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# Cross-Platform NATSBridge Tutorial
A step-by-step guide to get started with NATSBridge across **Julia**, **JavaScript**, and **Python/MicroPython**.
## Table of Contents
1. [Overview](#overview)
2. [Prerequisites](#prerequisites)
3. [Installation](#installation)
4. [Quick Start](#quick-start)
5. [Basic Examples](#basic-examples)
6. [Advanced Usage](#advanced-usage)
---
## Overview
NATSBridge enables seamless communication across platforms through NATS, with automatic transport selection based on payload size:
- **Direct Transport**: Payloads < 1MB are sent directly via NATS (Base64 encoded)
- **Link Transport**: Payloads >= 1MB are uploaded to an HTTP file server and referenced via URL
### Cross-Platform API Parity
All three platforms use the same high-level API:
```
# Input format
smartsend(subject, [(dataname, data, type), ...], options)
# Output format
(env, env_json_str) = smartsend(...)
env = smartreceive(msg, options)
```
### Supported Payload Types
| Type | Julia | JavaScript | Python | MicroPython |
|------|-------|------------|--------|-------------|
| `text` | `String` | `string` | `str` | `str` |
| `dictionary` | `Dict` | `Object` | `dict` | `dict` |
| `table` | `DataFrame` | `Array<Object>` | `DataFrame` | ❌ |
| `image` | `Vector{UInt8}` | `Uint8Array` | `bytes` | `bytearray` |
| `audio` | `Vector{UInt8}` | `Uint8Array` | `bytes` | `bytearray` |
| `video` | `Vector{UInt8}` | `Uint8Array` | `bytes` | `bytearray` |
| `binary` | `Vector{UInt8}` | `Uint8Array` | `bytes` | `bytearray` |
---
## Prerequisites
Before you begin, ensure you have:
1. **NATS Server** running (or accessible)
2. **HTTP File Server** (optional, for large payloads > 1MB)
3. **Platform-specific packages** installed
---
## Installation
### Julia
```julia
using Pkg
Pkg.add("NATS")
Pkg.add("Arrow")
Pkg.add("JSON3")
Pkg.add("HTTP")
Pkg.add("UUIDs")
Pkg.add("Dates")
```
### JavaScript (Node.js)
```bash
npm install nats uuid apache-arrow node-fetch
```
### JavaScript (Browser)
```html
<script src="https://unpkg.com/nats-js/dist/bundle/nats.min.js"></script>
<script src="https://unpkg.com/apache-arrow/arrow.min.js"></script>
```
### Python (Desktop)
```bash
pip install nats-py aiohttp pyarrow pandas
```
### MicroPython
Uses built-in modules: `network`, `socket`, `time`, `json`, `base64`
---
## Quick Start
### Step 1: Start NATS Server
```bash
docker run -p 4222:4222 nats:latest
```
### Step 2: Start HTTP File Server (Optional)
```bash
mkdir -p /tmp/fileserver
python3 -m http.server 8080 --directory /tmp/fileserver
```
### Step 3: Send Your First Message
#### Julia
```julia
using NATSBridge
# Send a text message
data = [("message", "Hello World", "text")]
env, env_json_str = smartsend("/chat/room1", data, broker_url="nats://localhost:4222")
# env: msg_envelope_v1 struct with all metadata and payloads
# env_json_str: JSON string representation of the envelope for publishing
println("Message sent!")
# Or use is_publish=false to get envelope and JSON without publishing
env, env_json_str = smartsend("/chat/room1", data, broker_url="nats://localhost:4222", is_publish=false)
# env: msg_envelope_v1 struct
# env_json_str: JSON string for publishing to NATS
```
#### JavaScript
```javascript
const NATSBridge = require('./src/natsbridge.js');
// Send a text message
const data = [["message", "Hello World", "text"]];
const [env, env_json_str] = await NATSBridge.smartsend(
"/chat/room1",
data,
{ broker_url: "nats://localhost:4222" }
);
// env: Object with all metadata and payloads
// env_json_str: JSON string for publishing
console.log("Message sent!");
// Or use is_publish=false
const [env, env_json_str] = await NATSBridge.smartsend(
"/chat/room1",
data,
{ broker_url: "nats://localhost:4222", is_publish: false }
);
```
#### Python
```python
from natsbridge import smartsend
# Send a text message
data = [("message", "Hello World", "text")]
env, env_json_str = await smartsend(
"/chat/room1",
data,
broker_url="nats://localhost:4222"
)
# env: Dict with all metadata and payloads
# env_json_str: JSON string for publishing
print("Message sent!")
