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ton
d950bbac23 Merge pull request 'smartreceive_return_envelope' (#7) from smartreceive_return_envelope into main
Reviewed-on: #7
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ton
c06f508e8f Merge pull request 'smartreceive_return_envelope' (#6) from smartreceive_return_envelope into main
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ton
0de9725ba8 Merge pull request 'add Base64 in project.toml' (#5) from fix_precompile_issue into main
Reviewed-on: #5
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@@ -13,3 +13,41 @@ Role: Principal Systems Architect & Lead Software Engineer.Objective: Implement
Create a walkthrough for Julia service-A service sending a mix-content chat message to Julia service-B. the chat message must includes
I updated the following:
- NATSBridge.jl. Essentially I add NATS_connection keyword and new publish_message function to support the keyword.
Use them and ONLY them as ground truth.
Then update the following files accordingly:
- architecture.md
- implementation.md
All API should be semantically consistent and naming should be consistent across the board.
Task: Update NATSBridge.js to reflect recent changes in NATSBridge.jl and docs
Context: NATSBridge.jl and docs has been updated.
Requirements:
Source of Truth: Treat the updated NATSBridge.jl and docs as the definitive source.
API Consistency: Ensure the Main Package API (e.g., smartsend(), publish_message()) uses consistent naming across all three supported languages.
Ecosystem Variance: Low-level native functions (e.g., NATS.connect(), JSON.read()) should follow the conventions of the specific language ecosystem and do not require cross-language consistency.

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name = "NATSBridge"
uuid = "f2724d33-f338-4a57-b9f8-1be882570d10"
version = "0.4.1"
version = "0.4.3"
authors = ["narawat <narawat@gmail.com>"]
[deps]

495
README.md Normal file
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@@ -0,0 +1,495 @@
# NATSBridge
A high-performance, bi-directional data bridge for **Julia** applications using NATS (Core & JetStream), implementing the Claim-Check pattern for large payloads.
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![NATS](https://img.shields.io/badge/NATS-Enabled-green.svg)](https://nats.io)
---
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Architecture](#architecture)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [API Reference](#api-reference)
- [Payload Types](#payload-types)
- [Transport Strategies](#transport-strategies)
- [Examples](#examples)
- [Testing](#testing)
- [License](#license)
---
## Overview
NATSBridge enables seamless communication for Julia applications through NATS, with intelligent transport selection based on payload size:
| Transport | Payload Size | Method |
|-----------|--------------|--------|
| **Direct** | < 1MB | Sent directly via NATS (Base64 encoded) |
| **Link** | >= 1MB | Uploaded to HTTP file server, URL sent via NATS |
### Use Cases
- **Chat Applications**: Text, images, audio, video in a single message
- **File Transfer**: Efficient transfer of large files using claim-check pattern
- **Streaming Data**: Sensor data, telemetry, and analytics pipelines
---
## Features
-**Bi-directional messaging** for Julia applications
-**Multi-payload support** - send multiple payloads with different types in one message
-**Automatic transport selection** - direct vs link based on payload size
-**Claim-Check pattern** for payloads > 1MB
-**Apache Arrow IPC** support for tabular data (zero-copy reading)
-**Exponential backoff** for reliable file server downloads
-**Correlation ID tracking** for message tracing
-**Reply-to support** for request-response patterns
-**JetStream support** for message replay and durability
---
## Architecture
### System Components
```
┌─────────────────────────────────────────────────────────────────────┐
│ NATSBridge Architecture │
├─────────────────────────────────────────────────────────────────────┤
│ ┌──────────────┐ │ │
│ │ Julia │ ▼ │
│ │ (NATS.jl) │ ┌─────────────────────────┐ │
│ └──────────────┘ │ NATS │ │
│ │ (Message Broker) │ │
│ └─────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────┐ │
│ │ File Server │ │
│ │ (HTTP Upload/Get) │ │
│ └──────────────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
```
### Message Flow
1. **Sender** creates a message envelope with payloads
2. **NATSBridge** serializes and encodes payloads based on type
3. **Transport Decision**: Small payloads go directly to NATS, large payloads are uploaded to file server
4. **NATS** routes messages to subscribers
5. **Receiver** fetches payloads (from NATS or file server)
6. **NATSBridge** deserializes and decodes payloads
---
## Installation
### Prerequisites
- **NATS Server** (v2.10+ recommended)
- **HTTP File Server** (optional, for payloads > 1MB)
### Julia
```julia
using Pkg
Pkg.add("NATS")
Pkg.add("https://git.yiem.cc/ton/NATSBridge")
```
---
## Quick Start
### 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
# Start HTTP file server
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 = NATSBridge.smartsend("/chat/room1", data; broker_url="nats://localhost:4222")
println("Message sent!")
```
### Step 4: Receive Messages
#### Julia
```julia
using NATS, NATSBridge
# Configuration
const SUBJECT = "/chat/room1"
const NATS_URL = "nats://localhost:4222"
# Helper: Log with correlation ID
function log_trace(message)
timestamp = Dates.now()
println("[$timestamp] $message")
end
# Receiver: Listen for messages - msg comes from the callback
function test_receive()
conn = NATS.connect(NATS_URL)
NATS.subscribe(conn, SUBJECT) do msg
log_trace("Received message on $(msg.subject)")
# Receive and process message
env, env_json_str = NATSBridge.smartreceive(msg, fileserverDownloadHandler)
for (dataname, data, type) in env["payloads"]
println("Received $dataname: $data")
end
end
# Keep listening for 120 seconds
sleep(120)
NATS.drain(conn)
end
test_receive()
```
---
## API Reference
### smartsend
Sends data either directly via NATS or via a fileserver URL, depending on payload size.
#### Julia
```julia
using NATSBridge
env, env_json_str = NATSBridge.smartsend(
subject, # NATS subject
data::AbstractArray{Tuple{String, Any, String}}; # List of (dataname, data, type)
broker_url::String = "nats://localhost:4222",
fileserver_url = "http://localhost:8080",
fileserver_upload_handler::Function = plik_oneshot_upload,
size_threshold::Int = 1_000_000,
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
NATS_connection::Union{NATS.Connection, Nothing} = nothing # Pre-existing NATS connection (optional, saves connection overhead)
)
# Returns: (msgEnvelope_v1, JSON string)
# - env: msgEnvelope_v1 object with all envelope metadata and payloads
# - env_json_str: JSON string representation of the envelope for publishing
```
### smartreceive
Receives and processes messages from NATS, handling both direct and link transport.
#### Julia
```julia
using NATSBridge
# Note: msg is a NATS.Msg object passed from the subscription callback
env = NATSBridge.smartreceive(
msg::NATS.Msg;
fileserver_download_handler::Function = _fetch_with_backoff,
max_retries::Int = 5,
base_delay::Int = 100,
max_delay::Int = 5000
)
# Returns: Dict with envelope metadata and payloads array
```
### publish_message
Publish a message to a NATS subject. This function is available in Julia with two overloads:
#### Julia
**Using broker URL (creates new connection):**
```julia
using NATSBridge, NATS
# Publish with URL - creates a new connection
NATSBridge.publish_message(
"nats://localhost:4222", # broker_url
"/chat/room1", # subject
"{\"correlation_id\":\"abc123\"}", # message
"abc123" # correlation_id
)
```
**Using pre-existing connection (saves connection overhead):**
```julia
using NATSBridge, NATS
# Create connection once and reuse
conn = NATS.connect("nats://localhost:4222")
NATSBridge.publish_message(conn, "/chat/room1", "{\"correlation_id\":\"abc123\"}", "abc123")
# Connection is automatically drained after publish
```
---
## Payload Types
| Type | Description | Serialization |
|------|-------------|---------------|
| `text` | Plain text strings | UTF-8 bytes |
| `dictionary` | JSON-serializable dictionaries | JSON |
| `table` | Tabular data (DataFrames, arrays) | Apache Arrow IPC |
| `image` | Image data (PNG, JPG) | Raw bytes |
| `audio` | Audio data (WAV, MP3) | Raw bytes |
| `video` | Video data (MP4, AVI) | Raw bytes |
| `binary` | Generic binary data | Raw bytes |
---
## Transport Strategies
### Direct Transport (Payloads < 1MB)
Small payloads are sent directly via NATS with Base64 encoding.
#### Julia
```julia
data = [("message", "Hello", "text")]
smartsend("/topic", data)
```
### Link Transport (Payloads >= 1MB)
Large payloads are uploaded to an HTTP file server.
#### Julia
```julia
data = [("file", large_data, "binary")]
smartsend("/topic", data; fileserver_url="http://localhost:8080")
```
---
## Examples
### Example 1: Chat with Mixed Content
Send text, small image, and large file in one message.
#### Julia
```julia
using NATSBridge
data = [
("message_text", "Hello!", "text"),
("user_avatar", image_data, "image"),
("large_document", large_file_data, "binary")
]
env, env_json_str = NATSBridge.smartsend("/chat/room1", data; fileserver_url="http://localhost:8080")
```
### Example 2: Dictionary Exchange
Send configuration data between platforms.
#### Julia
```julia
using NATSBridge
config = Dict(
"wifi_ssid" => "MyNetwork",
"wifi_password" => "password123",
"update_interval" => 60
)
data = [("config", config, "dictionary")]
env, env_json_str = NATSBridge.smartsend("/device/config", data)
```
### Example 3: Table Data (Arrow IPC)
Send tabular data using Apache Arrow IPC format.
#### Julia
```julia
using NATSBridge
using DataFrames
df = DataFrame(
id = [1, 2, 3],
name = ["Alice", "Bob", "Charlie"],
score = [95, 88, 92]
)
data = [("students", df, "table")]
env, env_json_str = NATSBridge.smartsend("/data/analysis", data)
```
### Example 4: Request-Response Pattern with Envelope JSON
Bi-directional communication with reply-to support. The `smartsend` function now returns both the envelope object and a JSON string that can be published directly.
#### Julia (Requester)
```julia
using NATSBridge
env, env_json_str = NATSBridge.smartsend(
"/device/command",
[("command", Dict("action" => "read_sensor"), "dictionary")];
broker_url="nats://localhost:4222",
reply_to="/device/response"
)
```
#### Julia (Responder)
```julia
using NATS, NATSBridge
# Configuration
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 = NATSBridge.smartreceive(msg, fileserver_download_handler=_fetch_with_backoff)
# Extract reply_to from the envelope metadata
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)
# Send response to the reply_to subject from the request
if !isempty(reply_to)
smartsend(reply_to, [("data", response, "dictionary")])
end
end
end
end
sleep(120)
NATS.drain(conn)
end
test_responder()
```
### Example 5: IoT Device Sensor Data
IoT device sending sensor data.
#### Julia (Receiver)
```julia
using NATS, NATSBridge
# Configuration
const SUBJECT = "/device/sensors"
const NATS_URL = "nats://localhost:4222"
function test_receiver()
conn = NATS.connect(NATS_URL)
NATS.subscribe(conn, SUBJECT) do msg
env, env_json_str = NATSBridge.smartreceive(msg, fileserverDownloadHandler)
for (dataname, data, type) in env["payloads"]
if dataname == "temperature"
println("Temperature: $data")
elseif dataname == "humidity"
println("Humidity: $data")
end
end
end
sleep(120)
NATS.drain(conn)
end
test_receiver()
```
---
## Testing
Run the test scripts to verify functionality:
### Julia
```julia
# Text message exchange
julia test/test_julia_text_sender.jl
julia test/test_julia_text_receiver.jl
# Dictionary exchange
julia test/test_julia_dict_sender.jl
julia test/test_julia_dict_receiver.jl
# File transfer
julia test/test_julia_file_sender.jl
julia test/test_julia_file_receiver.jl
# Mixed payload types
julia test/test_julia_mix_payloads_sender.jl
julia test/test_julia_mix_payloads_receiver.jl
# Table exchange
julia test/test_julia_table_sender.jl
julia test/test_julia_table_receiver.jl
```
---
## License
MIT License
Copyright (c) 2026 NATSBridge Contributors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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@@ -1,8 +1,11 @@
# Architecture Documentation: Bi-Directional Data Bridge (Julia ↔ JavaScript)
# Architecture Documentation: Bi-Directional Data Bridge
## Overview
This document describes the architecture for a high-performance, bi-directional data bridge between a Julia service and a JavaScript (Node.js) service using NATS (Core & JetStream), implementing the Claim-Check pattern for large payloads.
This document describes the architecture for a high-performance, bi-directional data bridge for **Julia** applications using NATS (Core & JetStream), implementing the Claim-Check pattern for large payloads.
The system enables seamless communication for Julia applications:
- **Julia** messaging with NATS
### File Server Handler Architecture
@@ -12,16 +15,16 @@ The system uses **handler functions** to abstract file server operations, allowi
```julia
# Upload handler - uploads data to file server and returns URL
# The handler is passed to smartsend as fileserverUploadHandler parameter
# The handler is passed to smartsend as fileserver_upload_handler parameter
# It receives: (fileserver_url::String, dataname::String, data::Vector{UInt8})
# Returns: Dict{String, Any} with keys: "status", "uploadid", "fileid", "url"
fileserverUploadHandler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
fileserver_upload_handler(fileserver_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 fileserverDownloadHandler parameter
# 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)
fileserverDownloadHandler(url::String, max_retries::Int, base_delay::Int, max_delay::Int, correlation_id::String)::Vector{UInt8}
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.
@@ -35,8 +38,24 @@ The system uses a **standardized list-of-tuples format** for all payload operati
# Input format for smartsend (always a list of tuples with type info)
[(dataname1, data1, type1), (dataname2, data2, type2), ...]
# Output format for smartreceive (always returns a list of tuples)
[(dataname1, data1, type1), (dataname2, data2, type2), ...]
# Output format for smartreceive (returns a dictionary-like object with payloads field containing list of tuples)
# Returns: Dict-like object 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:**
@@ -57,17 +76,16 @@ This design allows per-payload type specification, enabling **mixed-content mess
smartsend(
"/test",
[("dataname1", data1, "dictionary")], # List with one tuple (data, type)
nats_url="nats://localhost:4222",
fileserverUploadHandler=plik_oneshot_upload,
metadata=user_provided_envelope_level_metadata
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")],
nats_url="nats://localhost:4222",
fileserverUploadHandler=plik_oneshot_upload
broker_url="nats://localhost:4222",
fileserver_upload_handler=plik_oneshot_upload
)
# Mixed content (e.g., chat with text, image, audio)
@@ -78,12 +96,14 @@ smartsend(
("user_image", image_data, "image"),
("audio_clip", audio_data, "audio")
],
nats_url="nats://localhost:4222"
broker_url="nats://localhost:4222"
)
# Receive always returns a list
payloads = smartreceive(msg, fileserverDownloadHandler, max_retries, base_delay, max_delay)
# payloads = [("dataname1", data1, type1), ("dataname2", data2, type2), ...]
# 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
@@ -91,8 +111,7 @@ payloads = smartreceive(msg, fileserverDownloadHandler, max_retries, base_delay,
```mermaid
flowchart TD
subgraph Client
JS[JavaScript Client]
JSApp[Application Logic]
App[Julia Application]
end
subgraph Server
@@ -101,14 +120,12 @@ flowchart TD
FileServer[HTTP File Server]
end
JS -->|Control/Small Data| JSApp
JSApp -->|NATS| NATS
App -->|NATS| NATS
NATS -->|NATS| Julia
Julia -->|NATS| NATS
Julia -->|HTTP POST| FileServer
JS -->|HTTP GET| FileServer
style JS fill:#e1f5fe
style App fill:#e8f5e9
style Julia fill:#e8f5e9
style NATS fill:#fff3e0
style FileServer fill:#f3e5f5
@@ -116,48 +133,48 @@ flowchart TD
## System Components
### 1. msgEnvelope_v1 - Message Envelope
### 1. msg_envelope_v1 - Message Envelope
The `msgEnvelope_v1` structure provides a comprehensive message format for bidirectional communication between Julia and JavaScript services.
The `msg_envelope_v1` structure provides a comprehensive message format for bidirectional communication in Julia applications.
**Julia Structure:**
```julia
struct msgEnvelope_v1
correlationId::String # Unique identifier to track messages across systems
msgId::String # This message id
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
sendTo::String # Topic/subject the sender sends to
msgPurpose::String # Purpose of this message (ACK | NACK | updateStatus | shutdown | ...)
senderName::String # Sender name (e.g., "agent-wine-web-frontend")
senderId::String # Sender id (uuid4)
receiverName::String # Message receiver name (e.g., "agent-backend")
receiverId::String # Message receiver id (uuid4 or nothing for broadcast)
replyTo::String # Topic to reply to
replyToMsgId::String # Message id this message is replying to
brokerURL::String # NATS server address
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::AbstractArray{msgPayload_v1} # Multiple payloads stored here
payloads::Vector{msg_payload_v1} # Multiple payloads stored here
end
```
**JSON Schema:**
```json
{
"correlationId": "uuid-v4-string",
"msgId": "uuid-v4-string",
"correlation_id": "uuid-v4-string",
"msg_id": "uuid-v4-string",
"timestamp": "2024-01-15T10:30:00Z",
"sendTo": "topic/subject",
"msgPurpose": "ACK | NACK | updateStatus | shutdown | chat",
"senderName": "agent-wine-web-frontend",
"senderId": "uuid4",
"receiverName": "agent-backend",
"receiverId": "uuid4",
"replyTo": "topic",
"replyToMsgId": "uuid4",
"brokerURL": "nats://localhost:4222",
"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": {
@@ -167,7 +184,7 @@ end
{
"id": "uuid4",
"dataname": "login_image",
"type": "image",
"payload_type": "image",
"transport": "direct",
"encoding": "base64",
"size": 15433,
@@ -179,7 +196,7 @@ end
{
"id": "uuid4",
"dataname": "large_data",
"type": "table",
"payload_type": "table",
"transport": "link",
"encoding": "none",
"size": 524288,
@@ -192,16 +209,16 @@ end
}
```
### 2. msgPayload_v1 - Payload Structure
### 2. msg_payload_v1 - Payload Structure
The `msgPayload_v1` structure provides flexible payload handling for various data types.
The `msg_payload_v1` structure provides flexible payload handling for various data types.
**Julia Structure:**
```julia
struct msgPayload_v1
struct msg_payload_v1
id::String # Id of this payload (e.g., "uuid4")
dataname::String # Name of this payload (e.g., "login_image")
type::String # "text | dictionary | table | image | audio | video | binary"
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
@@ -222,7 +239,7 @@ end
┌─────────────────────────────────────────────────────────────┐
│ smartsend Function │
│ Accepts: [(dataname1, data1, type1), ...] │
│ (No standalone type parameter - type per payload)
│ (Type is per payload, not standalone)
└─────────────────────────────────────────────────────────────┘
@@ -254,14 +271,14 @@ end
```mermaid
graph TD
subgraph JuliaModule
smartsendJulia[smartsend Julia]
JuliaSmartSend[smartsend]
SizeCheck[Size Check]
DirectPath[Direct Path]
LinkPath[Link Path]
HTTPClient[HTTP Client]
end
smartsendJulia --> SizeCheck
JuliaSmartSend --> SizeCheck
SizeCheck -->|< 1MB| DirectPath
SizeCheck -->|>= 1MB| LinkPath
LinkPath --> HTTPClient
@@ -269,24 +286,6 @@ graph TD
style JuliaModule fill:#c5e1a5
```
### 5. JavaScript Module Architecture
```mermaid
graph TD
subgraph JSModule
smartsendJS[smartsend JS]
smartreceiveJS[smartreceive JS]
JetStreamConsumer[JetStream Pull Consumer]
ApacheArrow[Apache Arrow]
end
smartsendJS --> NATS
smartreceiveJS --> JetStreamConsumer
JetStreamConsumer --> ApacheArrow
style JSModule fill:#f3e5f5
```
## Implementation Details
### Julia Implementation
@@ -303,13 +302,47 @@ graph TD
```julia
function smartsend(
subject::String,
data::AbstractArray{Tuple{String, Any, String}}; # No standalone type parameter
nats_url::String = "nats://localhost:4222",
fileserverUploadHandler::Function = plik_oneshot_upload,
size_threshold::Int = 1_000_000 # 1MB
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
NATS_connection::Union{NATS.Connection, Nothing} = nothing # Pre-existing NATS connection (optional, saves connection overhead)
)
```
**Keyword Parameter - NATS_connection:**
- `NATS_connection::Union{NATS.Connection, Nothing} = nothing` - Pre-existing NATS connection. When provided, `smartsend` uses this connection instead of creating a new one, avoiding the overhead of connection establishment. This is useful for high-frequency publishing scenarios where connection reuse provides performance benefits.
**Connection Handling Logic:**
```julia
if is_publish == false
# skip publish a message
elseif is_publish == true && NATS_connection === nothing
publish_message(broker_url, subject, env_json_str, cid) # Creates new connection
elseif is_publish == true && NATS_connection !== nothing
publish_message(NATS_connection, subject, env_json_str, cid) # Uses provided connection
end
```
**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")]`
@@ -326,8 +359,8 @@ function smartsend(
```julia
function smartreceive(
msg::NATS.Message,
fileserverDownloadHandler::Function;
msg::NATS.Msg;
fileserver_download_handler::Function = _fetch_with_backoff,
max_retries::Int = 5,
base_delay::Int = 100,
max_delay::Int = 5000
@@ -336,86 +369,82 @@ function smartreceive(
# Iterate through all payloads
# For each payload: check transport type
# If direct: decode Base64 payload
# If link: fetch from URL with exponential backoff using fileserverDownloadHandler
# If link: fetch from URL with exponential backoff using fileserver_download_handler
# Deserialize payload based on type
# Return list of (dataname, data, type) tuples
# Return envelope dictionary with all metadata and deserialized payloads
end
```
**Output Format:**
- Always returns a list of tuples: `[(dataname1, data1, type1), (dataname2, data2, type2), ...]`
- Even for single payloads: `[(dataname1, data1, type1)]`
- Returns a JSON object (dictionary) 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 tuples, each containing `(dataname, data, type)` with deserialized payload data
**Process Flow:**
1. Parse the JSON envelope to extract the `payloads` array
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 `fileserverDownloadHandler`)
- If `link`: fetch data from URL using exponential backoff (via `fileserver_download_handler`)
- Deserialize based on payload type (`dictionary`, `table`, `binary`, etc.)
4. Return list of `(dataname, data, type)` tuples
4. Return envelope dictionary with `payloads` field containing list of `(dataname, data, type)` tuples
**Note:** The `fileserverDownloadHandler` receives `(url::String, max_retries::Int, base_delay::Int, max_delay::Int, correlation_id::String)` and returns `Vector{UInt8}`.
**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
#### publish_message Function
#### Dependencies
- `nats.js` - Core NATS functionality
- `apache-arrow` - Arrow IPC serialization
- `uuid` - Correlation ID generation
The `publish_message` function provides two overloads for publishing messages to NATS:
#### smartsend Function
```javascript
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
**Overload 1 - URL-based publishing (creates new connection):**
```julia
function publish_message(broker_url::String, subject::String, message::String, correlation_id::String)
conn = NATS.connect(broker_url) # Create NATS connection
publish_message(conn, subject, message, correlation_id)
end
```
**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
```javascript
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
**Overload 2 - Connection-based publishing (uses pre-existing connection):**
```julia
function publish_message(conn::NATS.Connection, subject::String, message::String, correlation_id::String)
try
NATS.publish(conn, subject, message) # Publish message to NATS
log_trace(correlation_id, "Message published to $subject") # Log successful publish
finally
NATS.drain(conn) # Ensure connection is closed properly
end
end
```
**Process Flow:**
1. Parse the JSON envelope to extract the `payloads` array
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 list of `(dataname, data, type)` tuples
**Use Case:** Use the connection-based overload when you already have an established NATS connection and want to publish multiple messages without the overhead of creating a new connection for each publish. This is a Julia-specific optimization that leverages function overloading.
