#!/usr/bin/env node
// Scenario 2: Deep Dive Analysis (Large Arrow Table)
// Tests large Arrow tables (> 1MB) sent via HTTP fileserver

const { SmartSend } = require('../js_bridge');

// Configuration
const ANALYSIS_SUBJECT = "analysis_results";
const NATS_URL = "nats://localhost:4222";

// Create correlation ID for tracing
const correlationId = require('uuid').v4();

// Sender: Send large Arrow table to Julia
async function sendLargeTable() {
    // Create a large DataFrame-like structure (10 million rows)
    // For testing, we'll create a smaller but still large table
    const numRows = 1000000; // 1 million rows
    
    const data = {
        id: Array.from({ length: numRows }, (_, i) => i + 1),
        value: Array.from({ length: numRows }, () => Math.random()),
        category: Array.from({ length: numRows }, () => ['A', 'B', 'C'][Math.floor(Math.random() * 3)])
    };
    
    // Convert to Arrow Table
    const { Table, Vector, RecordBatch } = require('apache-arrow');
    
    const idVector = Vector.from(data.id);
    const valueVector = Vector.from(data.value);
    const categoryVector = Vector.from(data.category);
    
    const table = Table.from({
        id: idVector,
        value: valueVector,
        category: categoryVector
    });
    
    // Send via SmartSend with type="table"
    const env = await SmartSend(
        ANALYSIS_SUBJECT,
        table,
        "table",
        { correlationId }
    );
    
    console.log(`Sent large table with ${numRows} rows`);
    console.log(`Correlation ID: ${correlationId}`);
    console.log(`Transport: ${env.transport}`);
    console.log(`URL: ${env.url || 'N/A'}`);
}

// Run the sender
sendLargeTable().catch(console.error);