# Or use is_publish=False
env, env_json_str = await smartsend(
"/chat/room1",
data,
broker_url="nats://localhost:4222",
is_publish=False
)
# env: Dict with all metadata and payloads
# env_json_str: JSON string for publishing to NATS
```
#### MicroPython
```python
from natsbridge_mpy import NATSBridge
bridge = NATSBridge()
# Send a text message (limited to small payloads)
data = [("message", "Hello World", "text")]
env, env_json_str = bridge.smartsend(
"/chat/room1",
data,
size_threshold=100000 # Lower threshold for MicroPython
)
print("Message sent!")
```
### Step 4: Receive Messages
#### Julia
```julia
using NATSBridge
# Receive and process message
env = smartreceive(msg; fileserver_download_handler=_fetch_with_backoff)
# Returns: ::JSON.Object{String, Any} with "payloads" field containing Vector{Tuple{String, Any, String}}
# Access payloads: for (dataname, data, type) in env["payloads"]
for (dataname, data, type) in env["payloads"]
println("Received $dataname: $data")
end
```
#### JavaScript
```javascript
const NATSBridge = require('./src/natsbridge.js');
// Receive and process message
const env = await NATSBridge.smartreceive(msg, {
fileserver_download_handler: NATSBridge.fetchWithBackoff
});
// env.payloads = [[dataname, data, type], ...]
for (const [dataname, data, type] of env.payloads) {
console.log(`Received ${dataname}:`, data);
}
```
#### Python
```python
from natsbridge import smartreceive, fetch_with_backoff
# Receive and process message
env = await smartreceive(
msg,
fileserver_download_handler=fetch_with_backoff
)
# env["payloads"] = [(dataname, data, type), ...]
for dataname, data, type_ in env["payloads"]:
print(f"Received {dataname}: {data}")
```
---
## Basic Examples
### Example 1: Sending a Dictionary
#### Julia
```julia
using NATSBridge
config = Dict(
"wifi_ssid" => "MyNetwork",
"wifi_password" => "password123",
"update_interval" => 60
)
data = [("config", config, "dictionary")]
env, env_json_str = smartsend("/device/config", data, broker_url="nats://localhost:4222")
```
#### JavaScript
```javascript
const NATSBridge = require('./src/natsbridge.js');
const config = {
wifi_ssid: "MyNetwork",
wifi_password: "password123",
update_interval: 60
};
const data = [["config", config, "dictionary"]];
const [env, env_json_str] = await NATSBridge.smartsend(
"/device/config",
data,
{ broker_url: "nats://localhost:4222" }
);
```
#### Python
```python
from natsbridge import smartsend
config = {
"wifi_ssid": "MyNetwork",
"wifi_password": "password123",
"update_interval": 60
}
data = [("config", config, "dictionary")]
env, env_json_str = await smartsend(
"/device/config",
data,
broker_url="nats://localhost:4222"
)
```
#### MicroPython
```python
from natsbridge_mpy import NATSBridge
bridge = NATSBridge()
config = {
"wifi_ssid": "MyNetwork",
"wifi_password": "password123",
"update_interval": 60
}
data = [("config", config, "dictionary")]
env, env_json_str = bridge.smartsend(
"/device/config",
data,
size_threshold=100000
)
```
### Example 2: Sending Binary Data (Image)
#### Julia
```julia
using NATSBridge
# Read image file
image_data = read("image.png")
data = [("user_image", image_data, "binary")]
env, env_json_str = smartsend("/chat/image", data, broker_url="nats://localhost:4222")
```
#### JavaScript
```javascript
const NATSBridge = require('./src/natsbridge.js');
const fs = require('fs');
// Read image file
const image_data = fs.readFileSync('image.png');
const data = [["user_image", image_data, "binary"]];
const [env, env_json_str] = await NATSBridge.smartsend(
"/chat/image",
data,
{ broker_url: "nats://localhost:4222" }
);
```
#### Python
```python
from natsbridge import smartsend
