download <day>

# output:
# [INFO  aoc] 🎄 aoc-cli - Advent of Code command-line tool
# [INFO  aoc_client] 🎅 Saved puzzle to 'data/puzzles/01.md'
# [INFO  aoc_client] 🎅 Saved input to 'data/inputs/01.txt'
# ---
# 🎄 Successfully wrote input to "data/inputs/01.txt".
# 🎄 Successfully wrote puzzle to "data/puzzles/01.md".

➡️ Run solutions for a day

# example: `cargo solve 01`
cargo solve <day>

# output:
#     Finished dev [unoptimized + debuginfo] target(s) in 0.13s
#     Running `target/debug/01`
# Part 1: 42 (166.0ns)
# Part 2: 42 (41.0ns)

The solve command runs your solution against real puzzle inputs. To run an optimized build of your code, append the --release flag as with any other rust program.

Submitting solutions

Important

This requires installing the aoc-cli crate.

Append the --submit <part> option to the solve command to submit your solution for checking.

➡️ Run all solutions

cargo all

# output:
#     Running `target/release/advent_of_code`
# ----------
# | Day 01 |
# ----------
# Part 1: 42 (19.0ns)
# Part 2: 42 (19.0ns)
# <...other days...>
# Total: 0.20ms

This runs all solutions sequentially and prints output to the command-line. Same as for the solve command, the --release flag runs an optimized build.

➡️ Benchmark your solutions

# example: `cargo time 8 --store`
cargo time <day> [--all] [--store]

# output:
# Day 08
# ------
# Part 1: 1 (39.0ns @ 10000 samples)
# Part 2: 2 (39.0ns @ 10000 samples)
#
# Total (Run): 0.00ms
#
# Stored updated benchmarks.

The cargo time command allows you to benchmark your code and store timings in the readme. When benching, the runner will run your code between 10 and 10.000 times, depending on execution time of first execution, and print the average execution time.

cargo time has three modes of execution:

1. cargo time without arguments incrementally benches solutions that do not have been stored in the readme yet and skips the rest.
2. cargo time <day> benches a single solution.
3. cargo time --all benches all solutions.

By default, cargo time does not write to the readme. In order to do so, append the --store flag: cargo time --store.

Please note that these are not scientific benchmarks, understand them as a fun approximation. 😉 Timings, especially in the microseconds range, might change a bit between invocations.

➡️ Run all tests

cargo test

To run tests for a specific day, append --bin <day>, e.g. cargo test --bin 01. You can further scope it down to a specific part, e.g. cargo test --bin 01 part_one.

➡️ Read puzzle description

Important

This command requires installing the aoc-cli crate.

# example: `cargo read 1`
cargo read <day>

# output:
# Loaded session cookie from "/Users/<snip>/.adventofcode.session".
# Fetching puzzle for day 1, 2022...
# ...the input...

➡️ Scaffold, download & read the current aoc day

Important

This command requires installing the aoc-cli crate.

During december, the today shorthand command can be used to:

• scaffold a solution for the current day
• download its input
• and read the puzzle

in one go.

# example: `cargo today` on December 1st
cargo today

# output:
# Created module file "src/bin/01.rs"
# Created empty input file "data/inputs/01.txt"
# Created empty example file "data/examples/01.txt"
# ---
# 🎄 Type `cargo solve 01` to run your solution.
# [INFO  aoc] 🎄 aoc-cli - Advent of Code command-line tool
# [INFO  aoc_client] 🎅 Saved puzzle to 'data/puzzles/01.md'
# [INFO  aoc_client] 🎅 Saved input to 'data/inputs/01.txt'
# ---
# 🎄 Successfully wrote input to "data/inputs/01.txt".
# 🎄 Successfully wrote puzzle to "data/puzzles/01.md".
#
# Loaded session cookie from "/Users/<snip>/.adventofcode.session".
# Fetching puzzle for day 1, 2022...
# ...the input...

➡️ Format code

cargo fmt

➡️ Lint code

cargo clippy

Optional template features

Configure aoc-cli integration

1. Install aoc-cli via cargo: cargo install aoc-cli --version 0.12.0
2. Create the file <home_directory>/.adventofcode.session and paste your session cookie into it. To retrieve the session cookie, press F12 anywhere on the Advent of Code website to open your browser developer tools. Look in Cookies under the Application or Storage tab, and copy out the session cookie value. ¹

Once installed, you can use the download command, the read command, and automatically submit solutions via the --submit flag.

Automatically track ⭐️ progress in the readme

This template includes a Github action that automatically updates the readme with your advent of code progress.

To enable it, complete the following steps:

1. Create a private leaderboard

Go to the leaderboard page of the year you want to track and click Private Leaderboard. If you have not created a leaderboard yet, create one by clicking Create It. Your leaderboard should be accessible under https://adventofcode.com/{year}/leaderboard/private/view/{aoc_user_id}.

2. Set repository secrets

Go to the Secrets tab in your repository settings and create the following secrets:

• AOC_USER_ID: Go to this page and copy your user id. It's the number behind the # symbol in the first name option. Example: 3031.
• AOC_YEAR: the year you want to track. Example: 2021.
• AOC_SESSION: an active session² for the advent of code website. To get this, press F12 anywhere on the Advent of Code website to open your browser developer tools. Look in your Cookies under the Application or Storage tab, and copy out the session cookie.

Go to the Variables tab in your repository settings and create the following variable:

• AOC_ENABLED: This variable controls whether the workflow is enabled. Set it to true to enable the progress tracker. After you complete AoC or no longer work on it, you can set this to false to disable the CI.

✨ You can now run this action manually via the Run workflow button on the workflow page. If you want the workflow to run automatically, uncomment the schedule section in the readme-stars.yml workflow file or add a push trigger.

Enable code formatting / clippy checks in the CI

Uncomment the respective sections in the ci.yml workflow.

Use DHAT to profile heap allocations

If you are not only interested in the runtime of your solution, but also its memory allocation profile, you can use the template's DHAT integration to analyze it. In order to activate DHAT, call the solve command with the --dhat flag.

cargo solve 1 --dhat

# output:
#     Running `target/dhat/1`
# dhat: Total:     276 bytes in 3 blocks
# dhat: At t-gmax: 232 bytes in 2 blocks
# dhat: At t-end:  0 bytes in 0 blocks
# dhat: The data has been saved to dhat-heap.json, and is viewable with dhat/dh_view.html
# Part 1: 9001 (4.1ms)

The command will output some basic stats to the command-line and generate a dhat-heap.json report in the repo root directory.

You can pass the report a tool like dh-view to view a detailed breakdown of heap allocations.

Use VS Code to debug your code

1. Install rust-analyzer and CodeLLDB.
2. Set breakpoints in your code. ³
3. Click Debug next to the unit test or the main function. ⁴
4. The debugger will halt your program at the specific line and allow you to inspect the local stack. ⁵

Useful crates

• itertools: Extends iterators with extra methods and adaptors. Frequently useful for aoc puzzles.
• regex: Official regular expressions implementation for Rust.

A curated list of popular crates can be found on blessed.rs.

Do you have aoc-specific crate recommendations? Share them!

Footnotes

Footnotes

1. The session cookie might expire after a while (~1 month) which causes the downloads to fail. To fix this issue, refresh the .adventofcode.session file. ↩

2. The session cookie might expire after a while (~1 month) which causes the automated workflow to fail. To fix this issue, refresh the AOC_SESSION secret. ↩

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