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

using NATS
using Arrow
using DataFrames
using JSON3
using UUIDs

# Include the bridge module
include("../src/julia_bridge.jl")
using .BiDirectionalBridge

# Configuration
const ANALYSIS_SUBJECT = "analysis_results"
const RESPONSE_SUBJECT = "analysis_response"
const NATS_URL = "nats://localhost:4222"

# Create correlation ID for tracing
correlation_id = string(uuid4())

# Receiver: Listen for analysis results
function start_analysis_listener()
    conn = NATS.Connection(NATS_URL)
    try
        NATS.subscribe(conn, ANALYSIS_SUBJECT) do msg
            log_trace("Received message from $(msg.subject)")
            
            # Parse the envelope
            env = MessageEnvelope(String(msg.data))
            
            # Use SmartReceive to handle the data
            result = SmartReceive(msg)
            
            # Process the data based on type
            if result.envelope.type == "table"
                df = result.data
                log_trace("Received DataFrame with $(nrows(df)) rows")
                log_trace("DataFrame columns: $(names(df))")
                
                # Send acknowledgment
                response = Dict(
                    "status" => "Processed",
                    "correlation_id" => env.correlation_id,
                    "row_count" => nrows(df)
                )
                NATS.publish(conn, RESPONSE_SUBJECT, JSON3.stringify(response))
            end
        end
        
        # Keep listening for 10 seconds
        sleep(10)
    finally
        NATS.close(conn)
    end
end

# Helper: Log with correlation ID
function log_trace(message)
    timestamp = Dates.now()
    println("[$timestamp] [Correlation: $correlation_id] $message")
end

# Run the listener
start_analysis_listener()