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narawat
2026-03-08 10:42:54 +07:00
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#!/usr/bin/env julia
# Test script for mixed-content message testing
# Tests receiving a mix of text, json, table, image, audio, video, and binary data
# from Julia serviceA to Julia serviceB using NATSBridge.jl smartreceive
#
# This test demonstrates that any combination and any number of mixed content
# can be sent and received correctly.
using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP, Base64
# Include the bridge module
include("./src/NATSBridge.jl")
using .NATSBridge
# Configuration
const SUBJECT = "/test/mix"
const NATS_URL = "nats.yiem.cc"
const FILESERVER_URL = "http://192.168.88.104:8080"
# ------------------------------------------------------------------------------------------------ #
# test mixed content transfer #
# ------------------------------------------------------------------------------------------------ #
# Helper: Log with correlation ID
function log_trace(message)
timestamp = Dates.now()
println("[$timestamp] $message")
end
# Receiver: Listen for messages and verify mixed content handling
function test_mix_receive()
conn = NATS.connect(NATS_URL)
incoming_msg = nothing
NATS.subscribe(conn, SUBJECT) do msg
log_trace("Received message on $(msg.subject)")
incoming_msg = msg
# # Use NATSBridge.smartreceive to handle the data
# # API: smartreceive(msg, download_handler; max_retries, base_delay, max_delay)
# result = NATSBridge.smartreceive(
# msg;
# max_retries = 5,
# base_delay = 100,
# max_delay = 5000
# )
# log_trace("Received $(length(result["payloads"])) payloads")
# # Result is an envelope dictionary with payloads field containing list of (dataname, data, data_type) tuples
# for (dataname, data, data_type) in result["payloads"]
# log_trace("\n=== Payload: $dataname (type: $data_type) ===")
# # Handle different data types
# if data_type == "text"
# # Text data - should be a String
# if isa(data, String)
# log_trace(" Type: String")
# log_trace(" Length: $(length(data)) characters")
# # Display first 200 characters
# if length(data) > 200
# log_trace(" First 200 chars: $(data[1:200])...")
# else
# log_trace(" Content: $data")
# end
# # Save to file
# output_path = "./received_$dataname.txt"
# write(output_path, data)
# log_trace(" Saved to: $output_path")
# else
# log_trace(" ERROR: Expected String, got $(typeof(data))")
# end
# elseif data_type == "dictionary"
# # Dictionary data - should be JSON object
# if isa(data, JSON.Object{String, Any})
# log_trace(" Type: Dict")
# log_trace(" Keys: $(keys(data))")
# # Display nested content
# for (key, value) in data
# log_trace(" $key => $value")
# end
# # Save to JSON file
# output_path = "./received_$dataname.json"
# json_str = JSON.json(data, 2)
# write(output_path, json_str)
# log_trace(" Saved to: $output_path")
# else
# log_trace(" ERROR: Expected Dict, got $(typeof(data))")
# end
# elseif data_type == "table"
# # Table data - should be a DataFrame
# tabledata = deepcopy(data)
# println("found table data")
# break
# # return data
# # if isa(data, DataFrame)
# # log_trace(" Type: DataFrame")
# # log_trace(" Dimensions: $(size(data, 1)) rows x $(size(data, 2)) columns")
# # log_trace(" Columns: $(names(data))")
# # # Display first few rows
# # log_trace(" First 5 rows:")
# # display(data[1:min(5, size(data, 1)), :])
# # # Save to Arrow file
# # output_path = "./received_$dataname.arrow"
# # io = IOBuffer()
# # Arrow.write(io, data)
# # write(output_path, take!(io))
# # log_trace(" Saved to: $output_path")
# # else
# # log_trace(" ERROR: Expected DataFrame, got $(typeof(data))")
# # end
# elseif data_type == "image"
# # Image data - should be Vector{UInt8}
# if isa(data, Vector{UInt8})
# log_trace(" Type: Vector{UInt8} (binary)")
# log_trace(" Size: $(length(data)) bytes")
# # Save to file
# output_path = "./received_$dataname.bin"
# write(output_path, data)
# log_trace(" Saved to: $output_path")
# else
# log_trace(" ERROR: Expected Vector{UInt8}, got $(typeof(data))")
# end
# elseif data_type == "audio"
# # Audio data - should be Vector{UInt8}
# if isa(data, Vector{UInt8})
# log_trace(" Type: Vector{UInt8} (binary)")
# log_trace(" Size: $(length(data)) bytes")
# # Save to file
# output_path = "./received_$dataname.bin"
# write(output_path, data)
# log_trace(" Saved to: $output_path")
# else
# log_trace(" ERROR: Expected Vector{UInt8}, got $(typeof(data))")
# end
# elseif data_type == "video"
# # Video data - should be Vector{UInt8}
# if isa(data, Vector{UInt8})
# log_trace(" Type: Vector{UInt8} (binary)")
# log_trace(" Size: $(length(data)) bytes")
# # Save to file
# output_path = "./received_$dataname.bin"
# write(output_path, data)
# log_trace(" Saved to: $output_path")
# else
# log_trace(" ERROR: Expected Vector{UInt8}, got $(typeof(data))")
# end
# elseif data_type == "binary"
# # Binary data - should be Vector{UInt8}
# if isa(data, Vector{UInt8})
# log_trace(" Type: Vector{UInt8} (binary)")
# log_trace(" Size: $(length(data)) bytes")
# # Save to file
# output_path = "./received_$dataname.bin"
# write(output_path, data)
# log_trace(" Saved to: $output_path")
# else
# log_trace(" ERROR: Expected Vector{UInt8}, got $(typeof(data))")
# end
# else
# log_trace(" ERROR: Unknown data type '$data_type'")
# end
# end
# Summary
# println("\n=== Verification Summary ===")
# text_count = count(x -> x[3] == "text", result["payloads"])
# dict_count = count(x -> x[3] == "dictionary", result["payloads"])
# table_count = count(x -> x[3] == "table", result["payloads"])
# image_count = count(x -> x[3] == "image", result["payloads"])
# audio_count = count(x -> x[3] == "audio", result["payloads"])
# video_count = count(x -> x[3] == "video", result["payloads"])
# binary_count = count(x -> x[3] == "binary", result["payloads"])
# log_trace("Text payloads: $text_count")
# log_trace("Dictionary payloads: $dict_count")
# log_trace("Table payloads: $table_count")
# log_trace("Image payloads: $image_count")
# log_trace("Audio payloads: $audio_count")
# log_trace("Video payloads: $video_count")
# log_trace("Binary payloads: $binary_count")
# # Print transport type info for each payload if available
# println("\n=== Payload Details ===")
# for (dataname, data, data_type) in result["payloads"]
# if data_type in ["image", "audio", "video", "binary"]
# log_trace("$dataname: $(length(data)) bytes (binary)")
# elseif data_type == "table"
# data = DataFrame(data)
# log_trace("$dataname: $(size(data, 1)) rows x $(size(data, 2)) columns (DataFrame)")
# elseif data_type == "dictionary"
# log_trace("$dataname: $(length(JSON.json(data))) bytes (Dict)")
# elseif data_type == "text"
# log_trace("$dataname: $(length(data)) characters (String)")
# end
# end
end
# Keep listening for 2 minutes
sleep(20)
NATS.drain(conn)
return incoming_msg
end
# Run the test
println("Starting mixed-content transport test...")
println("Note: This receiver will wait for messages from the sender.")
println("Run test_julia_to_julia_mix_sender.jl first to send test data.")
# Run receiver
println("\ntesting smartreceive for mixed content")
incoming_msg = test_mix_receive()
println("\nTest completed.")
Check architecture.md. For sending table I want to add JSON in addition to Apache Arrow.
Currently I use "table" datatype when sending table data using Arrow. Now table that I want to send using JSON
I will use "jsontable" as datatype while sending table using Arrow I will use "arrowtable" as datatype.
This will select how smartsend and smartreceive serialize/deserialize the table.
Can you help me do this? Save the updated architecture.md into updated_architecture.md file. I will deal with source code later.
Now update implementation.md and save into updated_implementation.md
Keep in mind that Julia DataFrame and Python Pandas rely on columnar-oriented dictionary to create as the following example:
julia> dict = Dict("customer age" => [15, 20, 25],
"first name" => ["Rohit", "Rahul", "Akshat"])
julia> DataFrame(dict)
python> data = {
"Name": ["Alice", "Bob", "Charlie"],
"Age": [25, 30, 35],
"Score": [88.5, 92.0, 79.5]
}
python> df = pd.DataFrame(data)
But JS use Array of Objects while MicroPython use list of lists. Both are row-oriented structure.
So use row-oriented JSON to send across these languages. For Julia and Python, only convert
row-oriented JSON to columnar-oriented dictionary for "going-into" and vise versa for "coming-out"
a dataframe while JS and MicroPython won't require such process.
You may add these info into architecture.md if you see fit.