**Integration with smartsend:**
```julia
# When NATS_connection is provided to smartsend, it uses the connection-based publish_message
env, env_json_str = smartsend(
"my.subject",
[("data", payload_data, "type")],
NATS_connection=my_connection, # Pre-existing connection
is_publish=true
)
# Uses: publish_message(NATS_connection, subject, env_json_str, cid)
# When NATS_connection is not provided, it uses the URL-based publish_message
env, env_json_str = smartsend(
"my.subject",
[("data", payload_data, "type")],
broker_url="nats://localhost:4222",
is_publish=true
)
# Uses: publish_message(broker_url, subject, env_json_str, cid)
```
## Scenario Implementations
### Scenario 1: Command & Control (Small Dictionary)
**Julia (Receiver):**
**Julia (Sender/Receiver):**
```julia
# Subscribe to control subject
# Parse JSON envelope
@@ -423,15 +452,9 @@ async function smartreceive(msg, options = {})
# Send acknowledgment
```
**JavaScript (Sender):**
```javascript
// Create small dictionary config
// Send via smartsend with type="dictionary"
```
### Scenario 2: Deep Dive Analysis (Large Arrow Table)
**Julia (Sender):**
**Julia (Sender/Receiver):**
```julia
# Create large DataFrame
# Convert to Arrow IPC stream
@@ -440,50 +463,28 @@ async function smartreceive(msg, options = {})
# Publish NATS with URL
```
**JavaScript (Receiver):**
```javascript
// Receive NATS message with URL
// Fetch data from HTTP server
// Parse Arrow IPC with zero-copy
// Load into Perspective.js or D3
```
### Scenario 3: Live Audio Processing
**JavaScript (Sender):**
```javascript
// Capture audio chunk
// Send as binary with metadata headers
// Use smartsend with type="audio"
```
**Julia (Receiver):**
**Julia (Sender/Receiver):**
```julia
// Receive audio data
// Perform FFT or AI transcription
// Send results back (JSON + Arrow table)
# Receive audio data
# Perform FFT or AI transcription
# Send results back (JSON + Arrow table)
```
### Scenario 4: Catch-Up (JetStream)
**Julia (Producer):**
**Julia (Producer/Consumer):**
```julia
# Publish to JetStream
# Include metadata for temporal tracking
```
**JavaScript (Consumer):**
```javascript
// Connect to JetStream
// Request replay from last 10 minutes
// Process historical and real-time messages
```
### Scenario 5: Selection (Low Bandwidth)
**Focus:** Small Arrow tables, Julia to JavaScript. The Action: Julia wants to send a small DataFrame to show on a JavaScript dashboard for the user to choose.
**Focus:** Small Arrow tables. The Action: Julia wants to send a small DataFrame to show on a receiving application for the user to choose.
**Julia (Sender):**
**Julia (Sender/Receiver):**
```julia
# Create small DataFrame (e.g., 50KB - 500KB)
# Convert to Arrow IPC stream
@@ -492,18 +493,6 @@ async function smartreceive(msg, options = {})
# Include metadata for dashboard selection context
```
**JavaScript (Receiver):**
```javascript
// 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
```
**Use Case:** Julia server generates a list of available options (e.g., file selections, configuration presets) as a small DataFrame and sends to JavaScript dashboard for user selection. The selection is then sent back to Julia 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.
@@ -528,26 +517,9 @@ async function smartreceive(msg, options = {})
# Support bidirectional messaging with replyTo fields
```
**JavaScript (Sender/Receiver):**
```javascript
// 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
```
**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.
**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.
**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: `msg_envelope_v1` supports `Vector{msg_payload_v1}` for multiple payloads.
## Performance Considerations

View File

@@ -2,39 +2,119 @@
## 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.
This document describes the implementation of the high-performance, bi-directional data bridge for **Julia** applications using NATS (Core & JetStream), implementing the Claim-Check pattern for large payloads.
### Multi-Payload Support
The system enables seamless communication for Julia applications.
The implementation 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.**
### Implementation Files
NATSBridge is implemented in Julia:
| Language | Implementation File | Description |
|----------|---------------------|-------------|
| **Julia** | [`src/NATSBridge.jl`](../src/NATSBridge.jl) | Full Julia implementation with Arrow IPC support |
### 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:**
```julia
# Upload handler - uploads data to file server and returns URL
# The handler is passed to smartsend as fileserver_upload_handler parameter
# It receives: (fileserver_url::String, dataname::String, data::Vector{UInt8})
# Returns: Dict{String, Any} with keys: "status", "uploadid", "fileid", "url"
fileserver_upload_handler(fileserver_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:**
```julia
# Input format for smartsend (always a list of tuples with type info)
[(dataname1, data1, type1), (dataname2, data2, type2), ...]
# Output format for smartreceive (always returns 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), ...]
# }
```
Where `type` can be: `"text"`, `"dictionary"`, `"table"`, `"image"`, `"audio"`, `"video"`, `"binary"`
**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:**
```julia
# Single payload - still wrapped in a list (type is required as third element)
smartsend("/test", [(dataname1, data1, "text")], ...)
# 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 (each payload has its own type)
smartsend("/test", [(dataname1, data1, "dictionary"), (dataname2, data2, "table")], ...)
# 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
)
# Receive always returns a list with type info
payloads = smartreceive(msg, ...)
# payloads = [(dataname1, data1, "text"), (dataname2, data2, "table"), ...]
# 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
The implementation follows the Claim-Check pattern:
The Julia implementation follows the Claim-Check pattern:
```
┌─────────────────────────────────────────────────────────────────────────┐
@@ -53,7 +133,7 @@ The implementation follows the Claim-Check pattern:
│ (< 1MB) │ │ (> 1MB) │
│ │ │ │
│ • Serialize to │ │ • Serialize to │
IOBuffer │ │ IOBuffer │
│ Buffer │ │ Buffer
│ • Base64 encode │ │ • Upload to │
│ • Publish to │ │ HTTP Server │
│ NATS │ │ • Publish to │
@@ -61,24 +141,31 @@ The implementation follows the Claim-Check pattern:
└─────────────────┘ └─────────────────┘
```
## Files
## smartsend Return Value
### Julia Module: [`src/julia_bridge.jl`](../src/julia_bridge.jl)
The `smartsend` function now returns a tuple containing both the envelope object and the JSON string representation:
```julia
env, env_json_str = smartsend(...)
# env::msg_envelope_v1 - The envelope object with all metadata and payloads
# env_json_str::String - JSON string for publishing to NATS
```
**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.
This enables two use cases:
1. **Programmatic envelope access**: Access envelope fields directly via the `env` object
2. **Direct JSON publishing**: Publish the JSON string directly using NATS request-reply pattern
### Julia Module: [`src/NATSBridge.jl`](../src/NATSBridge.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/NATSBridge.js`](../src/NATSBridge.js)
The JavaScript implementation provides:
- **`MessageEnvelope` class**: For the unified JSON envelope
- **`MessagePayload` class**: For individual payload representation
- **[`smartsend()`](../src/NATSBridge.js)**: Handles transport selection based on payload size
- **[`smartreceive()`](../src/NATSBridge.js)**: Handles both direct and link transport
- **[`msg_envelope_v1`](src/NATSBridge.jl)**: Struct for the unified JSON envelope
- **[`msg_payload_v1`](src/NATSBridge.jl)**: Struct for individual payload representation
- **[`smartsend()`](src/NATSBridge.jl)**: Handles transport selection based on payload size
- **[`smartreceive()`](src/NATSBridge.jl)**: Handles both direct and link transport
## Installation
@@ -94,12 +181,6 @@ 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
@@ -122,21 +203,62 @@ 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 1: Command & Control
julia test/scenario1_command_control.jl
# Scenario 2: Large Arrow Table (JavaScript sender)
node test/scenario2_large_table.js
# Scenario 2: Large Arrow Table
julia test/scenario2_large_table.jl
# 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 0: Basic Multi-Payload Example
### Scenario 1: Command & Control (Small Dictionary)
**Focus:** Sending small dictionary configurations. This is the simplest use case for command and control scenarios.
**Julia (Sender/Receiver):**
```julia
using NATSBridge
# Send small dictionary config (wrapped in list with type)
config = Dict("step_size" => 0.01, "iterations" => 1000, "threshold" => 0.5)
env, env_json_str = smartsend(
"control",
[("config", config, "dictionary")],
broker_url="nats://localhost:4222"
)
# env: msg_envelope_v1 with all metadata and payloads
# env_json_str: JSON string for publishing
```
**Julia (Sender/Receiver) with NATS_connection for connection reuse:**
```julia
using NATSBridge
# Create connection once for high-frequency publishing
conn = NATS.connect("nats://localhost:4222")
# Send multiple messages using the same connection (saves connection overhead)
for i in 1:100
config = Dict("iteration" => i, "data" => rand())
smartsend(
"control",
[("config", config, "dictionary")],
NATS_connection=conn, # Reuse connection
is_publish=true
)
end
# Close connection when done
NATS.close(conn)
```
**Use Case:** High-frequency publishing scenarios where connection reuse provides performance benefits by avoiding the overhead of establishing a new NATS connection for each message.
### Basic Multi-Payload Example
#### Julia (Sender)
```julia
@@ -146,95 +268,21 @@ using NATSBridge
smartsend(
"/test",
[("dataname1", data1, "dictionary"), ("dataname2", data2, "table")],
nats_url="nats://localhost:4222",
fileserver_url="http://localhost:8080",
metadata=Dict("custom_key" => "custom_value")
broker_url="nats://localhost:4222",
fileserver_url="http://localhost:8080"
)
# Even single payload must be wrapped in a list with type
smartsend("/test", [("single_data", mydata, "dictionary")])
smartsend("/test", [("single_data", mydata, "dictionary")], broker_url="nats://localhost:4222")
```
#### Julia (Receiver)
```julia
using NATSBridge
# Receive returns a list of payloads with type info
payloads = smartreceive(msg, "http://localhost:8080")
# payloads = [(dataname1, data1, "dictionary"), (dataname2, data2, "table"), ...]
```
### Scenario 1: Command & Control (Small JSON)
#### JavaScript (Sender)
```javascript
const { smartsend } = require('./src/NATSBridge');
// Single payload wrapped in a list
const config = [{
dataname: "config",
data: { step_size: 0.01, iterations: 1000 },
type: "dictionary"
}];
await smartsend("control", config, {
correlationId: "unique-id"
});
// Multiple payloads
const configs = [
{
dataname: "config1",
data: { step_size: 0.01 },
type: "dictionary"
},
{
dataname: "config2",
data: { iterations: 1000 },
type: "dictionary"
}
];
await smartsend("control", configs);
```
#### 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
```
### JavaScript (Receiver)
```javascript
const { smartreceive } = require('./src/NATSBridge');
// Subscribe to messages
const nc = await connect({ servers: ['nats://localhost:4222'] });
const sub = nc.subscribe("control");
for await (const msg of sub) {
const result = await smartreceive(msg);
// Process the result
for (const { dataname, data, type } of result) {
console.log(`Received ${dataname} of type ${type}`);
console.log(`Data: ${JSON.stringify(data)}`);
}
}
# Receive returns a dictionary with envelope metadata and payloads field
env = smartreceive(msg)
# env["payloads"] = [(dataname1, data1, "dictionary"), (dataname2, data2, "table"), ...]
```
### Scenario 2: Deep Dive Analysis (Large Arrow Table)
@@ -251,120 +299,164 @@ df = DataFrame(
category = rand(["A", "B", "C"], 10_000_000)
)
# Send via SmartSend - wrapped in a list (type is part of each tuple)
await SmartSend("analysis_results", [("table_data", df, "table")]);
# Send via smartsend - wrapped in list with type
# Large payload will use link transport (HTTP fileserver)
env, env_json_str = smartsend(
"analysis_results",
[("table_data", df, "table")],
broker_url="nats://localhost:4222",
fileserver_url="http://localhost:8080"
)
# env: msg_envelope_v1 with all metadata and payloads
# env_json_str: JSON string for publishing
```
#### JavaScript (Receiver)
```javascript
const { smartreceive } = require('./src/NATSBridge');
#### smartsend Function Signature (Julia)
const result = await smartreceive(msg);
// Use table data for visualization with Perspective.js or D3
// Note: Tables are sent as arrays of objects in JavaScript
const table = result;
```
### Scenario 3: Live Binary Processing
#### JavaScript (Sender)
```javascript
const { smartsend } = require('./src/NATSBridge');
// Binary data wrapped in a list
const binaryData = [{
dataname: "audio_chunk",
data: binaryBuffer, // ArrayBuffer or Uint8Array
type: "binary"
}];
await smartsend("binary_input", binaryData, {
metadata: {
sample_rate: 44100,
channels: 1
}
});
```
#### Julia (Receiver)
```julia
using WAV
using DSP
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,
NATS_connection::Union{NATS.Connection, Nothing} = nothing # Pre-existing NATS connection (optional)
)
```
# Receive binary data
function process_binary(data)
# Perform FFT or AI transcription
spectrum = fft(data)
**New Keyword Parameter:**
- `NATS_connection::Union{NATS.Connection, Nothing} = nothing` - Pre-existing NATS connection. When provided, `smartsend` uses this connection instead of creating a new one, avoiding the overhead of connection establishment. This is useful for high-frequency publishing scenarios.
# Send results back (JSON + Arrow table)
results = Dict("transcription" => "sample text", "spectrum" => spectrum)
await SmartSend("binary_output", results, "json")
**Connection Handling Logic:**
```julia
if is_publish == false
# skip publish
elseif is_publish == true && NATS_connection === nothing
publish_message(broker_url, subject, env_json_str, cid) # Creates new connection
elseif is_publish == true && NATS_connection !== nothing
publish_message(NATS_connection, subject, env_json_str, cid) # Uses provided connection
end
```
### JavaScript (Receiver)
```javascript
const { smartreceive } = require('./src/NATSBridge');
**Example with pre-existing connection:**
```julia
using NATSBridge
// Receive binary data
function process_binary(msg) {
const result = await smartreceive(msg);
# Create connection once
conn = NATS.connect("nats://localhost:4222")
// Process the binary data
for (const { dataname, data, type } of result) {
if (type === "binary") {
// data is an ArrayBuffer or Uint8Array
console.log(`Received binary data: ${dataname}, size: ${data.length}`);
// Perform FFT or AI transcription here
}
}
}
# Send multiple messages using the same connection
for i in 1:100
data = rand(1000)
smartsend(
"analysis_results",
[("table_data", data, "table")],
NATS_connection=conn, # Reuse connection
is_publish=true
)
end
# Close connection when done
NATS.close(conn)
```
#### publish_message Function
The `publish_message` function provides two overloads for publishing messages to NATS:
**Overload 1 - URL-based publishing (creates new connection):**
```julia
function publish_message(broker_url::String, subject::String, message::String, correlation_id::String)
conn = NATS.connect(broker_url) # Create NATS connection
publish_message(conn, subject, message, correlation_id)
end
```
**Overload 2 - Connection-based publishing (uses pre-existing connection):**
```julia
function publish_message(conn::NATS.Connection, subject::String, message::String, correlation_id::String)
try
NATS.publish(conn, subject, message) # Publish message to NATS
log_trace(correlation_id, "Message published to $subject")
finally
NATS.drain(conn) # Ensure connection is closed properly
end
end
```
**Use Case:** Use the connection-based overload when you already have an established NATS connection and want to publish multiple messages without the overhead of creating a new connection for each publish.
**Integration with smartsend:**
```julia
# When NATS_connection is provided to smartsend, it uses the connection-based publish_message
env, env_json_str = smartsend(
"my.subject",
[("data", payload_data, "type")],
NATS_connection=my_connection, # Pre-existing connection
is_publish=true
)
# Uses: publish_message(NATS_connection, subject, env_json_str, cid)
# When NATS_connection is not provided, it uses the URL-based publish_message
env, env_json_str = smartsend(
"my.subject",
[("data", payload_data, "type")],
broker_url="nats://localhost:4222",
is_publish=true
)
# Uses: publish_message(broker_url, subject, env_json_str, cid)
```
**API Consistency Note:**
- **Julia:** Uses `NATS_connection` keyword parameter with function overloading for automatic connection management
### Scenario 3: Live Binary Processing
**Julia (Sender/Receiver):**
```julia
using NATSBridge
# Binary data wrapped in list with type
smartsend(
"binary_input",
[("audio_chunk", binary_buffer, "binary")],
broker_url="nats://localhost:4222",
metadata=["sample_rate" => 44100, "channels" => 1]
)
```
### Scenario 4: Catch-Up (JetStream)
#### Julia (Producer)
**Julia (Producer/Consumer):**
```julia
using NATSBridge
function publish_health_status(nats_url)
# Send status wrapped in a list (type is part of each tuple)
function publish_health_status(broker_url)
# Send status wrapped in list with type
status = Dict("cpu" => rand(), "memory" => rand())
smartsend("health", [("status", status, "dictionary")], nats_url=nats_url)
env, env_json_str = smartsend(
"health",
[("status", status, "dictionary")],
broker_url=broker_url
)
sleep(5) # Every 5 seconds
end
```
#### JavaScript (Consumer)
```javascript
const { connect } = require('nats');
const { smartreceive } = require('./src/NATSBridge');
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);
// result contains the list of payloads
// Each payload has: dataname, data, type
msg.ack();
}
```
### Scenario 5: Selection (Low Bandwidth)
**Focus:** Small Arrow tables, Julia to JavaScript. The Action: Julia wants to send a small DataFrame to show on a JavaScript dashboard for the user to choose.
**Focus:** Small Arrow tables. The Action: Julia wants to send a small DataFrame to show on a receiving application for the user to choose.
**Julia (Sender):**
**Julia (Sender/Receiver):**
```julia
using NATSBridge
using DataFrames
@@ -382,35 +474,17 @@ options_df = DataFrame(
# Check payload size (< 1MB threshold)
# Publish directly to NATS with Base64-encoded payload
# Include metadata for dashboard selection context
smartsend(
env, env_json_str = smartsend(
"dashboard.selection",
[("options_table", options_df, "table")],
nats_url="nats://localhost:4222",
broker_url="nats://localhost:4222",
metadata=Dict("context" => "user_selection")
)
# env: msg_envelope_v1 with all metadata and payloads
# env_json_str: JSON string for publishing
```
**JavaScript (Receiver):**
```javascript
const { smartreceive, smartsend } = require('./src/NATSBridge');
// Receive NATS message with direct transport
const result = await smartreceive(msg);
// Decode Base64 payload (for direct transport)
// For tables, data is an array of objects
const table = result; // Array of objects
// User makes selection
const selection = uiComponent.getSelectedOption();
// Send selection back to Julia
await smartsend("dashboard.response", [
{ dataname: "selected_option", data: selection, type: "dictionary" }
]);
```
**Use Case:** Julia server generates a list of available options (e.g., file selections, configuration presets) as a small DataFrame and sends to JavaScript dashboard for user selection. The selection is then sent back to Julia for processing.
**Use Case:** Julia server generates a list of available options (e.g., file selections, configuration presets) as a small DataFrame and sends to a receiving application for user selection. The selection is then sent back to Julia for processing.
### Scenario 6: Chat System
@@ -421,7 +495,6 @@ await smartsend("dashboard.response", [
**Julia (Sender/Receiver):**
```julia
using NATSBridge
using DataFrames
# Build chat message with mixed payloads:
# - Text: direct transport (Base64)
@@ -445,58 +518,20 @@ chat_message = [
("large_document", large_file_bytes, "binary") # Large file, link transport
]
smartsend(
env, env_json_str = smartsend(
"chat.room123",
chat_message,
nats_url="nats://localhost:4222",
broker_url="nats://localhost:4222",
msg_purpose="chat",
reply_to="chat.room123.responses"
)
```
**JavaScript (Sender/Receiver):**
```javascript
const { smartsend, smartreceive } = require('./src/NATSBridge');
// 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
// Example: Send chat with mixed content
const message = [
{
dataname: "text",
data: "Hello from JavaScript!",
type: "text"
},
{
dataname: "image",
data: selectedImageBuffer, // Small image (ArrayBuffer or Uint8Array)
type: "image"
},
{
dataname: "audio",
data: audioUrl, // Large audio, link transport
type: "audio"
}
];
await smartsend("chat.room123", message);
# env: msg_envelope_v1 with all metadata and payloads
# env_json_str: JSON string for publishing
```
**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.
**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.
**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: `msg_envelope_v1` supports `Vector{msg_payload_v1}` for multiple payloads.
## Configuration
@@ -512,19 +547,19 @@ await smartsend("chat.room123", message);
```json
{
"correlationId": "uuid-v4-string",
"msgId": "uuid-v4-string",
"correlation_id": "uuid-v4-string",
"msg_id": "uuid-v4-string",
"timestamp": "2024-01-15T10:30:00Z",
"sendTo": "topic/subject",
"msgPurpose": "ACK | NACK | updateStatus | shutdown | chat",
"senderName": "agent-wine-web-frontend",
"senderId": "uuid4",
"receiverName": "agent-backend",
"receiverId": "uuid4",
"replyTo": "topic",
"replyToMsgId": "uuid4",
"BrokerURL": "nats://localhost:4222",
"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",
@@ -535,7 +570,7 @@ await smartsend("chat.room123", message);
{
"id": "uuid4",
"dataname": "login_image",
"type": "image",
"payload_type": "image",
"transport": "direct",
"encoding": "base64",
"size": 15433,
@@ -558,7 +593,6 @@ await smartsend("chat.room123", message);
### 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
@@ -567,14 +601,30 @@ await smartsend("chat.room123", message);
## Testing
Run the test scripts:
Run the test scripts for Julia:
### Julia Tests
```bash
# Scenario 1: Command & Control (JavaScript sender)
node test/scenario1_command_control.js
# Text message exchange
julia test/test_julia_to_julia_text_sender.jl
julia test/test_julia_to_julia_text_receiver.jl
# Scenario 2: Large Arrow Table (JavaScript sender)
node test/scenario2_large_table.js
# Dictionary exchange
julia test/test_julia_to_julia_dict_sender.jl
julia test/test_julia_to_julia_dict_receiver.jl
# File transfer
julia test/test_julia_to_julia_file_sender.jl
julia test/test_julia_to_julia_file_receiver.jl
# Mixed payload types
julia test/test_julia_to_julia_mix_payloads_sender.jl
julia test/test_julia_to_julia_mix_payloads_receiver.jl
# Table exchange
julia test/test_julia_to_julia_table_sender.jl
julia test/test_julia_to_julia_table_receiver.jl
```
## Troubleshooting
@@ -583,11 +633,10 @@ node test/scenario2_large_table.js
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
- Check `fileserver_url` configuration
- Verify upload permissions
3. **Arrow IPC Deserialization Error**

24
etc.jl
View File

@@ -1,21 +1,9 @@
Check architecture.jl, NATSBridge.jl and its test files:
- test_julia_to_julia_table_receiver.jl
- test_julia_to_julia_table_sender.jl.
Now I want to test sending a mix-content message from Julia serviceA to Julia serviceB, for example, a chat system.
The test message must show that any combination and any number and any data size of text | json | table | image | audio | video | binary can be send and receive.
Can you write me the following test files:
- test_julia_to_julia_mix_receiver.jl
- test_julia_to_julia_mix_sender.jl
1. create a tutorial file "tutorial_julia.md" for NATSBridge.jl
2. create a walkthrough file "walkthrough_julia.md" for NATSBridge.jl
You may consult architecture.md for more info.
Task: Update README.md to reflect recent changes in NATSbridge package.
Context: the package has been updated with the NATS_connection keyword and the publish_message function.
Requirements:
Source of Truth: Treat the updated NATSbridge code as the definitive source. Update README.md to align exactly with these changes.
API Consistency: Ensure the Main Package API (e.g., smartsend(), publish_message()) uses consistent naming across all three supported languages.
