NATSBridge/docs/architecture.md

Architecture Documentation: Bi-Directional Data Bridge

Overview

This document describes the architecture for a high-performance, bi-directional data bridge between Julia, JavaScript, and Python/Micropython applications using NATS (Core & JetStream), implementing the Claim-Check pattern for large payloads.

The system enables seamless communication across all three platforms:

• Julia ↔ JavaScript bi-directional messaging
• JavaScript ↔ Python/Micropython bi-directional messaging
• Julia ↔ Python/Micropython bi-directional messaging (via JSON serialization)

File Server Handler Architecture

The system uses handler functions to abstract file server operations, allowing support for different file server implementations (e.g., Plik, AWS S3, custom HTTP server).

Handler Function Signatures:

# Upload handler - uploads data to file server and returns URL
# The handler is passed to smartsend as fileserver_upload_handler parameter
# It receives: (file_server_url::String, dataname::String, data::Vector{UInt8})
# Returns: Dict{String, Any} with keys: "status", "uploadid", "fileid", "url"
fileserver_upload_handler(file_server_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}

# Download handler - fetches data from file server URL with exponential backoff
# The handler is passed to smartreceive as fileserver_download_handler parameter
# It receives: (url::String, max_retries::Int, base_delay::Int, max_delay::Int, correlation_id::String)
# Returns: Vector{UInt8} (the downloaded data)
fileserver_download_handler(url::String, max_retries::Int, base_delay::Int, max_delay::Int, correlation_id::String)::Vector{UInt8}

This design allows the system to support multiple file server backends without changing the core messaging logic.

Multi-Payload Support (Standard API)

The system uses a standardized list-of-tuples format for all payload operations. Even when sending a single payload, the user must wrap it in a list.

API Standard:

# Input format for smartsend (always a list of tuples with type info)
[(dataname1, data1, type1), (dataname2, data2, type2), ...]

# Output format for smartreceive (returns a dictionary with payloads field containing list of tuples)
# Returns: Dict with envelope metadata and payloads field containing Vector{Tuple{String, Any, String}}
# {
#   "correlation_id": "...",
#   "msg_id": "...",
#   "timestamp": "...",
#   "send_to": "...",
#   "msg_purpose": "...",
#   "sender_name": "...",
#   "sender_id": "...",
#   "receiver_name": "...",
#   "receiver_id": "...",
#   "reply_to": "...",
#   "reply_to_msg_id": "...",
#   "broker_url": "...",
#   "metadata": {...},
#   "payloads": [(dataname1, data1, type1), (dataname2, data2, type2), ...]
# }

Supported Types:

• "text" - Plain text
• "dictionary" - JSON-serializable dictionaries (Dict, NamedTuple)
• "table" - Tabular data (DataFrame, array of structs)
• "image" - Image data (Bitmap, PNG/JPG bytes)
• "audio" - Audio data (WAV, MP3 bytes)
• "video" - Video data (MP4, AVI bytes)
• "binary" - Generic binary data (Vector{UInt8})

This design allows per-payload type specification, enabling mixed-content messages where different payloads can use different serialization formats in a single message.

Examples:

# Single payload - still wrapped in a list
smartsend(
    "/test",
    [("dataname1", data1, "dictionary")],  # List with one tuple (data, type)
    broker_url="nats://localhost:4222",
    fileserver_upload_handler=plik_oneshot_upload
)

# Multiple payloads in one message with different types
smartsend(
    "/test",
    [("dataname1", data1, "dictionary"), ("dataname2", data2, "table")],
    broker_url="nats://localhost:4222",
    fileserver_upload_handler=plik_oneshot_upload
)

# Mixed content (e.g., chat with text, image, audio)
smartsend(
    "/chat",
    [
        ("message_text", "Hello!", "text"),
        ("user_image", image_data, "image"),
        ("audio_clip", audio_data, "audio")
    ],
    broker_url="nats://localhost:4222"
)