# Read image file
with open("image.png", "rb") as f:
image_data = f.read()
data = [("user_image", image_data, "binary")]
env, env_json_str = await smartsend(
"/chat/image",
data,
broker_url="nats://localhost:4222"
)
```
#### MicroPython
```python
from natsbridge_mpy import NATSBridge
bridge = NATSBridge()
# Read image file
with open("image.png", "rb") as f:
image_data = f.read()
data = [("user_image", image_data, "binary")]
env, env_json_str = bridge.smartsend(
"/chat/image",
data,
size_threshold=100000
)
```
### Example 3: Request-Response Pattern
#### Julia (Requester)
```julia
using NATSBridge
# Send command with reply-to
data = [("command", Dict("action" => "read_sensor"), "dictionary")]
env, env_json_str = smartsend(
"/device/command",
data,
broker_url="nats://localhost:4222",
reply_to="/device/response",
reply_to_msg_id="cmd-001"
)
```
#### JavaScript (Requester)
```javascript
const NATSBridge = require('./src/natsbridge.js');
// Send command with reply-to
const data = [["command", { action: "read_sensor" }, "dictionary"]];
const [env, env_json_str] = await NATSBridge.smartsend(
"/device/command",
data,
{
broker_url: "nats://localhost:4222",
reply_to: "/device/response",
reply_to_msg_id: "cmd-001"
}
);
```
#### Python (Requester)
```python
from natsbridge import smartsend
# Send command with reply-to
data = [("command", {"action": "read_sensor"}, "dictionary")]
env, env_json_str = await smartsend(
"/device/command",
data,
broker_url="nats://localhost:4222",
reply_to="/device/response",
reply_to_msg_id="cmd-001"
)
```
#### Julia (Responder)
```julia
using NATSBridge, NATS
const SUBJECT = "/device/command"
const NATS_URL = "nats://localhost:4222"
function test_responder()
conn = NATS.connect(NATS_URL)
NATS.subscribe(conn, SUBJECT) do msg
env = smartreceive(msg, fileserver_download_handler=_fetch_with_backoff)
reply_to = env["reply_to"]
for (dataname, data, type) in env["payloads"]
if dataname == "command" && data["action"] == "read_sensor"
response = Dict("sensor_id" => "sensor-001", "value" => 42.5)
if !isempty(reply_to)
smartsend(reply_to, [("data", response, "dictionary")])
end
end
end
end
sleep(120)
NATS.drain(conn)
end
test_responder()
```
---
## Advanced Usage
### Example 4: Large Payloads (File Server)
For payloads larger than 1MB, NATSBridge automatically uses the file server:
#### Julia
```julia
using NATSBridge
# Create large data (> 1MB)
large_data = rand(UInt8, 2_000_000)
env, env_json_str = smartsend(
"/data/large",
[("large_file", large_data, "binary")],
broker_url="nats://localhost:4222",
fileserver_url="http://localhost:8080"
)
println("File uploaded to: $(env.payloads[1].data)")
# Note: For link transport, data field contains the URL string
```
#### JavaScript
```javascript
const NATSBridge = require('./src/natsbridge.js');
// Create large data (> 1MB)
const large_data = Buffer.alloc(2_000_000);
for (let i = 0; i < large_data.length; i++) {
large_data[i] = Math.floor(Math.random() * 256);
}
const [env, env_json_str] = await NATSBridge.smartsend(
"/data/large",
[["large_file", large_data, "binary"]],
{
broker_url: "nats://localhost:4222",
fileserver_url: "http://localhost:8080"
}
);
console.log("File uploaded to:", env.payloads[0].data);
// Note: For link transport, data field contains the URL string
```
#### Python
```python
from natsbridge import smartsend
# Create large data (> 1MB)
import os
large_data = os.urandom(2_000_000)
env, env_json_str = await smartsend(
"/data/large",
[("large_file", large_data, "binary")],
broker_url="nats://localhost:4222",
fileserver_url="http://localhost:8080"
)
print(f"File uploaded to: {env['payloads'][0]['data']}")
