cjson-tools 1.6.0

Python bindings for the cJSON-Tools C library

cJSON-Tools

A high-performance C toolkit for transforming and analyzing JSON data with Python bindings. cJSON-Tools provides powerful tools for:

1. JSON Flattening: Converts nested JSON structures into flat key-value pairs
2. JSON Schema Generation: Analyzes JSON objects and generates a unified JSON schema
3. Multi-threading Support: Optimized performance for processing large JSON datasets
4. Python Bindings: Use the library directly from Python with native C performance

🚀 Quick Start

Python (Recommended)

pip install cjson-tools

import cjson_tools
import json

# Flatten nested JSON
data = {"user": {"name": "John", "address": {"city": "NYC"}}}
flattened = cjson_tools.flatten_json(json.dumps(data))
print(flattened)  # {"user.name": "John", "user.address.city": "NYC"}

# Generate JSON schema
schema = cjson_tools.generate_schema(json.dumps(data))
print(schema)

C Library

git clone https://github.com/amaye15/cJSON-Tools.git
cd cJSON-Tools
make
./bin/json_tools -f input.json

📁 Project Structure

• c-lib/ - C library source code, headers, and tests
  • src/ - C source files with optimized algorithms
  • include/ - C header files
  • tests/ - Dynamic test suite and benchmarks
• py-lib/ - Python bindings and related files
  • cjson_tools/ - Python package source
  • tests/ - Python unit tests
  • examples/ - Python usage examples
  • benchmarks/ - Performance testing scripts

✨ Features

🔄 JSON Flattening

• Dot Notation: Nested objects → address.city
• Array Indexing: Arrays → skills[0], skills[1]
• Type Preservation: Maintains strings, numbers, booleans, null
• Deep Nesting: Handles arbitrarily nested structures
• Batch Processing: Process thousands of objects efficiently

📋 JSON Schema Generation

• JSON Schema Draft-07: Standards-compliant schema generation
• Smart Type Detection: Handles nested objects, arrays, mixed types
• Required Properties: Automatically detects required vs optional fields
• Nullable Support: Identifies fields that can be null
• Array Analysis: Intelligent sampling for large arrays

⚡ Performance Optimizations

• Multi-threading: Parallel processing for large datasets
• Memory Pools: Optimized memory allocation for small strings
• Branch Prediction: Compiler hints for better CPU performance
• Adaptive Threading: Automatically chooses optimal thread count
• SIMD Optimizations: Vectorized operations where possible
• Zero-Copy: Minimal memory copying for better performance

🐍 Python Integration

• Native Performance: C-speed with Python convenience
• Pythonic API: Simple, intuitive function calls
• Type Safety: Proper error handling and validation
• Memory Management: Automatic cleanup, no memory leaks

📋 Requirements

Python Package (Recommended)

• Python: 3.6+ (3.8+ recommended)
• Compiler: Automatically handled by pip
• Platform: Linux, macOS, Windows

C Library Development

• Compiler: GCC 7+ or Clang 6+ with C99 support
• Dependencies: Integrated cJSON (no external dependencies)
• Threading: POSIX threads (pthread)
• Build Tools: Make, standard build utilities

Platform-Specific Setup

Ubuntu/Debian

sudo apt-get update
sudo apt-get install build-essential python3-dev

macOS

# Install Xcode Command Line Tools
xcode-select --install
# Or install via Homebrew
brew install python

Windows

• Install Visual Studio Build Tools or Visual Studio Community
• Python will automatically detect and use the compiler

🛠️ Installation & Setup

Option 1: Python Package (Recommended)

From PyPI

pip install cjson-tools

From Source

git clone https://github.com/amaye15/cJSON-Tools.git
cd cJSON-Tools/py-lib
pip install -e .  # Development mode
# or
pip install .     # Regular installation

Option 2: C Library Development

Quick Build

git clone https://github.com/amaye15/cJSON-Tools.git
cd cJSON-Tools
make                    # Build optimized version
make debug             # Build debug version