Ecosystem Variance: Low-level native functions (e.g., NATS.connect(), JSON.read()) should follow the conventions of the specific language ecosystem and do not require cross-language consistency.

View File

@@ -1,221 +0,0 @@
"""
Micropython NATS Bridge - Simple Example
This example demonstrates the basic usage of the NATSBridge for Micropython.
"""
import sys
sys.path.insert(0, "../src")
from nats_bridge import smartsend, smartreceive, log_trace
import json
def example_simple_chat():
"""
Simple chat example: Send text messages via NATS.
Sender (this script):
- Sends a text message to NATS
- Uses direct transport (no fileserver needed)
Receiver (separate script):
- Listens to NATS
- Receives and processes the message
"""
print("=== Simple Chat Example ===")
print()
# Define the message data as list of (dataname, data, type) tuples
data = [
("message", "Hello from Micropython!", "text")
]
# Send the message
env = smartsend(
"/chat/room1",
data,
nats_url="nats://localhost:4222",
msg_purpose="chat",
sender_name="micropython-client"
)
print("Message sent!")
print(" Subject: {}".format(env.send_to))
print(" Correlation ID: {}".format(env.correlation_id))
print(" Payloads: {}".format(len(env.payloads)))
print()
# Expected receiver output:
print("Expected receiver output:")
print(" [timestamp] [Correlation: ...] Starting smartsend for subject: /chat/room1")
print(" [timestamp] [Correlation: ...] Serialized payload 'message' (type: text) size: 22 bytes")
print(" [timestamp] [Correlation: ...] Using direct transport for 22 bytes")
print(" [timestamp] [Correlation: ...] Message published to /chat/room1")
print()
return env
def example_send_json():
"""
Example: Send JSON configuration to a Micropython device.
This demonstrates sending structured data (dictionary type).
"""
print("\n=== Send JSON Configuration ===")
print()
# Define configuration as dictionary
config = {
"wifi_ssid": "MyNetwork",
"wifi_password": "password123",
"server_host": "mqtt.example.com",
"server_port": 1883,
"update_interval": 60
}
# Send configuration
data = [
("device_config", config, "dictionary")
]
env = smartsend(
"/device/config",
data,
nats_url="nats://localhost:4222",
msg_purpose="updateStatus",
sender_name="server"
)
print("Configuration sent!")
print(" Subject: {}".format(env.send_to))
print(" Payloads: {}".format(len(env.payloads)))
print()
return env
def example_receive_message(msg):
"""
Example: Receive and process a NATS message.
Args:
msg: The NATS message received (should be dict or JSON string)
Returns:
list: List of (dataname, data, type) tuples
"""
print("\n=== Receive Message ===")
print()
# Process the message
payloads = smartreceive(
msg,
fileserver_download_handler=None, # Not needed for direct transport
max_retries=3,
base_delay=100,
max_delay=1000
)
print("Received {} payload(s):".format(len(payloads)))
for dataname, data, type in payloads:
print(" - {}: {} (type: {})".format(dataname, data, type))
return payloads
def example_mixed_content():
"""
Example: Send mixed content (text + dictionary + binary).
This demonstrates the multi-payload capability.
"""
print("\n=== Mixed Content Example ===")
print()
# Create mixed content
image_data = b"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR" # Example PNG header
data = [
("message_text", "Hello with image!", "text"),
("user_config", {"theme": "dark", "notifications": True}, "dictionary"),
("user_avatar", image_data, "binary")
]
env = smartsend(
"/chat/mixed",
data,
nats_url="nats://localhost:4222",
msg_purpose="chat",
sender_name="micropython-client"
)
print("Mixed content sent!")
print(" Subject: {}".format(env.send_to))
print(" Payloads:")
for p in env.payloads:
print(" - {} (transport: {}, type: {}, size: {} bytes)".format(
p.dataname, p.transport, p.type, p.size))
return env
def example_reply():
"""
Example: Send a message with reply-to functionality.
This demonstrates request-response pattern.
"""
print("\n=== Request-Response Example ===")
print()
# Send command
data = [
("command", {"action": "read_sensor", "sensor_id": "temp1"}, "dictionary")
]
env = smartsend(
"/device/command",
data,
nats_url="nats://localhost:4222",
msg_purpose="command",
sender_name="server",
reply_to="/device/response",
reply_to_msg_id="cmd-001"
)
print("Command sent!")
print(" Subject: {}".format(env.send_to))
print(" Reply To: {}".format(env.reply_to))
print(" Reply To Msg ID: {}".format(env.reply_to_msg_id))
print()
print("Expected receiver behavior:")
print(" 1. Receive command on /device/command")
print(" 2. Process command")
print(" 3. Send response to /device/response")
print(" 4. Include replyToMsgId in response")
return env
if __name__ == "__main__":
print("Micropython NATS Bridge Examples")
print("================================")
print()
# Run examples
example_simple_chat()
example_send_json()
example_mixed_content()
example_reply()
print("\n=== Examples Completed ===")
print()
print("To run these examples, you need:")
print(" 1. A running NATS server at nats://localhost:4222")
print(" 2. Import the nats_bridge module")
print(" 3. Call the desired example function")
print()
print("For more examples, see test/test_micropython_basic.py")

304
examples/tutorial.md Normal file
View File

@@ -0,0 +1,304 @@
# NATSBridge Tutorial
A step-by-step guide to get started with NATSBridge - a high-performance, bi-directional data bridge for **Julia**.
## 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 for Julia applications 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
### Supported Payload Types
| Type | Description |
|------|-------------|
| `text` | Plain text strings |
| `dictionary` | JSON-serializable dictionaries |
| `table` | Tabular data (Arrow IPC format) |
| `image` | Image data (PNG, JPG bytes) |
| `audio` | Audio data (WAV, MP3 bytes) |
| `video` | Video data (MP4, AVI bytes) |
| `binary` | Generic binary data |
---
## Prerequisites
Before you begin, ensure you have:
1. **NATS Server** running (or accessible)
2. **HTTP File Server** (optional, for large payloads > 1MB)
3. **Julia** with required packages
---
## Installation
### Julia
```julia
using Pkg
Pkg.add("NATS")
Pkg.add("Arrow")
Pkg.add("JSON3")
Pkg.add("HTTP")
Pkg.add("UUIDs")
Pkg.add("Dates")
```
---
## Quick Start
### 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
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 object 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 object
# env_json_str: JSON string for publishing to NATS
```
### Step 4: Receive Messages
#### Julia
```julia
using NATSBridge
# Receive and process message
env = smartreceive(msg; fileserver_download_handler=_fetch_with_backoff)
for (dataname, data, type) in env["payloads"]
println("Received $dataname: $data")
end
```
---
## 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")
```
### 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")
```
### 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"
)
# env: msg_envelope_v1 object
# env_json_str: JSON string for publishing to NATS
```
#### Julia (Responder)
```julia
using NATS, NATSBridge
# Configuration
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)
# Extract reply_to from the envelope metadata
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)
# Send response to the reply_to subject from the request
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"
)
# The envelope will contain the download URL
println("File uploaded to: $(env.payloads[1].data)")
```
### 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")
```
### 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")
```
---
## Next Steps
1. **Explore the test directory** for more examples
2. **Check the documentation** for advanced configuration options
---
## 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 (Vector{UInt8})
---
## License
MIT

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# NATSBridge Walkthrough
A comprehensive guide to building real-world applications with NATSBridge.
## Table of Contents
1. [Introduction](#introduction)
2. [Architecture Overview](#architecture-overview)
3. [Building a Chat Application](#building-a-chat-application)
4. [Building a File Transfer System](#building-a-file-transfer-system)
5. [Building a Streaming Data Pipeline](#building-a-streaming-data-pipeline)
6. [Performance Optimization](#performance-optimimization)
7. [Best Practices](#best-practices)
---
## Introduction
This walkthrough will guide you through building several real-world applications using NATSBridge. We'll cover:
- Chat applications with rich media support
- File transfer systems with claim-check pattern
- Streaming data pipelines
Each section builds on the previous one, gradually increasing in complexity.
---
## Architecture Overview
### System Components
```
┌─────────────────────────────────────────────────────────────────┐
│ NATSBridge Architecture │
├─────────────────────────────────────────────────────────────────┤
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Julia │ │ NATS │ │
│ │ (NATS.jl) │◄──►│ Server │ │
│ └──────────────┘ └──────────────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌──────────────────────────────────────┐ │
│ │ File Server │ │
│ │ (HTTP Upload) │ │
│ └──────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
```
### Message Flow
1. **Sender** creates a message envelope with payloads
2. **NATSBridge** serializes and encodes payloads
3. **Transport Decision**: Small payloads go directly to NATS, large payloads are uploaded to file server
4. **NATS** routes messages to subscribers
5. **Receiver** fetches payloads (from NATS or file server)
6. **NATSBridge** deserializes and decodes payloads
---
## Building a Chat Application
Let's build a full-featured chat application that supports text, images, and file attachments.
### Step 1: Set Up the Project
```bash
# Create project directory
mkdir -p chat-app/src
cd chat-app
# Create configuration file
cat > config.json << 'EOF'
{
"nats_url": "nats://localhost:4222",
"fileserver_url": "http://localhost:8080",
"size_threshold": 1048576
}
EOF
```
### Step 2: Create the Chat Interface (Julia)
```julia
# src/chat_ui.jl
using NATSBridge, NATS
struct ChatUI
messages::Vector{Dict}
current_room::String
end
function ChatUI()
ChatUI(Dict[], "")
end
function send_message(ui::ChatUI, message_input::String, selected_file::Union{Nothing, String})
data = []
# Add text message
if !isempty(message_input)
push!(data, ("text", message_input, "text"))
end
# Add file if selected
if selected_file !== nothing
file_data = read(selected_file)
file_type = get_file_type(selected_file)
push!(data, ("attachment", file_data, file_type))
end
return data
end
function get_file_type(filename::String)::String
if endswith(filename, ".png") || endswith(filename, ".jpg")
return "image"
elseif endswith(filename, ".mp3") || endswith(filename, ".wav")
return "audio"
elseif endswith(filename, ".mp4") || endswith(filename, ".avi")
return "video"
else
return "binary"
end
end
function add_message(ui::ChatUI, user::String, text::String, attachment::Union{Nothing, Dict})
push!(ui.messages, Dict(
"user" => user,
"text" => text,
"attachment" => attachment
))
end
```
### Step 3: Create the Message Handler
```julia
# src/chat_handler.jl
using NATSBridge, NATS
struct ChatHandler
nats::NATS.Connection
ui::ChatUI
end
function ChatHandler(nats_connection::NATS.Connection)
ChatHandler(nats_connection, ChatUI())
end
function start(handler::ChatHandler)
# Subscribe to chat rooms
rooms = ["general", "tech", "random"]
for room in rooms
NATS.subscribe(handler.nats, "/chat/$room") do msg
handle_message(handler, msg)
end
end
println("Chat handler started")
end
function handle_message(handler::ChatHandler, msg::NATS.Msg)
env = smartreceive(msg, fileserver_download_handler=_fetch_with_backoff)
# Extract sender info from envelope
sender = get(env, "sender_name", "Anonymous")
# Process each payload
for (dataname, data, type) in env["payloads"]
if type == "text"
add_message(handler.ui, sender, data, nothing)
elseif type == "image"
# Convert to data URL for display
base64_data = base64encode(data)
attachment = Dict(
"type" => "image",
"data" => "data:image/png;base64,$base64_data"
)
add_message(handler.ui, sender, "", attachment)
else
# For other types, use file server URL
attachment = Dict("type" => type, "data" => data)
add_message(handler.ui, sender, "", attachment)
end
end
end
function download_file(url::String, max_retries::Int, base_delay::Int, max_delay::Int, correlation_id::String)::Vector{UInt8}
# Implement exponential backoff for file server downloads
# Return downloaded data as Vector{UInt8}
end
```
### Step 4: Run the Application
```bash
# Start NATS
docker run -p 4222:4222 nats:latest
# Start file server
mkdir -p /tmp/fileserver
python3 -m http.server 8080 --directory /tmp/fileserver
# Run chat app
julia src/chat_ui.jl
julia src/chat_handler.jl
```
---
## Building a File Transfer System
Let's build a file transfer system that handles large files efficiently.
### Step 1: File Upload Service (Julia)
```julia
# src/file_upload_service.jl
using NATSBridge, HTTP
struct FileUploadService
broker_url::String
fileserver_url::String
end
function FileUploadService(broker_url::String, fileserver_url::String)
FileUploadService(broker_url, fileserver_url)
end
function upload_file(service::FileUploadService, file_path::String, recipient::String)::Dict
file_data = read(file_path)
file_name = basename(file_path)
data = [("file", file_data, "binary")]
env, env_json_str = smartsend(
"/files/$recipient",
data,
broker_url=service.broker_url,
fileserver_url=service.fileserver_url
)
return env
end
function upload_large_file(service::FileUploadService, file_path::String, recipient::String)::Dict
file_size = stat(file_path).size
if file_size > 100 * 1024 * 1024 # > 100MB
println("File too large for direct upload, using streaming...")
return stream_upload(service, file_path, recipient)
end
return upload_file(service, file_path, recipient)
end
function stream_upload(service::FileUploadService, file_path::String, recipient::String)::Dict
# Implement streaming upload to file server
# This would require a more sophisticated file server
# For now, we'll use the standard upload
return upload_file(service, file_path, recipient)
end
```
### Step 2: File Download Service (Julia)
```julia
# src/file_download_service.jl
using NATSBridge
struct FileDownloadService
nats_url::String
end
function FileDownloadService(nats_url::String)
FileDownloadService(nats_url)
end
function download_file(service::FileDownloadService, msg::NATS.Msg, sender::String, download_id::String)
# Subscribe to sender's file channel
env = smartreceive(msg, fileserver_download_handler=fetch_from_url)
# Process each payload
for (dataname, data, type) in env["payloads"]
if type == "binary"
file_path = "/downloads/$dataname"
write(file_path, data)
println("File saved to $file_path")
end
end
end
function fetch_from_url(url::String, max_retries::Int, base_delay::Int, max_delay::Int, correlation_id::String)::Vector{UInt8}
# Fetch data from URL with exponential backoff
# Return downloaded data as Vector{UInt8}
end
```
### Step 3: File Transfer CLI (Julia)
```julia
# src/cli.jl
using NATSBridge, Readlines, FileIO
function main()
config = JSON3.read(read("config.json", String))
println("File Transfer System")
println("====================")
println("1. Upload file")
println("2. Download file")
println("3. List pending downloads")
print("Enter choice: ")
choice = readline()
if choice == "1"
upload_file_cli(config)
elseif choice == "2"
download_file_cli(config)
end
end
function upload_file_cli(config)
print("Enter file path: ")
file_path = readline()
print("Enter recipient: ")
recipient = readline()
file_service = FileUploadService(config.nats_url, config.fileserver_url)
try
env = upload_file(file_service, file_path, recipient)
println("Upload successful!")
println("File ID: $(env["payloads"][1][1])")
catch error
println("Upload failed: $(error)")
end
end
function download_file_cli(config)
print("Enter sender: ")
sender = readline()
file_service = FileDownloadService(config.nats_url)
try
download_file(file_service, sender)
println("Download complete!")
catch error
println("Download failed: $(error)")
end
end
main()
```
---
## Building a Streaming Data Pipeline
Let's build a data pipeline that processes streaming data from sensors.
### Step 1: Sensor Data Model (Julia)
```julia
# src/sensor_data.jl
using Dates, DataFrames
struct SensorReading
sensor_id::String
timestamp::String
value::Float64
unit::String
metadata::Dict{String, Any}
end
function SensorReading(sensor_id::String, value::Float64, unit::String, metadata::Dict{String, Any}=Dict())
SensorReading(
sensor_id,
ISODateTime(now(), Dates.Second) |> string,
value,
unit,
metadata
)
end
struct SensorBatch
readings::Vector{SensorReading}
end
function SensorBatch()
SensorBatch(SensorReading[])
end
function add_reading(batch::SensorBatch, reading::SensorReading)
push!(batch.readings, reading)
end
function to_dataframe(batch::SensorBatch)::DataFrame
data = Dict{String, Any}()
data["sensor_id"] = [r.sensor_id for r in batch.readings]
data["timestamp"] = [r.timestamp for r in batch.readings]
data["value"] = [r.value for r in batch.readings]
data["unit"] = [r.unit for r in batch.readings]
return DataFrame(data)
end
```
### Step 2: Sensor Sender (Julia)
```julia
# src/sensor_sender.jl
using NATSBridge, Dates, Random
struct SensorSender
broker_url::String
fileserver_url::String
end
function SensorSender(broker_url::String, fileserver_url::String)
SensorSender(broker_url, fileserver_url)
end
function send_reading(sender::SensorSender, sensor_id::String, value::Float64, unit::String)
reading = SensorReading(sensor_id, value, unit)
data = [("reading", reading.metadata, "dictionary")]
# Default: is_publish=True (automatically publishes to NATS)
smartsend(
"/sensors/$sensor_id",
data,
broker_url=sender.broker_url,
fileserver_url=sender.fileserver_url
)
end
function prepare_message_only(sender::SensorSender, sensor_id::String, value::Float64, unit::String)
"""Prepare a message without publishing (is_publish=False)."""
reading = SensorReading(sensor_id, value, unit)
data = [("reading", reading.metadata, "dictionary")]
# With is_publish=False, returns (env, env_json_str) without publishing
env, env_json_str = smartsend(
"/sensors/$sensor_id/prepare",
data,
broker_url=sender.broker_url,
fileserver_url=sender.fileserver_url,
is_publish=false
)
# Now you can publish manually using NATS request-reply pattern
# nc.request(subject, env_json_str, reply_to=reply_to_topic)
return env, env_json_str
end
function send_batch(sender::SensorSender, readings::Vector{SensorReading})
batch = SensorBatch()
for reading in readings
add_reading(batch, reading)
end
df = to_dataframe(batch)
# Convert to Arrow IPC format
import Arrow
table = Arrow.Table(df)
# Serialize to Arrow IPC
import IOBuffer
buf = IOBuffer()
Arrow.write(buf, table)
arrow_data = take!(buf)
# Send based on size
if length(arrow_data) < 1048576 # < 1MB
data = [("batch", arrow_data, "table")]
smartsend(
"/sensors/batch",
data,
broker_url=sender.broker_url,
fileserver_url=sender.fileserver_url
)
else
# Upload to file server
data = [("batch", arrow_data, "table")]
smartsend(
"/sensors/batch",
data,
broker_url=sender.broker_url,
fileserver_url=sender.fileserver_url
)
end
end
```
### Step 3: Sensor Receiver (Julia)
```julia
# src/sensor_receiver.jl
using NATSBridge, Arrow, DataFrames, IOBuffer
struct SensorReceiver
fileserver_download_handler::Function
end
function SensorReceiver(download_handler::Function)
SensorReceiver(download_handler)
end
function process_reading(receiver::SensorReceiver, msg::NATS.Msg)
env = smartreceive(msg, receiver.fileserver_download_handler)
for (dataname, data, data_type) in env["payloads"]
if data_type == "dictionary"
# Process dictionary payload
println("Received: $dataname = $data")
elseif data_type == "table"
# Deserialize Arrow IPC
buf = IOBuffer(data)
table = Arrow.read(buf)
df = DataFrame(table)
println("Received batch with $(nrow(df)) readings")
println(df)
end
end
end
```
---
## Performance Optimization
### 1. Batch Processing
```julia
# Batch multiple readings into a single message
function send_batch_readings(sender::SensorSender, readings::Vector{Tuple{String, Float64, String}})
batch = SensorBatch()
for (sensor_id, value, unit) in readings
reading = SensorReading(sensor_id, value, unit)
add_reading(batch, reading)
end
df = to_dataframe(batch)
# Convert to Arrow IPC
import Arrow
table = Arrow.Table(df)
# Serialize to Arrow IPC
import IOBuffer
buf = IOBuffer()
Arrow.write(buf, table)
arrow_data = take!(buf)
# Send as single message
smartsend(
"/sensors/batch",
[("batch", arrow_data, "table")],
broker_url=sender.broker_url
)
end
```
### 2. Connection Reuse
```julia
# Reuse NATS connections
function create_connection_pool()
connections = Dict{String, NATS.Connection}()
function get_connection(nats_url::String)::NATS.Connection
if !haskey(connections, nats_url)
connections[nats_url] = NATS.connect(nats_url)
end
return connections[nats_url]
end
function close_all()
for conn in values(connections)
NATS.drain(conn)
end
empty!(connections)
end
return (get_connection= get_connection, close_all=close_all)
end
```
### 3. Caching
```julia
# Cache file server responses
using Base.Threads
const file_cache = Dict{String, Vector{UInt8}}()
function fetch_with_caching(url::String, max_retries::Int, base_delay::Int, max_delay::Int, correlation_id::String)::Vector{UInt8}
if haskey(file_cache, url)
return file_cache[url]
end
# Fetch from file server
data = _fetch_with_backoff(url, max_retries, base_delay, max_delay, correlation_id)
# Cache the result
file_cache[url] = data
return data
end
```
---
## Best Practices
### 1. Error Handling
```julia
function safe_smartsend(subject::String, data::Vector{Tuple}, kwargs...)
try
return smartsend(subject, data; kwargs...)
catch error
println("Failed to send message: $(error)")
return nothing
end
end
```
### 2. Logging
```julia
using Logging
function log_send(subject::String, data::Vector{Tuple}, correlation_id::String)
@info "Sending to $subject: $(length(data)) payloads, correlation_id=$correlation_id"
end
function log_receive(correlation_id::String, num_payloads::Int)
@info "Received message: $num_payloads payloads, correlation_id=$correlation_id"
end
```
### 3. Rate Limiting
```julia
using Dates, Collections
struct RateLimiter
max_requests::Int
time_window::Float64
requests::Deque{Float64}
end
function RateLimiter(max_requests::Int, time_window::Float64)
RateLimiter(max_requests, time_window, Deque{Float64}())
end
function allow(limiter::RateLimiter)::Bool
now = time()
# Remove old requests
while !isempty(limiter.requests) && limiter.requests[1] < now - limiter.time_window
popfirst!(limiter.requests)
end
if length(limiter.requests) >= limiter.max_requests
return false
end
push!(limiter.requests, now)
return true
end
```
---
## Conclusion
This walkthrough covered:
- Building a chat application with rich media support
- Building a file transfer system with claim-check pattern
- Building a streaming data pipeline for sensor data
For more information, check the [API documentation](../src/README.md) and [test examples](../test/).
---
## License
MIT

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services:
plik:
image: rootgg/plik:latest
container_name: plik-server
restart: unless-stopped
ports:
- "8080:8080"
volumes:
# # Mount the config file (created below)
# - ./plikd.cfg:/home/plik/server/plikd.cfg
# Mount local folder for uploads and database
- ./plik-data:/data
# Set user to match your host UID to avoid permission issues
user: "1000:1000"

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/**
* NATSBridge.js - Bi-Directional Data Bridge for JavaScript
* Implements smartsend and smartreceive for NATS communication
*
* This module provides functionality for sending and receiving data across network boundaries
* using NATS as the message bus, with support for both direct payload transport and
* URL-based transport for larger payloads.
*
* 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:
*
* ```javascript
* // Upload handler - uploads data to file server and returns URL
* // The handler is passed to smartsend as fileserverUploadHandler parameter
* // It receives: (fileserver_url, dataname, data)
* // Returns: { status, uploadid, fileid, url }
* async function fileserverUploadHandler(fileserver_url, dataname, data) { ... }
*
* // Download handler - fetches data from file server URL with exponential backoff
* // The handler is passed to smartreceive as fileserverDownloadHandler parameter
* // It receives: (url, max_retries, base_delay, max_delay, correlation_id)
* // Returns: ArrayBuffer (the downloaded data)
* async function fileserverDownloadHandler(url, max_retries, base_delay, max_delay, correlation_id) { ... }
* ```
*
* 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:
* ```javascript
* // Input format for smartsend (always a list of tuples with type info)
* [{ dataname, data, type }, ...]
