NATSBridge/docs/updated_architecture.md

# Cross-Platform Architecture Documentation: Bi-Directional Data Bridge

## Overview

This document describes the architecture for a high-performance, bi-directional data bridge using **NATS (Core & JetStream)**, implementing the Claim-Check pattern for large payloads. The system is implemented across three platforms with **high-level API parity** while maintaining **idiomatic implementations** for each language.

**Supported Platforms:**
- **Julia** - Ground truth implementation with full feature set
- **JavaScript** - Node.js and browser-compatible implementation
- **Python/MicroPython** - Desktop and embedded-compatible implementation

### Cross-Platform Design Principles

1. **High-Level API Parity**: All three platforms expose the same `smartsend()` and `smartreceive()` functions with identical signatures and behavior
2. **Idiomatic Implementations**: Each platform uses its native patterns (multiple dispatch in Julia, async/prototype in JS, class-based in Python)
3. **Message Format Consistency**: The `msg_envelope_v1` and `msg_payload_v1` JSON schemas are identical across all platforms
4. **Handler Function Abstraction**: File server operations are abstracted through handler functions for backend flexibility

---

## High-Level API Standard (Cross-Platform)

### Unified API Signature

All three platforms expose the same high-level API:

**Input Format (smartsend):**
```
[(dataname1, data1, type1), (dataname2, data2, type2), ...]
```

**Output Format (smartreceive):**
```
{
  "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 Payload Types

| Type | Julia | JavaScript | Python/MicroPython |
|------|-------|------------|-------------------|
| `text` | `String` | `string` | `str` |
| `dictionary` | `Dict`, `NamedTuple` | `Object`, `Array` | `dict`, `list` |
| `arrowtable` | `DataFrame`, `Arrow.Table` | `Array<Object>` (input) → `Buffer` (Arrow IPC) | `pandas.DataFrame`, `bytes` (Arrow IPC) |
| `jsontable` | `Vector{NamedTuple}`, `Vector{Dict}` | `Array<Object>` | `list[dict]`, `list` |
| `image` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes`, `bytearray` |
| `audio` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes`, `bytearray` |
| `video` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes`, `bytearray` |
| `binary` | `Vector{UInt8}`, `IOBuffer` | `Uint8Array`, `Buffer` | `bytes`, `bytearray`, `io.BytesIO` |

### Cross-Platform API Examples

**Julia:**
```julia
using NATSBridge

# Send
env, env_json_str = smartsend(
    "/chat",
    [("message", "Hello!", "text"), ("image", image_bytes, "image")],
    broker_url="nats://localhost:4222"
)

# Receive - returns JSON.Object{String, Any}
env = smartreceive(msg; fileserver_download_handler=_fetch_with_backoff)
# env is a JSON.Object{String, Any} with "payloads" field containing Vector{Tuple{String, Any, String}}
# Access payloads: for (dataname, data, type) in env["payloads]
```

**JavaScript:**
```javascript
const NATSBridge = require('natsbridge');

// Send
const [env, env_json_str] = await NATSBridge.smartsend(
    "/chat",
    [
        ["message", "Hello!", "text"],
        ["image", imageBuffer, "image"]
    ],
    { broker_url: "nats://localhost:4222" }
);

// Receive - returns Promise<object>
const env = await NATSBridge.smartreceive(msg, {
    fileserver_download_handler: fetchWithBackoff
});
// env is an object with "payloads" field containing Array of arrays
// Access payloads: for (const [dataname, data, type] of env.payloads)
```

**Python:**
```python
from natsbridge import NATSBridge

# Send
env, env_json_str = NATSBridge.smartsend(
    "/chat",
    [("message", "Hello!", "text"), ("image", image_bytes, "image")],
    broker_url="nats://localhost:4222"
)

# Receive - returns Tuple[Dict, str]
env = NATSBridge.smartreceive(
    msg,
    fileserver_download_handler=fetch_with_backoff
)
# env is a Dict with "payloads" key containing List[Tuple[str, Any, str]]
# Access payloads: for dataname, data, type_ in env["payloads"]
```

**MicroPython:**
```python
from natsbridge import NATSBridge

# Send (limited to direct transport due to memory constraints)
env, env_json_str = NATSBridge.smartsend(
    "/chat",
    [("message", "Hello!", "text")],
    broker_url="nats://localhost:4222"
)
```

---

## Architecture Diagram (Cross-Platform)

```mermaid
flowchart TB
    subgraph JuliaApp["Julia Application"]
        JuliaAppCode[App Code]
        JuliaBridge[NATSBridge.jl]
        JuliaNATS[<b>NATS.jl</b>]
    end

    subgraph JSApp["JavaScript Application"]
        JSAppCode[App Code]
        JSBridge[NATSBridge.js]
        JSNATS[<b>nats.js</b>]
    end

    subgraph PythonApp["Python/MicroPython Application"]
        PythonAppCode[App Code]
        PythonBridge[NATSBridge.py]
        PythonNATS[<b>nats.py</b>]
    end

    subgraph Infrastructure["Infrastructure"]
        NATS[<b>NATS Server</b><br/>Message Broker]
        FileServer[<b>HTTP File Server</b><br/>Upload/Download]
    end

