refactor: modular architecture with unified error handling and DashMap concurrency

- Break monolithic records.rs into commands/, output/, parsing/ modules
- Add lib.rs with shared types (AppError, Car, BestCarRecord, etc.)
- Replace Arc<Mutex<HashMap>> with DashMap for concurrent access
- Add unified AppError enum with thiserror
- Add configurable CLI paths (--my-cars, --cars-db, --times-dir, --tracks-db)
- Add serde deserialization for Car struct
- Add CSV escaping for fields with commas/quotes/newlines
- Fix track name splitting: split_once -> rsplit_once for _a handling
- Add integration tests, proptest property-based tests, and unit tests
- Add rustfmt.toml configuration
- Update documentation (API, ARCHITECTURE, DATA_STRUCTURES, README)
This commit is contained in:
Jan-Peter von Hunnius 2026-05-08 16:19:01 +02:00
parent 9d57b18a4e
commit a9a46ee551
24 changed files with 3541 additions and 1470 deletions

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@ -6,543 +6,267 @@ This document provides comprehensive API documentation for the Top Drives Record
## Core Modules
### Records Module (`src/records.rs`)
### Shared Library (`src/lib.rs`)
Primary analysis engine for processing JSON-based timing data.
Common types and utilities shared across the project.
#### Public Types
##### `Car`
**Core car information structure**
```rust
#[derive(Debug, Clone, Deserialize)]
pub struct Car {
pub rid: String, // Repository identifier
pub brand: String, // Manufacturer/brand name
pub only_name: String, // Model name (without brand)
pub year: u16, // Model year
pub rq: u8, // Rarity level (10-113)
pub prize: bool, // Whether car is prize-only
pub guid: String, // Game-specific card identifier
}
```
**Purpose**: Represents a car entity from the records database.
**Deserialization**: Uses `serde::Deserialize` with `#[serde(rename = "onlyName")]` for the `only_name` field and `#[serde(default)]` for `prize`.
##### `BestCarRecord`
**Best performing car record for a specific track/RQ/condition**
```rust
#[derive(Debug, Clone)]
pub struct BestCarRecord {
pub car_rid: String, // Car identifier
pub brand: String, // Manufacturer
pub only_name: String, // Model name
pub year: u16, // Year
pub rq: u8, // RQ level
pub config: String, // Tune config (111, 233, etc.)
pub time: f64, // Track time in seconds
pub owned: bool, // User ownership status
pub prize: bool, // Prize car status
pub guid: String, // Game identifier
}
```
##### `TripletKey`
**Composite key for unique track conditions**
```rust
pub type TripletKey = (String, u8, String);
// Track ID RQ Level Surface Condition
```
##### `AnalysisContext`
**Mutable analysis state**
```rust
pub struct AnalysisContext {
pub best_cars: Arc<Mutex<HashMap<TripletKey, Vec<BestCarRecord>>>>,
}
```
**Methods**:
- `new()` — Create a new, empty analysis context
- `lock_best_cars()` — Acquire a lock on the best_cars map (returns `Result`, converts poison errors to `AppError`)
##### `CarCounts`
**Aggregated per-car win counts**
```rust
pub struct CarCounts {
pub by_rid: HashMap<String, (BestCarRecord, usize)>,
pub guid_to_rid: HashMap<String, String>,
pub car_map: HashMap<String, Car>,
pub car_list: Vec<Car>,
}
```
##### `MyCarsData`
**Parsed user data from `my_cars.json`**
```rust
#[derive(Debug)]
pub struct MyCarsData {
pub owned: HashSet<String>, // Owned car GUIDs
pub wishlist: HashSet<String>, // Wishlist car GUIDs
}
```
##### `AppError`
**Unified error type**
```rust
#[derive(Debug, thiserror::Error)]
pub enum AppError {
#[error("I/O error: {0}")]
Io(#[from] std::io::Error),
#[error("Network error: {0}")]
Network(String),
#[error("Parse error in {file}: {reason}")]
Parse { file: String, reason: String },
#[error("Missing data file: {0}")]
MissingFile(String),
#[error("Internal error: mutex poisoned — {0}")]
Poisoned(String),
}
```
#### Public Functions
##### `main()`
**Entry point for the records analyzer**
##### `translate_track_name(track_code: &str) -> String`
**Translates internal track identifiers to human-readable names**
```rust
#[tokio::main]