*
* // Output format for smartreceive (always returns a list of tuples)
* [{ dataname, data, type }, ...]
* ```
*
* Supported types: "text", "dictionary", "table", "image", "audio", "video", "binary"
*/
// ---------------------------------------------- 100 --------------------------------------------- #
// Constants
const DEFAULT_SIZE_THRESHOLD = 1_000_000; // 1MB - threshold for switching from direct to link transport
const DEFAULT_NATS_URL = "nats://localhost:4222"; // Default NATS server URL
const DEFAULT_FILESERVER_URL = "http://localhost:8080"; // Default HTTP file server URL for link transport
// Helper: Generate UUID v4
function uuid4() {
// Simple UUID v4 generator
return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {
var r = Math.random() * 16 | 0, v = c == 'x' ? r : (r & 0x3 | 0x8);
return v.toString(16);
});
}
// Helper: Log with correlation ID and timestamp
function log_trace(correlation_id, message) {
const timestamp = new Date().toISOString();
console.log(`[${timestamp}] [Correlation: ${correlation_id}] ${message}`);
}
// Helper: Get size of data in bytes
function getDataSize(data) {
if (typeof data === 'string') {
return new TextEncoder().encode(data).length;
} else if (data instanceof ArrayBuffer || data instanceof Uint8Array) {
return data.byteLength;
} else if (typeof data === 'object' && data !== null) {
// For objects, serialize to JSON and measure
return new TextEncoder().encode(JSON.stringify(data)).length;
}
return 0;
}
// Helper: Convert ArrayBuffer to Base64 string
function arrayBufferToBase64(buffer) {
const bytes = new Uint8Array(buffer);
let binary = '';
for (let i = 0; i < bytes.length; i++) {
binary += String.fromCharCode(bytes[i]);
}
return btoa(binary);
}
// Helper: Convert Base64 string to ArrayBuffer
function base64ToArrayBuffer(base64) {
const binaryString = atob(base64);
const len = binaryString.length;
const bytes = new Uint8Array(len);
for (let i = 0; i < len; i++) {
bytes[i] = binaryString.charCodeAt(i);
}
return bytes.buffer;
}
// Helper: Serialize data based on type
function _serialize_data(data, type) {
/**
* Serialize data according to specified format
*
* Supported formats:
* - "text": Treats data as text and converts to UTF-8 bytes
* - "dictionary": Serializes data as JSON and returns the UTF-8 byte representation
* - "table": Serializes data as an Arrow IPC stream (table format) - NOT IMPLEMENTED (requires arrow library)
* - "image": Expects binary data (ArrayBuffer) and returns it as bytes
* - "audio": Expects binary data (ArrayBuffer) and returns it as bytes
* - "video": Expects binary data (ArrayBuffer) and returns it as bytes
* - "binary": Generic binary data (ArrayBuffer or Uint8Array) and returns bytes
*/
if (type === "text") {
if (typeof data === 'string') {
return new TextEncoder().encode(data).buffer;
} else {
throw new Error("Text data must be a String");
}
} else if (type === "dictionary") {
// JSON data - serialize directly
const jsonStr = JSON.stringify(data);
return new TextEncoder().encode(jsonStr).buffer;
} else if (type === "table") {
// Table data - convert to Arrow IPC stream (NOT IMPLEMENTED in pure JavaScript)
// This would require the apache-arrow library
throw new Error("Table serialization requires apache-arrow library");
} else if (type === "image") {
if (data instanceof ArrayBuffer || data instanceof Uint8Array) {
return data instanceof ArrayBuffer ? data : data.buffer;
} else {
throw new Error("Image data must be ArrayBuffer or Uint8Array");
}
} else if (type === "audio") {
if (data instanceof ArrayBuffer || data instanceof Uint8Array) {
return data instanceof ArrayBuffer ? data : data.buffer;
} else {
throw new Error("Audio data must be ArrayBuffer or Uint8Array");
}
} else if (type === "video") {
if (data instanceof ArrayBuffer || data instanceof Uint8Array) {
return data instanceof ArrayBuffer ? data : data.buffer;
} else {
throw new Error("Video data must be ArrayBuffer or Uint8Array");
}
} else if (type === "binary") {
if (data instanceof ArrayBuffer || data instanceof Uint8Array) {
return data instanceof ArrayBuffer ? data : data.buffer;
} else {
throw new Error("Binary data must be ArrayBuffer or Uint8Array");
}
} else {
throw new Error(`Unknown type: ${type}`);
}
}
// Helper: Deserialize bytes based on type
function _deserialize_data(data, type, correlation_id) {
/**
* Deserialize bytes to data based on type
*
* Supported formats:
* - "text": Converts bytes to string
* - "dictionary": Parses JSON string
* - "table": Parses Arrow IPC stream - NOT IMPLEMENTED (requires apache-arrow library)
* - "image": Returns binary data
* - "audio": Returns binary data
* - "video": Returns binary data
* - "binary": Returns binary data
*/
if (type === "text") {
const decoder = new TextDecoder();
return decoder.decode(new Uint8Array(data));
} else if (type === "dictionary") {
const decoder = new TextDecoder();
const jsonStr = decoder.decode(new Uint8Array(data));
return JSON.parse(jsonStr);
} else if (type === "table") {
// Table data - deserialize Arrow IPC stream (NOT IMPLEMENTED in pure JavaScript)
throw new Error("Table deserialization requires apache-arrow library");
} else if (type === "image") {
return data;
} else if (type === "audio") {
return data;
} else if (type === "video") {
return data;
} else if (type === "binary") {
return data;
} else {
throw new Error(`Unknown type: ${type}`);
}
}
// Helper: Upload data to file server
async function _upload_to_fileserver(fileserver_url, dataname, data, correlation_id) {
/**
* Upload data to HTTP file server (plik-like API)
*
* This function implements the plik one-shot upload mode:
* 1. Creates a one-shot upload session by sending POST request with {"OneShot": true}
* 2. Uploads the file data as multipart form data
* 3. Returns identifiers and download URL for the uploaded file
*/
log_trace(correlation_id, `Uploading ${dataname} to fileserver: ${fileserver_url}`);
// Step 1: Get upload ID and token
const url_getUploadID = `${fileserver_url}/upload`;
const headers = {
"Content-Type": "application/json"
};
const body = JSON.stringify({ OneShot: true });
let response = await fetch(url_getUploadID, {
method: "POST",
headers: headers,
body: body
});
if (!response.ok) {
throw new Error(`Failed to get upload ID: ${response.status} ${response.statusText}`);
}
const responseJson = await response.json();
const uploadid = responseJson.id;
const uploadtoken = responseJson.uploadToken;
// Step 2: Upload file data
const url_upload = `${fileserver_url}/file/${uploadid}`;
// Create multipart form data
const formData = new FormData();
// Create a Blob from the ArrayBuffer
const blob = new Blob([data], { type: "application/octet-stream" });
formData.append("file", blob, dataname);
response = await fetch(url_upload, {
method: "POST",
headers: {
"X-UploadToken": uploadtoken
},
body: formData
});
if (!response.ok) {
throw new Error(`Failed to upload file: ${response.status} ${response.statusText}`);
}
const fileResponseJson = await response.json();
const fileid = fileResponseJson.id;
// Build the download URL
const url = `${fileserver_url}/file/${uploadid}/${fileid}/${encodeURIComponent(dataname)}`;
log_trace(correlation_id, `Uploaded to URL: ${url}`);
return {
status: response.status,
uploadid: uploadid,
fileid: fileid,
url: url
};
}
// Helper: Fetch data from URL with exponential backoff
async function _fetch_with_backoff(url, max_retries, base_delay, max_delay, correlation_id) {
/**
* Fetch data from URL with retry logic using exponential backoff
*/
let delay = base_delay;
for (let attempt = 1; attempt <= max_retries; attempt++) {
try {
const response = await fetch(url);
if (response.status === 200) {
log_trace(correlation_id, `Successfully fetched data from ${url} on attempt ${attempt}`);
const arrayBuffer = await response.arrayBuffer();
return arrayBuffer;
} else {
throw new Error(`Failed to fetch: ${response.status} ${response.statusText}`);
}
} catch (e) {
log_trace(correlation_id, `Attempt ${attempt} failed: ${e.message}`);
if (attempt < max_retries) {
// Sleep with exponential backoff
await new Promise(resolve => setTimeout(resolve, delay));
delay = Math.min(delay * 2, max_delay);
}
}
}
throw new Error(`Failed to fetch data after ${max_retries} attempts`);
}
// Helper: Get payload bytes from data
function _get_payload_bytes(data) {
if (data instanceof ArrayBuffer || data instanceof Uint8Array) {
return data instanceof ArrayBuffer ? new Uint8Array(data) : data;
} else if (typeof data === 'string') {
return new TextEncoder().encode(data);
} else {
// For objects, serialize to JSON
return new TextEncoder().encode(JSON.stringify(data));
}
}
// MessagePayload class
class MessagePayload {
/**
* Represents a single payload in the message envelope
*
* @param {Object} options - Payload options
* @param {string} options.id - ID of this payload (e.g., "uuid4")
* @param {string} options.dataname - Name of this payload (e.g., "login_image")
* @param {string} options.type - Payload type: "text", "dictionary", "table", "image", "audio", "video", "binary"
* @param {string} options.transport - "direct" or "link"
* @param {string} options.encoding - "none", "json", "base64", "arrow-ipc"
* @param {number} options.size - Data size in bytes
* @param {string|ArrayBuffer} options.data - Payload data (direct) or URL (link)
* @param {Object} options.metadata - Metadata for this payload
*/
constructor(options) {
this.id = options.id || uuid4();
this.dataname = options.dataname;
this.type = options.type;
this.transport = options.transport;
this.encoding = options.encoding;
this.size = options.size;
this.data = options.data;
this.metadata = options.metadata || {};
}
// Convert to JSON object
toJSON() {
const obj = {
id: this.id,
dataname: this.dataname,
type: this.type,
transport: this.transport,
encoding: this.encoding,
size: this.size
};
// Include data based on transport type
if (this.transport === "direct" && this.data !== null) {
if (this.encoding === "base64" || this.encoding === "json") {
obj.data = this.data;
} else {
// For other encodings, use base64
const payloadBytes = _get_payload_bytes(this.data);
obj.data = arrayBufferToBase64(payloadBytes);
}
} else if (this.transport === "link" && this.data !== null) {
// For link transport, data is a URL string
obj.data = this.data;
}
if (Object.keys(this.metadata).length > 0) {
obj.metadata = this.metadata;
}
return obj;
}
}
// MessageEnvelope class
class MessageEnvelope {
/**
* Represents the message envelope containing metadata and payloads
*
* @param {Object} options - Envelope options
* @param {string} options.sendTo - Topic/subject the sender sends to
* @param {Array<MessagePayload>} options.payloads - Array of payloads
* @param {string} options.correlationId - Unique identifier to track messages
* @param {string} options.msgId - This message id
* @param {string} options.timestamp - Message published timestamp
* @param {string} options.msgPurpose - Purpose of this message
* @param {string} options.senderName - Name of the sender
* @param {string} options.senderId - UUID of the sender
* @param {string} options.receiverName - Name of the receiver
* @param {string} options.receiverId - UUID of the receiver
* @param {string} options.replyTo - Topic to reply to
* @param {string} options.replyToMsgId - Message id this message is replying to
* @param {string} options.brokerURL - NATS server address
* @param {Object} options.metadata - Metadata for the envelope
*/
constructor(options) {
this.correlationId = options.correlationId || uuid4();
this.msgId = options.msgId || uuid4();
this.timestamp = options.timestamp || new Date().toISOString();
this.sendTo = options.sendTo;
this.msgPurpose = options.msgPurpose || "";
this.senderName = options.senderName || "";
this.senderId = options.senderId || uuid4();
this.receiverName = options.receiverName || "";
this.receiverId = options.receiverId || "";
this.replyTo = options.replyTo || "";
this.replyToMsgId = options.replyToMsgId || "";
this.brokerURL = options.brokerURL || DEFAULT_NATS_URL;
this.metadata = options.metadata || {};
this.payloads = options.payloads || [];
}
// Convert to JSON string
toJSON() {
const obj = {
correlationId: this.correlationId,
msgId: this.msgId,
timestamp: this.timestamp,
sendTo: this.sendTo,
msgPurpose: this.msgPurpose,
senderName: this.senderName,
senderId: this.senderId,
receiverName: this.receiverName,
receiverId: this.receiverId,
replyTo: this.replyTo,
replyToMsgId: this.replyToMsgId,
brokerURL: this.brokerURL
};
if (Object.keys(this.metadata).length > 0) {
obj.metadata = this.metadata;
}
if (this.payloads.length > 0) {
obj.payloads = this.payloads.map(p => p.toJSON());
}
return obj;
}
// Convert to JSON string
toString() {
return JSON.stringify(this.toJSON());
}
}
// SmartSend function
async function smartsend(subject, data, options = {}) {
/**
* Send data either directly via NATS or via a fileserver URL, depending on payload size
*
* This function intelligently routes data delivery based on payload size relative to a threshold.
* If the serialized payload is smaller than `size_threshold`, it encodes the data as Base64 and publishes directly over NATS.
* Otherwise, it uploads the data to a fileserver and publishes only the download URL over NATS.
*
* @param {string} subject - NATS subject to publish the message to
* @param {Array} data - List of {dataname, data, type} objects to send
* @param {Object} options - Additional options
* @param {string} options.natsUrl - URL of the NATS server (default: "nats://localhost:4222")
* @param {string} options.fileserverUrl - Base URL of the file server (default: "http://localhost:8080")
* @param {Function} options.fileserverUploadHandler - Function to handle fileserver uploads
* @param {number} options.sizeThreshold - Threshold in bytes separating direct vs link transport (default: 1MB)
* @param {string} options.correlationId - Optional correlation ID for tracing
* @param {string} options.msgPurpose - Purpose of the message (default: "chat")
* @param {string} options.senderName - Name of the sender (default: "NATSBridge")
* @param {string} options.receiverName - Name of the receiver (default: "")
* @param {string} options.receiverId - UUID of the receiver (default: "")
* @param {string} options.replyTo - Topic to reply to (default: "")
* @param {string} options.replyToMsgId - Message ID this message is replying to (default: "")
*
* @returns {Promise<MessageEnvelope>} - The envelope for tracking
*/
const {
natsUrl = DEFAULT_NATS_URL,
fileserverUrl = DEFAULT_FILESERVER_URL,
fileserverUploadHandler = _upload_to_fileserver,
sizeThreshold = DEFAULT_SIZE_THRESHOLD,
correlationId = uuid4(),
msgPurpose = "chat",
senderName = "NATSBridge",
receiverName = "",
receiverId = "",
replyTo = "",
replyToMsgId = ""
} = options;
log_trace(correlationId, `Starting smartsend for subject: ${subject}`);
// Generate message metadata
const msgId = uuid4();
// Process each payload in the list
const payloads = [];
for (const payload of data) {
const dataname = payload.dataname;
const payloadData = payload.data;
const payloadType = payload.type;
// Serialize data based on type
const payloadBytes = _serialize_data(payloadData, payloadType);
const payloadSize = payloadBytes.byteLength;
log_trace(correlationId, `Serialized payload '${dataname}' (type: ${payloadType}) size: ${payloadSize} bytes`);
// Decision: Direct vs Link
if (payloadSize < sizeThreshold) {
// Direct path - Base64 encode and send via NATS
const payloadB64 = arrayBufferToBase64(payloadBytes);
log_trace(correlationId, `Using direct transport for ${payloadSize} bytes`);
// Create MessagePayload for direct transport
const payloadObj = new MessagePayload({
dataname: dataname,
type: payloadType,
transport: "direct",
encoding: "base64",
size: payloadSize,
data: payloadB64,
metadata: { payload_bytes: payloadSize }
});
payloads.push(payloadObj);
} else {
// Link path - Upload to HTTP server, send URL via NATS
log_trace(correlationId, `Using link transport, uploading to fileserver`);
// Upload to HTTP server
const response = await fileserverUploadHandler(fileserverUrl, dataname, payloadBytes, correlationId);
if (response.status !== 200) {
throw new Error(`Failed to upload data to fileserver: ${response.status}`);
}
const url = response.url;
log_trace(correlationId, `Uploaded to URL: ${url}`);
// Create MessagePayload for link transport
const payloadObj = new MessagePayload({
dataname: dataname,
type: payloadType,
transport: "link",
encoding: "none",
size: payloadSize,
data: url,
metadata: {}
});
payloads.push(payloadObj);
}
}
// Create MessageEnvelope with all payloads
const env = new MessageEnvelope({
correlationId: correlationId,
msgId: msgId,
sendTo: subject,
msgPurpose: msgPurpose,
senderName: senderName,
receiverName: receiverName,
receiverId: receiverId,
replyTo: replyTo,
replyToMsgId: replyToMsgId,
brokerURL: natsUrl,
payloads: payloads
});
// Publish message to NATS
await publish_message(natsUrl, subject, env.toString(), correlationId);
return env;
}
// Helper: Publish message to NATS
async function publish_message(natsUrl, subject, message, correlation_id) {
/**
* Publish a message to a NATS subject with proper connection management
*
* @param {string} natsUrl - NATS server URL
* @param {string} subject - NATS subject to publish to
* @param {string} message - JSON message to publish
* @param {string} correlation_id - Correlation ID for logging
*/
log_trace(correlation_id, `Publishing message to ${subject}`);
// For Node.js, we would use nats.js library
// This is a placeholder that throws an error
// In production, you would import and use the actual nats library
// Example with nats.js:
// import { connect } from 'nats';
// const nc = await connect({ servers: [natsUrl] });
// await nc.publish(subject, message);
// nc.close();
// For now, just log the message
console.log(`[NATS PUBLISH] Subject: ${subject}, Message: ${message.substring(0, 100)}...`);
}
// SmartReceive function
async function smartreceive(msg, options = {}) {
/**
* Receive and process messages from NATS
*
* This function processes incoming NATS messages, handling both direct transport
* (base64 decoded payloads) and link transport (URL-based payloads).
*
* @param {Object} msg - NATS message object with payload property
* @param {Object} options - Additional options
* @param {Function} options.fileserverDownloadHandler - Function to handle downloading data from file server URLs
* @param {number} options.maxRetries - Maximum retry attempts for fetching URL (default: 5)
* @param {number} options.baseDelay - Initial delay for exponential backoff in ms (default: 100)
* @param {number} options.maxDelay - Maximum delay for exponential backoff in ms (default: 5000)
*
* @returns {Promise<Array>} - List of {dataname, data, type} objects
*/
const {
fileserverDownloadHandler = _fetch_with_backoff,
maxRetries = 5,
baseDelay = 100,
maxDelay = 5000
} = options;
// Parse the JSON envelope
const jsonStr = typeof msg.payload === 'string' ? msg.payload : new TextDecoder().decode(msg.payload);
const json_data = JSON.parse(jsonStr);
log_trace(json_data.correlationId, `Processing received message`);
// Process all payloads in the envelope
const payloads_list = [];
// Get number of payloads
const num_payloads = json_data.payloads ? json_data.payloads.length : 0;
for (let i = 0; i < num_payloads; i++) {
const payload = json_data.payloads[i];
const transport = payload.transport;
const dataname = payload.dataname;
if (transport === "direct") {
// Direct transport - payload is in the message
log_trace(json_data.correlationId, `Direct transport - decoding payload '${dataname}'`);
// Extract base64 payload from the payload
const payload_b64 = payload.data;
// Decode Base64 payload
const payload_bytes = base64ToArrayBuffer(payload_b64);
// Deserialize based on type
const data_type = payload.type;
const data = _deserialize_data(payload_bytes, data_type, json_data.correlationId);
payloads_list.push({ dataname, data, type: data_type });
} else if (transport === "link") {
// Link transport - payload is at URL
const url = payload.data;
log_trace(json_data.correlationId, `Link transport - fetching '${dataname}' from URL: ${url}`);
// Fetch with exponential backoff using the download handler
const downloaded_data = await fileserverDownloadHandler(
url, maxRetries, baseDelay, maxDelay, json_data.correlationId
);
// Deserialize based on type
const data_type = payload.type;
const data = _deserialize_data(downloaded_data, data_type, json_data.correlationId);
payloads_list.push({ dataname, data, type: data_type });
} else {
throw new Error(`Unknown transport type for payload '${dataname}': ${transport}`);
}
}
return payloads_list;
}
// Export for Node.js
if (typeof module !== 'undefined' && module.exports) {
module.exports = {
MessageEnvelope,
MessagePayload,
smartsend,
smartreceive,
_serialize_data,
_deserialize_data,
_fetch_with_backoff,
_upload_to_fileserver,
DEFAULT_SIZE_THRESHOLD,
DEFAULT_NATS_URL,
DEFAULT_FILESERVER_URL,
uuid4,
log_trace
};
}
// Export for browser
if (typeof window !== 'undefined') {
window.NATSBridge = {
MessageEnvelope,
MessagePayload,
smartsend,
smartreceive,
_serialize_data,
_deserialize_data,
_fetch_with_backoff,
_upload_to_fileserver,
DEFAULT_SIZE_THRESHOLD,
DEFAULT_NATS_URL,
DEFAULT_FILESERVER_URL,
uuid4,
log_trace
};
}

View File

@@ -1,212 +0,0 @@
# NATSBridge for Micropython
A high-performance, bi-directional data bridge for Micropython devices using NATS (Core & JetStream), implementing the Claim-Check pattern for large payloads.
## Overview
This module provides functionality for sending and receiving data over 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
## Features
- ✅ Bi-directional NATS communication
- ✅ Multi-payload support (mixed content in single message)
- ✅ Automatic transport selection based on payload size
- ✅ File server integration for large payloads
- ✅ Exponential backoff for URL fetching
- ✅ Correlation ID tracking
- ✅ Reply-to support for request-response pattern
## Supported Payload Types
| Type | Description |
|------|-------------|
| `text` | Plain text strings |
| `dictionary` | JSON-serializable dictionaries |
| `table` | Tabular data (Arrow IPC format) |
| `image` | Image data (PNG, JPG bytes) |
| `audio` | Audio data (WAV, MP3 bytes) |
| `video` | Video data (MP4, AVI bytes) |
| `binary` | Generic binary data |
## Installation
1. Copy `nats_bridge.py` to your Micropython device
2. Ensure you have the following dependencies:
- `urequests` for HTTP requests
- `ubinascii` for base64 encoding
- `ujson` for JSON handling
- `usocket` for networking
## Usage
### Basic Text Message
```python
from nats_bridge import smartsend, smartreceive
# Sender
data = [("message", "Hello World", "text")]
env = smartsend("/chat/room1", data, nats_url="nats://localhost:4222")
# Receiver
payloads = smartreceive(msg)
for dataname, data, type in payloads:
print("Received {}: {}".format(dataname, data))
```
### Sending JSON Configuration
```python
from nats_bridge import smartsend
config = {
"wifi_ssid": "MyNetwork",
"wifi_password": "password123",
"update_interval": 60
}
data = [("config", config, "dictionary")]
env = smartsend("/device/config", data, nats_url="nats://localhost:4222")
```
### Mixed Content (Chat with Text + Image)
```python
from nats_bridge import smartsend
image_data = b"\x89PNG..." # PNG bytes
data = [
("message_text", "Hello with image!", "text"),
("user_avatar", image_data, "binary")
]
env = smartsend("/chat/mixed", data, nats_url="nats://localhost:4222")
```
### Request-Response Pattern
```python
from nats_bridge import smartsend
# Send command with reply-to
data = [("command", {"action": "read_sensor"}, "dictionary")]
env = smartsend(
"/device/command",
data,
nats_url="nats://localhost:4222",
reply_to="/device/response",
reply_to_msg_id="cmd-001"
)
```
### Large Payloads (File Server)
```python
from nats_bridge import smartsend
# Large data (> 1MB)
large_data = b"A" * 2000000 # 2MB
env = smartsend(
"/data/large",
[("large_file", large_data, "binary")],
nats_url="nats://localhost:4222",
fileserver_url="http://localhost:8080",
size_threshold=1000000 # 1MB threshold
)
```
## API Reference
### `smartsend(subject, data, ...)`
Send data via NATS with automatic transport selection.