    JuliaAppCode --> JuliaBridge
    JuliaBridge --> JuliaNATS
    JSAppCode --> JSBridge
    JSBridge --> JSNATS
    PythonAppCode --> PythonBridge
    PythonBridge --> PythonNATS

    JuliaNATS --> NATS
    JSNATS --> NATS
    PythonNATS --> NATS

    NATS --> JuliaNATS
    NATS --> JSNATS
    NATS --> PythonNATS

    JuliaBridge -.->|HTTP POST upload| FileServer
    JSBridge -.->|HTTP POST upload| FileServer
    PythonBridge -.->|HTTP POST upload| FileServer

    FileServer -.->|HTTP GET download| JuliaBridge
    FileServer -.->|HTTP GET download| JSBridge
    FileServer -.->|HTTP GET download| PythonBridge

    style JuliaApp fill:#c5e1a5
    style JSApp fill:#bbdefb
    style PythonApp fill:#f8bbd0
    style NATS fill:#fff3e0
    style FileServer fill:#f3e5f5
```

---

## System Components

### 1. msg_envelope_v1 - Message Envelope

**JSON Schema (Identical Across All Platforms):**
```json
{
  "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": {
    "content_type": "application/octet-stream",
    "content_length": 123456
  },
  
  "payloads": [
    {
      "id": "uuid4",
      "dataname": "login_image",
      "payload_type": "image",
      "transport": "direct",
      "encoding": "base64",
      "size": 15433,
      "data": "base64-encoded-string",
      "metadata": {
        "checksum": "sha256_hash"
      }
    },
    {
      "id": "uuid4",
      "dataname": "large_arrow_table",
      "payload_type": "arrowtable",
      "transport": "link",
      "encoding": "arrow-ipc",
      "size": 524288,
      "data": "http://localhost:8080/file/UPLOAD_ID/FILE_ID/data.arrow",
      "metadata": {}
    },
    {
      "id": "uuid4",
      "dataname": "json_table",
      "payload_type": "jsontable",
      "transport": "direct",
      "encoding": "json",
      "size": 1024,
      "data": "[{\"id\": 1, \"name\": \"Alice\"}, {\"id\": 2, \"name\": \"Bob\"}]",
      "metadata": {}
    }
  ]
}
```

### 2. msg_payload_v1 - Payload Structure

**JSON Schema (Identical Across All Platforms):**
```json
{
  "id": "uuid4",
  "dataname": "login_image",
  "payload_type": "image | dictionary | arrowtable | jsontable | text | audio | video | binary",
  "transport": "direct | link",
  "encoding": "none | json | base64 | arrow-ipc",
  "size": 15433,
  "data": "base64-encoded-string | http-url | json-string",
  "metadata": {
    "checksum": "sha256_hash"
  }
}
```

### 3. Transport Strategy Decision Logic (Cross-Platform)

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

---

## Platform-Specific Implementations

### Julia Implementation

#### Architecture Patterns

**Multiple Dispatch:** Julia's core strength is leveraged through function overloading:

```julia
# publish_message has two overloads based on argument types
function publish_message(broker_url::String, subject::String, message::String, correlation_id::String)
    conn = NATS.connect(broker_url)
    publish_message(conn, subject, message, correlation_id)
end

function publish_message(conn::NATS.Connection, subject::String, message::String, correlation_id::String)
    try
        NATS.publish(conn, subject, message)
        log_trace(correlation_id, "Message published to $subject")
    finally
        NATS.drain(conn)
    end
end
```

**Struct-Based Data Models:**
```julia
struct msg_payload_v1
    id::String
    dataname::String
    payload_type::String
    transport::String
    encoding::String
    size::Integer
    data::Any
    metadata::Dict{String, Any}
end

struct msg_envelope_v1
    correlation_id::String
    msg_id::String
    timestamp::String
    send_to::String
    msg_purpose::String
    sender_name::String
    sender_id::String
    receiver_name::String
    receiver_id::String
    reply_to::String
    reply_to_msg_id::String
    broker_url::String
    metadata::Dict{String, Any}
    payloads::Vector{msg_payload_v1}
end
```

#### Dependencies

| Package | Purpose |
|---------|---------|
| `NATS.jl` | Core NATS functionality |
| `Arrow.jl` | Arrow IPC serialization |
| `JSON.jl` | JSON parsing |
| `HTTP.jl` | HTTP client for file server |
| `UUIDs.jl` | UUID generation |
| `Dates.jl` | Timestamps |
| `Base64` | Base64 encoding |

#### File Server Handler Signatures