Advanced Build Options

# Clean build
make clean && make

# Install system-wide (optional)
sudo make install

# Build with specific optimizations
CFLAGS="-O3 -march=native" make

# Build tests
cd c-lib/tests
make

Option 3: Development Setup

Full Development Environment

# Clone repository
git clone https://github.com/amaye15/cJSON-Tools.git
cd cJSON-Tools

# Build C library
make

# Setup Python development
cd py-lib
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e .

# Run tests
python3 tests/test_cjson_tools.py
cd ../c-lib/tests
./run_dynamic_tests.sh

🚀 Usage Guide

Python API (Recommended)

Basic Operations

import cjson_tools
import json

# Single object flattening
data = {"user": {"name": "John", "details": {"age": 30, "city": "NYC"}}}
flattened = cjson_tools.flatten_json(json.dumps(data))
result = json.loads(flattened)
print(result)  # {"user.name": "John", "user.details.age": 30, "user.details.city": "NYC"}

# Schema generation
schema = cjson_tools.generate_schema(json.dumps(data))
schema_obj = json.loads(schema)
print(schema_obj["properties"])

Batch Processing

# Process multiple objects efficiently
objects = [
    '{"id": 1, "user": {"name": "Alice"}}',
    '{"id": 2, "user": {"name": "Bob", "age": 25}}',
    '{"id": 3, "user": {"name": "Charlie", "location": {"city": "SF"}}}'
]

# Flatten all objects
flattened_batch = cjson_tools.flatten_json_batch(objects)
for flat in flattened_batch:
    print(json.loads(flat))

# Generate unified schema from all objects
unified_schema = cjson_tools.generate_schema_batch(objects)
print(json.loads(unified_schema))

Performance Optimization

# For large datasets, enable multi-threading
large_dataset = [json.dumps({"data": i, "nested": {"value": i*2}}) for i in range(10000)]

# Auto-detect optimal thread count
result = cjson_tools.flatten_json_batch(large_dataset, use_threads=True)

# Specify thread count
result = cjson_tools.flatten_json_batch(large_dataset, use_threads=True, num_threads=4)

# Single-threaded for comparison
result = cjson_tools.flatten_json_batch(large_dataset, use_threads=False)

C Command Line Interface

# JSON Tools - High-performance JSON processing utility
# Usage: json_tools [options] [input_file]

# Options:
#   -h, --help                 Show help message
#   -f, --flatten              Flatten nested JSON (default)
#   -s, --schema               Generate JSON schema
#   -t, --threads [num]        Use multi-threading (auto-detect optimal count)
#   -p, --pretty               Pretty-print output
#   -o, --output <file>        Write to file instead of stdout

C CLI Examples

JSON Flattening

# From file
./bin/json_tools -f input.json

# From stdin with pretty printing
cat input.json | ./bin/json_tools -f -p

# Multi-threaded processing
./bin/json_tools -f -t 4 large_batch.json

# Save to file
./bin/json_tools -f -o flattened.json input.json

Schema Generation

# Generate schema from file
./bin/json_tools -s input.json

# Multi-threaded schema generation
./bin/json_tools -s -t 4 large_batch.json

# Pretty-printed schema to file
./bin/json_tools -s -p -o schema.json input.json

Example Input/Output

JSON Flattening

Input:

{
  "name": "John Doe",
  "age": 30,
  "address": {
    "street": "123 Main St",
    "city": "Anytown"
  },
  "skills": ["programming", "design"]
}

Output:

{
  "name": "John Doe",
  "age": 30,
  "address.street": "123 Main St",
  "address.city": "Anytown",
  "skills[0]": "programming",
  "skills[1]": "design"
}

JSON Schema Generation

Input:

[
  {
    "id": 1,
    "name": "John",
    "email": "[e-mail]",
    "active": true
  },
  {
    "id": 2,
    "name": "Jane",
    "email": "[e-mail]",
    "active": false,
    "tags": ["admin", "user"]
  }
]

Output:

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "properties": {
    "id": {
      "type": "integer"
    },
    "name": {
      "type": "string"
    },
    "email": {
      "type": "string"
    },
    "active": {
      "type": "boolean"
    },
    "tags": {
      "type": ["array", "null"],
      "items": {
        "type": "string"
      }
    }
  },
  "required": ["id", "name", "email", "active"]
}

Performance

The multi-threaded implementation is designed for processing large batches of JSON objects, though our benchmarks show interesting results:

• For small files: Single-threaded processing is generally more efficient
• For medium files: Multi-threading shows minimal improvements for specific operations
• For large files: Current implementation shows thread overhead may outweigh benefits

Benchmarks

The project includes comprehensive benchmarking tools to measure performance:

# Run mini benchmarks (quick demonstration)
cd c-lib/tests
./run_mini_benchmarks.sh

# Run full benchmarks (may take several minutes)
./run_benchmarks.sh

# Generate visualization charts
cd ../../py-lib/benchmarks
python visualize_benchmarks.py

# Generate comprehensive visualization
python visualize_comprehensive_benchmarks.py

Benchmark results are saved to c-lib/tests/benchmark_results.md and c-lib/tests/comprehensive_benchmark_results.md, with visualizations in the corresponding PNG files.

Actual Benchmark Results

Our comprehensive benchmarks revealed:

• Small files (< 100KB): Multi-threading overhead outweighs benefits, with performance similar to or worse than single-threaded processing
• Medium files (100KB-1MB): Multi-threading provides minimal improvement (0-12.5%) for schema generation
• Large files (1MB-10MB): Current multi-threaded implementation shows slight performance degradation (-7% to -8%)

Key findings:

1. Thread Overhead: For most file sizes, the overhead of creating and managing threads outweighs the benefits of parallel processing.

2. Optimal Use Case: Multi-threading appears to be most beneficial for medium-sized files (around 1,000 objects) for schema generation.

3. Future Optimizations: The multi-threaded implementation could be improved with:

   • More efficient work distribution
   • Thread pooling to reduce creation overhead
   • Optimized memory usage to reduce cache misses
   • Adaptive threading that only uses multiple threads when beneficial

For detailed benchmark analysis, see c-lib/tests/comprehensive_benchmark_results.md.

Python Usage

The Python bindings provide a simple, Pythonic interface to the cJSON-Tools library.

Installation

pip install cjson-tools

Examples

Flattening JSON

import json
from cjson_tools import flatten_json

# Flatten a single JSON object
nested_json = '''
{
    "person": {
        "name": "John Doe",
        "age": 30,
        "address": {
            "street": "123 Main St",
            "city": "Anytown",
            "zip": "12345"
        }
    }
}
'''

flattened = flatten_json(nested_json)
print(json.loads(flattened))
# Output: {"person.name": "John Doe", "person.age": 30, "person.address.street": "123 Main St", ...}

Batch Processing

from cjson_tools import flatten_json_batch

# Process multiple JSON objects at once
json_objects = [
    '{"a": {"b": 1}}',
    '{"x": {"y": {"z": 2}}}'
]

flattened_batch = flatten_json_batch(json_objects)
print(flattened_batch)
# Output: ['{"a.b": 1}', '{"x.y.z": 2}']

Schema Generation

from cjson_tools import generate_schema

# Generate a schema from a JSON object
json_obj = '''
{
    "id": 1,
    "name": "Product",
    "price": 29.99,
    "tags": ["electronics", "gadget"]
}
'''

schema = generate_schema(json_obj)
print(json.loads(schema))
# Output: {"type": "object", "properties": {"id": {"type": "number"}, ...}}

Multi-threading

from cjson_tools import flatten_json_batch, generate_schema_batch

# Enable multi-threading with a specific number of threads
flattened = flatten_json_batch(large_json_list, use_threads=True, num_threads=4)

# Auto-detect the optimal number of threads
schema = generate_schema_batch(large_json_list, use_threads=True)

For more examples, see the py-lib/examples directory.