async fn main()
```
- **Input**: Track code (e.g., `"laguna"`)
- **Output**: Track name (e.g., `"Laguna Seca"`)
- **Fallback**: Returns original code if no mapping found
- **Data source**: `track_names.json` (compiled into binary via `include_str!`)
**Purpose**: Command-line interface and operation dispatcher
##### `translate_track_type(track_code: &str) -> &'static str`
**Translates condition codes to surface names**
**Arguments**: Command line arguments
- `args[1]`: Command (`fetch`, `best`, `missing`, `trash`, `details`, `track`, `full_script`)
- **Input**: Condition code (e.g., `"00"`)
- **Output**: Surface name (e.g., `"Dry"`)
- **Fallback**: Returns `"Unknown"` for unrecognized codes
**Commands**:
- `fetch`: Download all data files
- `best`: Show best owned cars
- `missing`: Show best unowned cars
- `trash`: Show removable cars
- `details`: Export all timing data
- `track`: Track-specific analysis
- `full_script`: Run fetch + all analysis commands
##### `get_file(file_url: &str, local_file_path: &str) -> Result<(), AppError>`
**Downloads a file from a URL and saves it locally**
**Example**:
```bash
cargo run --bin records -- best
```
- Removes existing file before downloading
- Returns `AppError::Network` on HTTP failures
- Returns `AppError::Io` on file write failures
##### `print_best_cars(filter_out_owned: bool)`
**Displays ranked list of best performing cars**
##### `load_my_cars_data(path: &str) -> Result<MyCarsData, AppError>`
**Loads and parses user car collection data**
```rust
fn print_best_cars(filter_out_owned: bool)
```
- **Input**: Path to `my_cars.json`
- **Returns**: `MyCarsData` with owned and wishlist GUIDs
- **Errors**: `AppError::MissingFile` if file not found, `AppError::Parse` if JSON invalid
**Purpose**: Aggregates performance data across all tracks and conditions
##### `compare_cars(a: &Car, b: &Car) -> std::cmp::Ordering`
**Sorting comparator for cars**
**Arguments**:
- `filter_out_owned`: If true, excludes owned cars from output
- Sorts by: RQ → Brand → Model name
**Output Format**:
```csv
RQ,Brand,Model,Year,Count
85,McLaren,F1,1993,15
82,Ferrari,250 GTO,1962,12
```
##### `compute_car_counts(ctx: &AnalysisContext, car_list: Vec<Car>) -> Result<CarCounts, AppError>`
**Aggregates per-car win counts from analysis context**
**Algorithm**:
1. Count occurrences of each car across all track/RQ/condition combinations
2. Sort by RQ level, then by car identifier
3. Apply ownership filter if requested
- Returns `CarCounts` with by_rid, guid_to_rid, car_map, and car_list
##### `print_best_times()`
**Exports comprehensive timing data for all combinations**
##### `escape_csv_field(field: &str) -> String`
**Escapes a field for safe CSV inclusion**
```rust
fn print_best_times()
```
**Purpose**: Detailed performance export for external analysis
**Output Format**:
```csv
RQ,TrackID,Condition,CarRQ,Brand,Model,Year,Config,Time
85,laguna,Dry,85,McLaren,F1,1993,233,112.45
```
**Data Volume**: Large output file with all track/condition/RQ combinations
##### `print_best_car(track: &String, condition: &String, rq: &String)`
**Finds best car for specific track conditions**
```rust
fn print_best_car(track: &String, condition: &String, rq: &String)
```
**Purpose**: Targeted analysis for specific racing scenarios
**Arguments**:
- `track`: Track identifier (e.g., "laguna", "monaco")
- `condition`: Surface condition (e.g., "Dry", "Wet")
- `rq`: RQ level limit
**Example**:
```rust
print_best_car(&"laguna".to_string(), &"Dry".to_string(), &"85".to_string());
```
##### `print_trash()`
**Identifies cars safe for removal from collection**
```rust
fn print_trash()
```
**Purpose**: Collection optimization recommendations
**Safety Criteria**:
- Car not among best performers on any track
- RQ level below 80 (preserves high-end cars)
- Not a prize car (may have collection value)
**Output Format**:
```csv
RQ,Brand,Model,Year
45,Toyota,Corolla,1995
```
##### `parse_car_times(car: Car)`
**Processes timing data for a single car**
```rust
async fn parse_car_times(car: Car)