```julia
# Upload handler
fileserver_upload_handler(
    fileserver_url::String,
    dataname::String,
    data::Vector{UInt8}
)::Dict{String, Any}

# Download handler
fileserver_download_handler(
    url::String,
    max_retries::Int,
    base_delay::Int,
    max_delay::Int,
    correlation_id::String
)::Vector{UInt8}
```

#### Key Functions

```julia
# Main send/receive functions
function smartsend(
    subject::String,
    data::AbstractArray{Tuple{String, Any, String}, 1};
    broker_url::String = DEFAULT_BROKER_URL,
    fileserver_url = DEFAULT_FILESERVER_URL,
    fileserver_upload_handler::Function = plik_oneshot_upload,
    size_threshold::Int = DEFAULT_SIZE_THRESHOLD,
    correlation_id::String = string(uuid4()),
    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,
    NATS_connection::Union{NATS.Connection, Nothing} = nothing,
    msg_id::String = string(uuid4()),
    sender_id::String = string(uuid4())
)::Tuple{msg_envelope_v1, String}

function smartreceive(
    msg::NATS.Msg;
    fileserver_download_handler::Function = _fetch_with_backoff,
    max_retries::Int = 5,
    base_delay::Int = 100,
    max_delay::Int = 5000
)::JSON.Object{String, Any}
```

#### Serialization Logic for Tables

```julia
# Serialize table data based on payload_type
function _serialize_table_data(data::Any, payload_type::String)::Vector{UInt8}
    if payload_type == "arrowtable"
        # Serialize to Apache Arrow IPC format
        buffer = IOBuffer()
        Arrow.write(buffer, data)
        return take!(buffer)
    elseif payload_type == "jsontable"
        # Serialize to JSON format
        json_str = JSON.json(data)
        return Vector{UInt8}(json_str)
    else
        throw(ArgumentError("Unknown payload_type: $payload_type"))
    end
end

# Deserialize table data based on payload_type
function _deserialize_table_data(data::Vector{UInt8}, payload_type::String)::Any
    if payload_type == "arrowtable"
        # Deserialize from Apache Arrow IPC format
        buffer = Buffer(data)
        return Arrow.read(buffer)
    elseif payload_type == "jsontable"
        # Deserialize from JSON format
        json_str = String(data)
        return JSON.parse(json_str)
    else
        throw(ArgumentError("Unknown payload_type: $payload_type"))
    end
end
```

---

### JavaScript Implementation

#### Architecture Patterns

**Async/Await Pattern:** JavaScript uses async/await for non-blocking I/O:

```javascript
// smartsend is async and returns a Promise
async function smartsend(subject, data, options = {}) {
    const {
        broker_url = DEFAULT_BROKER_URL,
        fileserver_url = DEFAULT_FILESERVER_URL,
        fileserver_upload_handler = plikOneshotUpload,
        size_threshold = DEFAULT_SIZE_THRESHOLD,
        correlation_id = generateUUID(),
        msg_purpose = "chat",
        sender_name = "NATSBridge",
        receiver_name = "",
        receiver_id = "",
        reply_to = "",
        reply_to_msg_id = "",
        is_publish = true,
        nats_connection = null,
        msg_id = generateUUID(),
        sender_id = generateUUID()
    } = options;

    // Process payloads
    const payloads = [];
    for (const [dataname, payloadData, payloadType] of data) {
        const payloadBytes = await serializeData(payloadData, payloadType);
        const payloadSize = payloadBytes.byteLength;

        if (payloadSize < size_threshold) {
            // Direct path
            const payloadB64 = base64Encode(payloadBytes);
            payloads.push({
                id: generateUUID(),
                dataname,
                payload_type: payloadType,
                transport: "direct",
                encoding: "base64",
                size: payloadSize,
                data: payloadB64
            });
        } else {
            // Link path
            const response = await fileserver_upload_handler(
                fileserver_url, dataname, payloadBytes
            );
            payloads.push({
                id: generateUUID(),
                dataname,
                payload_type: payloadType,
                transport: "link",
                encoding: "none",
                size: payloadSize,
                data: response.url
            });
        }
    }

    const env = buildEnvelope(subject, payloads, {
        correlation_id, msg_id, msg_purpose,
        sender_name, sender_id, receiver_name,
        receiver_id, reply_to, reply_to_msg_id,
        broker_url
    });

    const env_json_str = JSON.stringify(env);

    if (is_publish) {
        if (nats_connection) {
            await publishMessage(nats_connection, subject, env_json_str, correlation_id);
        } else {
            await publishMessage(broker_url, subject, env_json_str, correlation_id);
        }
    }

    return [env, env_json_str];
}
```

**Prototype-Based Utilities:**
```javascript
// NATS client wrapper (prototype-based)
class NATSClient {
    constructor(url) {
        this.url = url;
        this.connection = null;
    }

    async connect() {
        this.connection = await nats.connect({ servers: this.url });
        return this.connection;
    }