# Receive returns a dictionary envelope with all metadata and deserialized payloads
env = smartreceive(msg; fileserver_download_handler=_fetch_with_backoff, max_retries=5, base_delay=100, max_delay=5000)
# env["payloads"] = [("dataname1", data1, type1), ("dataname2", data2, type2), ...]
# env["correlation_id"], env["msg_id"], etc.
# env is a dictionary containing envelope metadata and payloads field

Architecture Diagram

flowchart TD
    subgraph Client
        JS[JavaScript Client]
        JSApp[Application Logic]
    end

    subgraph Server
        Julia[Julia Service]
        NATS[NATS Server]
        FileServer[HTTP File Server]
    end

    JS -->|Control/Small Data| JSApp
    JSApp -->|NATS| NATS
    NATS -->|NATS| Julia
    Julia -->|NATS| NATS
    Julia -->|HTTP POST| FileServer
    JS -->|HTTP GET| FileServer

    style JS fill:#e1f5fe
    style Julia fill:#e8f5e9
    style NATS fill:#fff3e0
    style FileServer fill:#f3e5f5

System Components

1. msg_envelope_v1 - Message Envelope

The msg_envelope_v1 structure provides a comprehensive message format for bidirectional communication between Julia, JavaScript, and Python/Micropython applications.

Julia Structure:

struct msg_envelope_v1
  correlation_id::String       # Unique identifier to track messages across systems
  msg_id::String               # This message id
  timestamp::String            # Message published timestamp
  
  send_to::String              # Topic/subject the sender sends to
  msg_purpose::String          # Purpose of this message (ACK | NACK | updateStatus | shutdown | ...)
  sender_name::String          # Sender name (e.g., "agent-wine-web-frontend")
  sender_id::String            # Sender id (uuid4)
  receiver_name::String        # Message receiver name (e.g., "agent-backend")
  receiver_id::String          # Message receiver id (uuid4 or nothing for broadcast)
  reply_to::String             # Topic to reply to
  reply_to_msg_id::String      # Message id this message is replying to
  broker_url::String           # NATS server address
  
  metadata::Dict{String, Any}
  payloads::Vector{msg_payload_v1}  # Multiple payloads stored here
end

JSON Schema:

{
  "correlation_id": "uuid-v4-string",
  "msg_id": "uuid-v4-string",
  "timestamp": "2024-01-15T10:30:00Z",
  
  "send_to": "topic/subject",
  "msg_purpose": "ACK | NACK | updateStatus | shutdown | chat",
  "sender_name": "agent-wine-web-frontend",
  "sender_id": "uuid4",
  "receiver_name": "agent-backend",
  "receiver_id": "uuid4",
  "reply_to": "topic",
  "reply_to_msg_id": "uuid4",
  "broker_url": "nats://localhost:4222",
  
  "metadata": {

  },
  
  "payloads": [
    {
      "id": "uuid4",
      "dataname": "login_image",
      "payload_type": "image",
      "transport": "direct",
      "encoding": "base64",
      "size": 15433,
      "data": "base64-encoded-string",
      "metadata": {

      }
    },
    {
      "id": "uuid4",
      "dataname": "large_data",
      "payload_type": "table",
      "transport": "link",
      "encoding": "none",
      "size": 524288,
      "data": "http://localhost:8080/file/UPLOAD_ID/FILE_ID/data.arrow",
      "metadata": {

      }
    }
  ]
}

2. msg_payload_v1 - Payload Structure

The msg_payload_v1 structure provides flexible payload handling for various data types across all supported platforms.

Julia Structure:

struct msg_payload_v1
  id::String                    # Id of this payload (e.g., "uuid4")
  dataname::String              # Name of this payload (e.g., "login_image")
  payload_type::String          # "text | dictionary | table | image | audio | video | binary"
  transport::String             # "direct | link"
  encoding::String              # "none | json | base64 | arrow-ipc"
  size::Integer                 # Data size in bytes
  data::Any                     # Payload data in case of direct transport or a URL in case of link
  metadata::Dict{String, Any}   # Dict("checksum" => "sha256_hash", ...)
end

Key Features:

