xerg - High-Performance Parallel Grep Tool

Published on crates.io as xerg

An ultra-fast, parallel grep implementation in Rust with syntax highlighting and detailed search statistics. Built for performance with multi-core processing and optimized dependencies.

This repository is part of Pragmatic AI Labs Rust Bootcamp

Features

• ✅ Parallel Processing: Multi-core file processing with intelligent thread pool management
• ✅ Pattern Matching: Regular expression engine with optimized performance
• ✅ Structured Streaming: Streams structured matches per file with headers and statistics
• ✅ Directory Traversal: Recursive scanning with symlink support
• ✅ Colorized Output: Customizable syntax highlighting (red, green, blue, bold)
• ✅ Search Statistics: Optional detailed metrics with --stats flag
• ✅ Quality Assurance: Comprehensive test suite and optimized dependencies

Quick Start

Installation

Install from crates.io (Recommended)

cargo install xerg

Build from Source

1. Clone the repository:

   git clone https://github.com/yinkam/xtreme-grep.git
   cd xtreme-grep
   

2. Build the project:

   cargo build --release
   

Development Setup

Use the included Makefile for common development tasks:

make help        # Show all available commands
make build       # Build the project
make test        # Run all tests
make run         # Run with default parameters
make clean       # Clean build artifacts
make release     # Build optimized release binary
make all         # Build, test, and create release

Usage

Using the installed binary:

# Basic search in current directory
xerg "pattern" .

# Search with colored output, statistics, and specific path
xerg --color blue --stats "pattern" src/

For development (from source):

# Basic search in current directory
cargo run "pattern" .

# Search with colored output, statistics, and specific path
cargo run --color blue --stats "pattern" src/

# Or use the built binary directly
./target/release/xgrep --color green --stats "pattern" /path/to/search

Command-Line Options

Search Statistics

$ xerg --stats --color blue "use" src/
--- /Users/user/rust-grep/src/lib.rs ---
    8:  use colors::Color;
    9:  use crawler::get_files;
   10:  use result::print_result;
--- /Users/user/rust-grep/src/main.rs ---
   15:  use greprs::{run_search, Config};
   16:  use std::process;
  lines: 45, matches: 2, skipped: 0
result: files:8; lines:1186; matches:207; skipped:0; errors:0; time:0.012s;

Structured Result Format: Machine-readable summary with semicolon delimiters and millisecond-precision timing. Perfect for performance analysis and automated testing.

Metrics: files = processed files, lines = total lines read, matches = pattern occurrences, skipped = unreadable lines, errors = access failures, time = execution time

Architecture

The project follows a modular architecture with clear separation of concerns:

Core Modules

• main.rs: CLI entry point and argument parsing
• lib.rs: Core integration layer connecting all modules
• search.rs: Parallel file processing with Rayon
• crawler.rs: Directory traversal with symlink support
• highlighter.rs: Regex-based text highlighting
• colors.rs: ANSI color management
• result.rs: Message handling and statistics formatting

Dependencies

Binary Size: 2.2MB optimized release build with minimal feature flags enabled

Performance

Multi-core Processing: Utilizes cores - 1 threads for optimal performance without system lock-up
Memory Efficient: Line-by-line processing handles files of any size
Structured Streaming: Streams structured matches per file as processing completes
Optimized I/O: Buffered reading and compiled regex reuse

Planned Features

• Auto-color detection (--color=auto)
• Silent mode (-s, --silent)
• Case insensitive search (-i, --ignore-case)
• Invert matching (-v, --invert-match)
• Multi-pattern support
• File type filtering
• Line number display (-n, --line-number)

Contributing

See CONTRIBUTING.md for guidelines on how to contribute to this project.

This is a learning-focused project demonstrating comprehensive Rust development practices. Contributions that enhance the educational value are especially welcome.

License

This project is open source and available under the MIT License.

Built during the Pragmatic AI Labs Rust Bootcamp