```
**Purpose**: Core parsing logic for individual car performance data
**Process Flow**:
1. Load car's JSON timing file
2. Parse each configuration (111, 233, 323, etc.)
3. Extract timing data for each track/condition
4. Apply quality filters (downvotes, time validation)
5. Update global best cars cache
**Quality Filters**:
- Minimum time: 1.2 seconds
- No downvotes from community
- Special handling for Test Bowl track
##### `parse_all_times()`
**Orchestrates concurrent processing of all cars**
```rust
async fn parse_all_times()
```
**Purpose**: Coordinates parallel processing of car timing files
**Concurrency**: One async task per car file, bounded by a semaphore (limit 20)
**Synchronization**: Waits for all tasks to complete
##### `update_best_car(track_id: &str, car_rq: u8, surface: &str, new_record: BestCarRecord)`
**Updates global best cars database with new performance data**
```rust
fn update_best_car(track_id: &str, car_rq: u8, surface: &str, new_record: BestCarRecord)
```
**Purpose**: Implements core best-car selection algorithm
**RQ Range Analysis**:
- Tests car from base RQ up to `MAX_RQ` (113)
- Compares against existing best times
- Updates records for improved performance
**Logic**:
1. For each RQ level from car_rq to `MAX_RQ` (113):
2. If new time is better → replace record
3. If new time ties → add to list (if not duplicate)
4. If new time is worse → ignore
##### `get_car_list() -> Vec<Car>`
**Loads complete car database from repository file**
```rust
fn get_car_list() -> Vec<Car>
```
**Purpose**: Provides access to all known cars in the database
**Source**: `cars_repo.json` file
**Returns**: Vector of all cars with complete metadata
##### `get_my_car_list() -> Vec<String>`
**Loads user's car collection from game export**
```rust
fn get_my_car_list() -> Vec<String>
```
**Purpose**: Identifies which cars the user owns
**Source**: `my_cars.json` game export
**Filter**: Only `locked: true` cards
**Returns**: Vector of owned car GUIDs
##### `fetch_times()`
**Downloads timing files for all cars with rate limiting**
```rust
async fn fetch_times()
```
**Purpose**: Batch download of car timing data
**Rate Limiting**: 20 concurrent downloads maximum (semaphore)
**Progress**: Shows download progress for each file
##### `fetch_car(rid: String)`
**Downloads timing data for single car**
```rust
async fn fetch_car(rid: String)
```
**Purpose**: Individual car data fetching
**URL Format**: `https://api.topdrivesrecords.com/car/{rid}`
**Storage**: `times/{rid}.json`
##### `get_cars_file()`
**Downloads main car database**
```rust
async fn get_cars_file()
```
**Purpose**: Updates car metadata database
**Source**: GitHub repository cars database
**Destination**: `cars_repo.json`
##### `get_tracks_file()`
**Downloads track definitions**
```rust
async fn get_tracks_file()
```
**Purpose**: Updates track name mappings
**Source**: GitHub repository tracks database
**Destination**: `tracks_repo.json`
##### `get_file(file_url: &str, local_file_path: &str)`
**Generic file download utility**
```rust
async fn get_file(file_url: &str, local_file_path: &str)
```
**Purpose**: Downloads files from remote URLs
**Features**:
- Automatic existing file removal
- Error handling with graceful failure
- Progress indication through errors
### Shared Library (`src/lib.rs`)
Common utilities shared across the project.
##### `translate_track_name(track_code: &str) -> &str`
**Converts track codes to human-readable names**
```rust
fn translate_track_name(track_code: &str) -> &str
```
**Purpose**: User-friendly track name display
**Implementation**: Loads mappings from `track_names.json` at compile time via `include_str!`
**Coverage**: All tracks defined in `track_names.json`
**Fallback**: Returns original code if not found
##### `translate_track_type(track_code: &str) -> &str`
**Converts condition codes to surface descriptions**
```rust
fn translate_track_type(track_code: &str) -> &str
```
**Purpose**: Human-readable surface condition names
**Mappings**:
- `"00"` → `"Dry"`
- `"01"` → `"Wet"`
- `"10"` → `"Dirt"`
- `"11"` → `"Dirt Wet"`
##### `split_prefix_postfix<'a>(input: &'a str, delimiter: &'a str) -> Option<(&'a str, &'a str)>`
**String splitting utility for track ID parsing**
```rust
fn split_prefix_postfix<'a>(input: &'a str, delimiter: &'a str) -> Option<(&'a str, &'a str)>
```
**Purpose**: Parse track IDs with condition suffixes
**Example**:
```rust
split_prefix_postfix("laguna_a00", "_a") // Some(("laguna", "00"))
```
## Data Structures
### Records Module Data Types
#### `Card`
**User's car collection entry**
```rust
struct Card {
cardId: String, // Unique card identifier
locked: bool, // Ownership status
}
```
#### `Car`
**Car metadata from database**
```rust
struct Car {
rid: String, // Repository ID
brand: String, // Manufacturer
only_name: String, // Model name
year: u16, // Model year
rq: u8, // Rarity level
prize: bool, // Prize car status
guid: String, // Game GUID
}
```
#### `BestCarRecord`
**Performance record for best cars**
```rust
struct BestCarRecord {
car_rid: String, // Car identifier
brand: String, // Manufacturer
only_name: String, // Model name
year: u16, // Year
rq: u8, // RQ level
config: String, // Tune configuration
time: f64, // Track time
owned: bool, // User ownership
guid: String, // Game identifier
}
```
#### `TripletKey`
**Composite key for track conditions**
```rust
type TripletKey = (String, u8, String);
// Track ID RQ Level Surface Condition
```
### Constants
#### `MAX_RQ`
```rust
const MAX_RQ: u8 = 113;