• Supports multiple data types: text, dictionary, table, image, audio, video, binary
• Flexible transport: "direct" (NATS) or "link" (HTTP fileserver)
• Multiple payloads per message (essential for chat with mixed content)
• Per-payload and per-envelope metadata support

3. Transport Strategy Decision Logic

┌─────────────────────────────────────────────────────────────┐
│                     smartsend Function                      │
│  Accepts: [(dataname1, data1, type1), ...]                  │
│  (Type is per payload, not standalone)                      │
└─────────────────────────────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────┐
│  For each payload:                                          │
│  1. Extract type from tuple                                 │
│  2. Serialize based on type                                 │
│  3. Check payload size                                      │
└─────────────────────────────────────────────────────────────┘
                             │
            ┌────────────────┴─-────────────────┐
            ▼                                   ▼
      ┌─────────────────┐                 ┌─────────────────┐
      │  Direct Path    │                 │  Link Path      │
      │  (< 1MB)        │                 │  (> 1MB)        │
      │                 │                 │                 │
      │ • Serialize to  │                 │ • Serialize to  │
      │   IOBuffer      │                 │   IOBuffer      │
      │ • Base64 encode │                 │ • Upload to     │
      │ • Publish to    │                 │   HTTP Server   │
      │   NATS          │                 │ • Publish to    │
      │   (with payload │                 │   NATS with URL │
      │    in envelope) │                 │   (in envelope) │
      └─────────────────┘                 └─────────────────┘

4. Cross-Platform Architecture

flowchart TD
    subgraph PythonMicropython
        Py[Python/Micropython]
        PySmartSend[smartsend]
        PySmartReceive[smartreceive]
    end

    subgraph JavaScript
        JS[JavaScript]
        JSSmartSend[smartsend]
        JSSmartReceive[smartreceive]
    end

    subgraph Julia
        Julia[Julia]
        JuliaSmartSend[smartsend]
        JuliaSmartReceive[smartreceive]
    end

    subgraph NATS
        NATSServer[NATS Server]
    end

    PySmartSend --> NATSServer
    JSSmartSend --> NATSServer
    JuliaSmartSend --> NATSServer
    NATSServer --> PySmartReceive
    NATSServer --> JSSmartReceive
    NATSServer --> JuliaSmartReceive

    style PythonMicropython fill:#e1f5fe
    style JavaScript fill:#f3e5f5
    style Julia fill:#e8f5e9

5. Python/Micropython Module Architecture

graph TD
    subgraph PyModule
        PySmartSend[smartsend]
        SizeCheck[Size Check]
        DirectPath[Direct Path]
        LinkPath[Link Path]
        HTTPClient[HTTP Client]
    end

    PySmartSend --> SizeCheck
    SizeCheck -->|< 1MB| DirectPath
    SizeCheck -->|>= 1MB| LinkPath
    LinkPath --> HTTPClient

    style PyModule fill:#b3e5fc

6. Julia Module Architecture

graph TD
    subgraph JuliaModule
        JuliaSmartSend[smartsend]
        SizeCheck[Size Check]
        DirectPath[Direct Path]
        LinkPath[Link Path]
        HTTPClient[HTTP Client]
    end

    JuliaSmartSend --> SizeCheck
    SizeCheck -->|< 1MB| DirectPath
    SizeCheck -->|>= 1MB| LinkPath
    LinkPath --> HTTPClient

    style JuliaModule fill:#c5e1a5

7. JavaScript Module Architecture

graph TD
    subgraph JSModule
        JSSmartSend[smartsend]
        JSSmartReceive[smartreceive]
        JetStreamConsumer[JetStream Pull Consumer]
        ApacheArrow[Apache Arrow]
    end