**Arguments:**
- `subject` (str): NATS subject to publish to
- `data` (list): List of `(dataname, data, type)` tuples
- `nats_url` (str): NATS server URL (default: `nats://localhost:4222`)
- `fileserver_url` (str): HTTP file server URL (default: `http://localhost:8080`)
- `size_threshold` (int): Threshold in bytes (default: 1,000,000)
- `correlation_id` (str): Optional correlation ID for tracing
- `msg_purpose` (str): Message purpose (default: `"chat"`)
- `sender_name` (str): Sender name (default: `"NATSBridge"`)
- `receiver_name` (str): Receiver name (default: `""`)
- `receiver_id` (str): Receiver ID (default: `""`)
- `reply_to` (str): Reply topic (default: `""`)
- `reply_to_msg_id` (str): Reply message ID (default: `""`)
**Returns:** `MessageEnvelope` object
### `smartreceive(msg, ...)`
Receive and process NATS messages.
**Arguments:**
- `msg`: NATS message (dict or JSON string)
- `fileserver_download_handler` (function): Function to fetch data from URLs
- `max_retries` (int): Maximum retry attempts (default: 5)
- `base_delay` (int): Initial delay in ms (default: 100)
- `max_delay` (int): Maximum delay in ms (default: 5000)
**Returns:** List of `(dataname, data, type)` tuples
### `MessageEnvelope`
Represents a complete NATS message envelope.
**Attributes:**
- `correlation_id`: Unique identifier for tracing
- `msg_id`: Unique message identifier
- `timestamp`: Message publication timestamp
- `send_to`: NATS subject
- `msg_purpose`: Message purpose
- `sender_name`: Sender name
- `sender_id`: Sender UUID
- `receiver_name`: Receiver name
- `receiver_id`: Receiver UUID
- `reply_to`: Reply topic
- `reply_to_msg_id`: Reply message ID
- `broker_url`: NATS broker URL
- `metadata`: Message-level metadata
- `payloads`: List of MessagePayload objects
### `MessagePayload`
Represents a single payload within a message envelope.
**Attributes:**
- `id`: Unique payload identifier
- `dataname`: Name of the payload
- `type`: Payload type ("text", "dictionary", etc.)
- `transport`: Transport method ("direct" or "link")
- `encoding`: Encoding method ("none", "base64", etc.)
- `size`: Payload size in bytes
- `data`: Payload data (bytes for direct, URL for link)
- `metadata`: Payload-level metadata
## Examples
See `examples/micropython_example.py` for more detailed examples.
## Testing
Run the test suite:
```bash
python test/test_micropython_basic.py
```
## Requirements
- Micropython with networking support
- NATS server (nats.io)
- HTTP file server (optional, for large payloads)
## License
MIT

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@@ -1,664 +0,0 @@
"""
Micropython NATS Bridge - Bi-Directional Data Bridge for Micropython
This module provides functionality for sending and receiving data over NATS
using the Claim-Check pattern for large payloads.
Supported types: "text", "dictionary", "table", "image", "audio", "video", "binary"
"""
import json
import random
import time
import usocket
import uselect
import ustruct
import uuid
try:
import ussl
HAS_SSL = True
except ImportError:
HAS_SSL = False
# Constants
DEFAULT_SIZE_THRESHOLD = 1000000 # 1MB - threshold for switching from direct to link transport
DEFAULT_NATS_URL = "nats://localhost:4222"
DEFAULT_FILESERVER_URL = "http://localhost:8080"
# ============================================= 100 ============================================== #
class MessagePayload:
"""Internal message payload structure representing a single payload within a NATS message envelope."""
def __init__(self, data, msg_type, id="", dataname="", transport="direct",
encoding="none", size=0, metadata=None):
"""
Initialize a MessagePayload.
Args:
data: Payload data (bytes for direct, URL string for link)
msg_type: Payload type ("text", "dictionary", "table", "image", "audio", "video", "binary")
id: Unique identifier for this payload (auto-generated if empty)
dataname: Name of the payload (auto-generated UUID if empty)
transport: Transport method ("direct" or "link")
encoding: Encoding method ("none", "json", "base64", "arrow-ipc")
size: Size of the payload in bytes
metadata: Optional metadata dictionary
"""
self.id = id if id else self._generate_uuid()
self.dataname = dataname if dataname else self._generate_uuid()
self.type = msg_type
self.transport = transport
self.encoding = encoding
self.size = size
self.data = data
self.metadata = metadata if metadata else {}
def _generate_uuid(self):
"""Generate a UUID string."""
return str(uuid.uuid4())
def to_dict(self):
"""Convert payload to dictionary for JSON serialization."""
payload_dict = {
"id": self.id,
"dataname": self.dataname,
"type": self.type,
"transport": self.transport,
"encoding": self.encoding,
"size": self.size,
}
# Include data based on transport type
if self.transport == "direct" and self.data is not None:
if self.encoding == "base64" or self.encoding == "json":
payload_dict["data"] = self.data
else:
# For other encodings, use base64
payload_dict["data"] = self._to_base64(self.data)
elif self.transport == "link" and self.data is not None:
# For link transport, data is a URL string
payload_dict["data"] = self.data
if self.metadata:
payload_dict["metadata"] = self.metadata
return payload_dict
def _to_base64(self, data):
"""Convert bytes to base64 string."""
if isinstance(data, bytes):
# Simple base64 encoding without library
import ubinascii
return ubinascii.b2a_base64(data).decode('utf-8').strip()
return data
def _from_base64(self, data):
"""Convert base64 string to bytes."""
import ubinascii
return ubinascii.a2b_base64(data)
class MessageEnvelope:
"""Internal message envelope structure containing multiple payloads with metadata."""
def __init__(self, send_to, payloads, correlation_id="", msg_id="", timestamp="",
msg_purpose="", sender_name="", sender_id="", receiver_name="",
receiver_id="", reply_to="", reply_to_msg_id="", broker_url=DEFAULT_NATS_URL,
metadata=None):
"""
Initialize a MessageEnvelope.
Args:
send_to: NATS subject/topic to publish the message to
payloads: List of MessagePayload objects
correlation_id: Unique identifier to track messages (auto-generated if empty)
msg_id: Unique message identifier (auto-generated if empty)
timestamp: Message publication timestamp
msg_purpose: Purpose of the message ("ACK", "NACK", "updateStatus", "shutdown", "chat", etc.)
sender_name: Name of the sender
sender_id: UUID of the sender
receiver_name: Name of the receiver (empty means broadcast)
receiver_id: UUID of the receiver (empty means broadcast)
reply_to: Topic where receiver should reply
reply_to_msg_id: Message ID this message is replying to
broker_url: NATS broker URL
metadata: Optional message-level metadata
"""
self.correlation_id = correlation_id if correlation_id else self._generate_uuid()
self.msg_id = msg_id if msg_id else self._generate_uuid()
self.timestamp = timestamp if timestamp else self._get_timestamp()
self.send_to = send_to
self.msg_purpose = msg_purpose
self.sender_name = sender_name
self.sender_id = sender_id if sender_id else self._generate_uuid()
self.receiver_name = receiver_name
self.receiver_id = receiver_id if receiver_id else self._generate_uuid()
self.reply_to = reply_to
self.reply_to_msg_id = reply_to_msg_id
self.broker_url = broker_url
self.metadata = metadata if metadata else {}
self.payloads = payloads
def _generate_uuid(self):
"""Generate a UUID string."""
return str(uuid.uuid4())
def _get_timestamp(self):
"""Get current timestamp in ISO format."""
# Simplified timestamp - Micropython may not have full datetime
return "2026-02-21T" + time.strftime("%H:%M:%S", time.localtime())
def to_json(self):
"""Convert envelope to JSON string."""
obj = {
"correlationId": self.correlation_id,
"msgId": self.msg_id,
"timestamp": self.timestamp,
"sendTo": self.send_to,
"msgPurpose": self.msg_purpose,
"senderName": self.sender_name,
"senderId": self.sender_id,
"receiverName": self.receiver_name,
"receiverId": self.receiver_id,
"replyTo": self.reply_to,
"replyToMsgId": self.reply_to_msg_id,
"brokerURL": self.broker_url
}
# Include metadata if not empty
if self.metadata:
obj["metadata"] = self.metadata
# Convert payloads to JSON array
if self.payloads:
payloads_json = []
for payload in self.payloads:
payloads_json.append(payload.to_dict())
obj["payloads"] = payloads_json
return json.dumps(obj)
def log_trace(correlation_id, message):
"""Log a trace message with correlation ID and timestamp."""
timestamp = time.strftime("%Y-%m-%dT%H:%M:%S", time.localtime())
print("[{}] [Correlation: {}] {}".format(timestamp, correlation_id, message))
def _serialize_data(data, msg_type):
"""Serialize data according to specified format.
Args:
data: Data to serialize
msg_type: Target format ("text", "dictionary", "table", "image", "audio", "video", "binary")
Returns:
bytes: Binary representation of the serialized data
"""
if msg_type == "text":
if isinstance(data, str):
return data.encode('utf-8')
else:
raise ValueError("Text data must be a string")
elif msg_type == "dictionary":
if isinstance(data, dict):
json_str = json.dumps(data)
return json_str.encode('utf-8')
else:
raise ValueError("Dictionary data must be a dict")
elif msg_type in ("image", "audio", "video", "binary"):
if isinstance(data, bytes):
return data
else:
raise ValueError("{} data must be bytes".format(msg_type.capitalize()))
else:
raise ValueError("Unknown type: {}".format(msg_type))
def _deserialize_data(data_bytes, msg_type, correlation_id):
"""Deserialize bytes to data based on type.
Args:
data_bytes: Serialized data as bytes
msg_type: Data type ("text", "dictionary", "table", "image", "audio", "video", "binary")
correlation_id: Correlation ID for logging
Returns:
Deserialized data
"""
if msg_type == "text":
return data_bytes.decode('utf-8')
elif msg_type == "dictionary":
json_str = data_bytes.decode('utf-8')
return json.loads(json_str)
elif msg_type in ("image", "audio", "video", "binary"):
return data_bytes
else:
raise ValueError("Unknown type: {}".format(msg_type))
class NATSConnection:
"""Simple NATS connection for Micropython."""
def __init__(self, url=DEFAULT_NATS_URL):
"""Initialize NATS connection.
Args:
url: NATS server URL (e.g., "nats://localhost:4222")
"""
self.url = url
self.host = "localhost"
self.port = 4222
self.conn = None
self._parse_url(url)
def _parse_url(self, url):
"""Parse NATS URL to extract host and port."""
if url.startswith("nats://"):
url = url[7:]
elif url.startswith("tls://"):
url = url[6:]
if ":" in url:
self.host, port_str = url.split(":")
self.port = int(port_str)
else:
self.host = url
def connect(self):
"""Connect to NATS server."""
addr = usocket.getaddrinfo(self.host, self.port)[0][-1]
self.conn = usocket.socket()
self.conn.connect(addr)
log_trace("", "Connected to NATS server at {}:{}".format(self.host, self.port))
def publish(self, subject, message):
"""Publish a message to a NATS subject.
Args:
subject: NATS subject to publish to
message: Message to publish (should be bytes or string)
"""
if isinstance(message, str):
message = message.encode('utf-8')
# Simple NATS protocol implementation
msg = "PUB {} {}\r\n".format(subject, len(message))
msg = msg.encode('utf-8') + message + b"\r\n"
self.conn.send(msg)
log_trace("", "Message published to {}".format(subject))
def subscribe(self, subject, callback):
"""Subscribe to a NATS subject.
Args:
subject: NATS subject to subscribe to
callback: Callback function to handle incoming messages
"""
log_trace("", "Subscribed to {}".format(subject))
# Simplified subscription - in a real implementation, you'd handle SUB/PUB messages
# For Micropython, we'll use a simple polling approach
self.subscribed_subject = subject
self.subscription_callback = callback
def wait_message(self, timeout=1000):
"""Wait for incoming message.
Args:
timeout: Timeout in milliseconds
Returns:
NATS message object or None if timeout
"""
# Simplified message reading
# In a real implementation, you'd read from the socket
# For now, this is a placeholder
return None
def close(self):
"""Close the NATS connection."""
if self.conn:
self.conn.close()
self.conn = None
log_trace("", "NATS connection closed")
def _fetch_with_backoff(url, max_retries=5, base_delay=100, max_delay=5000, correlation_id=""):
"""Fetch data from URL with exponential backoff.
Args:
url: URL to fetch from
max_retries: Maximum number of retry attempts
base_delay: Initial delay in milliseconds
max_delay: Maximum delay in milliseconds
correlation_id: Correlation ID for logging
Returns:
bytes: Fetched data
Raises:
Exception: If all retry attempts fail
"""
delay = base_delay
for attempt in range(1, max_retries + 1):
try:
# Simple HTTP GET request
# This is a simplified implementation
# For production, you'd want a proper HTTP client
import urequests
response = urequests.get(url)
if response.status_code == 200:
log_trace(correlation_id, "Successfully fetched data from {} on attempt {}".format(url, attempt))
return response.content
else:
raise Exception("Failed to fetch: {}".format(response.status_code))
except Exception as e:
log_trace(correlation_id, "Attempt {} failed: {}".format(attempt, str(e)))
if attempt < max_retries:
time.sleep(delay / 1000.0)
delay = min(delay * 2, max_delay)
def plik_oneshot_upload(file_server_url, filename, data):
"""Upload a single file to a plik server using one-shot mode.
Args:
file_server_url: Base URL of the plik server
filename: Name of the file being uploaded
data: Raw byte data of the file content
Returns:
dict: Dictionary with keys:
- "status": HTTP server response status
- "uploadid": ID of the one-shot upload session
- "fileid": ID of the uploaded file within the session
- "url": Full URL to download the uploaded file
"""
import urequests
import json
# Get upload ID
url_get_upload_id = "{}/upload".format(file_server_url)
headers = {"Content-Type": "application/json"}
body = json.dumps({"OneShot": True})
response = urequests.post(url_get_upload_id, headers=headers, data=body)
response_json = json.loads(response.content)
uploadid = response_json.get("id")
uploadtoken = response_json.get("uploadToken")
# Upload file
url_upload = "{}/file/{}".format(file_server_url, uploadid)
headers = {"X-UploadToken": uploadtoken}
# For Micropython, we need to construct the multipart form data manually
# This is a simplified approach
boundary = "----WebKitFormBoundary{}".format(uuid.uuid4().hex[:16])
# Create multipart body
part1 = "--{}\r\n".format(boundary)
part1 += "Content-Disposition: form-data; name=\"file\"; filename=\"{}\"\r\n".format(filename)
part1 += "Content-Type: application/octet-stream\r\n\r\n"
part1_bytes = part1.encode('utf-8')
part2 = "\r\n--{}--".format(boundary)
part2_bytes = part2.encode('utf-8')
# Combine all parts
full_body = part1_bytes + data + part2_bytes
# Set content type with boundary
content_type = "multipart/form-data; boundary={}".format(boundary)
response = urequests.post(url_upload, headers={"Content-Type": content_type}, data=full_body)
response_json = json.loads(response.content)
fileid = response_json.get("id")
url = "{}/file/{}/{}".format(file_server_url, uploadid, filename)
return {
"status": response.status_code,
"uploadid": uploadid,
"fileid": fileid,
"url": url
}
def smartsend(subject, data, nats_url=DEFAULT_NATS_URL, fileserver_url=DEFAULT_FILESERVER_URL,
fileserver_upload_handler=plik_oneshot_upload, size_threshold=DEFAULT_SIZE_THRESHOLD,
correlation_id=None, msg_purpose="chat", sender_name="NATSBridge",
receiver_name="", receiver_id="", reply_to="", reply_to_msg_id=""):
"""Send data either directly via NATS or via a fileserver URL, depending on payload size.
This function intelligently routes data delivery based on payload size relative to a threshold.
If the serialized payload is smaller than `size_threshold`, it encodes the data as Base64 and
publishes directly over NATS. Otherwise, it uploads the data to a fileserver and publishes
only the download URL over NATS.
Args:
subject: NATS subject to publish the message to
data: List of (dataname, data, type) tuples to send
nats_url: URL of the NATS server
fileserver_url: URL of the HTTP file server
fileserver_upload_handler: Function to handle fileserver uploads
size_threshold: Threshold in bytes separating direct vs link transport
correlation_id: Optional correlation ID for tracing
msg_purpose: Purpose of the message
sender_name: Name of the sender
receiver_name: Name of the receiver
receiver_id: UUID of the receiver
reply_to: Topic to reply to
reply_to_msg_id: Message ID this message is replying to
Returns:
MessageEnvelope: The envelope object for tracking
"""
# Generate correlation ID if not provided
cid = correlation_id if correlation_id else str(uuid.uuid4())
log_trace(cid, "Starting smartsend for subject: {}".format(subject))
# Generate message metadata
msg_id = str(uuid.uuid4())
# Process each payload in the list
payloads = []
for dataname, payload_data, payload_type in data:
# Serialize data based on type
payload_bytes = _serialize_data(payload_data, payload_type)
payload_size = len(payload_bytes)
log_trace(cid, "Serialized payload '{}' (type: {}) size: {} bytes".format(
dataname, payload_type, payload_size))
# Decision: Direct vs Link
if payload_size < size_threshold:
# Direct path - Base64 encode and send via NATS
payload_b64 = _serialize_data(payload_bytes, "binary") # Already bytes
# Convert to base64 string for JSON
import ubinascii
payload_b64_str = ubinascii.b2a_base64(payload_bytes).decode('utf-8').strip()
log_trace(cid, "Using direct transport for {} bytes".format(payload_size))
# Create MessagePayload for direct transport
payload = MessagePayload(
payload_b64_str,
payload_type,
id=str(uuid.uuid4()),
dataname=dataname,
transport="direct",
encoding="base64",
size=payload_size,
metadata={"payload_bytes": payload_size}
)
payloads.append(payload)
else:
# Link path - Upload to HTTP server, send URL via NATS
log_trace(cid, "Using link transport, uploading to fileserver")
# Upload to HTTP server
response = fileserver_upload_handler(fileserver_url, dataname, payload_bytes)
if response["status"] != 200:
raise Exception("Failed to upload data to fileserver: {}".format(response["status"]))
url = response["url"]
log_trace(cid, "Uploaded to URL: {}".format(url))
# Create MessagePayload for link transport
payload = MessagePayload(
url,
payload_type,
id=str(uuid.uuid4()),
dataname=dataname,
transport="link",
encoding="none",
size=payload_size,
metadata={}
)
payloads.append(payload)
# Create MessageEnvelope with all payloads
env = MessageEnvelope(
subject,
payloads,
correlation_id=cid,
msg_id=msg_id,
msg_purpose=msg_purpose,
sender_name=sender_name,
sender_id=str(uuid.uuid4()),
receiver_name=receiver_name,
receiver_id=receiver_id,
reply_to=reply_to,
reply_to_msg_id=reply_to_msg_id,
broker_url=nats_url,
metadata={}
)
msg_json = env.to_json()
# Publish to NATS
nats_conn = NATSConnection(nats_url)
nats_conn.connect()
nats_conn.publish(subject, msg_json)
nats_conn.close()
return env
def smartreceive(msg, fileserver_download_handler=_fetch_with_backoff, max_retries=5,
base_delay=100, max_delay=5000):
"""Receive and process messages from NATS.
This function processes incoming NATS messages, handling both direct transport
(base64 decoded payloads) and link transport (URL-based payloads).
Args:
msg: NATS message to process (dict with payload data)
fileserver_download_handler: Function to handle downloading data from file server URLs
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
Returns:
list: List of (dataname, data, type) tuples
"""
# Parse the JSON envelope
json_data = msg if isinstance(msg, dict) else json.loads(msg)
log_trace(json_data.get("correlationId", ""), "Processing received message")
# Process all payloads in the envelope
payloads_list = []
# Get number of payloads
num_payloads = len(json_data.get("payloads", []))
for i in range(num_payloads):
payload = json_data["payloads"][i]
transport = payload.get("transport", "")
dataname = payload.get("dataname", "")
if transport == "direct":
log_trace(json_data.get("correlationId", ""),
"Direct transport - decoding payload '{}'".format(dataname))
# Extract base64 payload from the payload
payload_b64 = payload.get("data", "")
# Decode Base64 payload
import ubinascii
payload_bytes = ubinascii.a2b_base64(payload_b64.encode('utf-8'))
# Deserialize based on type
data_type = payload.get("type", "")
data = _deserialize_data(payload_bytes, data_type, json_data.get("correlationId", ""))
payloads_list.append((dataname, data, data_type))
elif transport == "link":
# Extract download URL from the payload
url = payload.get("data", "")
log_trace(json_data.get("correlationId", ""),
"Link transport - fetching '{}' from URL: {}".format(dataname, url))
# Fetch with exponential backoff
downloaded_data = fileserver_download_handler(
url, max_retries, base_delay, max_delay, json_data.get("correlationId", "")
)
# Deserialize based on type
data_type = payload.get("type", "")
data = _deserialize_data(downloaded_data, data_type, json_data.get("correlationId", ""))
payloads_list.append((dataname, data, data_type))
else:
raise ValueError("Unknown transport type for payload '{}': {}".format(dataname, transport))
return payloads_list
# Utility functions
def generate_uuid():
"""Generate a UUID string."""
return str(uuid.uuid4())
def get_timestamp():
"""Get current timestamp in ISO format."""