```
**Purpose**: Maximum RQ level for range analysis. Replaces former magic number 115 with a named constant.
## Global State
### `BEST_CARS` Cache
**Thread-safe global performance database**
```rust
static BEST_CARS: Lazy<Mutex<HashMap<TripletKey, Vec<BestCarRecord>>>>
```
**Initialization**: One-time setup with `once_cell::Lazy`
**Thread Safety**: Protected by `Mutex` for concurrent access
**Structure**: HashMap with composite keys for efficient lookups
## Error Handling
### Network Errors
- **HTTP Failures**: Logged but don't crash application
- **Invalid JSON**: Graceful fallback to defaults
- **Missing Files**: Clear error messages for user
### Parse Errors
- **Malformed Data**: Skip invalid entries
- **Type Conversion**: Use safe defaults
- **Missing Fields**: Provide sensible fallbacks
### User Input Errors
- **Invalid Commands**: Clear usage instructions
- **Missing Arguments**: Descriptive error messages
- **Out of Range**: Validation with helpful feedback
## Usage Examples
### Basic Analysis Workflow
```rust
// 1. Load user's car collection
let my_cars = get_my_car_list();
// 2. Process all car timing data
parse_all_times().await;
// 3. Generate best cars report
print_best_cars(false);
// 4. Find collection optimization opportunities
print_trash();
```
### Track-Specific Analysis
```rust
// Analyze specific track conditions
let track = "laguna".to_string();
let condition = "Dry".to_string();
let rq = "85".to_string();
print_best_car(&track, &condition, &rq);
```
### Data Fetching
```rust
// Download fresh data
get_cars_file().await;
get_tracks_file().await;
fetch_times().await;
```
## Performance Characteristics
### Time Complexity
- **Parsing**: O(n) where n = number of cars × tracks
- **Best Car Selection**: O(1) HashMap operations
- **Sorting**: O(n log n) for output ordering
### Memory Usage
- **Peak**: ~200-300MB during full analysis
- **Baseline**: ~50MB application overhead
- **Per Car**: ~1-2KB metadata + timing data
### Concurrency
- **Downloads**: 20 concurrent connections max (semaphore bounded)
- **Processing**: One task per car file
- **Thread Safety**: Mutex-protected shared state
## Extension Points
### Adding New Commands
1. Add command parsing in `main()`
2. Implement analysis function
3. Update help documentation
### Adding New Tracks
1. Update `tracks_repo.json` with new track definitions
2. Add track name mapping to `track_names.json`
3. Track names are compiled in at build time
### Custom Analysis Types
1. Create new output formatting functions
2. Add aggregation algorithms
3. Extend data structures as needed
## Security Considerations
### Network Security
- HTTPS-only external requests
- Certificate validation (with fallback)
- No sensitive data transmission
### File System Security
- Read-only access to game files
- No modification of user data
- Safe file path handling
### Input Validation
- Command injection prevention
- Path traversal protection
- Input sanitization
## Troubleshooting API Issues
### Common Problems
**"Function not found"**
- Check module imports
- Verify function signatures
- Ensure proper visibility
**"Type mismatch"**
- Review struct field types
- Check function signatures
- Verify generic parameters
**"Deadlock"**
- Avoid nested Mutex locks
- Check lock ordering
- Use timeout where possible
### Debug Strategies
**Logging**:
```rust
println!("Processing car: {}", car.rid);
eprintln!("Error: {}", error);
```
**Tracing**:
```rust
// Add at function entry
let _span = tracing::span!(Level::INFO, "function_name");
let _enter = _span.enter();
```
**Profiling**:
```bash
cargo flamegraph --bin records -- best
```
## Migration Guide
### Version Compatibility
- **Current**: 0.2.0 API stable
- **Previous**: 0.1.0 fully compatible
- **Future**: Semantic versioning maintained
### Breaking Changes
- Club module (`src/club.rs`) has been removed
- `scraper` dependency removed
- `tracks.txt` removed (replaced by `track_names.json`)
- Magic number 115 replaced with `const MAX_RQ: u8 = 113`
- `translate_track_name()` now loads from `track_names.json` instead of hardcoded match
- Wraps in quotes if field contains comma, quote, newline, or carriage return
- Doubles internal quotes per CSV spec
---
**API Version**: 0.2.0
**Last Updated**: 2026-05-06
**Compatibility**: Rust 1.70.0+
### Records Binary (`src/records.rs`)
Primary analysis engine for processing JSON-based timing data.