    async publish(subject, message) {
        if (!this.connection) {
            await this.connect();
        }
        await this.connection.publish(subject, message);
    }

    async close() {
        if (this.connection) {
            this.connection.close();
        }
    }
}
```

#### Dependencies (Node.js)

| Package | Purpose |
|---------|---------|
| `nats` | Core NATS functionality (nats.js) |
| `crypto` (built-in) | UUID generation (Node.js) |
| `node-fetch` or `axios` | HTTP client for file server |
| `apache-arrow` | Arrow IPC serialization |

#### Dependencies (Browser)

| Package | Purpose |
|---------|---------|
| `nats` | Browser-compatible NATS client |
| `crypto` (built-in) | UUID generation (browser) |
| `fetch` (native) | HTTP client for file server |
| `apache-arrow` | Arrow IPC serialization |

#### Dependencies (MicroPython)

| Module | Purpose |
|--------|---------|
| `nats` (custom) | MicroPython NATS client |
| `time` | Timestamps |
| `uos` | File operations |
| `base64` | Base64 encoding |

#### File Server Handler Signatures

```javascript
// Upload handler - async function returning Promise
async function fileserver_upload_handler(
    fileserver_url,
    dataname,
    data  // Uint8Array
) {
    // Returns: { status, uploadid, fileid, url }
}

// Download handler - async function returning Promise
async function fileserver_download_handler(
    url,
    max_retries,
    base_delay,
    max_delay,
    correlation_id
) {
    // Returns: Uint8Array
}
```

#### Key Functions

```javascript
// Main send/receive functions
async function smartsend(subject, data, options = {}) {
    // data: Array of [dataname, data, type] tuples
    // Returns: Promise<[env, env_json_str]>
}

async function smartreceive(msg, options = {}) {
    // msg: NATS message object
    // Returns: Promise<env_object>
}

// Utility functions
async function serializeData(data, payload_type) {
    // Returns: Uint8Array
}

async function deserializeData(data, payload_type) {
    // Returns: deserialized data
}

async function fetchWithBackoff(url, max_retries, base_delay, max_delay, correlation_id) {
    // Returns: Uint8Array
}
```

#### Serialization Logic for Tables

```javascript
// Serialize table data based on payload_type
async function serializeTableData(data, payload_type) {
    if (payload_type === "arrowtable") {
        // Serialize to Apache Arrow IPC format
        const schema = new arrow.Schema([...]); // Define schema
        const arr = arrow.tableToArrowTable(data, schema);
        const buffer = arrow.RecordBatch.from(arr).toBuffer();
        return new Uint8Array(buffer);
    } else if (payload_type === "jsontable") {
        // Serialize to JSON format
        const jsonStr = JSON.stringify(data);
        return new TextEncoder().encode(jsonStr);
    } else {
        throw new Error(`Unknown payload_type: ${payload_type}`);
    }
}

// Deserialize table data based on payload_type
async function deserializeTableData(data, payload_type) {
    if (payload_type === "arrowtable") {
        // Deserialize from Apache Arrow IPC format
        const buffer = arrow.arrayBufferToBuffer(data.buffer);
        const batch = arrow.RecordBatch.deserialize(buffer);
        return arrow.tableFromBatch(batch);
    } else if (payload_type === "jsontable") {
        // Deserialize from JSON format
        const jsonStr = new TextDecoder().decode(data);
        return JSON.parse(jsonStr);
    } else {
        throw new Error(`Unknown payload_type: ${payload_type}`);
    }
}
```

---

### Python/MicroPython Implementation

#### Architecture Patterns

**Class-Based Design:** Python uses classes for stateful operations:

```python
class NATSBridge:
    """Cross-platform NATS bridge implementation."""
    
    DEFAULT_SIZE_THRESHOLD = 1_000_000  # 1MB
    DEFAULT_BROKER_URL = "nats://localhost:4222"
    DEFAULT_FILESERVER_URL = "http://localhost:8080"
    
    def __init__(self, broker_url=None, fileserver_url=None):
        self.broker_url = broker_url or self.DEFAULT_BROKER_URL
        self.fileserver_url = fileserver_url or self.DEFAULT_FILESERVER_URL
        self._nats_client = None
    
    async def smartsend(self, subject, data, **kwargs):
        """
        Send data via NATS with automatic transport selection.
        
        Args:
            subject: NATS subject to publish to
            data: List of (dataname, data, type) tuples
            **kwargs: Additional options (broker_url, fileserver_url, etc.)
        