    JSSmartSend --> NATS
    JSSmartReceive --> JetStreamConsumer
    JetStreamConsumer --> ApacheArrow

    style JSModule fill:#f3e5f5

Implementation Details

Julia Implementation

Dependencies

• NATS.jl - Core NATS functionality
• Arrow.jl - Arrow IPC serialization
• JSON3.jl - JSON parsing
• HTTP.jl - HTTP client for file server
• Dates.jl - Timestamps for logging

smartsend Function

function smartsend(
  subject::String,
  data::AbstractArray{Tuple{String, Any, String}, 1};  # List of (dataname, data, type) tuples
  broker_url::String = DEFAULT_BROKER_URL,  # NATS server URL
  fileserver_url = DEFAULT_FILESERVER_URL,
  fileserver_upload_handler::Function = plik_oneshot_upload,
  size_threshold::Int = DEFAULT_SIZE_THRESHOLD,
  correlation_id::Union{String, Nothing} = nothing,
  msg_purpose::String = "chat",
  sender_name::String = "NATSBridge",
  receiver_name::String = "",
  receiver_id::String = "",
  reply_to::String = "",
  reply_to_msg_id::String = "",
  is_publish::Bool = true   # Whether to automatically publish to NATS
)

Return Value:

• Returns a tuple (env, env_json_str) where:
  • env::msg_envelope_v1 - The envelope object containing all metadata and payloads
  • env_json_str::String - JSON string representation of the envelope for publishing

Options:

• is_publish::Bool = true - When true (default), the message is automatically published to NATS. When false, the function returns the envelope and JSON string without publishing, allowing manual publishing via NATS request-reply pattern.

The envelope object can be accessed directly for programmatic use, while the JSON string can be published directly to NATS using the request-reply pattern.

Input Format:

• data::AbstractArray{Tuple{String, Any, String}} - Must be a list of (dataname, data, type) tuples: [("dataname1", data1, "type1"), ("dataname2", data2, "type2"), ...]
• Even for single payloads: [(dataname1, data1, "type1")]
• Each payload can have a different type, enabling mixed-content messages

Flow:

1. Iterate through the list of (dataname, data, type) tuples
2. For each payload: extract the type from the tuple and serialize accordingly
3. Check payload size
4. If < threshold: publish directly to NATS with Base64-encoded payload
5. If >= threshold: upload to HTTP server, publish NATS with URL

smartreceive Handler

function smartreceive(
    msg::NATS.Msg;
    fileserver_download_handler::Function = _fetch_with_backoff,
    max_retries::Int = 5,
    base_delay::Int = 100,
    max_delay::Int = 5000
)
    # Parse envelope
    # Iterate through all payloads
    # For each payload: check transport type
    #   If direct: decode Base64 payload
    #   If link: fetch from URL with exponential backoff using fileserver_download_handler
    # Deserialize payload based on type
    # Return envelope dictionary with all metadata and deserialized payloads
end

Output Format:

• Returns a dictionary (key-value map) containing all envelope fields:
  • correlation_id, msg_id, timestamp, send_to, msg_purpose, sender_name, sender_id, receiver_name, receiver_id, reply_to, reply_to_msg_id, broker_url
  • metadata - Message-level metadata dictionary
  • payloads - List of dictionaries, each containing deserialized payload data

Process Flow:

1. Parse the JSON envelope to extract all fields
2. Iterate through each payload in payloads
3. For each payload:
   • Determine transport type (direct or link)
   • If direct: decode Base64 data from the message
   • If link: fetch data from URL using exponential backoff (via fileserver_download_handler)
   • Deserialize based on payload type (dictionary, table, binary, etc.)
4. Return envelope dictionary with payloads field containing list of (dataname, data, type) tuples

Note: The fileserver_download_handler receives (url::String, max_retries::Int, base_delay::Int, max_delay::Int, correlation_id::String) and returns Vector{UInt8}.

JavaScript Implementation

Dependencies

• nats.js - Core NATS functionality
• apache-arrow - Arrow IPC serialization
• uuid - Correlation ID generation

smartsend Function

async function smartsend(subject, data, options = {})
    // data format: [(dataname, data, type), ...]
    // options object should include:
    // - natsUrl: NATS server URL
    // - fileserverUrl: base URL of the file server
    // - sizeThreshold: threshold in bytes for transport selection
    // - correlationId: optional correlation ID for tracing

Input Format:

• data - Must be a list of (dataname, data, type) tuples: [(dataname1, data1, "type1"), (dataname2, data2, "type2"), ...]
• Even for single payloads: [(dataname1, data1, "type1")]
• Each payload can have a different type, enabling mixed-content messages

Flow:

1. Iterate through the list of (dataname, data, type) tuples
2. For each payload: extract the type from the tuple and serialize accordingly
3. Check payload size
4. If < threshold: publish directly to NATS
5. If >= threshold: upload to HTTP server, publish NATS with URL

smartreceive Handler

async function smartreceive(msg, options = {})
    // options object should include:
    // - fileserverDownloadHandler: function to fetch data from file server URL
    // - max_retries: maximum retry attempts for fetching URL
    // - base_delay: initial delay for exponential backoff in ms
    // - max_delay: maximum delay for exponential backoff in ms
    // - correlationId: optional correlation ID for tracing

Output Format:

• Returns a dictionary (key-value map) containing all envelope fields:
  • correlationId, msgId, timestamp, sendTo, msgPurpose, senderName, senderId, receiverName, receiverId, replyTo, replyToMsgId, brokerURL
  • metadata - Message-level metadata dictionary
  • payloads - List of dictionaries, each containing deserialized payload data

Process Flow:

1. Parse the JSON envelope to extract all fields
2. Iterate through each payload in payloads
3. For each payload:
   • Determine transport type (direct or link)
   • If direct: decode Base64 data from the message
   • If link: fetch data from URL using exponential backoff
   • Deserialize based on payload type (dictionary, table, binary, etc.)
4. Return envelope dictionary with payloads field containing list of (dataname, data, type) tuples

Scenario Implementations

Scenario 1: Command & Control (Small Dictionary)

Julia (Sender/Receiver):

# Subscribe to control subject
# Parse JSON envelope
# Execute simulation with parameters
# Send acknowledgment

JavaScript (Sender/Receiver):

// Create small dictionary config
// Send via smartsend with type="dictionary"

Python/Micropython (Sender/Receiver):

# Create small dictionary config
# Send via smartsend with type="dictionary"

Scenario 2: Deep Dive Analysis (Large Arrow Table)

Julia (Sender/Receiver):

# Create large DataFrame
# Convert to Arrow IPC stream
# Check size (> 1MB)
# Upload to HTTP server
# Publish NATS with URL

JavaScript (Sender/Receiver):

// Receive NATS message with URL
// Fetch data from HTTP server
// Parse Arrow IPC with zero-copy
// Load into Perspective.js or D3

Python/Micropython (Sender/Receiver):

# Create large DataFrame
# Convert to Arrow IPC stream
# Check size (> 1MB)
# Upload to HTTP server
# Publish NATS with URL

Scenario 3: Live Audio Processing

JavaScript (Sender/Receiver):

// Capture audio chunk
// Send as binary with metadata headers
// Use smartsend with type="audio"

Julia (Sender/Receiver):

# Receive audio data
# Perform FFT or AI transcription
# Send results back (JSON + Arrow table)

Python/Micropython (Sender/Receiver):

# Capture audio chunk
# Send as binary with metadata headers
# Use smartsend with type="audio"

Scenario 4: Catch-Up (JetStream)

Julia (Producer/Consumer):

# Publish to JetStream
# Include metadata for temporal tracking

JavaScript (Producer/Consumer):

// Connect to JetStream
// Request replay from last 10 minutes
// Process historical and real-time messages

Python/Micropython (Producer/Consumer):

# Publish to JetStream
# Include metadata for temporal tracking

Scenario 5: Selection (Low Bandwidth)

Focus: Small Arrow tables, cross-platform communication. The Action: Any platform wants to send a small DataFrame to show on any receiving application for the user to choose.