#### Constants
| Constant | Value | Description |
|----------|-------|-------------|
| `MAX_RQ` | 113 | Maximum RQ level for range analysis |
| `MIN_VALID_TIME` | 1.2 | Minimum valid track time (seconds) |
| `TIME_TOLERANCE` | 0.005 | Tolerance for tie detection (seconds) |
| `TEST_BOWL_FACTOR` | 2000.0 | Multiplier for Test Bowl time inversion |
| `CONCURRENCY_LIMIT` | 20 | Maximum concurrent tasks |
| `DEFAULT_MY_CARS_PATH` | `"my_cars.json"` | Default user data file path |
| `CARS_DB_PATH` | `"cars_repo.json"` | Car database file path |
| `TIMES_DIR` | `"times"` | Timing data directory |
| `CARS_DB_URL` | GitHub URL | Remote car database URL |
| `TRACKS_DB_URL` | GitHub URL | Remote tracks database URL |
| `CAR_TIMING_API_BASE` | API URL | Base URL for per-car timing data |
#### CLI Commands
| Command | Description | Flags |
|---------|-------------|-------|
| `fetch` | Download latest car and track data | None |
| `best` | Generate `best.csv` and `very_best.csv` | `--min-count N` (default: 10) |
| `trash` | Generate `trash.csv` | None |
| `wishlist` | Generate `wishlist.csv`, `add.csv`, `remove.csv` | `--min-count N`, `--max-wishlist M` |
| `all` | Generate all output files in a single pass | `--min-count N`, `--max-wishlist M` |
| `details` | Generate `details.csv` with all timing data | None |
| `track` | Show best car for specific track/condition/RQ | `<track> <condition> <rq>` |
#### Key Internal Functions
##### `run_analysis(owned: &HashSet<String>) -> Result<(AnalysisContext, CarCounts), AppError>`
**Common analysis setup**
Loads user data, parses all timing files, and computes car counts.
##### `parse_car_times(car: Car, my_cars: &HashSet<String>, ctx: &AnalysisContext)`
**Processes timing data for a single car**
- Runs inside `tokio::task::spawn_blocking` to avoid blocking async runtime
- Reads car JSON file from `times/` directory
- Processes each configuration and track
- Applies quality filters (downvotes, minimum time)
- Handles Test Bowl time inversion
##### `update_best_car(best_cars, track_id, car_rq, surface, new_record)`
**Updates best car records with a new performance entry**
- Iterates RQ range from car's RQ to `MAX_RQ`
- Replaces existing record if new time is faster
- Adds to tie list if within `TIME_TOLERANCE`
- Prevents duplicate `car_rid` entries in tie lists
##### `parse_all_times(my_cars, ctx) -> Vec<Car>`
**Orchestrates concurrent parsing of all car timing files**
- Uses `Semaphore(CONCURRENCY_LIMIT)` for bounded concurrency
- Spawns blocking tasks via `tokio::task::spawn_blocking`
- Returns the full car list for downstream use
##### `compute_ideal_wishlist(counts, owned, min_count, max_wishlist) -> Vec<(BestCarRecord, usize)>`
**Computes the ideal wishlist**
- Filters to cars with count >= `min_count`
- Excludes owned cars
- Sorts by RQ
- Caps at `max_wishlist`
#### Output Functions
##### `write_sorted_csv(path, header, entries, cmp, format_row)`
**Generic sorted CSV writer**
- Sorts by index (avoids cloning entries)
- Writes header row
- Formats each row via closure
##### `write_best_csv(counts)`, `write_very_best_csv(counts, min_count)`
**Generate best car CSV files**
- Format: `RQ,Brand,Model,Year,Count`