        Returns:
            Tuple of (env, env_json_str)
        """
        # Extract options with defaults
        options = self._merge_options(kwargs)
        
        # Process payloads
        payloads = []
        for dataname, payload_data, payload_type in data:
            payload_bytes = self._serialize_data(payload_data, payload_type)
            payload_size = len(payload_bytes)
            
            if payload_size < options['size_threshold']:
                # Direct path
                payload_b64 = base64.b64encode(payload_bytes).decode('utf-8')
                payloads.append({
                    'id': uuid.uuid4().hex,
                    'dataname': dataname,
                    'payload_type': payload_type,
                    'transport': 'direct',
                    'encoding': 'base64',
                    'size': payload_size,
                    'data': payload_b64
                })
            else:
                # Link path
                response = await options['fileserver_upload_handler'](
                    options['fileserver_url'], dataname, payload_bytes
                )
                payloads.append({
                    'id': uuid.uuid4().hex,
                    'dataname': dataname,
                    'payload_type': payload_type,
                    'transport': 'link',
                    'encoding': 'none',
                    'size': payload_size,
                    'data': response['url']
                })
        
        # Build envelope
        env = self._build_envelope(subject, payloads, options)
        env_json_str = json.dumps(env)
        
        if options['is_publish']:
            await self._publish_message(
                subject, env_json_str, options['correlation_id'],
                nats_connection=options.get('nats_connection')
            )
        
        return env, env_json_str
    
    async def smartreceive(self, msg, **kwargs):
        """
        Receive and process NATS message.
        
        Args:
            msg: NATS message object
            **kwargs: Additional options (fileserver_download_handler, etc.)
        
        Returns:
            Dict with envelope metadata and payloads
        """
        # Parse envelope
        env_json_obj = json.loads(msg.payload)
        
        # Process payloads
        payloads_list = []
        for payload in env_json_obj['payloads']:
            transport = payload['transport']
            dataname = payload['dataname']
            
            if transport == 'direct':
                payload_b64 = payload['data']
                payload_bytes = base64.b64decode(payload_b64)
                data_type = payload['payload_type']
                data = self._deserialize_data(payload_bytes, data_type)
                payloads_list.append((dataname, data, data_type))
            elif transport == 'link':
                url = payload['data']
                downloaded_data = await options['fileserver_download_handler'](
                    url,
                    options['max_retries'],
                    options['base_delay'],
                    options['max_delay'],
                    env_json_obj['correlation_id']
                )
                data_type = payload['payload_type']
                data = self._deserialize_data(downloaded_data, data_type)
                payloads_list.append((dataname, data, data_type))
        
        env_json_obj['payloads'] = payloads_list
        return env_json_obj
```

**Dataclass for Type Safety:**
```python
from dataclasses import dataclass, field
from typing import Any, Dict, List, Tuple, Union

@dataclass
class MsgPayloadV1:
    """Message payload structure."""
    id: str
    dataname: str
    payload_type: str
    transport: str
    encoding: str
    size: int
    data: Union[str, bytes]  # URL for link, base64 for direct
    metadata: Dict[str, Any] = field(default_factory=dict)

@dataclass
class MsgEnvelopeV1:
    """Message envelope structure."""
    correlation_id: str
    msg_id: str
    timestamp: str
    send_to: str
    msg_purpose: str
    sender_name: str
    sender_id: str
    receiver_name: str
    receiver_id: str
    reply_to: str
    reply_to_msg_id: str
    broker_url: str
    metadata: Dict[str, Any] = field(default_factory=dict)
    payloads: List[MsgPayloadV1] = field(default_factory=list)
```

#### Dependencies (Desktop Python)

| Package | Purpose |
|---------|---------|
| `nats-py` | Core NATS functionality |
| `uuid` | UUID generation (stdlib) |
| `aiohttp` or `requests` | HTTP client for file server |
| `pyarrow` | Arrow IPC serialization |
| `pandas` | DataFrame support (optional) |
| `python-dateutil` | Timestamps |
| `base64` | Base64 encoding (stdlib) |

#### Dependencies (MicroPython)

| Module | Purpose |
|--------|---------|
| `network` | NATS connection (custom) |
| `time` | Timestamps |
| `uos` | File operations |
| `base64` | Base64 encoding |
| `json` | JSON parsing |
| `struct` | Binary data handling |

**MicroPython Limitations:**
- No Arrow IPC support (memory constraints)
- Only direct transport (< 1MB threshold enforced)
- Simplified UUID generation
- No async/await (use callbacks or uasyncio)

#### File Server Handler Signatures

```python
# Upload handler - async function
async def fileserver_upload_handler(
    fileserver_url: str,
    dataname: str,
    data: bytes
) -> Dict[str, Any]:
    """
    Upload data to file server.
    
    Args:
        fileserver_url: Base URL of file server
        dataname: Name of the file
        data: Binary data
    
    Returns:
        Dict with keys: 'status', 'uploadid', 'fileid', 'url'
    """
    pass

# Download handler - async function
async def fileserver_download_handler(
    url: str,
    max_retries: int,
    base_delay: int,
    max_delay: int,
    correlation_id: str
) -> bytes:
    """
    Download data from URL with exponential backoff.
    