return time.strftime("%Y-%m-%dT%H:%M:%S", time.localtime())
# Example usage
if __name__ == "__main__":
print("NATSBridge for Micropython")
print("=========================")
print("This module provides:")
print(" - MessageEnvelope: Message envelope structure")
print(" - MessagePayload: Payload structure")
print(" - smartsend: Send data via NATS with automatic transport selection")
print(" - smartreceive: Receive and process messages from NATS")
print(" - plik_oneshot_upload: Upload files to HTTP file server")
print(" - _fetch_with_backoff: Fetch data from URLs with retry logic")
print()
print("Usage:")
print(" from nats_bridge import smartsend, smartreceive")
print(" data = [(\"message\", \"Hello World\", \"text\")]")
print(" env = smartsend(\"my.subject\", data)")
print()
print(" # On receiver:")
print(" payloads = smartreceive(msg)")
print(" for dataname, data, type in payloads:")
print(" print(f\"Received {dataname} of type {type}: {data}\")")

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@@ -1,79 +0,0 @@
#!/usr/bin/env node
// Test script for Dictionary transport testing
// Tests receiving 1 large and 1 small Dictionaries via direct and link transport
// Uses NATSBridge.js smartreceive with "dictionary" type
const { smartreceive, log_trace } = require('./src/NATSBridge');
// Configuration
const SUBJECT = "/NATSBridge_dict_test";
const NATS_URL = "nats.yiem.cc";
// Helper: Log with correlation ID
function log_trace(message) {
const timestamp = new Date().toISOString();
console.log(`[${timestamp}] ${message}`);
}
// Receiver: Listen for messages and verify Dictionary handling
async function test_dict_receive() {
// Connect to NATS
const { connect } = require('nats');
const nc = await connect({ servers: [NATS_URL] });
// Subscribe to the subject
const sub = nc.subscribe(SUBJECT);
for await (const msg of sub) {
log_trace(`Received message on ${msg.subject}`);
// Use NATSBridge.smartreceive to handle the data
const result = await smartreceive(
msg,
{
maxRetries: 5,
baseDelay: 100,
maxDelay: 5000
}
);
// Result is a list of {dataname, data, type} objects
for (const { dataname, data, type } of result) {
if (typeof data === 'object' && data !== null && !Array.isArray(data)) {
log_trace(`Received Dictionary '${dataname}' of type ${type}`);
// Display dictionary contents
console.log(" Contents:");
for (const [key, value] of Object.entries(data)) {
console.log(` ${key} => ${value}`);
}
// Save to JSON file
const fs = require('fs');
const output_path = `./received_${dataname}.json`;
const json_str = JSON.stringify(data, null, 2);
fs.writeFileSync(output_path, json_str);
log_trace(`Saved Dictionary to ${output_path}`);
} else {
log_trace(`Received unexpected data type for '${dataname}': ${typeof data}`);
}
}
}
// Keep listening for 10 seconds
setTimeout(() => {
nc.close();
process.exit(0);
}, 120000);
}
// Run the test
console.log("Starting Dictionary transport test...");
console.log("Note: This receiver will wait for messages from the sender.");
console.log("Run test_js_to_js_dict_sender.js first to send test data.");
// Run receiver
console.log("testing smartreceive");
test_dict_receive();
console.log("Test completed.");

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@@ -1,164 +0,0 @@
#!/usr/bin/env node
// Test script for Dictionary transport testing
// Tests sending 1 large and 1 small Dictionaries via direct and link transport
// Uses NATSBridge.js smartsend with "dictionary" type
const { smartsend, uuid4, log_trace } = require('./src/NATSBridge');
// Configuration
const SUBJECT = "/NATSBridge_dict_test";
const NATS_URL = "nats.yiem.cc";
const FILESERVER_URL = "http://192.168.88.104:8080";
// Create correlation ID for tracing
const correlation_id = uuid4();
// Helper: Log with correlation ID
function log_trace(message) {
const timestamp = new Date().toISOString();
console.log(`[${timestamp}] [Correlation: ${correlation_id}] ${message}`);
}
// File upload handler for plik server
async function plik_upload_handler(fileserver_url, dataname, data, correlation_id) {
// Get upload ID
const url_getUploadID = `${fileserver_url}/upload`;
const headers = {
"Content-Type": "application/json"
};
const body = JSON.stringify({ OneShot: true });
let response = await fetch(url_getUploadID, {
method: "POST",
headers: headers,
body: body
});
if (!response.ok) {
throw new Error(`Failed to get upload ID: ${response.status} ${response.statusText}`);
}
const responseJson = await response.json();
const uploadid = responseJson.id;
const uploadtoken = responseJson.uploadToken;
// Upload file
const formData = new FormData();
const blob = new Blob([data], { type: "application/octet-stream" });
formData.append("file", blob, dataname);
response = await fetch(`${fileserver_url}/file/${uploadid}`, {
method: "POST",
headers: {
"X-UploadToken": uploadtoken
},
body: formData
});
if (!response.ok) {
throw new Error(`Failed to upload file: ${response.status} ${response.statusText}`);
}
const fileResponseJson = await response.json();
const fileid = fileResponseJson.id;
const url = `${fileserver_url}/file/${uploadid}/${fileid}/${encodeURIComponent(dataname)}`;
return {
status: response.status,
uploadid: uploadid,
fileid: fileid,
url: url
};
}
// Sender: Send Dictionaries via smartsend
async function test_dict_send() {
// Create a small Dictionary (will use direct transport)
const small_dict = {
name: "Alice",
age: 30,
scores: [95, 88, 92],
metadata: {
height: 155,
weight: 55
}
};
// Create a large Dictionary (will use link transport if > 1MB)
const large_dict_ids = [];
const large_dict_names = [];
const large_dict_scores = [];
const large_dict_categories = [];
for (let i = 0; i < 50000; i++) {
large_dict_ids.push(i + 1);
large_dict_names.push(`User_${i}`);
large_dict_scores.push(Math.floor(Math.random() * 100) + 1);
large_dict_categories.push(`Category_${Math.floor(Math.random() * 10) + 1}`);
}
const large_dict = {
ids: large_dict_ids,
names: large_dict_names,
scores: large_dict_scores,
categories: large_dict_categories,
metadata: {
source: "test_generator",
timestamp: new Date().toISOString()
}
};
// Test data 1: small Dictionary
const data1 = { dataname: "small_dict", data: small_dict, type: "dictionary" };
// Test data 2: large Dictionary
const data2 = { dataname: "large_dict", data: large_dict, type: "dictionary" };
// Use smartsend with dictionary type
// For small Dictionary: will use direct transport (JSON encoded)
// For large Dictionary: will use link transport (uploaded to fileserver)
const env = await smartsend(
SUBJECT,
[data1, data2],
{
natsUrl: NATS_URL,
fileserverUrl: FILESERVER_URL,
fileserverUploadHandler: plik_upload_handler,
sizeThreshold: 1_000_000,
correlationId: correlation_id,
msgPurpose: "chat",
senderName: "dict_sender",
receiverName: "",
receiverId: "",
replyTo: "",
replyToMsgId: ""
}
);
log_trace(`Sent message with ${env.payloads.length} payloads`);
// Log transport type for each payload
for (let i = 0; i < env.payloads.length; i++) {
const payload = env.payloads[i];
log_trace(`Payload ${i + 1} ('${payload.dataname}'):`);
log_trace(` Transport: ${payload.transport}`);
log_trace(` Type: ${payload.type}`);
log_trace(` Size: ${payload.size} bytes`);
log_trace(` Encoding: ${payload.encoding}`);
if (payload.transport === "link") {
log_trace(` URL: ${payload.data}`);
}
}
}
// Run the test
console.log("Starting Dictionary transport test...");
console.log(`Correlation ID: ${correlation_id}`);
// Run sender
console.log("start smartsend for dictionaries");
test_dict_send();
console.log("Test completed.");

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@@ -1,70 +0,0 @@
#!/usr/bin/env node
// Test script for large payload testing using binary transport
// Tests receiving a large file (> 1MB) via smartsend with binary type
const { smartreceive, log_trace } = require('./src/NATSBridge');
// Configuration
const SUBJECT = "/NATSBridge_test";
const NATS_URL = "nats.yiem.cc";
// Helper: Log with correlation ID
function log_trace(message) {
const timestamp = new Date().toISOString();
console.log(`[${timestamp}] ${message}`);
}
// Receiver: Listen for messages and verify large payload handling
async function test_large_binary_receive() {
// Connect to NATS
const { connect } = require('nats');
const nc = await connect({ servers: [NATS_URL] });
// Subscribe to the subject
const sub = nc.subscribe(SUBJECT);
for await (const msg of sub) {
log_trace(`Received message on ${msg.subject}`);
// Use NATSBridge.smartreceive to handle the data
const result = await smartreceive(
msg,
{
maxRetries: 5,
baseDelay: 100,
maxDelay: 5000
}
);
// Result is a list of {dataname, data, type} objects
for (const { dataname, data, type } of result) {
if (data instanceof Uint8Array || Array.isArray(data)) {
const file_size = data.length;
log_trace(`Received ${file_size} bytes of binary data for '${dataname}' of type ${type}`);
// Save received data to a test file
const fs = require('fs');
const output_path = `./new_${dataname}`;
fs.writeFileSync(output_path, Buffer.from(data));
log_trace(`Saved received data to ${output_path}`);
} else {
log_trace(`Received unexpected data type for '${dataname}': ${typeof data}`);
}
}
}
// Keep listening for 10 seconds
setTimeout(() => {
nc.close();
process.exit(0);
}, 120000);
}
// Run the test
console.log("Starting large binary payload test...");
// Run receiver
console.log("testing smartreceive");
test_large_binary_receive();
console.log("Test completed.");

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@@ -1,143 +0,0 @@
#!/usr/bin/env node
// Test script for large payload testing using binary transport
// Tests sending a large file (> 1MB) via smartsend with binary type
const { smartsend, uuid4, log_trace } = require('./src/NATSBridge');
// Configuration
const SUBJECT = "/NATSBridge_test";
const NATS_URL = "nats.yiem.cc";
const FILESERVER_URL = "http://192.168.88.104:8080";
// Create correlation ID for tracing
const correlation_id = uuid4();
// Helper: Log with correlation ID
function log_trace(message) {
const timestamp = new Date().toISOString();
console.log(`[${timestamp}] [Correlation: ${correlation_id}] ${message}`);
}
// File upload handler for plik server
async function plik_upload_handler(fileserver_url, dataname, data, correlation_id) {
log_trace(correlation_id, `Uploading ${dataname} to fileserver: ${fileserver_url}`);
// Step 1: Get upload ID and token
const url_getUploadID = `${fileserver_url}/upload`;
const headers = {
"Content-Type": "application/json"
};
const body = JSON.stringify({ OneShot: true });
let response = await fetch(url_getUploadID, {
method: "POST",
headers: headers,
body: body
});
if (!response.ok) {
throw new Error(`Failed to get upload ID: ${response.status} ${response.statusText}`);
}
const responseJson = await response.json();
const uploadid = responseJson.id;
const uploadtoken = responseJson.uploadToken;
// Step 2: Upload file data
const url_upload = `${fileserver_url}/file/${uploadid}`;
// Create multipart form data
const formData = new FormData();
const blob = new Blob([data], { type: "application/octet-stream" });
formData.append("file", blob, dataname);
response = await fetch(url_upload, {
method: "POST",
headers: {
"X-UploadToken": uploadtoken
},
body: formData
});
if (!response.ok) {
throw new Error(`Failed to upload file: ${response.status} ${response.statusText}`);
}
const fileResponseJson = await response.json();
const fileid = fileResponseJson.id;
// Build the download URL
const url = `${fileserver_url}/file/${uploadid}/${fileid}/${encodeURIComponent(dataname)}`;
log_trace(correlation_id, `Uploaded to URL: ${url}`);
return {
status: response.status,
uploadid: uploadid,
fileid: fileid,
url: url
};
}
// Sender: Send large binary file via smartsend
async function test_large_binary_send() {
// Read the large file as binary data
const fs = require('fs');
// Test data 1
const file_path1 = './testFile_large.zip';
const file_data1 = fs.readFileSync(file_path1);
const filename1 = 'testFile_large.zip';
const data1 = { dataname: filename1, data: file_data1, type: "binary" };
// Test data 2
const file_path2 = './testFile_small.zip';
const file_data2 = fs.readFileSync(file_path2);
const filename2 = 'testFile_small.zip';
const data2 = { dataname: filename2, data: file_data2, type: "binary" };
// Use smartsend with binary type - will automatically use link transport
// if file size exceeds the threshold (1MB by default)
const env = await smartsend(
SUBJECT,
[data1, data2],
{
natsUrl: NATS_URL,
fileserverUrl: FILESERVER_URL,
fileserverUploadHandler: plik_upload_handler,
sizeThreshold: 1_000_000,
correlationId: correlation_id,
msgPurpose: "chat",
senderName: "sender",
receiverName: "",
receiverId: "",
replyTo: "",
replyToMsgId: ""
}
);
log_trace(`Sent message with transport: ${env.payloads[0].transport}`);
log_trace(`Envelope type: ${env.payloads[0].type}`);
// Check if link transport was used
if (env.payloads[0].transport === "link") {
log_trace("Using link transport - file uploaded to HTTP server");
log_trace(`URL: ${env.payloads[0].data}`);
} else {
log_trace("Using direct transport - payload sent via NATS");
}
}
// Run the test
console.log("Starting large binary payload test...");
console.log(`Correlation ID: ${correlation_id}`);
// Run sender first
console.log("start smartsend");
test_large_binary_send();
// Run receiver
// console.log("testing smartreceive");
// test_large_binary_receive();
console.log("Test completed.");

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@@ -1,276 +0,0 @@
#!/usr/bin/env node
// Test script for mixed-content message testing
// Tests sending a mix of text, json, table, image, audio, video, and binary data
// from JavaScript serviceA to JavaScript serviceB using NATSBridge.js smartsend
//
// This test demonstrates that any combination and any number of mixed content
// can be sent and received correctly.
const { smartsend, uuid4, log_trace, _serialize_data } = require('./src/NATSBridge');
// Configuration
const SUBJECT = "/NATSBridge_mix_test";
const NATS_URL = "nats.yiem.cc";
const FILESERVER_URL = "http://192.168.88.104:8080";
// Create correlation ID for tracing
const correlation_id = uuid4();
// Helper: Log with correlation ID
function log_trace(message) {
const timestamp = new Date().toISOString();
console.log(`[${timestamp}] [Correlation: ${correlation_id}] ${message}`);
}
// File upload handler for plik server
async function plik_upload_handler(fileserver_url, dataname, data, correlation_id) {
log_trace(correlation_id, `Uploading ${dataname} to fileserver: ${fileserver_url}`);
// Step 1: Get upload ID and token
const url_getUploadID = `${fileserver_url}/upload`;
const headers = {
"Content-Type": "application/json"
};
const body = JSON.stringify({ OneShot: true });
let response = await fetch(url_getUploadID, {
method: "POST",
headers: headers,
body: body
});
if (!response.ok) {
throw new Error(`Failed to get upload ID: ${response.status} ${response.statusText}`);
}
const responseJson = await response.json();
const uploadid = responseJson.id;
const uploadtoken = responseJson.uploadToken;
// Step 2: Upload file data
const url_upload = `${fileserver_url}/file/${uploadid}`;
// Create multipart form data
const formData = new FormData();
const blob = new Blob([data], { type: "application/octet-stream" });
formData.append("file", blob, dataname);
response = await fetch(url_upload, {
method: "POST",
headers: {
"X-UploadToken": uploadtoken
},
body: formData
});
if (!response.ok) {
throw new Error(`Failed to upload file: ${response.status} ${response.statusText}`);
}
const fileResponseJson = await response.json();
const fileid = fileResponseJson.id;
// Build the download URL
const url = `${fileserver_url}/file/${uploadid}/${fileid}/${encodeURIComponent(dataname)}`;
log_trace(correlation_id, `Uploaded to URL: ${url}`);
return {
status: response.status,
uploadid: uploadid,
fileid: fileid,
url: url
};
}
// Helper: Create sample data for each type
function create_sample_data() {
// Text data (small - direct transport)
const text_data = "Hello! This is a test chat message. 🎉\nHow are you doing today? 😊";
// Dictionary/JSON data (medium - could be direct or link)
const dict_data = {
type: "chat",
sender: "serviceA",
receiver: "serviceB",
metadata: {
timestamp: new Date().toISOString(),
priority: "high",
tags: ["urgent", "chat", "test"]
},
content: {
text: "This is a JSON-formatted chat message with nested structure.",
format: "markdown",
mentions: ["user1", "user2"]
}
};
// Table data (small - direct transport) - NOT IMPLEMENTED (requires apache-arrow)
// const table_data_small = {...};
// Table data (large - link transport) - NOT IMPLEMENTED (requires apache-arrow)
// const table_data_large = {...};
// Image data (small binary - direct transport)
// Create a simple 10x10 pixel PNG-like data
const image_width = 10;
const image_height = 10;
let image_data = new Uint8Array(128); // PNG header + pixel data
// PNG header
image_data[0] = 0x89;
image_data[1] = 0x50;
image_data[2] = 0x4E;
image_data[3] = 0x47;
image_data[4] = 0x0D;
image_data[5] = 0x0A;
image_data[6] = 0x1A;
image_data[7] = 0x0A;
// Simple RGB data (10*10*3 = 300 bytes)
for (let i = 0; i < 300; i++) {
image_data[i + 8] = 0xFF; // Red pixel
}
// Image data (large - link transport)
const large_image_width = 500;
const large_image_height = 1000;
const large_image_data = new Uint8Array(large_image_width * large_image_height * 3 + 8);
// PNG header
large_image_data[0] = 0x89;
large_image_data[1] = 0x50;
large_image_data[2] = 0x4E;
large_image_data[3] = 0x47;
large_image_data[4] = 0x0D;
large_image_data[5] = 0x0A;
large_image_data[6] = 0x1A;
large_image_data[7] = 0x0A;
// Random RGB data
for (let i = 0; i < large_image_width * large_image_height * 3; i++) {
large_image_data[i + 8] = Math.floor(Math.random() * 255);
}
// Audio data (small binary - direct transport)
const audio_data = new Uint8Array(100);
for (let i = 0; i < 100; i++) {
audio_data[i] = Math.floor(Math.random() * 255);
}
// Audio data (large - link transport)
const large_audio_data = new Uint8Array(1_500_000);
for (let i = 0; i < 1_500_000; i++) {
large_audio_data[i] = Math.floor(Math.random() * 255);
}
// Video data (small binary - direct transport)
const video_data = new Uint8Array(150);
for (let i = 0; i < 150; i++) {
video_data[i] = Math.floor(Math.random() * 255);
}
// Video data (large - link transport)
const large_video_data = new Uint8Array(1_500_000);
for (let i = 0; i < 1_500_000; i++) {
large_video_data[i] = Math.floor(Math.random() * 255);
}
// Binary data (small - direct transport)
const binary_data = new Uint8Array(200);
for (let i = 0; i < 200; i++) {
binary_data[i] = Math.floor(Math.random() * 255);
}
// Binary data (large - link transport)
const large_binary_data = new Uint8Array(1_500_000);
for (let i = 0; i < 1_500_000; i++) {
large_binary_data[i] = Math.floor(Math.random() * 255);
}
return {
text_data,
dict_data,
// table_data_small,
// table_data_large,
image_data,
large_image_data,
audio_data,
large_audio_data,
video_data,
large_video_data,
binary_data,
large_binary_data
};
}
// Sender: Send mixed content via smartsend
async function test_mix_send() {
// Create sample data
const { text_data, dict_data, image_data, large_image_data, audio_data, large_audio_data, video_data, large_video_data, binary_data, large_binary_data } = create_sample_data();
// Create payloads list - mixed content with both small and large data
// Small data uses direct transport, large data uses link transport
const payloads = [
// Small data (direct transport) - text, dictionary
{ dataname: "chat_text", data: text_data, type: "text" },
{ dataname: "chat_json", data: dict_data, type: "dictionary" },
// { dataname: "chat_table_small", data: table_data_small, type: "table" },
// Large data (link transport) - large image, large audio, large video, large binary
// { dataname: "chat_table_large", data: table_data_large, type: "table" },
{ dataname: "user_image_large", data: large_image_data, type: "image" },
{ dataname: "audio_clip_large", data: large_audio_data, type: "audio" },
{ dataname: "video_clip_large", data: large_video_data, type: "video" },
{ dataname: "binary_file_large", data: large_binary_data, type: "binary" }
];
// Use smartsend with mixed content
const env = await smartsend(
SUBJECT,
payloads,
{
natsUrl: NATS_URL,
fileserverUrl: FILESERVER_URL,
fileserverUploadHandler: plik_upload_handler,
sizeThreshold: 1_000_000,
correlationId: correlation_id,
msgPurpose: "chat",
senderName: "mix_sender",
receiverName: "",
receiverId: "",
replyTo: "",
replyToMsgId: ""
}
);
log_trace(`Sent message with ${env.payloads.length} payloads`);
// Log transport type for each payload
for (let i = 0; i < env.payloads.length; i++) {
const payload = env.payloads[i];
log_trace(`Payload ${i + 1} ('${payload.dataname}'):`);
log_trace(` Transport: ${payload.transport}`);
log_trace(` Type: ${payload.type}`);
log_trace(` Size: ${payload.size} bytes`);
log_trace(` Encoding: ${payload.encoding}`);
if (payload.transport === "link") {
log_trace(` URL: ${payload.data}`);
}
}
// Summary
console.log("\n--- Transport Summary ---");
const direct_count = env.payloads.filter(p => p.transport === "direct").length;
const link_count = env.payloads.filter(p => p.transport === "link").length;
log_trace(`Direct transport: ${direct_count} payloads`);
log_trace(`Link transport: ${link_count} payloads`);
}
// Run the test
console.log("Starting mixed-content transport test...");
console.log(`Correlation ID: ${correlation_id}`);
// Run sender
console.log("start smartsend for mixed content");
test_mix_send();
console.log("\nTest completed.");
console.log("Note: Run test_js_to_js_mix_receiver.js to receive the messages.");

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@@ -1,172 +0,0 @@
#!/usr/bin/env node
// Test script for mixed-content message testing
// Tests receiving a mix of text, json, table, image, audio, video, and binary data
// from JavaScript serviceA to JavaScript serviceB using NATSBridge.js smartreceive
//
// This test demonstrates that any combination and any number of mixed content
// can be sent and received correctly.
const { smartreceive, log_trace } = require('./src/NATSBridge');
// Configuration
const SUBJECT = "/NATSBridge_mix_test";
const NATS_URL = "nats.yiem.cc";
// Helper: Log with correlation ID
function log_trace(message) {
const timestamp = new Date().toISOString();
console.log(`[${timestamp}] ${message}`);
}
// Receiver: Listen for messages and verify mixed content handling
async function test_mix_receive() {
// Connect to NATS
const { connect } = require('nats');
const nc = await connect({ servers: [NATS_URL] });
// Subscribe to the subject
const sub = nc.subscribe(SUBJECT);
for await (const msg of sub) {
log_trace(`Received message on ${msg.subject}`);
// Use NATSBridge.smartreceive to handle the data
const result = await smartreceive(
msg,
{
maxRetries: 5,
baseDelay: 100,
maxDelay: 5000
}
);
log_trace(`Received ${result.length} payloads`);
// Result is a list of {dataname, data, type} objects
for (const { dataname, data, type } of result) {
log_trace(`\n=== Payload: ${dataname} (type: ${type}) ===`);
// Handle different data types
if (type === "text") {
// Text data - should be a String
if (typeof data === 'string') {
log_trace(` Type: String`);
log_trace(` Length: ${data.length} characters`);
// Display first 200 characters
if (data.length > 200) {
log_trace(` First 200 chars: ${data.substring(0, 200)}...`);
} else {
log_trace(` Content: ${data}`);
}
// Save to file
const fs = require('fs');
const output_path = `./received_${dataname}.txt`;
fs.writeFileSync(output_path, data);
log_trace(` Saved to: ${output_path}`);
} else {
log_trace(` ERROR: Expected String, got ${typeof data}`);
}
} else if (type === "dictionary") {
// Dictionary data - should be an object
if (typeof data === 'object' && data !== null && !Array.isArray(data)) {
log_trace(` Type: Object`);
log_trace(` Keys: ${Object.keys(data).join(', ')}`);
// Display nested content
for (const [key, value] of Object.entries(data)) {
log_trace(` ${key} => ${value}`);
}
// Save to JSON file
const fs = require('fs');
const output_path = `./received_${dataname}.json`;
const json_str = JSON.stringify(data, null, 2);
fs.writeFileSync(output_path, json_str);
log_trace(` Saved to: ${output_path}`);
} else {
log_trace(` ERROR: Expected Object, got ${typeof data}`);
}
} else if (type === "table") {
// Table data - should be an array of objects (requires apache-arrow)
log_trace(` Type: Array (requires apache-arrow for full deserialization)`);
if (Array.isArray(data)) {
log_trace(` Length: ${data.length} items`);
log_trace(` First item: ${JSON.stringify(data[0])}`);
} else {
log_trace(` ERROR: Expected Array, got ${typeof data}`);
}
} else if (type === "image" || type === "audio" || type === "video" || type === "binary") {
// Binary data - should be Uint8Array
if (data instanceof Uint8Array || Array.isArray(data)) {
log_trace(` Type: Uint8Array (binary)`);
log_trace(` Size: ${data.length} bytes`);
// Save to file
const fs = require('fs');
const output_path = `./received_${dataname}.bin`;
fs.writeFileSync(output_path, Buffer.from(data));
log_trace(` Saved to: ${output_path}`);
} else {
log_trace(` ERROR: Expected Uint8Array, got ${typeof data}`);
}
} else {
log_trace(` ERROR: Unknown data type '${type}'`);
}
}
// Summary
console.log("\n=== Verification Summary ===");
const text_count = result.filter(x => x.type === "text").length;
const dict_count = result.filter(x => x.type === "dictionary").length;
const table_count = result.filter(x => x.type === "table").length;
const image_count = result.filter(x => x.type === "image").length;
const audio_count = result.filter(x => x.type === "audio").length;
const video_count = result.filter(x => x.type === "video").length;
const binary_count = result.filter(x => x.type === "binary").length;
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: ${binary_count}`);
// Print transport type info for each payload if available
console.log("\n=== Payload Details ===");
for (const { dataname, data, type } of result) {
if (["image", "audio", "video", "binary"].includes(type)) {
log_trace(`${dataname}: ${data.length} bytes (binary)`);
} else if (type === "table") {
log_trace(`${dataname}: ${data.length} items (Array)`);
} else if (type === "dictionary") {
log_trace(`${dataname}: ${JSON.stringify(data).length} bytes (Object)`);
} else if (type === "text") {
log_trace(`${dataname}: ${data.length} characters (String)`);
}
}
}
// Keep listening for 2 minutes
setTimeout(() => {
nc.close();
process.exit(0);
}, 120000);
}
// Run the test
console.log("Starting mixed-content transport test...");
console.log("Note: This receiver will wait for messages from the sender.");
console.log("Run test_js_to_js_mix_sender.js first to send test data.");
// Run receiver
console.log("\ntesting smartreceive for mixed content");
test_mix_receive();
console.log("\nTest completed.");

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@@ -1,86 +0,0 @@
#!/usr/bin/env node
// Test script for Table transport testing
// Tests receiving 1 large and 1 small Tables via direct and link transport
// Uses NATSBridge.js smartreceive with "table" type
//
// Note: This test requires the apache-arrow library to deserialize table data.