- Fields are CSV-escaped
##### `write_trash_csv(counts, owned)`
**Generate trash car CSV file**
- Filters: not best on any track, RQ < 80, not prize car
- Format: `RQ,Brand,Model,Year`
##### `write_wishlist_files(counts, owned, wishlist, min_count, max_wishlist)`
**Generate wishlist, add, and remove CSV files**
- Computes ideal wishlist once
- Derives all three files from it
- Format: `RQ,Brand,Model,Year,Count`
##### `write_details_csv(ctx)`
**Generate comprehensive timing data CSV**
- Format: `RQ,TrackID,Condition,CarRQ,Brand,Model,Year,Config,Time`
- Sorted by RQ then track ID
##### `print_best_car(ctx, track, condition, rq)`
**Print best car for a specific track/condition/RQ to stdout**

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@ -6,7 +6,29 @@ The Top Drives Records Analyzer consists of a main analysis binary and a shared
## Component Architecture
### 1. Records Module (`src/records.rs`)
### 1. Shared Library (`src/lib.rs`)
**Common types and utilities** shared across the project.
#### Key Responsibilities
- **Data Types**: Defines `Car`, `BestCarRecord`, `AnalysisContext`, `CarCounts`, `MyCarsData`, `AppError`
- **Track Name Translation**: Converts track codes to human-readable names via `translate_track_name()`
- **Track Type Translation**: Converts condition codes to surface descriptions via `translate_track_type()`
- **File Download**: Async file download utility via `get_file()`
- **User Data Loading**: Parses `my_cars.json` via `load_my_cars_data()`
- **Car Comparison**: Sorting comparator via `compare_cars()`
- **Count Aggregation**: Per-car win count computation via `compute_car_counts()`
- **CSV Escaping**: Safe CSV field formatting via `escape_csv_field()`
#### Error Handling
All public functions return `Result<T, AppError>` where `AppError` is a unified error type:
- `AppError::Io` — File system errors
- `AppError::Network` — HTTP/download errors
- `AppError::Parse` — JSON parsing errors with file context
- `AppError::MissingFile` — Required data file not found
- `AppError::Poisoned` — Mutex poisoning from panicked threads
### 2. Records Binary (`src/records.rs`)
**Primary analysis engine** that processes pre-downloaded JSON timing data.
@ -15,9 +37,14 @@ The Top Drives Records Analyzer consists of a main analysis binary and a shared
- **File Processing**: Parses JSON timing files for each car configuration
- **Performance Analysis**: Compares timing data across RQ ranges and track conditions
- **Collection Management**: Identifies owned cars and optimization opportunities
- **CSV Output**: Writes analysis results to CSV files with proper escaping
#### Key Constants
- `MAX_RQ: u8 = 113` — Maximum RQ level used for range analysis (replaces former magic number 115)
- `MAX_RQ: u8 = 113` — Maximum RQ level used for range analysis
- `MIN_VALID_TIME: f64 = 1.2` — Minimum valid track time threshold
- `TIME_TOLERANCE: f64 = 0.005` — Tolerance for tie detection (5ms)
- `TEST_BOWL_FACTOR: f64 = 2000.0` — Multiplier for Test Bowl time inversion
- `CONCURRENCY_LIMIT: usize = 20` — Maximum concurrent tasks
#### Data Flow
@ -32,21 +59,9 @@ JSON Parsing → BestCarRecord Analysis
↓
User Collection (my_cars.json)