    Args:
        url: URL to download from
        max_retries: Maximum retry attempts
        base_delay: Initial delay in ms
        max_delay: Maximum delay in ms
        correlation_id: Correlation ID for logging
    
    Returns:
        Downloaded bytes
    """
    pass
```

#### Key Functions

```python
# Main send/receive functions (standalone or class methods)
async def smartsend(
    subject: str,
    data: List[Tuple[str, Any, str]],
    broker_url: str = DEFAULT_BROKER_URL,
    fileserver_url: str = DEFAULT_FILESERVER_URL,
    fileserver_upload_handler: Callable = plik_oneshot_upload,
    size_threshold: int = DEFAULT_SIZE_THRESHOLD,
    correlation_id: str = None,
    msg_purpose: str = "chat",
    sender_name: str = "NATSBridge",
    receiver_name: str = "",
    receiver_id: str = "",
    reply_to: str = "",
    reply_to_msg_id: str = "",
    is_publish: bool = True,
    nats_connection: Any = None,
    msg_id: str = None,
    sender_id: str = None
) -> Tuple[Dict, str]:
    """Send data via NATS."""
    pass

async def smartreceive(
    msg: Any,
    fileserver_download_handler: Callable = fetch_with_backoff,
    max_retries: int = 5,
    base_delay: int = 100,
    max_delay: int = 5000
) -> Dict:
    """Receive and process NATS message."""
    pass

# Utility functions
def _serialize_data(data: Any, payload_type: str) -> bytes:
    """Serialize data to bytes."""
    pass

def _deserialize_data(data: bytes, payload_type: str) -> Any:
    """Deserialize bytes to data."""
    pass

async def fetch_with_backoff(
    url: str,
    max_retries: int,
    base_delay: int,
    max_delay: int,
    correlation_id: str
) -> bytes:
    """Fetch URL with exponential backoff."""
    pass
```

#### Serialization Logic for Tables

```python
# Serialize table data based on payload_type
def serialize_table_data(data: Any, payload_type: str) -> bytes:
    if payload_type == "arrowtable":
        # Serialize to Apache Arrow IPC format
        import pyarrow as pa
        import pyarrow.feather as feather
        import io
        
        if isinstance(data, pd.DataFrame):
            table = pa.Table.from_pandas(data)
            buffer = io.BytesIO()
            feather.write_feather(table, buffer)
            return buffer.getvalue()
        else:
            raise TypeError("Expected pandas DataFrame for arrowtable")
    
    elif payload_type == "jsontable":
        # Serialize to JSON format
        if isinstance(data, list) and all(isinstance(row, dict) for row in data):
            return json.dumps(data).encode('utf-8')
        else:
            raise TypeError("Expected list of dicts for jsontable")
    
    else:
        raise ValueError(f"Unknown payload_type: {payload_type}")

# Deserialize table data based on payload_type
def deserialize_table_data(data: bytes, payload_type: str) -> Any:
    if payload_type == "arrowtable":
        # Deserialize from Apache Arrow IPC format
        import pyarrow as pa
        import pyarrow.feather as feather
        import io
        
        buffer = io.BytesIO(data)
        table = feather.read_table(buffer)
        return table.to_pandas()
    
    elif payload_type == "jsontable":
        # Deserialize from JSON format
        json_str = data.decode('utf-8')
        return json.loads(json_str)
    
    else:
        raise ValueError(f"Unknown payload_type: {payload_type}")
```

---

## Platform Comparison Matrix

| Feature | Julia | JavaScript | Python | MicroPython |
|---------|-------|------------|--------|-------------|
| **Multiple Dispatch** | ✅ Native | ❌ (Prototypes) | ❌ (Overload via `@overload`) | ❌ |
| **Async/Await** | ❌ (Tasks) | ✅ Native | ✅ Native | ⚠️ (uasyncio) |
| **Type Safety** | ✅ Strong | ⚠️ (TypeScript) | ✅ (Type hints) | ❌ |
| **Memory Management** | ✅ GC | ✅ GC | ✅ GC | ⚠️ (Manual) |
| **Arrow IPC** | ✅ Native | ✅ (arrow package) | ✅ (pyarrow) | ❌ |
| **JSON Serialization** | ✅ (JSON.jl) | ✅ (native) | ✅ (json) | ✅ (json) |
| **arrowtable Support** | ✅ | ✅ | ✅ | ❌ |
| **jsontable Support** | ✅ | ✅ | ✅ | ✅ |
| **Direct Transport** | ✅ | ✅ | ✅ | ✅ |
| **Link Transport** | ✅ | ✅ | ✅ | ⚠️ (Limited) |
| **Handler Functions** | ✅ | ✅ | ✅ | ✅ |
| **Cross-Platform API** | ✅ | ✅ | ✅ | ✅ |

---

## Implementation Details by Platform

### Julia: Multiple Dispatch Pattern

```julia
# Function overloading based on argument types
function publish_message(broker_url::String, subject::String, message::String, correlation_id::String)
    # Creates new connection
end

function publish_message(conn::NATS.Connection, subject::String, message::String, correlation_id::String)
    # Uses pre-existing connection
end

