Frequently Asked Questions

Common questions and troubleshooting tips for the ARFF Format Converter v2.0.

General Questions

What is ARFF Format Converter v2.0?

ARFF Format Converter v2.0 is a high-performance Python package that converts ARFF (Attribute-Relation File Format) files to modern data formats like Parquet, CSV, JSON, XLSX, XML, and ORC. It delivers up to 100x performance improvements over traditional converters using Polars, PyArrow, and parallel processing optimizations.

What makes v2.0 ultra-fast?

Version 2.0 uses several cutting-edge optimizations:

• Polars integration: Ultra-fast DataFrame operations
• PyArrow optimization: Zero-copy memory operations
• Parallel processing: Multi-core utilization
• Memory mapping: Efficient handling of large files
• Chunked processing: Scalable memory management
• Fast mode: Skip validation for production workloads

What formats are supported?

The converter supports conversion to these modern formats:

• Parquet - Best performance and compression (recommended)
• CSV - Universal compatibility
• JSON - Web APIs and NoSQL databases
• XLSX - Excel spreadsheets
• XML - Structured documents
• ORC - Big data analytics (Spark, Hive)

How fast is it really?

Performance benchmarks show remarkable improvements:

• 100x faster than traditional converters
• Processing speed: Up to 1M+ rows/second
• Memory efficiency: 50-80% reduction in RAM usage
• File size: Up to 90% compression with Parquet

Installation & Setup

How do I install the converter?

Install using pip or uv (recommended for speed):

# Using uv (fastest)
uv add arff-format-converter

# Using pip
pip install arff-format-converter

# For development
pip install arff-format-converter[dev]

What are the system requirements?

• Python: 3.8+ (3.11+ recommended for best performance)
• Memory: 512MB minimum, 2GB+ recommended for large files
• CPU: Multi-core systems benefit from parallel processing
• Storage: Fast SSD recommended for optimal I/O performance

Can I use it without Polars or PyArrow?

Yes! The converter gracefully falls back to pandas if Polars/PyArrow are unavailable, though you'll lose the ultra-performance benefits. Install with:

pip install arff-format-converter[minimal]

Usage Questions

What's the fastest way to convert a file?

For maximum speed, use ultra-performance mode with Parquet output:

from arff_format_converter import ARFFConverter

converter = ARFFConverter(
    fast_mode=True,      # Skip validation
    parallel=True,       # Multi-core processing
    use_polars=True,     # Polars optimization
    memory_map=True,     # Memory mapping
    chunk_size=100000    # Large chunks
)

result = converter.convert(
    input_file="data.arff",
    output_dir="./output",
    output_format="parquet"
)

How do I handle very large ARFF files?

For files larger than available memory, use these settings:

# Memory-efficient configuration
converter = ARFFConverter(
    memory_map=True,      # Essential for large files
    chunk_size=50000,     # Adjust based on available RAM
    use_polars=True,      # Better memory management
    parallel=False        # Reduce memory pressure
)

# Process large file
result = converter.convert(
    input_file="huge_dataset.arff",
    output_dir="./output",
    output_format="parquet"  # Best compression
)

Can I process multiple files at once?

Yes! Use batch processing for optimal efficiency:

from pathlib import Path

input_files = list(Path("datasets").glob("*.arff"))
results = converter.batch_convert(
    input_files=input_files,
    output_dir=Path("./output"),
    output_format="parquet",
    parallel=True  # Process files in parallel
)

How do I use the CLI interface?

The command-line interface provides full functionality:

# Basic conversion
arff-format-converter --file data.arff --output ./output --format parquet

# Ultra-fast mode
arff-format-converter --file data.arff --output ./output --format parquet --fast --parallel

# Benchmark performance
arff-format-converter --file data.arff --output ./benchmarks --benchmark

Performance Optimization

Which output format should I choose?

Format recommendations based on use case:

• Parquet: Best overall choice (speed + compression + compatibility)
• CSV: Maximum compatibility, human-readable
• JSON: Web APIs, JavaScript applications
• ORC: Big data analytics (Spark, Hive, Presto)
• XLSX: Business reports, Excel integration
• XML: Legacy systems, structured documents

How do I optimize for my specific use case?

Use the built-in benchmark feature to find optimal settings:

# Benchmark all formats
results = converter.benchmark(
    input_file="sample_data.arff",
    formats=["csv", "json", "parquet", "xlsx"],
    iterations=3
)

# Find optimal format
fastest = min(results.items(), key=lambda x: x[1]['duration'])
print(f"Fastest format: {fastest[0]}")

What chunk size should I use?

Chunk size depends on your system and file characteristics:

• Small files (<100MB): Default (10,000 rows)
• Medium files (100MB-1GB): 50,000-100,000 rows
• Large files (>1GB): 100,000-500,000 rows
• Memory-constrained: 1,000-5,000 rows

Troubleshooting

The conversion is slower than expected. What should I check?

Common performance issues and solutions:

• Check fast_mode: Enable with fast_mode=True for 20-30% speedup
• Enable parallel processing: Set parallel=True on multi-core systems
• Use Polars: Ensure use_polars=True (default)
• Increase chunk_size: Try larger values for better throughput
• Check disk I/O: Use fast SSD storage for input/output
• Memory availability: Ensure sufficient RAM for chunk size

I'm getting memory errors with large files. How do I fix this?

Memory optimization strategies:

# Memory-constrained settings
converter = ARFFConverter(
    fast_mode=False,      # Enable validation (uses less memory)
    parallel=False,       # Single-threaded processing
    use_polars=False,     # Use pandas (sometimes more memory-efficient)
    chunk_size=5000,      # Smaller chunks
    memory_map=True       # Essential for large files
)

The output file is corrupted or incomplete. What's wrong?

Common causes and solutions:

• Insufficient disk space: Check available storage
• Invalid ARFF file: Validate input with fast_mode=False
• Encoding issues: Try different encoding settings
• Interrupted process: Ensure stable execution environment
• Permission errors: Check write permissions on output directory

Some data values are missing or incorrect. Why?

Data integrity checks:

• Missing values: ARFF '?' symbols are preserved as NaN/null
• Data types: Some formats have limited type support
• Encoding: Ensure proper character encoding
• Validation: Use fast_mode=False for data validation

Can I recover from failed conversions?

Yes! Use the fallback pattern for robust processing:

def safe_convert(input_file, output_dir):
    # Try ultra-fast mode first
    try:
        fast_converter = ARFFConverter(fast_mode=True, parallel=True)
        return fast_converter.convert(input_file, output_dir, "parquet")
    except:
        # Fallback to safe mode
        safe_converter = ARFFConverter(fast_mode=False, parallel=False)
        return safe_converter.convert(input_file, output_dir, "csv")

Integration & Development

How do I integrate this into my data pipeline?

The converter is designed for easy integration:

import logging
from arff_format_converter import ARFFConverter

class DataPipeline:
    def __init__(self):
        self.converter = ARFFConverter(fast_mode=True, parallel=True)
    
    def process_arff(self, input_path, output_path):
        result = self.converter.convert(input_path, output_path, "parquet")
        
        if result.success:
            logging.info(f"Processed {input_path}: {result.duration:.2f}s")
            return result.output_file
        else:
            logging.error(f"Failed to process {input_path}")
            return None

Is there a REST API available?

The package focuses on Python and CLI interfaces. For web APIs, you can easily wrap the converter in Flask, FastAPI, or Django. See the examples section for integration patterns.

How do I contribute to the project?

Visit the GitHub repository for contribution guidelines, issue reporting, and development setup instructions.

Advanced Topics

Can I customize the conversion process?

Yes! The converter provides extensive configuration options:

converter = ARFFConverter(
    fast_mode=True,           # Skip validation
    parallel=True,            # Enable parallelism
    use_polars=True,          # Use Polars optimization
    memory_map=True,          # Memory mapping
    chunk_size=50000,         # Custom chunk size
    encoding='utf-8',         # Character encoding
    delimiter=',',            # CSV delimiter
    compression='snappy'      # Parquet compression
)

How does the converter handle edge cases?

The converter is designed to handle various ARFF file variations:

• Missing values: Properly converted to format-appropriate nulls
• Special characters: Unicode and encoding support
• Large numbers: High-precision numeric handling
• Date attributes: ISO format conversion
• Quoted strings: Proper escaping and parsing

What about data privacy and security?

The converter processes data locally and doesn't transmit information externally:

• Local processing: All conversion happens on your machine
• No network calls: No data sent to external services
• Memory safety: Secure memory handling with modern libraries
• File permissions: Respects system file access controls