↓
Output: CSV/Terminal Results
Output: CSV files / Terminal Results
```
### 2. Shared Library (`src/lib.rs`)
**Common utilities** shared across the project.
#### Key Responsibilities
- **Track Name Translation**: Converts track codes to human-readable names via `translate_track_name()`
- **Track Type Translation**: Converts condition codes to surface descriptions via `translate_track_type()`
#### Configuration
- Track name mappings are loaded from `track_names.json` at compile time using `include_str!`
- This external config file maps track codes (e.g., `"laguna"`) to human-readable names (e.g., `"Laguna Seca"`)
## Data Structures
### Core Types
@ -63,14 +78,15 @@ type TripletKey = (String, u8, String);
#### Car
```rust
#[derive(Debug, Clone, Deserialize)]
struct Car {
rid: String, // Repository ID
brand: String, // Manufacturer
only_name: String, // Model name
year: u16, // Model year
rq: u8, // Rarity level
prize: bool, // Prize car status
guid: String, // Game GUID
year: u16, // Model year
rq: u8, // Rarity level
prize: bool, // Prize car status
guid: String, // Game GUID
}
```
@ -80,25 +96,41 @@ struct BestCarRecord {
car_rid: String, // Car identifier
brand: String, // Manufacturer
only_name: String, // Model name
year: u16, // Year
rq: u8, // RQ level
year: u16, // Year
rq: u8, // RQ level
config: String, // Tune config (111, 233, etc.)
time: f64, // Track time (seconds)
owned: bool, // User ownership status
guid: String, // Game identifier
time: f64, // Track time (seconds)
owned: bool, // User ownership status
prize: bool, // Prize car status
guid: String, // Game identifier
}
```
## Global State Management
## State Management
### BEST_CARS Cache
### AnalysisContext
```rust
static BEST_CARS: Lazy<Mutex<HashMap<TripletKey, Vec<BestCarRecord>>>>
struct AnalysisContext {
best_cars: Arc<Mutex<HashMap<TripletKey, Vec<BestCarRecord>>>>,
}
```
**Thread Safety**: Protected by `Mutex` for concurrent access during parsing
**Initialization**: Uses `once_cell::Lazy` for one-time setup
**Structure**: HashMap with composite keys for efficient lookups
**Lifetime**: Created in `main()`, passed through the call chain
**Access**: `lock_best_cars()` method returns `Result` with proper error handling
### CarCounts
```rust
struct CarCounts {
by_rid: HashMap<String, (BestCarRecord, usize)>,
guid_to_rid: HashMap<String, String>,
car_map: HashMap<String, Car>,
car_list: Vec<Car>,
}
```
**Purpose**: Aggregated per-car win counts computed once from `AnalysisContext`
**Usage**: Shared across all output functions to avoid redundant computation
## Algorithm Details
@ -116,20 +148,21 @@ The system analyzes car performance across multiple RQ brackets:
Multiple validation layers ensure data quality:
1. **Minimum Time**: Times below 1.2 seconds are discarded
1. **Minimum Time**: Times below `MIN_VALID_TIME` (1.2s) are discarded
2. **Downvote Filter**: Community-downvoted times are excluded
3. **Special Cases**: Test Bowl track uses inverted time calculation (2000.0 / time)
3. **Tie Detection**: Times within `TIME_TOLERANCE` (5ms) are considered equal
4. **Special Cases**: Test Bowl track uses inverted time calculation (`TEST_BOWL_FACTOR / time`)
### Concurrent Processing
#### Bounded Concurrency Pattern
- **Task Spawning**: One async task per car file
- **Semaphore**: Limits concurrent operations to 20 (bounded concurrency)
- **Task Spawning**: One blocking task per car file via `tokio::task::spawn_blocking`
- **Semaphore**: Limits concurrent operations to `CONCURRENCY_LIMIT` (20)
- **Error Isolation**: Individual car failures don't stop batch processing
#### Synchronization
- **Join Handles**: Waits for all parsing tasks to complete
- **Mutex Protection**: Safe concurrent access to shared BEST_CARS cache
- **Mutex Protection**: Safe concurrent access to shared `AnalysisContext`
## File Organization
@ -138,8 +171,8 @@ Multiple validation layers ensure data quality:
├── cars_repo.json # Car database from repository
├── tracks_repo.json # Track definitions
├── track_names.json # Track code → name mappings (compiled in)
├── my_cars.json # User's car collection (game export)
└── times/ # Individual car timing files
├── my_cars.json # User's car collection (game export)
└── times/ # Individual car timing files
├── Ferrari_F40_1987.json
├── McLaren_F1_1993.json
└── ...
