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# Implementation Guide: Bi-Directional Data Bridge
## Overview
This document describes the implementation of the high-performance, bi-directional data bridge between Julia and JavaScript services using NATS (Core & JetStream), implementing the Claim-Check pattern for large payloads.
## Architecture
The implementation follows the Claim-Check pattern:
```
┌─────────────────────────────────────────────────────────────────────────┐
│ SmartSend Function │
└─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────┐
│ Is payload size < 1MB? │
└─────────────────────────────────────────────────────────────────────────┘
┌─────────────────┴─────────────────┐
▼ ▼
┌─────────────────┐ ┌─────────────────┐
│ Direct Path │ │ Link Path │
│ (< 1MB) │ │ (> 1MB) │
│ │ │ │
│ • Serialize to │ │ • Serialize to │
│ IOBuffer │ │ IOBuffer │
│ • Base64 encode │ │ • Upload to │
│ • Publish to │ │ HTTP Server │
│ NATS │ │ • Publish to │
│ │ │ NATS with URL │
└─────────────────┘ └─────────────────┘
```
## Files
### Julia Module: [`src/julia_bridge.jl`](../src/julia_bridge.jl)
The Julia implementation provides:
- **[`MessageEnvelope`](../src/julia_bridge.jl)**: Struct for the unified JSON envelope
- **[`SmartSend()`](../src/julia_bridge.jl)**: Handles transport selection based on payload size
- **[`SmartReceive()`](../src/julia_bridge.jl)**: Handles both direct and link transport
### JavaScript Module: [`src/js_bridge.js`](../src/js_bridge.js)
The JavaScript implementation provides:
- **`MessageEnvelope` class**: For the unified JSON envelope
- **[`SmartSend()`](../src/js_bridge.js)**: Handles transport selection based on payload size
- **[`SmartReceive()`](../src/js_bridge.js)**: Handles both direct and link transport
## Installation
### Julia Dependencies
```julia
using Pkg
Pkg.add("NATS")
Pkg.add("Arrow")
Pkg.add("JSON3")
Pkg.add("HTTP")
Pkg.add("UUIDs")
Pkg.add("Dates")
```
### JavaScript Dependencies
```bash
npm install nats.js apache-arrow uuid base64-url
```
## Usage Tutorial
### Step 1: Start NATS Server
```bash
docker run -p 4222:4222 nats:latest
```
### Step 2: Start HTTP File Server (optional)
```bash
# Create a directory for file uploads
mkdir -p /tmp/fileserver
# Use any HTTP server that supports POST for file uploads
# Example: Python's built-in server
python3 -m http.server 8080 --directory /tmp/fileserver
```
### Step 3: Run Test Scenarios
```bash
# Scenario 1: Command & Control (JavaScript sender)
node test/scenario1_command_control.js
# Scenario 2: Large Arrow Table (JavaScript sender)
node test/scenario2_large_table.js
# Scenario 3: Julia-to-Julia communication
# Run both Julia and JavaScript versions
julia test/scenario3_julia_to_julia.jl
node test/scenario3_julia_to_julia.js
```
## Usage
### Scenario 1: Command & Control (Small JSON)
#### JavaScript (Sender)
```javascript
const { SmartSend } = require('./js_bridge');
const config = {
step_size: 0.01,
iterations: 1000
};
await SmartSend("control", config, "json", {
correlationId: "unique-id"
});
```
#### Julia (Receiver)
```julia
using NATS
using JSON3
# Subscribe to control subject
subscribe(nats, "control") do msg
env = MessageEnvelope(String(msg.data))
config = JSON3.read(env.payload)
# Execute simulation with parameters
step_size = config.step_size
iterations = config.iterations
# Send acknowledgment
response = Dict("status" => "Running", "correlation_id" => env.correlation_id)
publish(nats, "control_response", JSON3.stringify(response))
end
```
### Scenario 2: Deep Dive Analysis (Large Arrow Table)
#### Julia (Sender)
```julia
using Arrow
using DataFrames
# Create large DataFrame
df = DataFrame(
id = 1:10_000_000,
value = rand(10_000_000),
category = rand(["A", "B", "C"], 10_000_000)
)
# Send via SmartSend with type="table"
await SmartSend("analysis_results", df, "table");
```
#### JavaScript (Receiver)
```javascript
const { SmartReceive } = require('./js_bridge');
const result = await SmartReceive(msg);
// Use table data for visualization with Perspective.js or D3
const table = result.data;
```
### Scenario 3: Live Binary Processing
#### JavaScript (Sender)
```javascript
const { SmartSend } = require('./js_bridge');
// Capture binary chunk
const binaryData = await navigator.mediaDevices.getUserMedia({ binary: true });
await SmartSend("binary_input", binaryData, "binary", {
metadata: {
sample_rate: 44100,
channels: 1
}
});
```
#### Julia (Receiver)
```julia
using WAV
using DSP
# Receive binary data
function process_binary(data)
# Perform FFT or AI transcription
spectrum = fft(data)
# Send results back (JSON + Arrow table)
results = Dict("transcription" => "sample text", "spectrum" => spectrum)
await SmartSend("binary_output", results, "json")
end
```
### Scenario 4: Catch-Up (JetStream)
#### Julia (Producer)
```julia
using NATS
function publish_health_status(nats)
jetstream = JetStream(nats, "health_updates")
while true
status = Dict("cpu" => rand(), "memory" => rand())
publish(jetstream, "health", status)
sleep(5) # Every 5 seconds
end
end
```
#### JavaScript (Consumer)
```javascript
const { connect } = require('nats');
const nc = await connect({ servers: ['nats://localhost:4222'] });
const js = nc.jetstream();
// Request replay from last 10 minutes
const consumer = await js.pullSubscribe("health", {
durable_name: "catchup",
max_batch: 100,
max_ack_wait: 30000
});
// Process historical and real-time messages
for await (const msg of consumer) {
const result = await SmartReceive(msg);
// Process the data
msg.ack();
}
```
## Configuration
### Environment Variables
| Variable | Default | Description |
|----------|---------|-------------|
| `NATS_URL` | `nats://localhost:4222` | NATS server URL |
| `FILESERVER_URL` | `http://localhost:8080/upload` | HTTP file server URL |
| `SIZE_THRESHOLD` | `1_000_000` | Size threshold in bytes (1MB) |
### Message Envelope Schema
```json
{
"correlation_id": "uuid-v4-string",
"type": "json|table|binary",
"transport": "direct|link",
"payload": "base64-encoded-string", // Only if transport=direct
"url": "http://fileserver/path/to/data", // Only if transport=link
"metadata": {
"content_type": "application/octet-stream",
"content_length": 123456,
"format": "arrow_ipc_stream"
}
}
```
## 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
- Maximum retry count: 5
- Base delay: 100ms, max delay: 5000ms
- Implemented in both Julia and JavaScript implementations
### Correlation ID Logging
- Log correlation_id at every stage
- Include: send, receive, serialize, deserialize
- Use structured logging format
## Testing
Run the test scripts:
```bash
# Scenario 1: Command & Control (JavaScript sender)
node test/scenario1_command_control.js
# Scenario 2: Large Arrow Table (JavaScript sender)
node test/scenario2_large_table.js
```
## Troubleshooting
### Common Issues
1. **NATS Connection Failed**
- Ensure NATS server is running
- Check NATS_URL configuration
2. **HTTP Upload Failed**
- Ensure file server is running
- Check FILESERVER_URL configuration
- Verify upload permissions
3. **Arrow IPC Deserialization Error**
- Ensure data is properly serialized to Arrow format
- Check Arrow version compatibility
## License
MIT