Julia (Sender/Receiver):

# Create small DataFrame (e.g., 50KB - 500KB)
# Convert to Arrow IPC stream
# Check payload size (< 1MB threshold)
# Publish directly to NATS with Base64-encoded payload
# Include metadata for dashboard selection context

JavaScript (Sender/Receiver):

// Receive NATS message with direct transport
// Decode Base64 payload
// Parse Arrow IPC with zero-copy
// Load into selection UI component (e.g., dropdown, table)
// User makes selection
// Send selection back to Julia

Python/Micropython (Sender/Receiver):

# Create small DataFrame (e.g., 50KB - 500KB)
# Convert to Arrow IPC stream
# Check payload size (< 1MB threshold)
# Publish directly to NATS with Base64-encoded payload
# Include metadata for dashboard selection context

Use Case: Any server generates a list of available options (e.g., file selections, configuration presets) as a small DataFrame and sends to any receiving application for user selection. The selection is then sent back to the sender for processing.

Scenario 6: Chat System

Focus: Every conversational message is composed of any number and any combination of components, spanning the full spectrum from small to large. This includes text, images, audio, video, tables, and files—specifically accommodating everything from brief snippets to high-resolution images, large audio files, extensive tables, and massive documents. Support for claim-check delivery and full bi-directional messaging across all platforms.

Multi-Payload Support: The system supports mixed-payload messages where a single message can contain multiple payloads with different transport strategies. The smartreceive function iterates through all payloads in the envelope and processes each according to its transport type.

Julia (Sender/Receiver):

# Build chat message with mixed payloads:
# - Text: direct transport (Base64)
# - Small images: direct transport (Base64)
# - Large images: link transport (HTTP URL)
# - Audio/video: link transport (HTTP URL)
# - Tables: direct or link depending on size
# - Files: link transport (HTTP URL)
# 
# Each payload uses appropriate transport strategy:
# - Size < 1MB → direct (NATS + Base64)
# - Size >= 1MB → link (HTTP upload + NATS URL)
# 
# Include claim-check metadata for delivery tracking
# Support bidirectional messaging with replyTo fields

JavaScript (Sender/Receiver):

// Build chat message with mixed content:
// - User input text: direct transport
// - Selected image: check size, use appropriate transport
// - Audio recording: link transport for large files
// - File attachment: link transport
// 
// Parse received message:
// - Direct payloads: decode Base64
// - Link payloads: fetch from HTTP with exponential backoff
// - Deserialize all payloads appropriately
// 
// Render mixed content in chat interface
// Support bidirectional reply with claim-check delivery confirmation

Python/Micropython (Sender/Receiver):

# Build chat message with mixed payloads:
# - Text: direct transport (Base64)
# - Small images: direct transport (Base64)
# - Large images: link transport (HTTP URL)
# - Audio/video: link transport (HTTP URL)
# - Tables: direct or link depending on size
# - Files: link transport (HTTP URL)
# 
# Each payload uses appropriate transport strategy:
# - Size < 1MB → direct (NATS + Base64)
# - Size >= 1MB → link (HTTP upload + NATS URL)
# 
# Include claim-check metadata for delivery tracking
# Support bidirectional messaging with replyTo fields

Use Case: Full-featured chat system supporting rich media. User can send text, small images directly, or upload large files that get uploaded to HTTP server and referenced via URLs. Claim-check pattern ensures reliable delivery tracking for all message components across all platforms.

Implementation Note: The smartreceive function iterates through all payloads in the envelope and processes each according to its transport type. See the standard API format in Section 1: msgEnvelope_v1 supports AbstractArray{msgPayload_v1} for multiple payloads.

Performance Considerations

Zero-Copy Reading

• Use Arrow's memory-mapped file reading
• Avoid unnecessary data copying during deserialization
• Use Apache Arrow's native IPC reader

Exponential Backoff

• Implement exponential backoff for HTTP link fetching
• Maximum retry count: 5
• Base delay: 100ms, max delay: 5000ms

Correlation ID Logging

• Log correlation_id at every stage
• Include: send, receive, serialize, deserialize
• Use structured logging format

Testing Strategy

Unit Tests

• Test smartsend with various payload sizes
• Test smartreceive with direct and link transport
• Test Arrow IPC serialization/deserialization

Integration Tests

• Test full flow with NATS server
• Test large data transfer (> 100MB)
• Test audio processing pipeline

Performance Tests

• Measure throughput for small payloads
• Measure throughput for large payloads