# Type-specific serialization
function _serialize_data(data::String, payload_type::String)
    # Text handling
end

function _serialize_data(data::Dict, payload_type::String)
    # Dictionary handling
end

function _serialize_data(data::DataFrame, payload_type::String)
    # Table handling - arrowtable
end

function _serialize_data(data::Vector{NamedTuple}, payload_type::String)
    # Table handling - jsontable
end
```

### JavaScript: Prototype + Async Pattern

```javascript
// Class-based NATS client
class NATSClient {
    constructor(url) {
        this.url = url;
    }
    
    async connect() {
        // Connection logic
    }
    
    async publish(subject, message) {
        // Publish logic
    }
}

// Module-level utility functions
function generateUUID() {
    return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, c => {
        const r = Math.random() * 16 | 0;
        return (c === 'x' ? r : (r & 0x3 | 0x8)).toString(16);
    });
}

async function serializeData(data, payload_type) {
    // Serialization logic for arrowtable and jsontable
}
```

### Python: Class-Based Pattern

```python
class NATSBridge:
    """Main bridge class."""
    
    def __init__(self, broker_url=None):
        self.broker_url = broker_url or DEFAULT_BROKER_URL
    
    async def smartsend(self, subject, data, **kwargs):
        """Send data."""
        pass
    
    async def smartreceive(self, msg, **kwargs):
        """Receive message."""
        pass

# Module-level convenience functions
def smartsend(subject, data, **kwargs):
    """Convenience function using default NATSBridge instance."""
    bridge = NATSBridge()
    return await bridge.smartsend(subject, data, **kwargs)

def smartreceive(msg, **kwargs):
    """Convenience function using default NATSBridge instance."""
    bridge = NATSBridge()
    return await bridge.smartreceive(msg, **kwargs)
```

---

## Scenario Implementations (Cross-Platform)

### Scenario 1: Command & Control (Small Dictionary)

| Platform | Code |
|----------|------|
| **Julia** | ```julia<br>config = Dict("step_size" => 0.01)<br>env, env_json_str = smartsend("control", [("config", config, "dictionary")])``` |
| **JavaScript** | ```javascript<br>const config = { step_size: 0.01 };<br>[env, env_json_str] = await smartsend("control", [["config", config, "dictionary"]]);``` |
| **Python** | ```python<br>config = {"step_size": 0.01}<br>env, env_json_str = await smartsend("control", [("config", config, "dictionary")])``` |

### Scenario 2: Deep Dive Analysis (Large Arrow Table)

| Platform | Code |
|----------|------|
| **Julia** | ```julia<br>df = DataFrame(id=1:1000000, value=rand(1000000))<br>env, env_json_str = smartsend("analysis", [("table_data", df, "arrowtable")])``` |
| **JavaScript** | ```javascript<br>const df = [{ id: 1, value: 0.5 }, ...];<br>[env, env_json_str] = await smartsend("analysis", [["table_data", df, "arrowtable"]]);``` |
| **Python** | ```python<br>import pandas as pd<br>df = pd.DataFrame({"id": range(1000000), "value": np.random.rand(1000000)})<br>env, env_json_str = await smartsend("analysis", [("table_data", df, "arrowtable")])``` |

### Scenario 3: Chat System (Multi-Payload)

| Platform | Code |
|----------|------|
| **Julia** | ```julia<br>chat = [("text", "Hello!", "text"), ("image", img_bytes, "image")]<br>env, env_json_str = smartsend("chat", chat)``` |
| **JavaScript** | ```javascript<br>const chat = [["text", "Hello!", "text"], ["image", imgBuffer, "image"]];<br>[env, env_json_str] = await smartsend("chat", chat);``` |
| **Python** | ```python<br>chat = [("text", "Hello!", "text"), ("image", img_bytes, "image")]<br>env, env_json_str = await smartsend("chat", chat)``` |

### Scenario 4: JSON Table Transfer (Cross-Platform)

| Platform | Code |
|----------|------|
| **Julia** | ```julia<br>rows = [Dict("id" => 1, "name" => "Alice"), Dict("id" => 2, "name" => "Bob")]<br>env, env_json_str = smartsend("data", [("users", rows, "jsontable")])``` |
| **JavaScript** | ```javascript<br>const users = [{ id: 1, name: "Alice" }, { id: 2, name: "Bob" }];<br>[env, env_json_str] = await smartsend("data", [["users", users, "jsontable"]]);``` |
| **Python** | ```python<br>users = [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]<br>env, env_json_str = await smartsend("data", [("users", users, "jsontable")])``` |

### Scenario 5: Smart Transport Selection

The `smartsend` function automatically selects the transport method based on payload size:

- **Direct Transport (< 1MB)**: Payload is serialized and embedded directly in the NATS message
  - `arrowtable`: Serialized to Arrow IPC, base64 encoded
  - `jsontable`: Serialized to JSON, base64 encoded
  - `dictionary`: Serialized to JSON, base64 encoded
  - `text`: Serialized to UTF-8, base64 encoded
  - `image/audio/video/binary`: Base64 encoded

- **Link Transport (>= 1MB)**: Payload is uploaded to HTTP file server, URL embedded in message
  - All types supported
  - Receiver downloads from URL and deserializes

---

## Performance Considerations (Cross-Platform)

### Zero-Copy Reading

| Platform | Strategy |
|----------|----------|
| **Julia** | `Arrow.read()` with memory-mapped files |
| **JavaScript** | `ArrayBuffer` with `DataView` |
| **Python** | `pyarrow` memory mapping |
| **MicroPython** | Not available (streaming only) |

### Exponential Backoff