// The JavaScript implementation uses apache-arrow for Arrow IPC deserialization.
const { smartreceive, log_trace } = require('./src/NATSBridge');
// Configuration
const SUBJECT = "/NATSBridge_table_test";
const NATS_URL = "nats.yiem.cc";
// Helper: Log with correlation ID
function log_trace(message) {
const timestamp = new Date().toISOString();
console.log(`[${timestamp}] ${message}`);
}
// Receiver: Listen for messages and verify Table handling
async function test_table_receive() {
// Connect to NATS
const { connect } = require('nats');
const nc = await connect({ servers: [NATS_URL] });
// Subscribe to the subject
const sub = nc.subscribe(SUBJECT);
for await (const msg of sub) {
log_trace(`Received message on ${msg.subject}`);
// Use NATSBridge.smartreceive to handle the data
const result = await smartreceive(
msg,
{
maxRetries: 5,
baseDelay: 100,
maxDelay: 5000
}
);
// Result is a list of {dataname, data, type} objects
for (const { dataname, data, type } of result) {
if (Array.isArray(data)) {
log_trace(`Received Table '${dataname}' of type ${type}`);
// Display table contents
console.log(` Dimensions: ${data.length} rows x ${data.length > 0 ? Object.keys(data[0]).length : 0} columns`);
console.log(` Columns: ${data.length > 0 ? Object.keys(data[0]).join(', ') : ''}`);
// Display first few rows
console.log(` First 5 rows:`);
for (let i = 0; i < Math.min(5, data.length); i++) {
console.log(` Row ${i}: ${JSON.stringify(data[i])}`);
}
// Save to JSON file
const fs = require('fs');
const output_path = `./received_${dataname}.json`;
const json_str = JSON.stringify(data, null, 2);
fs.writeFileSync(output_path, json_str);
log_trace(`Saved Table to ${output_path}`);
} else {
log_trace(`Received unexpected data type for '${dataname}': ${typeof data}`);
}
}
}
// Keep listening for 10 seconds
setTimeout(() => {
nc.close();
process.exit(0);
}, 120000);
}
// Run the test
console.log("Starting Table transport test...");
console.log("Note: This receiver will wait for messages from the sender.");
console.log("Run test_js_to_js_table_sender.js first to send test data.");
// Run receiver
console.log("testing smartreceive");
test_table_receive();
console.log("Test completed.");

View File

@@ -1,164 +0,0 @@
#!/usr/bin/env node
// Test script for Table transport testing
// Tests sending 1 large and 1 small Tables via direct and link transport
// Uses NATSBridge.js smartsend with "table" type
//
// Note: This test requires the apache-arrow library to serialize/deserialize table data.
// The JavaScript implementation uses apache-arrow for Arrow IPC serialization.
const { smartsend, uuid4, log_trace } = require('./src/NATSBridge');
// Configuration
const SUBJECT = "/NATSBridge_table_test";
const NATS_URL = "nats.yiem.cc";
const FILESERVER_URL = "http://192.168.88.104:8080";
// Create correlation ID for tracing
const correlation_id = uuid4();
// Helper: Log with correlation ID
function log_trace(message) {
const timestamp = new Date().toISOString();
console.log(`[${timestamp}] [Correlation: ${correlation_id}] ${message}`);
}
// File upload handler for plik server
async function plik_upload_handler(fileserver_url, dataname, data, correlation_id) {
log_trace(correlation_id, `Uploading ${dataname} to fileserver: ${fileserver_url}`);
// Step 1: Get upload ID and token
const url_getUploadID = `${fileserver_url}/upload`;
const headers = {
"Content-Type": "application/json"
};
const body = JSON.stringify({ OneShot: true });
let response = await fetch(url_getUploadID, {
method: "POST",
headers: headers,
body: body
});
if (!response.ok) {
throw new Error(`Failed to get upload ID: ${response.status} ${response.statusText}`);
}
const responseJson = await response.json();
const uploadid = responseJson.id;
const uploadtoken = responseJson.uploadToken;
// Step 2: Upload file data
const url_upload = `${fileserver_url}/file/${uploadid}`;
// Create multipart form data
const formData = new FormData();
const blob = new Blob([data], { type: "application/octet-stream" });
formData.append("file", blob, dataname);
response = await fetch(url_upload, {
method: "POST",
headers: {
"X-UploadToken": uploadtoken
},
body: formData
});
if (!response.ok) {
throw new Error(`Failed to upload file: ${response.status} ${response.statusText}`);
}
const fileResponseJson = await response.json();
const fileid = fileResponseJson.id;
// Build the download URL
const url = `${fileserver_url}/file/${uploadid}/${fileid}/${encodeURIComponent(dataname)}`;
log_trace(correlation_id, `Uploaded to URL: ${url}`);
return {
status: response.status,
uploadid: uploadid,
fileid: fileid,
url: url
};
}
// Sender: Send Tables via smartsend
async function test_table_send() {
// Note: This test requires apache-arrow library to create Arrow IPC data.
// For now, we'll use a simple array of objects as table data.
// In production, you would use the apache-arrow library to create Arrow IPC data.
// Create a small Table (will use direct transport)
const small_table = [
{ id: 1, name: "Alice", score: 95 },
{ id: 2, name: "Bob", score: 88 },
{ id: 3, name: "Charlie", score: 92 }
];
// Create a large Table (will use link transport if > 1MB)
// Generate a larger dataset (~2MB to ensure link transport)
const large_table = [];
for (let i = 0; i < 50000; i++) {
large_table.push({
id: i,
message: `msg_${i}`,
sender: `sender_${i}`,
timestamp: new Date().toISOString(),
priority: Math.floor(Math.random() * 3) + 1
});
}
// Test data 1: small Table
const data1 = { dataname: "small_table", data: small_table, type: "table" };
// Test data 2: large Table
const data2 = { dataname: "large_table", data: large_table, type: "table" };
// Use smartsend with table type
// For small Table: will use direct transport (Arrow IPC encoded)
// For large Table: will use link transport (uploaded to fileserver)
const env = await smartsend(
SUBJECT,
[data1, data2],
{
natsUrl: NATS_URL,
fileserverUrl: FILESERVER_URL,
fileserverUploadHandler: plik_upload_handler,
sizeThreshold: 1_000_000,
correlationId: correlation_id,
msgPurpose: "chat",
senderName: "table_sender",
receiverName: "",
receiverId: "",
replyTo: "",
replyToMsgId: ""
}
);
log_trace(`Sent message with ${env.payloads.length} payloads`);
// Log transport type for each payload
for (let i = 0; i < env.payloads.length; i++) {
const payload = env.payloads[i];
log_trace(`Payload ${i + 1} ('${payload.dataname}'):`);
log_trace(` Transport: ${payload.transport}`);
log_trace(` Type: ${payload.type}`);
log_trace(` Size: ${payload.size} bytes`);
log_trace(` Encoding: ${payload.encoding}`);
if (payload.transport === "link") {
log_trace(` URL: ${payload.data}`);
}
}
}
// Run the test
console.log("Starting Table transport test...");
console.log(`Correlation ID: ${correlation_id}`);
// Run sender
console.log("start smartsend for tables");
test_table_send();
console.log("Test completed.");

View File

@@ -1,80 +0,0 @@
#!/usr/bin/env node
// Test script for text transport testing
// Tests receiving 1 large and 1 small text from JavaScript serviceA to JavaScript serviceB
// Uses NATSBridge.js smartreceive with "text" type
const { smartreceive, log_trace } = require('./src/NATSBridge');
// Configuration
const SUBJECT = "/NATSBridge_text_test";
const NATS_URL = "nats.yiem.cc";
// Helper: Log with correlation ID
function log_trace(message) {
const timestamp = new Date().toISOString();
console.log(`[${timestamp}] ${message}`);
}
// Receiver: Listen for messages and verify text handling
async function test_text_receive() {
// Connect to NATS
const { connect } = require('nats');
const nc = await connect({ servers: [NATS_URL] });
// Subscribe to the subject
const sub = nc.subscribe(SUBJECT);
for await (const msg of sub) {
log_trace(`Received message on ${msg.subject}`);
// Use NATSBridge.smartreceive to handle the data
const result = await smartreceive(
msg,
{
maxRetries: 5,
baseDelay: 100,
maxDelay: 5000
}
);
// Result is a list of {dataname, data, type} objects
for (const { dataname, data, type } of result) {
if (typeof data === 'string') {
log_trace(`Received text '${dataname}' of type ${type}`);
log_trace(` Length: ${data.length} characters`);
// Display first 100 characters
if (data.length > 100) {
log_trace(` First 100 characters: ${data.substring(0, 100)}...`);
} else {
log_trace(` Content: ${data}`);
}
// Save to file
const fs = require('fs');
const output_path = `./received_${dataname}.txt`;
fs.writeFileSync(output_path, data);
log_trace(`Saved text to ${output_path}`);
} else {
log_trace(`Received unexpected data type for '${dataname}': ${typeof data}`);
}
}
}
// Keep listening for 10 seconds
setTimeout(() => {
nc.close();
process.exit(0);
}, 120000);
}
// Run the test
console.log("Starting text transport test...");
console.log("Note: This receiver will wait for messages from the sender.");
console.log("Run test_js_to_js_text_sender.js first to send test data.");
// Run receiver
console.log("testing smartreceive for text");
test_text_receive();
console.log("Test completed.");

View File

@@ -1,140 +0,0 @@
#!/usr/bin/env node
// Test script for text transport testing
// Tests sending 1 large and 1 small text from JavaScript serviceA to JavaScript serviceB
// Uses NATSBridge.js smartsend with "text" type
const { smartsend, uuid4, log_trace } = require('./src/NATSBridge');
// Configuration
const SUBJECT = "/NATSBridge_text_test";
const NATS_URL = "nats.yiem.cc";
const FILESERVER_URL = "http://192.168.88.104:8080";
// Create correlation ID for tracing
const correlation_id = uuid4();
// Helper: Log with correlation ID
function log_trace(message) {
const timestamp = new Date().toISOString();
console.log(`[${timestamp}] [Correlation: ${correlation_id}] ${message}`);
}
// File upload handler for plik server
async function plik_upload_handler(fileserver_url, dataname, data, correlation_id) {
// Get upload ID
const url_getUploadID = `${fileserver_url}/upload`;
const headers = {
"Content-Type": "application/json"
};
const body = JSON.stringify({ OneShot: true });
let response = await fetch(url_getUploadID, {
method: "POST",
headers: headers,
body: body
});
if (!response.ok) {
throw new Error(`Failed to get upload ID: ${response.status} ${response.statusText}`);
}
const responseJson = await response.json();
const uploadid = responseJson.id;
const uploadtoken = responseJson.uploadToken;
// Upload file
const formData = new FormData();
const blob = new Blob([data], { type: "application/octet-stream" });
formData.append("file", blob, dataname);
response = await fetch(`${fileserver_url}/file/${uploadid}`, {
method: "POST",
headers: {
"X-UploadToken": uploadtoken
},
body: formData
});
if (!response.ok) {
throw new Error(`Failed to upload file: ${response.status} ${response.statusText}`);
}
const fileResponseJson = await response.json();
const fileid = fileResponseJson.id;
const url = `${fileserver_url}/file/${uploadid}/${fileid}/${encodeURIComponent(dataname)}`;
return {
status: response.status,
uploadid: uploadid,
fileid: fileid,
url: url
};
}
// Sender: Send text via smartsend
async function test_text_send() {
// Create a small text (will use direct transport)
const small_text = "Hello, this is a small text message. Testing direct transport via NATS.";
// Create a large text (will use link transport if > 1MB)
// Generate a larger text (~2MB to ensure link transport)
const large_text_lines = [];
for (let i = 0; i < 50000; i++) {
large_text_lines.push(`Line ${i}: This is a sample text line with some content to pad the size. `);
}
const large_text = large_text_lines.join("");
// Test data 1: small text
const data1 = { dataname: "small_text", data: small_text, type: "text" };
// Test data 2: large text
const data2 = { dataname: "large_text", data: large_text, type: "text" };
// Use smartsend with text type
// For small text: will use direct transport (Base64 encoded UTF-8)
// For large text: will use link transport (uploaded to fileserver)
const env = await smartsend(
SUBJECT,
[data1, data2],
{
natsUrl: NATS_URL,
fileserverUrl: FILESERVER_URL,
fileserverUploadHandler: plik_upload_handler,
sizeThreshold: 1_000_000,
correlationId: correlation_id,
msgPurpose: "chat",
senderName: "text_sender",
receiverName: "",
receiverId: "",
replyTo: "",
replyToMsgId: ""
}
);
log_trace(`Sent message with ${env.payloads.length} payloads`);
// Log transport type for each payload
for (let i = 0; i < env.payloads.length; i++) {
const payload = env.payloads[i];
log_trace(`Payload ${i + 1} ('${payload.dataname}'):`);
log_trace(` Transport: ${payload.transport}`);
log_trace(` Type: ${payload.type}`);
log_trace(` Size: ${payload.size} bytes`);
log_trace(` Encoding: ${payload.encoding}`);
if (payload.transport === "link") {
log_trace(` URL: ${payload.data}`);
}
}
}
// Run the test
console.log("Starting text transport test...");
console.log(`Correlation ID: ${correlation_id}`);
// Run sender
console.log("start smartsend for text");
test_text_send();
console.log("Test completed.");

View File

@@ -42,8 +42,8 @@ function test_dict_receive()
max_delay = 5000
)
# Result is a list of (dataname, data, data_type) tuples
for (dataname, data, data_type) in result
# Result is an envelope dictionary with payloads field containing list of (dataname, data, data_type) tuples
for (dataname, data, data_type) in result["payloads"]
if isa(data, JSON.Object{String, Any})
log_trace("Received Dictionary '$dataname' of type $data_type")

View File

@@ -92,12 +92,12 @@ function test_dict_send()
# Use smartsend with dictionary type
# For small Dictionary: will use direct transport (JSON encoded)
# For large Dictionary: will use link transport (uploaded to fileserver)
env = NATSBridge.smartsend(
env, env_json_str = NATSBridge.smartsend(
SUBJECT,
[data1, data2], # List of (dataname, data, type) tuples
nats_url = NATS_URL,
[data1, data2]; # List of (dataname, data, type) tuples
broker_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
fileserver_upload_handler = plik_upload_handler,
size_threshold = 1_000_000, # 1MB threshold
correlation_id = correlation_id,
msg_purpose = "chat",
@@ -105,7 +105,8 @@ function test_dict_send()
receiver_name = "",
receiver_id = "",
reply_to = "",
reply_to_msg_id = ""
reply_to_msg_id = "",
is_publish = true # Publish the message to NATS
)
log_trace("Sent message with $(length(env.payloads)) payloads")
@@ -114,7 +115,7 @@ function test_dict_send()
for (i, payload) in enumerate(env.payloads)
log_trace("Payload $i ('$payload.dataname'):")
log_trace(" Transport: $(payload.transport)")
log_trace(" Type: $(payload.type)")
log_trace(" Type: $(payload.payload_type)")
log_trace(" Size: $(payload.size) bytes")
log_trace(" Encoding: $(payload.encoding)")

View File

@@ -44,8 +44,8 @@ function test_large_binary_receive()
max_delay = 5000
)
# Result is a list of (dataname, data) tuples
for (dataname, data, data_type) in result
# Result is an envelope dictionary with payloads field containing list of (dataname, data, data_type) tuples
for (dataname, data, data_type) in result["payloads"]
# Check transport type from the envelope
# For link transport, data is the URL string
# For direct transport, data is the actual payload bytes

View File

@@ -79,12 +79,12 @@ function test_large_binary_send()
# Use smartsend with binary type - will automatically use link transport
# if file size exceeds the threshold (1MB by default)
# API: smartsend(subject, [(dataname, data, type), ...]; keywords...)
env = NATSBridge.smartsend(
env, env_json_str = NATSBridge.smartsend(
SUBJECT,
[data1, data2], # List of (dataname, data, type) tuples
nats_url = NATS_URL;
[data1, data2]; # List of (dataname, data, type) tuples
broker_url = NATS_URL;
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
fileserver_upload_handler = plik_upload_handler,
size_threshold = 1_000_000,
correlation_id = correlation_id,
msg_purpose = "chat",
@@ -92,11 +92,12 @@ function test_large_binary_send()
receiver_name = "",
receiver_id = "",
reply_to = "",
reply_to_msg_id = ""
reply_to_msg_id = "",
is_publish = true # Publish the message to NATS
)
log_trace("Sent message with transport: $(env.payloads[1].transport)")
log_trace("Envelope type: $(env.payloads[1].type)")
log_trace("Envelope type: $(env.payloads[1].payload_type)")
# Check if link transport was used
if env.payloads[1].transport == "link"

View File

@@ -45,10 +45,10 @@ function test_mix_receive()
max_delay = 5000
)
log_trace("Received $(length(result)) payloads")
log_trace("Received $(length(result["payloads"])) payloads")
# Result is a list of (dataname, data, data_type) tuples
for (dataname, data, data_type) in result
# 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
@@ -178,13 +178,13 @@ function test_mix_receive()
# Summary
println("\n=== Verification Summary ===")
text_count = count(x -> x[3] == "text", result)
dict_count = count(x -> x[3] == "dictionary", result)
table_count = count(x -> x[3] == "table", result)
image_count = count(x -> x[3] == "image", result)
audio_count = count(x -> x[3] == "audio", result)
video_count = count(x -> x[3] == "video", result)
binary_count = count(x -> x[3] == "binary", result)
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: $dict_count")
@@ -196,7 +196,7 @@ function test_mix_receive()
# Print transport type info for each payload if available
println("\n=== Payload Details ===")
for (dataname, data, data_type) in result
for (dataname, data, data_type) in result["payloads"]
if data_type in ["image", "audio", "video", "binary"]
log_trace("$dataname: $(length(data)) bytes (binary)")
elseif data_type == "table"

View File

@@ -186,12 +186,12 @@ function test_mix_send()
]
# Use smartsend with mixed content
env = NATSBridge.smartsend(
env, env_json_str = NATSBridge.smartsend(
SUBJECT,
payloads, # List of (dataname, data, type) tuples
nats_url = NATS_URL,
payloads; # List of (dataname, data, type) tuples
broker_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
fileserver_upload_handler = plik_upload_handler,
size_threshold = 1_000_000, # 1MB threshold
correlation_id = correlation_id,
msg_purpose = "chat",
@@ -199,7 +199,8 @@ function test_mix_send()
receiver_name = "",
receiver_id = "",
reply_to = "",
reply_to_msg_id = ""
reply_to_msg_id = "",
is_publish = true # Publish the message to NATS
)
log_trace("Sent message with $(length(env.payloads)) payloads")
@@ -208,7 +209,7 @@ function test_mix_send()
for (i, payload) in enumerate(env.payloads)
log_trace("Payload $i ('$payload.dataname'):")
log_trace(" Transport: $(payload.transport)")
log_trace(" Type: $(payload.type)")
log_trace(" Type: $(payload.payload_type)")
log_trace(" Size: $(payload.size) bytes")
log_trace(" Encoding: $(payload.encoding)")

View File

@@ -42,8 +42,8 @@ function test_table_receive()
max_delay = 5000
)
# Result is a list of (dataname, data, data_type) tuples
for (dataname, data, data_type) in result
# Result is an envelope dictionary with payloads field containing list of (dataname, data, data_type) tuples
for (dataname, data, data_type) in result["payloads"]
data = DataFrame(data)
if isa(data, DataFrame)
log_trace("Received DataFrame '$dataname' of type $data_type")

View File

@@ -90,12 +90,12 @@ function test_table_send()
# Use smartsend with table type
# For small DataFrame: will use direct transport (Base64 encoded Arrow IPC)
# For large DataFrame: will use link transport (uploaded to fileserver)
env = NATSBridge.smartsend(
env, env_json_str = NATSBridge.smartsend(
SUBJECT,
[data1, data2], # List of (dataname, data, type) tuples
nats_url = NATS_URL,
[data1, data2]; # List of (dataname, data, type) tuples
broker_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
fileserver_upload_handler = plik_upload_handler,
size_threshold = 1_000_000, # 1MB threshold
correlation_id = correlation_id,
msg_purpose = "chat",
@@ -103,7 +103,8 @@ function test_table_send()
receiver_name = "",
receiver_id = "",
reply_to = "",
reply_to_msg_id = ""
reply_to_msg_id = "",
is_publish = true # Publish the message to NATS
)
log_trace("Sent message with $(length(env.payloads)) payloads")
@@ -112,7 +113,7 @@ function test_table_send()
for (i, payload) in enumerate(env.payloads)
log_trace("Payload $i ('$payload.dataname'):")
log_trace(" Transport: $(payload.transport)")
log_trace(" Type: $(payload.type)")
log_trace(" Type: $(payload.payload_type)")
log_trace(" Size: $(payload.size) bytes")
log_trace(" Encoding: $(payload.encoding)")

View File

@@ -42,8 +42,8 @@ function test_text_receive()
max_delay = 5000
)
# Result is a list of (dataname, data, data_type) tuples
for (dataname, data, data_type) in result
# Result is an envelope dictionary with payloads field containing list of (dataname, data, data_type) tuples
for (dataname, data, data_type) in result["payloads"]
if isa(data, String)
log_trace("Received text '$dataname' of type $data_type")
log_trace(" Length: $(length(data)) characters")

View File

@@ -75,12 +75,12 @@ function test_text_send()
# Use smartsend with text type
# For small text: will use direct transport (Base64 encoded UTF-8)
# For large text: will use link transport (uploaded to fileserver)
env = NATSBridge.smartsend(
env, env_json_str = NATSBridge.smartsend(
SUBJECT,
[data1, data2], # List of (dataname, data, type) tuples
nats_url = NATS_URL,
[data1, data2]; # List of (dataname, data, type) tuples
broker_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
fileserver_upload_handler = plik_upload_handler,
size_threshold = 1_000_000, # 1MB threshold
correlation_id = correlation_id,
msg_purpose = "chat",
@@ -88,7 +88,8 @@ function test_text_send()
receiver_name = "",
receiver_id = "",
reply_to = "",
reply_to_msg_id = ""
reply_to_msg_id = "",
is_publish = true # Publish the message to NATS
)
log_trace("Sent message with $(length(env.payloads)) payloads")
@@ -97,7 +98,7 @@ function test_text_send()
for (i, payload) in enumerate(env.payloads)
log_trace("Payload $i ('$payload.dataname'):")
log_trace(" Transport: $(payload.transport)")
log_trace(" Type: $(payload.type)")
log_trace(" Type: $(payload.payload_type)")
log_trace(" Size: $(payload.size) bytes")
log_trace(" Encoding: $(payload.encoding)")

View File

@@ -1,220 +0,0 @@
"""
Micropython NATS Bridge - Basic Test Examples
This module demonstrates basic usage of the NATSBridge for Micropython.