# Note: For link transport, data field contains the URL string
```
#### MicroPython
MicroPython enforces a hard limit of 50KB per payload:
```python
from natsbridge_mpy import NATSBridge
bridge = NATSBridge()
# MicroPython has a hard limit of 50KB per payload
# Use streaming or chunking for larger data
small_data = bytes(1000) # 1KB
data = [("small_file", small_data, "binary")]
env, env_json_str = bridge.smartsend(
"/data/small",
data,
size_threshold=100000 # Enforced max: 50000 bytes
)
```
### Example 5: Mixed Content (Chat with Text + Image)
NATSBridge supports sending multiple payloads with different types in a single message:
#### Julia
```julia
using NATSBridge
image_data = read("avatar.png")
data = [
("message_text", "Hello with image!", "text"),
("user_avatar", image_data, "image")
]
env, env_json_str = smartsend("/chat/mixed", data, broker_url="nats://localhost:4222")
```
#### JavaScript
```javascript
const NATSBridge = require('./src/natsbridge.js');
const fs = require('fs');
const image_data = fs.readFileSync('avatar.png');
const data = [
["message_text", "Hello with image!", "text"],
["user_avatar", image_data, "image"]
];
const [env, env_json_str] = await NATSBridge.smartsend(
"/chat/mixed",
data,
{ broker_url: "nats://localhost:4222" }
);
```
#### Python
```python
from natsbridge import smartsend
with open("avatar.png", "rb") as f:
image_data = f.read()
data = [
("message_text", "Hello with image!", "text"),
("user_avatar", image_data, "image")
]
env, env_json_str = await smartsend(
"/chat/mixed",
data,
broker_url="nats://localhost:4222"
)
# env: Dict with all metadata and payloads
```
### Example 6: Table Data (Arrow IPC)
For tabular data, NATSBridge uses Apache Arrow IPC format:
#### Julia
```julia
using NATSBridge
using DataFrames
# Create DataFrame
df = DataFrame(
id = [1, 2, 3],
name = ["Alice", "Bob", "Charlie"],
score = [95, 88, 92]
)
data = [("students", df, "table")]
env, env_json_str = smartsend("/data/students", data, broker_url="nats://localhost:4222")
```
#### JavaScript
```javascript
const NATSBridge = require('./src/natsbridge.js');
// Create table data (array of objects)
const table_data = [
{ id: 1, name: "Alice", score: 95 },
{ id: 2, name: "Bob", score: 88 },
{ id: 3, name: "Charlie", score: 92 }
];
const data = [["students", table_data, "table"]];
const [env, env_json_str] = await NATSBridge.smartsend(
"/data/students",
data,
{ broker_url: "nats://localhost:4222" }
);
```
#### Python
```python
from natsbridge import smartsend
import pandas as pd
# Create DataFrame
df = pd.DataFrame({
'id': [1, 2, 3],
'name': ['Alice', 'Bob', 'Charlie'],
'score': [95, 88, 92]
})
data = [("students", df, "table")]
env, env_json_str = await smartsend(
"/data/students",
data,
broker_url="nats://localhost:4222"
)
```
#### MicroPython
MicroPython does not support table type due to memory constraints. Use dictionary or binary instead.
---
## Next Steps
1. **Explore the test directory** for more examples
2. **Check the documentation** for advanced configuration options
3. **Read the walkthrough** for building real-world applications
---
## Troubleshooting
### Connection Issues
- Ensure NATS server is running: `docker ps | grep nats`
- Check firewall settings
- Verify NATS URL configuration
### File Server Issues
- Ensure file server is running and accessible
- Check upload permissions
- Verify file server URL configuration
### Serialization Errors
- Verify data type matches the specified type
- Check that binary data is in the correct format
- MicroPython: Ensure payload size < 50KB
---
## License
MIT

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