```python
# Python/MicroPython
async def fetch_with_backoff(url, max_retries, base_delay, max_delay, correlation_id):
    delay = base_delay
    for attempt in range(1, max_retries + 1):
        try:
            async with aiohttp.ClientSession() as session:
                async with session.get(url) as response:
                    if response.status == 200:
                        return await response.read()
        except Exception as e:
            if attempt < max_retries:
                await asyncio.sleep(delay / 1000.0)
                delay = min(delay * 2, max_delay)
    raise Exception("Failed to fetch after max retries")
```

### Correlation ID Logging

All platforms use correlation IDs for distributed tracing:

```
[timestamp] [Correlation: abc123] Message published to subject
```

### Serialization Performance Comparison

| Format | Use Case | Pros | Cons |
|--------|----------|------|------|
| `arrowtable` | Large tabular data | Fast, zero-copy, schema-preserving | Binary format, requires Arrow library |
| `jsontable` | Small/medium tabular data | Human-readable, universal support | Slower, larger size, no schema |

---

## Testing Strategy (Cross-Platform)

### Unit Tests

| Test Type | Julia | JavaScript | Python |
|-----------|-------|------------|--------|
| **Serialization** | `test/test_julia_text_sender.jl` | `test/test_js_text_sender.js` | `test/test_py_text_sender.py` |
| **Deserialization** | `test/test_julia_text_receiver.jl` | `test/test_js_text_receiver.js` | `test/test_py_text_receiver.py` |
| **Large Payload** | `test/test_julia_file_sender.jl` | `test/test_js_file_sender.js` | `test/test_py_file_sender.py` |
| **Multi-Payload** | `test/test_julia_mix_payloads_sender.jl` | `test/test_js_mix_payloads_sender.js` | `test/test_py_mix_payloads_sender.py` |
| **Arrow Table** | `test/test_julia_table_sender.jl` | `test/test_js_table_sender.js` | `test/test_py_table_sender.py` |

### Integration Tests

- NATS server communication
- File server upload/download
- Cross-platform message exchange
- Arrow table serialization/deserialization
- JSON table serialization/deserialization

---

## Configuration

### Environment Variables

| Variable | Default | Description |
|----------|---------|-------------|
| `NATS_URL` | `nats://localhost:4222` | NATS server URL |
| `FILESERVER_URL` | `http://localhost:8080` | HTTP file server URL |
| `SIZE_THRESHOLD` | `1000000` | Size threshold in bytes (1MB) |

### MicroPython-Specific Configuration

```python
# micropython.conf
NATS_URL = "nats://broker.local:4222"
FILESERVER_URL = "http://fileserver.local:8080"
SIZE_THRESHOLD = 100000  # Lower threshold for memory-constrained devices
MAX_PAYLOAD_SIZE = 50000  # Hard limit for MicroPython
```

---

## Summary

This cross-platform NATS bridge provides:

1. **High-Level API Parity**: Identical `smartsend()` and `smartreceive()` signatures across Julia, JavaScript, and Python/MicroPython
2. **Idiomatic Implementations**: 
   - Julia: Multiple dispatch and struct-based design
   - JavaScript: Async/await and prototype-based utilities
   - Python: Class-based design with type hints
3. **Message Format Consistency**: Identical `msg_envelope_v1` and `msg_payload_v1` JSON schemas
4. **Handler Abstraction**: File server operations abstracted through configurable handlers
5. **Platform-Specific Optimizations**: 
   - **Arrow IPC** (`arrowtable`): Efficient binary format for large tabular data
   - **JSON** (`jsontable`): Universal human-readable format for smaller tables
   - Streaming support in MicroPython

The Julia implementation serves as the **ground truth** for API design and behavior, while JavaScript and Python implementations maintain interface parity while leveraging their respective language idioms.

### Datatype Summary

| Datatype | Serialization | Use Case | Encoding |
|----------|---------------|----------|----------|
| `arrowtable` | Apache Arrow IPC | Large tabular data, schema-preserving | `arrow-ipc` → `base64` |
| `jsontable` | JSON | Small/medium tabular data, human-readable | `json` → `base64` |