xerg - High-Performance Parallel Grep Tool

Published on crates.io as xerg

An ultra-fast grep alternative with structured output and smart performance optimizations. Perfect for developers who need speed without sacrificing readability.

Why xerg?

🚀 Up to 34x faster than system grep on large directory structures with structured output. Optional -x (xtreme) mode delivers maximum speedup when raw speed matters most.

Features

• ⚡ Parallel processing: Multi-core file scanning for maximum speed
• 🎨 Pretty output: Syntax highlighting and structured results
• 🔍 Modern regex: PCRE support with advanced features beyond standard grep
• 🔧 Drop-in replacement: Familiar grep-like interface
• 🚀 Smart modes: Default (pretty) or -x (raw speed)
• 📊 Statistics: Optional detailed metrics with --stats

Performance Benchmarks

xerg excels in different scenarios depending on your use case:

Performance Comparison: xerg vs System grep

Mode Comparison: xerg (default) vs xerg -x

Usage Recommendations

Use xerg (default mode)

✅ Structured output with pretty formatting
✅ Code exploration and development work
✅ Human-readable results with file headers
✅ Most users - balanced speed + readability

Use xerg -x (xtreme mode)

✅ Maximum raw speed when structure isn't needed
✅ Shell pipelines and automation scripts
✅ CI/CD tasks where every millisecond counts
✅ Grep-compatible output for tool integration

Use System grep

✅ Single file searches
✅ Simple shell scripting
✅ One-off quick searches

Test Environment: macOS, Multi-core system, Release builds
Key Finding: Default mode provides excellent performance with readability; -x maximizes raw speed

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/xerg --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

Multi-core Processing: Utilizes cores - 1 threads for optimal performance
Smart File Reading: Adaptive strategy based on file size (streaming/bulk/memory-mapped)
Memory Efficient: Handles files of any size without excessive memory usage

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