🧪 Testing

Python Tests

cd py-lib

# Run unit tests
python3 tests/test_cjson_tools.py

# Run example script
python3 examples/example.py

# Performance testing
python3 -c "
import cjson_tools
import json
import time

# Quick performance test
data = [json.dumps({'id': i, 'nested': {'value': i*2}}) for i in range(1000)]
start = time.time()
result = cjson_tools.flatten_json_batch(data, use_threads=True)
print(f'Processed {len(data)} objects in {time.time()-start:.3f}s')
"

C Library Tests

cd c-lib/tests

# Run comprehensive dynamic tests
./run_dynamic_tests.sh

# Run specific test sizes
./run_dynamic_tests.sh --sizes "100,1000,10000"

# Build and run manual tests
make
./generate_test_data test.json 1000 3
../../bin/json_tools -f test.json

Benchmarking

# Python benchmarks
cd py-lib/benchmarks
python3 benchmark.py --quick

# C library benchmarks
cd c-lib/tests
./run_benchmarks.sh

# Memory profiling (requires valgrind)
valgrind --tool=memcheck --leak-check=full ../../bin/json_tools -f large_test.json

📦 Publishing & Distribution

Python Package Publishing

Setup for PyPI

cd py-lib

# Install build tools
pip install build twine

# Update version in setup.py
# Edit setup.py: version='1.4.0'

# Build package
python3 -m build

# Test upload to TestPyPI
twine upload --repository testpypi dist/*

# Upload to PyPI
twine upload dist/*

Automated Publishing (GitHub Actions)

# .github/workflows/publish.yml
name: Publish to PyPI
on:
  release:
    types: [published]
jobs:
  publish:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v3
    - name: Set up Python
      uses: actions/setup-python@v4
      with:
        python-version: '3.8'
    - name: Build and publish
      env:
        TWINE_USERNAME: __token__
        TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
      run: |
        cd py-lib
        pip install build twine
        python -m build
        twine upload dist/*

C Library Distribution

Creating Release Packages

# Create source distribution
make clean
tar -czf cjson-tools-1.4.0.tar.gz \
  --exclude='.git*' \
  --exclude='*.o' \
  --exclude='bin/*' \
  --exclude='py-lib/build' \
  --exclude='py-lib/*.egg-info' \
  .

# Create binary distribution (Linux)
make clean && make
mkdir -p cjson-tools-1.4.0-linux-x64/{bin,lib,include}
cp bin/json_tools cjson-tools-1.4.0-linux-x64/bin/
cp c-lib/include/*.h cjson-tools-1.4.0-linux-x64/include/
tar -czf cjson-tools-1.4.0-linux-x64.tar.gz cjson-tools-1.4.0-linux-x64/

Package Managers

Homebrew (macOS)

# Formula for Homebrew
class CjsonTools < Formula
  desc "High-performance JSON processing toolkit"
  homepage "https://github.com/amaye15/cJSON-Tools"
  url "https://github.com/amaye15/cJSON-Tools/archive/v1.4.0.tar.gz"
  sha256 "..."

  def install
    system "make"
    bin.install "bin/json_tools"
  end

  test do
    system "#{bin}/json_tools", "--help"
  end
end

APT Repository (Ubuntu/Debian)

# Build .deb package
mkdir -p cjson-tools-1.4.0/DEBIAN
cat > cjson-tools-1.4.0/DEBIAN/control << EOF
Package: cjson-tools
Version: 1.4.0
Architecture: amd64
Maintainer: Your Name <[e-mail]>
Description: High-performance JSON processing toolkit
EOF

mkdir -p cjson-tools-1.4.0/usr/bin
cp bin/json_tools cjson-tools-1.4.0/usr/bin/
dpkg-deb --build cjson-tools-1.4.0

� Automated CI/CD Pipeline

This project includes comprehensive GitHub Actions workflows for automated testing, security scanning, performance monitoring, and deployment:

🔄 Continuous Integration (ci.yml)

• Multi-platform testing: Ubuntu, macOS, Windows
• Python version matrix: 3.8, 3.9, 3.10, 3.11, 3.12
• C library testing: Build verification, memory leak detection with Valgrind
• Python package testing: Installation, functionality, performance validation
• Code quality: Black formatting, isort, flake8 linting
• Security scanning: Bandit, Safety dependency checks
• Integration tests: C CLI with Python validation, consistency checks

🔒 Security & Vulnerability Scanning (security.yml)

• Dependency scanning: Python package vulnerabilities with Safety
• Static analysis: CodeQL security analysis for C and Python
• Container security: Trivy vulnerability scanning
• License compliance: Automated license checking
• Memory safety: AddressSanitizer and Valgrind testing
• Weekly automated scans: Scheduled security monitoring

📊 Performance Monitoring (performance.yml)

• Automated benchmarks: Multi-platform performance testing
• Regression detection: Performance validation on PRs
• Scalability testing: Dataset sizes from 100 to 100K objects
• Memory efficiency: Memory usage monitoring and optimization
• Performance visualization: Automated chart generation
• Benchmark history: Long-term performance tracking

📦 Automated Publishing (publish.yml)

• Multi-platform wheels: Linux, macOS, Windows (x86_64, ARM64)
• Source distribution: Complete source package building
• PyPI publishing: Automated release to PyPI on tags
• Test PyPI: Optional test publishing for validation
• GitHub releases: Automated release asset creation
• Trusted publishing: Secure PyPI publishing with OIDC

📚 Documentation (docs.yml)

• API documentation: Automated C and Python API docs
• Usage examples: Comprehensive example generation
• Performance guides: Optimization documentation
• GitHub Pages: Automated documentation deployment
• Release validation: Version consistency checking

🎯 Workflow Features

Quality Assurance:

• ✅ Comprehensive test coverage across platforms and Python versions
• ✅ Memory leak detection with Valgrind
• ✅ Security vulnerability scanning
• ✅ Performance regression detection
• ✅ Code quality enforcement

Automation:

• 🔄 Automatic testing on every PR and push
• 📦 Automated PyPI publishing on releases
• 📊 Weekly performance and security monitoring
• 📚 Automatic documentation updates
• 🏷️ Badge updates for README

Security:

• 🔒 CodeQL static analysis
• 🛡️ Container security scanning
• 📋 License compliance monitoring
• 🔍 Dependency vulnerability tracking
• 🧪 Memory safety validation

Performance:

• ⚡ Multi-platform benchmarking
• 📈 Performance visualization
• 🎯 Regression detection
• 💾 Memory efficiency monitoring
• 📊 Throughput analysis

🚀 Getting Started with CI/CD

1. Fork the repository and enable GitHub Actions
2. Configure secrets for PyPI publishing (if needed)
3. Create a release to trigger automated publishing
4. Monitor workflows in the Actions tab

The CI/CD pipeline ensures high code quality, security, and performance while automating the entire release process from development to production deployment.

�🔧 Development & Contributing

Development Setup

# Full development environment
git clone https://github.com/amaye15/cJSON-Tools.git
cd cJSON-Tools

# Setup pre-commit hooks
pip install pre-commit
pre-commit install

# Build everything
make clean && make
cd py-lib && pip install -e . && cd ..

# Run all tests
cd c-lib/tests && ./run_dynamic_tests.sh && cd ../..
cd py-lib && python3 tests/test_cjson_tools.py && cd ..

Code Quality

# C code formatting (if clang-format available)
find c-lib -name "*.c" -o -name "*.h" | xargs clang-format -i

# Python code formatting
cd py-lib
pip install black isort flake8
black .
isort .
flake8 .

Performance Profiling

# Profile C library
cd c-lib/tests
perf record ../../bin/json_tools -f large_test.json
perf report

# Profile Python extension
cd py-lib
python3 -m cProfile -o profile.stats examples/example.py
python3 -c "import pstats; pstats.Stats('profile.stats').sort_stats('cumulative').print_stats(20)"

📄 License

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

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Areas for Contribution

• Performance optimizations
• Additional output formats
• Language bindings (Rust, Go, etc.)
• Documentation improvements
• Bug fixes and testing

📞 Support

• Issues: GitHub Issues
• Documentation: See py-lib/examples/ and c-lib/tests/
• Performance: Run benchmarks with ./run_dynamic_tests.sh