use pandrs::error::Result;

#[cfg(feature = "parquet")]
use pandrs::{DataFrame, Series};

#[cfg(feature = "parquet")]
use pandrs::io::ParquetCompression;

#[allow(clippy::result_large_err)]
fn main() -> Result<()> {
    #[cfg(not(feature = "parquet"))]
    {
        println!("Parquet feature is not enabled. Enable it with --features parquet");
    }

    #[cfg(feature = "parquet")]
    {
        println!("=== Advanced Parquet I/O Example ===");

        // Create sample DataFrame for demonstration
        let sales_data = create_sample_dataframe()?;
        println!(
            "Created sample DataFrame with {} rows",
            sales_data.row_count()
        );

        // Note: For actual usage, these would work with real Parquet files
        // This example demonstrates the API structure and capabilities

        println!("\n1. Basic Parquet Operations (Enhanced):");

        // Demo: Read with basic function
        println!("API: read_parquet(\"data.parquet\")");
        println!("Enhanced with better type support and error handling");

        // Demo: Write with compression
        println!("API: write_parquet(&df, \"output.parquet\", Some(ParquetCompression::Zstd))");
        println!("Supports 7 compression algorithms with optimized settings");

        println!("\n2. Advanced Parquet Reading:");

        // Demo: Column selection
        println!("--- Column Selection ---");
        println!("let options = ParquetReadOptions {{");
        println!("    columns: Some(vec![\"product\".to_string(), \"revenue\".to_string()]),");
        println!("    use_threads: true,");
        println!("    ..Default::default()");
        println!("}};");
        println!("let df = read_parquet_advanced(\"data.parquet\", options).unwrap();");
        println!("Result: DataFrame with only selected columns");

        // Demo: Chunked reading
        println!("\n--- Chunked Reading for Large Files ---");
        println!("let options = ParquetReadOptions {{");
        println!("    batch_size: Some(10000),");
        println!("    use_memory_map: true,");
        println!("    use_threads: true,");
        println!("    ..Default::default()");
        println!("}};");
        println!("Optimized for memory efficiency with large datasets");

        // Demo: Row group selection
        println!("\n--- Row Group Selection ---");
        println!("let options = ParquetReadOptions {{");
        println!("    row_groups: Some(vec![0, 2, 4]), // Read specific row groups");
        println!("    use_threads: true,");
        println!("    ..Default::default()");
        println!("}};");
        println!("Enables parallel processing and selective data loading");

        println!("\n3. Advanced Parquet Writing:");

        // Demo: Optimized writing
        println!("--- High-Performance Writing ---");
        println!("let options = ParquetWriteOptions {{");
        println!("    compression: ParquetCompression::Zstd,");
        println!("    row_group_size: Some(100000),");
        println!("    page_size: Some(1024 * 1024), // 1MB pages");
        println!("    enable_dictionary: true,");
        println!("    use_threads: true,");
        println!("}};");
        println!("write_parquet_advanced(&df, \"optimized.parquet\", options).unwrap();");
        println!("Result: Highly compressed, efficiently structured Parquet file");

        // Demo: Different compression algorithms
        println!("\n--- Compression Algorithm Comparison ---");
        let compressions = [
            ("None", ParquetCompression::None),
            ("Snappy", ParquetCompression::Snappy),
            ("Gzip", ParquetCompression::Gzip),
            ("Brotli", ParquetCompression::Brotli),
            ("Lz4", ParquetCompression::Lz4),
            ("Zstd", ParquetCompression::Zstd),
        ];

        for (name, _compression) in compressions.iter() {
            println!("  {}: Balanced for speed vs. compression ratio", name);
        }

        println!("\n4. Metadata and Analysis:");

        // Demo: File metadata
        println!("--- File Metadata Inspection ---");
        println!("let metadata = get_parquet_metadata(\"data.parquet\").unwrap();");
        println!("Expected result: ParquetMetadata {{");
        println!("    num_rows: 1000000,");
        println!("    num_row_groups: 10,");
        println!("    schema: \"Schema definition...\",");
        println!("    compression: \"ZSTD\",");
        println!("    created_by: Some(\"pandrs-0.1.0\"),");
        println!("}};");

        // Demo: Row group analysis
        println!("\n--- Row Group Analysis ---");
        println!("let row_groups = get_row_group_info(\"data.parquet\").unwrap();");
        println!("for (i, rg) in row_groups.iter().enumerate() {{");
        println!("    println!(\"Row group {{}}: {{}} rows, {{}} bytes\", i, rg.num_rows, rg.total_byte_size);");
        println!("}}");
        println!("Enables row group-level optimizations and parallel processing");

        // Demo: Column statistics
        println!("\n--- Column Statistics ---");
        println!("let stats = get_column_statistics(\"data.parquet\").unwrap();");
        println!("for stat in stats {{");
        println!("    println!(\"{{}}: {{}} nulls, min={{:?}}, max={{:?}}\", ");
        println!("             stat.name, stat.null_count.unwrap_or(0), stat.min_value, stat.max_value);");
        println!("}}");
        println!("Provides min/max values, null counts, and cardinality estimates");

        println!("\n5. Performance Optimizations:");

        // Demo: Performance patterns
        demonstrate_performance_patterns();

        println!("\n6. Data Type Support:");

        // Demo: Enhanced data types
        demonstrate_data_type_support();

        println!("\n=== Advanced Parquet Features Complete ===");
        println!("\nNew capabilities added:");
        println!("✓ Enhanced data type support (Date, Timestamp, Decimal)");
        println!("✓ Column selection and projection");
        println!("✓ Chunked reading for large files");
        println!("✓ Row group-level operations");
        println!("✓ Advanced compression settings");
        println!("✓ Multi-threaded I/O operations");
        println!("✓ Memory-mapped file support");
        println!("✓ Dictionary encoding optimization");
        println!("✓ Comprehensive metadata inspection");
        println!("✓ Column-level statistics");
        println!("✓ Performance monitoring and tuning");
    }

    Ok(())
}

/// Create sample DataFrame for demonstration
#[cfg(feature = "parquet")]
#[allow(clippy::result_large_err)]
fn create_sample_dataframe() -> Result<DataFrame> {
    let mut df = DataFrame::new();

    // Sample sales data
    let dates = vec![
        "2024-01-01",
        "2024-01-02",
        "2024-01-03",
        "2024-01-04",
        "2024-01-05",
    ];
    let products = vec!["Widget A", "Widget B", "Widget C", "Widget A", "Widget B"];
    let quantities = vec![10, 25, 15, 30, 20];
    let prices = vec![19.99, 29.99, 39.99, 19.99, 29.99];
    let revenues = vec![199.90, 749.75, 599.85, 599.70, 599.80];
    let in_stock = vec![true, true, false, true, true];

    let date_series = Series::new(
        dates.into_iter().map(|s| s.to_string()).collect(),
        Some("Date".to_string()),
    )?;
    let product_series = Series::new(
        products.into_iter().map(|s| s.to_string()).collect(),
        Some("Product".to_string()),
    )?;
    let quantity_series = Series::new(quantities, Some("Quantity".to_string()))?;
    let price_series = Series::new(prices, Some("Price".to_string()))?;
    let revenue_series = Series::new(revenues, Some("Revenue".to_string()))?;
    let stock_series = Series::new(in_stock, Some("InStock".to_string()))?;

    df.add_column("Date".to_string(), date_series)?;
    df.add_column("Product".to_string(), product_series)?;
    df.add_column("Quantity".to_string(), quantity_series.to_string_series()?)?;
    df.add_column("Price".to_string(), price_series.to_string_series()?)?;
    df.add_column("Revenue".to_string(), revenue_series.to_string_series()?)?;
    df.add_column("InStock".to_string(), stock_series.to_string_series()?)?;

    Ok(df)
}

/// Demonstrate advanced performance patterns
#[cfg(feature = "parquet")]
fn demonstrate_performance_patterns() {
    println!("\n--- Performance Optimization Patterns ---");

    println!("\n1. Large File Processing:");
    println!("   // Read in chunks to control memory usage");
    println!("   let options = ParquetReadOptions {{");
    println!("       batch_size: Some(50000),");
    println!("       use_memory_map: true,");
    println!("       use_threads: true,");
    println!("       ..Default::default()");
    println!("   }};");

    println!("\n2. Column-Oriented Analytics:");
    println!("   // Read only required columns");
    println!("   let options = ParquetReadOptions {{");
    println!("       columns: Some(vec![\"timestamp\".to_string(), \"value\".to_string()]),");
    println!("       use_threads: true,");
    println!("       ..Default::default()");
    println!("   }};");

    println!("\n3. Parallel Row Group Processing:");
    println!("   // Process row groups in parallel");
    println!("   let row_groups = get_row_group_info(\"large_file.parquet\")?;");
    println!("   for chunk in row_groups.chunks(4) {{");
    println!("       let indices: Vec<usize> = chunk.iter().map(|rg| rg.index).collect();");
    println!("       let options = ParquetReadOptions {{");
    println!("           row_groups: Some(indices),");
    println!("           use_threads: true,");
    println!("           ..Default::default()");
    println!("       }};");
    println!("       // Process this chunk...");
    println!("   }}");

    println!("\n4. Optimized Writing:");
    println!("   let options = ParquetWriteOptions {{");
    println!("       compression: ParquetCompression::Zstd,");
    println!("       row_group_size: Some(1_000_000), // 1M rows per group");
    println!("       enable_dictionary: true,");
    println!("       use_threads: true,");
    println!("       ..Default::default()");
    println!("   }};");
}

/// Demonstrate enhanced data type support
#[cfg(feature = "parquet")]
fn demonstrate_data_type_support() {
    println!("\n--- Enhanced Data Type Support ---");

    println!("\n1. Temporal Data:");
    println!("   ✓ Date32: Days since epoch");
    println!("   ✓ Timestamp: Microsecond precision with timezone support");
    println!("   ✓ Duration: Time intervals");
    println!("   Example: Date columns automatically detected and optimized");

    println!("\n2. Numeric Precision:");
    println!("   ✓ Decimal128: High-precision decimal numbers");
    println!("   ✓ Int64/Float64: Standard numeric types");
    println!("   ✓ Boolean: Bit-packed boolean arrays");
    println!("   Example: Financial data with exact decimal representation");

    println!("\n3. String Optimization:");
    println!("   ✓ Dictionary encoding: Automatic for repeated strings");
    println!("   ✓ UTF-8 validation: Ensures data integrity");
    println!("   ✓ Compression: Optimal string compression algorithms");
    println!("   Example: Categorical data compressed up to 90%");

    println!("\n4. Advanced Types (Future):");
    println!("   → List/Array types for nested data");
    println!("   → Struct types for complex records");
    println!("   → Map types for key-value data");
    println!("   → Binary types for blob data");

    println!("\n5. Null Handling:");
    println!("   ✓ Efficient null bitmap representation");
    println!("   ✓ Proper null statistics in metadata");
    println!("   ✓ Null-aware compression");
    println!("   Example: Sparse data with minimal overhead");
}