@ -147,14 +180,13 @@ Multiple validation layers ensure data quality:
### Output Files
```
├── best.csv # Best cars analysis results
├── details.csv # Detailed timing data
├── add.csv # Cars to add to wishlist
├── remove.csv # Cars to remove from wishlist
├── best.csv # All cars best on at least one track
├── very_best.csv # Cars best on ≥ min_count tracks
├── details.csv # Detailed timing data for all combinations
├── trash.csv # Owned cars safe to remove
├── sorted.csv # Best cars sorted by count
├── very_best.csv # Best cars with count ≥ 10
└── wishlist.csv # Missing cars already on wishlist
├── wishlist.csv # Ideal wishlist (very_best unowned, capped)
├── add.csv # Cars to add to wishlist
└── remove.csv # Cars to remove from wishlist
```
## Performance Characteristics
@ -168,33 +200,47 @@ Multiple validation layers ensure data quality:
## Error Handling Strategy
### Unified Error Type
All errors are represented as `AppError`:
- **I/O Errors**: File read/write failures
- **Network Errors**: Download failures with descriptive messages
- **Parse Errors**: JSON parsing failures with file name context
- **Missing Files**: Specific variant for absent data files
- **Poisoned Mutex**: Thread panic detection
### Graceful Degradation
- **Missing Files**: Continue with available data
- **Network Errors**: Log errors but don't crash
- **Parse Failures**: Use default values or skip invalid entries
- **Missing User Data**: `load_my_cars_or_warn()` returns empty data with a warning
- **Network Errors**: Fetch errors logged but don't crash
- **Parse Failures**: Individual car file failures emit warnings and skip
### User Feedback
- **Progress Indication**: Shows fetching/download progress
- **Error Messages**: Clear descriptions of what went wrong
- **Error Messages**: Clear descriptions via `AppError` Display impl
- **Warning Messages**: Non-fatal issues reported via stderr
## Extension Points
### Adding New Tracks
1. Update `tracks_repo.json` with new track definitions
2. Add track name mapping in `track_names.json`
3. Add condition mappings in `translate_track_type()`
3. Add condition mappings in `translate_track_type()` if needed
### Adding New Analysis Types
1. Create new output functions (similar to `print_best_cars()`)
2. Add command parsing in main function
3. Update help text and documentation
1. Create new output function(s) using `write_sorted_csv`
2. Add command variant to the `Commands` enum
3. Add command handler in `main()`
4. Update help text and documentation
### Adding New Output Formats
1. Create new output function(s) alongside existing CSV writers
2. Reuse `CarCounts` for data access
3. Add CLI flag for format selection if needed
## Security Considerations
### Network Access
- **HTTPS Only**: All external requests use HTTPS
- **Certificate Validation**: Accepts invalid certificates for compatibility
- **Rate Limiting**: Respects server limits with controlled concurrency (semaphore limit of 20)
- **Rate Limiting**: Respects server limits with controlled concurrency
### Local File Access
- **Read Only**: No modification of game files
@ -209,6 +255,8 @@ Multiple validation layers ensure data quality:
3. **Configuration Files**: External configuration for analysis parameters
4. **GUI Interface**: Web-based interface for non-technical users
5. **Export Formats**: Multiple output formats (JSON, XML, etc.)
6. **Module Split**: Break `records.rs` into a module tree for better organization
7. **Concurrent HashMap**: Use `DashMap` for finer-grained locking
### Scalability Considerations
1. **Pagination**: Handle large datasets with pagination

View file

@ -235,15 +235,10 @@ const MAX_RQ: u8 = 113;
**Car Database Processing:**
```rust
// Parse cars_repo.json
let cars_data: Vec<Value> = serde_json::from_str(&file_content)?;
for car in cars_data {
let car = Car {
rid: car["rid"].as_str().unwrap_or(""),
brand: car["brand"].as_str().unwrap_or(""),
// ... other fields
};
}
// Parse cars_repo.json — uses serde derive for deserialization
let cars: Vec<Car> = serde_json::from_str(&content)?;
// Falls back to manual Value-based parsing if serde deserialization fails
// (for backward compatibility with older JSON formats)
```
**Timing Data Processing:**
@ -303,8 +298,9 @@ BestCarRecord {
**RQ Range Analysis:**
- Test each car from its base RQ up to `MAX_RQ` (113)
- Compare against existing best times
- Compare against existing best times using `DashMap` entry API
- Update records if better performance found
- Uses `DashMap` for fine-grained concurrent access (no global lock)
## Data Quality Measures
@ -345,8 +341,8 @@ BestCarRecord {
### Memory Usage
**Large Data Structures**:
- `BEST_CARS` HashMap: Contains all best records across RQ ranges
- Individual timing files: Loaded and parsed sequentially
- `AnalysisContext.best_cars` (`DashMap<TripletKey, Vec<BestCarRecord>>`): Contains all best records across RQ ranges
- Individual timing files: Loaded and parsed sequentially via `spawn_blocking`
- User collection: Small, loaded once
**Optimization Strategies**: