We're thrilled to announce the release of bx-csv, a powerful premium module that brings enterprise-grade CSV processing capabilities to BoxLang+ subscribers! Built on Apache Commons CSV, this module provides both traditional functions and a modern, fluent API that makes working with CSV files a breeze.

✨ What's New?

The bx-csv module transforms how you work with CSV files in BoxLang, offering:

• 🚀 Modern Fluent API - Chainable, elegant code that's easy to read and maintain
• ⚡ Streaming Support - Process massive files without memory concerns
• 🎯 Smart Data Transformation - Built-in filtering and mapping during load operations
• 📦 Multi-Format Support - Seamless conversion between CSV, JSON, Query, and arrays
• 🎨 Flexible Configuration - Custom delimiters, quotes, escapes, and line separators
• 💪 Production Ready - Battle-tested Apache Commons CSV foundation

🎟️ Subscription & Trial Information

The bx-csv module is available exclusively to BoxLang+ and BoxLang++ subscribers. However, we want everyone to experience its power, so it comes with a generous 60-day trial when installed alongside the bx-plus module.

Ready to try it? Install it now:

box install bx-csv

Not a subscriber yet? Check out our plans and start your trial today!

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🚀 The Modern Way: Fluent API

We've designed the fluent API to be intuitive and powerful. Here's how easy it is to create and manipulate CSV files:

Creating a CSV File

// Create a simple contacts file
CSV()
    .setHeaders( "Name", "Email", "Age" )
    .addRow( [ "John Doe", "john@example.com", 30 ] )
    .addRow( [ "Jane Smith", "jane@example.com", 25 ] )
    .addRow( [ "Bob Johnson", "bob@example.com", 35 ] )
    .save( "contacts.csv" );

That's it! Clean, readable, and maintainable. The fluent API chains methods together, making your intent crystal clear.

Loading and Transforming Data

// Load, filter, and transform in one elegant chain
CSV( "raw-data.csv" )
    .filter( ( row ) => {
        // Only include rows with valid emails
        return row[ 2 ].find( "@" ) > 0;
    } )
    .map( ( row ) => {
        // Normalize the data
        row[ 0 ] = row[ 0 ].trim().toUpperCase(); // Name
        row[ 1 ] = row[ 1 ].trim(); // Phone
        row[ 2 ] = row[ 2 ].trim().toLowerCase(); // Email
        return row;
    } )
    .save( "cleaned-data.csv" );

⚡ Streaming Large Files

One of the most powerful features is the ability to process huge files without loading them entirely into memory:

// Process a multi-gigabyte file efficiently
CSV().process( "huge-file.csv", ( row ) => {
    // Process each row individually
    queryExecute(
        "INSERT INTO customers (name, email) VALUES ( ?, ? )",
        [ row[ 1 ], row[ 2 ] ]
    );
} );

The streaming API ensures your application remains responsive even when processing files with millions of rows.

📊 Seamless Format Conversion

The bx-csv module makes it trivial to convert between different data formats:

From Database to CSV

// Export query results directly to CSV
qData = queryExecute( "
    SELECT
        customer_name,
        order_date,
        product_name,
        quantity,
        unit_price,
        ( quantity * unit_price ) AS total
    FROM orders
    WHERE order_date >= ?
", [ dateAdd( "m", -1, now() ) ] );

CSVFile.fromQuery( qData )
    .delimiter( ',' )
    .quoteMode( "MINIMAL" )
    .save( "reports/monthly-orders.csv" );

From CSV to JSON

// Convert CSV to pretty-printed JSON
csv = CSV()
    .setHeaders( "Name", "Age", "City" )
    .addRow( [ "John", 30, "New York" ] )
    .addRow( [ "Jane", 25, "Los Angeles" ] );

jsonString = csv.toJson( true );
// Returns formatted JSON array of objects

From JSON to CSV

// Import JSON data and save as CSV
jsonData = '[
    { "Product": "Widget A", "Price": 10.99, "InStock": true },
    { "Product": "Widget B", "Price": 15.99, "InStock": false }
]';

CSVFile.fromJson( jsonData ).save( "products.csv" );

🎨 Flexible Configuration

The module supports virtually any CSV format you might encounter:

// Pipe-delimited with custom quote character
CSV()
    .delimiter( '|' )
    .quote( '\'' )
    .trim( true )
    .setHeaders( "Name", "Age", "City" )
    .addRow( [ "John O'Brien", 30, "New York" ] )
    .save( "data.txt" );

// Tab-separated values (TSV)
CSV()
    .delimiter( '\t' )
    .load( "data.tsv" );

// Handle comments and empty lines
CSV()
    .commentMarker( '#' )
    .ignoreEmptyLines( true )
    .load( "data-with-comments.csv" );

💼 Real-World Use Cases

Data Import Pipeline

// Build a robust data import system
CSV()
    .setPath( "imports/daily-feed.csv" )
    .delimiter( '|' )
    .trim( true )
    .ignoreEmptyLines( true )
    .filter( ( row ) => {
        // Validate required fields
        return row[ 1 ] != "" && row[ 2 ] != "" && row[ 3 ] > 0;
    } )
    .process( ( row ) => {
        // Insert validated rows into database
        try {
            queryExecute(
                "INSERT INTO products ( sku, name, price ) VALUES ( ?, ?, ? )",
                [ row[ 1 ], row[ 2 ], row[ 3 ] ]
            );
        } catch( any e ) {
            logError( "Failed to import row: " & row[ 1 ], e );
        }
    } );

Report Generation

// Generate monthly sales report
qSales = queryExecute( "
    SELECT
        DATE_FORMAT( order_date, '%Y-%m' ) as month,
        COUNT(*) as total_orders,
        SUM( order_total ) as revenue,
        AVG( order_total ) as avg_order
    FROM orders
    WHERE order_date >= DATE_SUB( NOW(), INTERVAL 12 MONTH )
    GROUP BY month
    ORDER BY month DESC
" );

CSVFile.fromQuery( qSales )
    .headerComments(
        "Monthly Sales Report",
        "Generated: " & now(),
        "Period: Last 12 Months"
    )
    .commentMarker( '#' )
    .quoteMode( "NON_NUMERIC" )
    .save( "reports/monthly-sales.csv" );

Data Consolidation

// Combine multiple CSV files from different sources
combined = CSV().setHeaders( "Name", "Email", "Source", "ImportDate" );

// Process each source file
sources = [
    { file: "mailchimp-export.csv", name: "MailChimp" },
    { file: "salesforce-export.csv", name: "Salesforce" },
    { file: "hubspot-export.csv", name: "HubSpot" }
];

sources.each( ( source ) => {
    CSV().process( source.file, ( row ) => {
        // Skip header rows
        if ( row[ 1 ] != "Name" && row[ 1 ] != "name" ) {
            combined.addRow( [
                row[ 1 ],
                row[ 2 ],
                source.name,
                now()
            ] );
        }
    } );
} );

combined.save( "combined-contacts.csv" );

📚 Complete Documentation

The bx-csv module is fully documented in our official BoxLang documentation, and we've even created a dedicated MCP Server for it:

• Complete Documentation - Full API reference and guides
• MCP Server - Connect directly to our documentation system
• Fluent API Reference - All chainable methods
• BIF Reference - Built-in function documentation

🎁 Get Access

bx-ldap is available exclusively to BoxLang +/++ subscribers. Join our subscription program to access this and other premium modules that extend BoxLang's capabilities:

• Priority Support - Get help when you need it
• Premium Modules - Access subscriber-only modules
• Early Access - Be first to try new features
• Exclusive Benefits - CFCasts account, FORGEBOX Pro, and more

🛒 Purchase Options

Ready to unlock bx-ldap and other premium modules? Choose your plan:

🌟 View BoxLang Plans & Pricing

Need help choosing the right plan or have questions? Contact us directly:

📧 info@boxlang.io