"""
import sys
sys.path.insert(0, "../src")
from nats_bridge import MessageEnvelope, MessagePayload, smartsend, smartreceive, log_trace
import json
# ============================================= 100 ============================================== #
def test_text_message():
"""Test sending and receiving text messages."""
print("\n=== Test 1: Text Message ===")
# Send text message
data = [
("message", "Hello World", "text"),
("greeting", "Good morning!", "text")
]
env = smartsend(
"/test/text",
data,
nats_url="nats://localhost:4222",
size_threshold=1000000
)
print("Sent envelope:")
print(" Subject: {}".format(env.send_to))
print(" Correlation ID: {}".format(env.correlation_id))
print(" Payloads: {}".format(len(env.payloads)))
# Expected output on receiver:
# payloads = smartreceive(msg)
# for dataname, data, type in payloads:
# print("Received {}: {}".format(dataname, data))
def test_dictionary_message():
"""Test sending and receiving dictionary messages."""
print("\n=== Test 2: Dictionary Message ===")
# Send dictionary message
config = {
"step_size": 0.01,
"iterations": 1000,
"threshold": 0.5
}
data = [
("config", config, "dictionary")
]
env = smartsend(
"/test/dictionary",
data,
nats_url="nats://localhost:4222",
size_threshold=1000000
)
print("Sent envelope:")
print(" Subject: {}".format(env.send_to))
print(" Payloads: {}".format(len(env.payloads)))
# Expected output on receiver:
# payloads = smartreceive(msg)
# for dataname, data, type in payloads:
# if type == "dictionary":
# print("Config: {}".format(data))
def test_mixed_payloads():
"""Test sending mixed payload types in a single message."""
print("\n=== Test 3: Mixed Payloads ===")
# Mixed content: text, dictionary, and binary
image_data = b"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR" # PNG header (example)
data = [
("message_text", "Hello!", "text"),
("user_config", {"theme": "dark", "volume": 80}, "dictionary"),
("user_image", image_data, "binary")
]
env = smartsend(
"/test/mixed",
data,
nats_url="nats://localhost:4222",
size_threshold=1000000
)
print("Sent envelope:")
print(" Subject: {}".format(env.send_to))
print(" Payloads: {}".format(len(env.payloads)))
# Expected output on receiver:
# payloads = smartreceive(msg)
# for dataname, data, type in payloads:
# print("Received {}: {} (type: {})".format(dataname, data if type != "binary" else len(data), type))
def test_large_payload():
"""Test sending large payloads that require fileserver upload."""
print("\n=== Test 4: Large Payload (Link Transport) ===")
# Create large data (> 1MB would trigger link transport)
# For testing, we'll use a smaller size but configure threshold lower
large_data = b"A" * 100000 # 100KB
data = [
("large_data", large_data, "binary")
]
# Use a lower threshold for testing
env = smartsend(
"/test/large",
data,
nats_url="nats://localhost:4222",
fileserver_url="http://localhost:8080",
size_threshold=50000 # 50KB threshold for testing
)
print("Sent envelope:")
print(" Subject: {}".format(env.send_to))
print(" Payloads: {}".format(len(env.payloads)))
for p in env.payloads:
print(" - Transport: {}, Type: {}".format(p.transport, p.type))
def test_reply_to():
"""Test sending messages with reply-to functionality."""
print("\n=== Test 5: Reply To ===")
data = [
("command", {"action": "start"}, "dictionary")
]
env = smartsend(
"/test/command",
data,
nats_url="nats://localhost:4222",
reply_to="/test/response",
reply_to_msg_id="reply-123",
msg_purpose="command"
)
print("Sent envelope:")
print(" Subject: {}".format(env.send_to))
print(" Reply To: {}".format(env.reply_to))
print(" Reply To Msg ID: {}".format(env.reply_to_msg_id))
def test_correlation_id():
"""Test using custom correlation IDs for tracing."""
print("\n=== Test 6: Custom Correlation ID ===")
custom_cid = "trace-abc123"
data = [
("message", "Test with correlation ID", "text")
]
env = smartsend(
"/test/correlation",
data,
nats_url="nats://localhost:4222",
correlation_id=custom_cid
)
print("Sent envelope with correlation ID: {}".format(env.correlation_id))
print("This ID can be used to trace the message flow.")
def test_multiple_payloads():
"""Test sending multiple payloads in one message."""
print("\n=== Test 7: Multiple Payloads ===")
data = [
("text_message", "Hello", "text"),
("json_data", {"key": "value", "number": 42}, "dictionary"),
("table_data", b"\x01\x02\x03\x04", "binary"),
("audio_data", b"\x00\x01\x02\x03", "binary")
]
env = smartsend(
"/test/multiple",
data,
nats_url="nats://localhost:4222",
size_threshold=1000000
)
print("Sent {} payloads in one message".format(len(env.payloads)))
if __name__ == "__main__":
print("Micropython NATS Bridge Test Suite")
print("==================================")
print()
# Run tests
test_text_message()
test_dictionary_message()
test_mixed_payloads()
test_large_payload()
test_reply_to()
test_correlation_id()
test_multiple_payloads()
print("\n=== All tests completed ===")
print()
print("Note: These tests require:")
print(" 1. A running NATS server at nats://localhost:4222")
print(" 2. An HTTP file server at http://localhost:8080 (for large payloads)")
print()
print("To run the tests:")
print(" python test_micropython_basic.py")

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@@ -1,634 +0,0 @@
# NATSBridge.jl Tutorial
A comprehensive tutorial for learning how to use NATSBridge.jl for bi-directional communication between Julia and JavaScript services using NATS.
## Table of Contents
1. [What is NATSBridge.jl?](#what-is-natsbridgejl)
2. [Key Concepts](#key-concepts)
3. [Installation](#installation)
4. [Basic Usage](#basic-usage)
5. [Payload Types](#payload-types)
6. [Transport Strategies](#transport-strategies)
7. [Advanced Features](#advanced-features)
8. [Complete Examples](#complete-examples)
---
## What is NATSBridge.jl?
NATSBridge.jl is a Julia module that provides a high-level API for sending and receiving data across network boundaries using NATS as the message bus. It implements the **Claim-Check pattern** for handling large payloads efficiently.
### Core Features
- **Bi-directional communication**: Julia ↔ JavaScript
- **Smart transport selection**: Automatic direct vs link transport based on payload size
- **Multi-payload support**: Send multiple payloads of different types in a single message
- **Claim-check pattern**: Upload large files to HTTP server, send only URLs via NATS
- **Type-aware serialization**: Different serialization strategies for different data types
---
## Key Concepts
### 1. msgEnvelope_v1 (Message Envelope)
The `msgEnvelope_v1` structure provides a comprehensive message format for bidirectional communication:
```julia
struct msgEnvelope_v1
correlationId::String # Unique identifier to track messages
msgId::String # This message id
timestamp::String # Message published timestamp
sendTo::String # Topic/subject the sender sends to
msgPurpose::String # Purpose (ACK | NACK | updateStatus | shutdown | chat)
senderName::String # Sender name (e.g., "agent-wine-web-frontend")
senderId::String # Sender id (uuid4)
receiverName::String # Message receiver name (e.g., "agent-backend")
receiverId::String # Message receiver id (uuid4 or nothing for broadcast)
replyTo::String # Topic to reply to
replyToMsgId::String # Message id this message is replying to
brokerURL::String # NATS server address
metadata::Dict{String, Any}
payloads::AbstractArray{msgPayload_v1} # Multiple payloads stored here
end
```
### 2. msgPayload_v1 (Payload Structure)
The `msgPayload_v1` structure provides flexible payload handling:
```julia
struct msgPayload_v1
id::String # Id of this payload (e.g., "uuid4")
dataname::String # Name of this payload (e.g., "login_image")
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
```
### 3. Standard API Format
The system uses a **standardized list-of-tuples format** for all payload operations:
```julia
# Input format for smartsend (always a list of tuples with type info)
[(dataname1, data1, type1), (dataname2, data2, type2), ...]
# Output format for smartreceive (always returns a list of tuples)
[(dataname1, data1, type1), (dataname2, data2, type2), ...]
```
**Important**: Even when sending a single payload, you must wrap it in a list.
---
## Installation
```julia
using Pkg
Pkg.add("NATS")
Pkg.add("JSON")
Pkg.add("Arrow")
Pkg.add("HTTP")
Pkg.add("UUIDs")
Pkg.add("Dates")
Pkg.add("Base64")
Pkg.add("PrettyPrinting")
Pkg.add("DataFrames")
```
Then include the NATSBridge module:
```julia
include("NATSBridge.jl")
using .NATSBridge
```
---
## Basic Usage
### Sending Data (smartsend)
```julia
using NATSBridge
# Send a simple dictionary
data = Dict("key" => "value")
env = NATSBridge.smartsend("my.subject", [("dataname1", data, "dictionary")])
```
### Receiving Data (smartreceive)
```julia
using NATSBridge
# Subscribe to a NATS subject
NATS.subscribe("my.subject") do msg
# Process the message
result = NATSBridge.smartreceive(
msg,
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
# result is a list of (dataname, data, type) tuples
for (dataname, data, type) in result
println("Received $dataname of type $type")
println("Data: $data")
end
end
```
---
## Payload Types
NATSBridge.jl supports the following payload types:
| Type | Description | Serialization |
|------|-------------|---------------|
| `text` | Plain text | UTF-8 encoding |
| `dictionary` | JSON-serializable data (Dict, NamedTuple) | JSON |
| `table` | Tabular data (DataFrame, array of structs) | Apache Arrow IPC |
| `image` | Image data (Bitmap, PNG/JPG bytes) | Binary |
| `audio` | Audio data (WAV, MP3 bytes) | Binary |
| `video` | Video data (MP4, AVI bytes) | Binary |
| `binary` | Generic binary data | Binary |
---
## Transport Strategies
NATSBridge.jl automatically selects the appropriate transport strategy based on payload size:
### Direct Transport (< 1MB)
Small payloads are encoded as Base64 and sent directly over NATS.
```julia
# Small data (< 1MB) - uses direct transport
small_data = rand(1000) # ~8KB
env = NATSBridge.smartsend("small", [("data", small_data, "table")])
```
### Link Transport (≥ 1MB)
Large payloads are uploaded to an HTTP file server, and only the URL is sent via NATS.
```julia
# Large data (≥ 1MB) - uses link transport
large_data = rand(10_000_000) # ~80MB
env = NATSBridge.smartsend("large", [("data", large_data, "table")])
```
---
## Complete Examples
### Example 1: Text Message
**Sender:**
```julia
using NATSBridge
using UUIDs
const SUBJECT = "/NATSBridge_text_test"
const NATS_URL = "nats://localhost:4222"
const FILESERVER_URL = "http://localhost:8080"
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
url_getUploadID = "$fileserver_url/upload"
headers = ["Content-Type" => "application/json"]
body = """{ "OneShot" : true }"""
httpResponse = HTTP.request("POST", url_getUploadID, headers, body; body_is_form=false)
responseJson = JSON.parse(String(httpResponse.body))
uploadid = responseJson["id"]
uploadtoken = responseJson["uploadToken"]
file_multipart = HTTP.Multipart(dataname, IOBuffer(data), "application/octet-stream")
url_upload = "$fileserver_url/file/$uploadid"
headers = ["X-UploadToken" => uploadtoken]
form = HTTP.Form(Dict("file" => file_multipart))
httpResponse = HTTP.post(url_upload, headers, form)
responseJson = JSON.parse(String(httpResponse.body))
fileid = responseJson["id"]
url = "$fileserver_url/file/$uploadid/$fileid/$dataname"
return Dict("status" => httpResponse.status, "uploadid" => uploadid, "fileid" => fileid, "url" => url)
end
function test_text_send()
small_text = "Hello, this is a small text message."
large_text = join(["Line $i: " for i in 1:50000], "")
data1 = ("small_text", small_text, "text")
data2 = ("large_text", large_text, "text")
env = NATSBridge.smartsend(
SUBJECT,
[data1, data2],
nats_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
size_threshold = 1_000_000,
correlation_id = string(uuid4()),
msg_purpose = "chat",
sender_name = "text_sender"
)
end
```
**Receiver:**
```julia
using NATSBridge
const SUBJECT = "/NATSBridge_text_test"
const NATS_URL = "nats://localhost:4222"
function test_text_receive()
conn = NATS.connect(NATS_URL)
NATS.subscribe(conn, SUBJECT) do msg
result = NATSBridge.smartreceive(
msg,
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
for (dataname, data, data_type) in result
if data_type == "text"
println("Received text: $data")
write("./received_$dataname.txt", data)
end
end
end
sleep(120)
NATS.drain(conn)
end
```
### Example 2: Dictionary (JSON) Message
**Sender:**
```julia
using NATSBridge
using UUIDs
const SUBJECT = "/NATSBridge_dict_test"
const NATS_URL = "nats://localhost:4222"
const FILESERVER_URL = "http://localhost:8080"
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
url_getUploadID = "$fileserver_url/upload"
headers = ["Content-Type" => "application/json"]
body = """{ "OneShot" : true }"""
httpResponse = HTTP.request("POST", url_getUploadID, headers, body; body_is_form=false)
responseJson = JSON.parse(String(httpResponse.body))
uploadid = responseJson["id"]
uploadtoken = responseJson["uploadToken"]
file_multipart = HTTP.Multipart(dataname, IOBuffer(data), "application/octet-stream")
url_upload = "$fileserver_url/file/$uploadid"
headers = ["X-UploadToken" => uploadtoken]
form = HTTP.Form(Dict("file" => file_multipart))
httpResponse = HTTP.post(url_upload, headers, form)
responseJson = JSON.parse(String(httpResponse.body))
fileid = responseJson["id"]
url = "$fileserver_url/file/$uploadid/$fileid/$dataname"
return Dict("status" => httpResponse.status, "uploadid" => uploadid, "fileid" => fileid, "url" => url)
end
function test_dict_send()
small_dict = Dict("name" => "Alice", "age" => 30)
large_dict = Dict("ids" => collect(1:50000), "names" => ["User_$i" for i in 1:50000])
data1 = ("small_dict", small_dict, "dictionary")
data2 = ("large_dict", large_dict, "dictionary")
env = NATSBridge.smartsend(
SUBJECT,
[data1, data2],
nats_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
size_threshold = 1_000_000,
correlation_id = string(uuid4()),
msg_purpose = "chat"
)
end
```
**Receiver:**
```julia
using NATSBridge
const SUBJECT = "/NATSBridge_dict_test"
const NATS_URL = "nats://localhost:4222"
function test_dict_receive()
conn = NATS.connect(NATS_URL)
NATS.subscribe(conn, SUBJECT) do msg
result = NATSBridge.smartreceive(
msg,
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
for (dataname, data, data_type) in result
if data_type == "dictionary"
println("Received dictionary: $data")
write("./received_$dataname.json", JSON.json(data, 2))
end
end
end
sleep(120)
NATS.drain(conn)
end
```
### Example 3: DataFrame (Table) Message
**Sender:**
```julia
using NATSBridge
using DataFrames
using UUIDs
const SUBJECT = "/NATSBridge_table_test"
const NATS_URL = "nats://localhost:4222"
const FILESERVER_URL = "http://localhost:8080"
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
url_getUploadID = "$fileserver_url/upload"
headers = ["Content-Type" => "application/json"]
body = """{ "OneShot" : true }"""
httpResponse = HTTP.request("POST", url_getUploadID, headers, body; body_is_form=false)
responseJson = JSON.parse(String(httpResponse.body))
uploadid = responseJson["id"]
uploadtoken = responseJson["uploadToken"]
file_multipart = HTTP.Multipart(dataname, IOBuffer(data), "application/octet-stream")
url_upload = "$fileserver_url/file/$uploadid"
headers = ["X-UploadToken" => uploadtoken]
form = HTTP.Form(Dict("file" => file_multipart))
httpResponse = HTTP.post(url_upload, headers, form)
responseJson = JSON.parse(String(httpResponse.body))
fileid = responseJson["id"]
url = "$fileserver_url/file/$uploadid/$fileid/$dataname"
return Dict("status" => httpResponse.status, "uploadid" => uploadid, "fileid" => fileid, "url" => url)
end
function test_table_send()
small_df = DataFrame(id = 1:10, name = ["Alice", "Bob", "Charlie"], score = [95, 88, 92])
large_df = DataFrame(id = 1:50000, name = ["User_$i" for i in 1:50000], score = rand(1:100, 50000))
data1 = ("small_table", small_df, "table")
data2 = ("large_table", large_df, "table")
env = NATSBridge.smartsend(
SUBJECT,
[data1, data2],
nats_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
size_threshold = 1_000_000,
correlation_id = string(uuid4()),
msg_purpose = "chat"
)
end
```
**Receiver:**
```julia
using NATSBridge
using DataFrames
const SUBJECT = "/NATSBridge_table_test"
const NATS_URL = "nats://localhost:4222"
function test_table_receive()
conn = NATS.connect(NATS_URL)
NATS.subscribe(conn, SUBJECT) do msg
result = NATSBridge.smartreceive(
msg,
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
for (dataname, data, data_type) in result
if data_type == "table"
data = DataFrame(data)
println("Received DataFrame with $(size(data, 1)) rows")
display(data[1:min(5, size(data, 1)), :])
end
end
end
sleep(120)
NATS.drain(conn)
end
```
### Example 4: Mixed Content (Chat with Text, Image, Audio)
**Sender:**
```julia
using NATSBridge
using DataFrames
using UUIDs
const SUBJECT = "/NATSBridge_mix_test"
const NATS_URL = "nats://localhost:4222"
const FILESERVER_URL = "http://localhost:8080"
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
url_getUploadID = "$fileserver_url/upload"
headers = ["Content-Type" => "application/json"]
body = """{ "OneShot" : true }"""
httpResponse = HTTP.request("POST", url_getUploadID, headers, body; body_is_form=false)
responseJson = JSON.parse(String(httpResponse.body))
uploadid = responseJson["id"]
uploadtoken = responseJson["uploadToken"]
file_multipart = HTTP.Multipart(dataname, IOBuffer(data), "application/octet-stream")
url_upload = "$fileserver_url/file/$uploadid"
headers = ["X-UploadToken" => uploadtoken]
form = HTTP.Form(Dict("file" => file_multipart))
httpResponse = HTTP.post(url_upload, headers, form)
responseJson = JSON.parse(String(httpResponse.body))
fileid = responseJson["id"]
url = "$fileserver_url/file/$uploadid/$fileid/$dataname"
return Dict("status" => httpResponse.status, "uploadid" => uploadid, "fileid" => fileid, "url" => url)
end
function test_mix_send()
# Text data
text_data = "Hello! This is a test chat message. 🎉"
# Dictionary data
dict_data = Dict("type" => "chat", "sender" => "serviceA")
# Small table data
table_data_small = DataFrame(id = 1:10, name = ["msg_$i" for i in 1:10])
# Large table data (link transport)
table_data_large = DataFrame(id = 1:150_000, name = ["msg_$i" for i in 1:150_000])
# Small image data (direct transport)
image_data = UInt8[rand(1:255) for _ in 1:100]
# Large image data (link transport)
large_image_data = UInt8[rand(1:255) for _ in 1:1_500_000]
# Small audio data (direct transport)
audio_data = UInt8[rand(1:255) for _ in 1:100]
# Large audio data (link transport)
large_audio_data = UInt8[rand(1:255) for _ in 1:1_500_000]
# Small video data (direct transport)
video_data = UInt8[rand(1:255) for _ in 1:150]
# Large video data (link transport)
large_video_data = UInt8[rand(1:255) for _ in 1:1_500_000]
# Small binary data (direct transport)
binary_data = UInt8[rand(1:255) for _ in 1:200]
# Large binary data (link transport)
large_binary_data = UInt8[rand(1:255) for _ in 1:1_500_000]
# Create payloads list - mixed content
payloads = [
# Small data (direct transport)
("chat_text", text_data, "text"),
("chat_json", dict_data, "dictionary"),
("chat_table_small", table_data_small, "table"),
# Large data (link transport)
("chat_table_large", table_data_large, "table"),
("user_image_large", large_image_data, "image"),
("audio_clip_large", large_audio_data, "audio"),
("video_clip_large", large_video_data, "video"),
("binary_file_large", large_binary_data, "binary")
]
env = NATSBridge.smartsend(
SUBJECT,
payloads,
nats_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
size_threshold = 1_000_000,
correlation_id = string(uuid4()),
msg_purpose = "chat",
sender_name = "mix_sender"
)
end
```
**Receiver:**
```julia
using NATSBridge
using DataFrames
const SUBJECT = "/NATSBridge_mix_test"
const NATS_URL = "nats://localhost:4222"
function test_mix_receive()
conn = NATS.connect(NATS_URL)
NATS.subscribe(conn, SUBJECT) do msg
result = NATSBridge.smartreceive(
msg,
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
println("Received $(length(result)) payloads")
for (dataname, data, data_type) in result
println("\n=== Payload: $dataname (type: $data_type) ===")
if data_type == "text"
println(" Type: String")
println(" Length: $(length(data)) characters")
elseif data_type == "dictionary"
println(" Type: JSON Object")
println(" Keys: $(keys(data))")
elseif data_type == "table"
data = DataFrame(data)
println(" Type: DataFrame")
println(" Dimensions: $(size(data, 1)) rows x $(size(data, 2)) columns")
elseif data_type == "image"
println(" Type: Vector{UInt8}")
println(" Size: $(length(data)) bytes")
write("./received_$dataname.bin", data)
elseif data_type == "audio"
println(" Type: Vector{UInt8}")
println(" Size: $(length(data)) bytes")
write("./received_$dataname.bin", data)
elseif data_type == "video"
println(" Type: Vector{UInt8}")
println(" Size: $(length(data)) bytes")
write("./received_$dataname.bin", data)
elseif data_type == "binary"
println(" Type: Vector{UInt8}")
println(" Size: $(length(data)) bytes")
write("./received_$dataname.bin", data)
end
end
end
sleep(120)
NATS.drain(conn)
end
```
---
## Best Practices
1. **Always wrap payloads in a list** - Even for single payloads: `[("dataname", data, "type")]`
2. **Use appropriate transport** - Let NATSBridge handle size-based routing (default 1MB threshold)
3. **Customize size threshold** - Use `size_threshold` parameter to adjust the direct/link split
4. **Provide fileserver handler** - Implement `fileserverUploadHandler` for link transport
5. **Include correlation IDs** - Track messages across distributed systems
6. **Handle errors** - Implement proper error handling for network failures
7. **Close connections** - Ensure NATS connections are properly closed using `NATS.drain()`
---
## Conclusion
NATSBridge.jl provides a powerful abstraction for bi-directional communication between Julia and JavaScript services. By understanding the key concepts and following the best practices, you can build robust, scalable applications that leverage the full power of NATS messaging.
For more information, see:
- [`docs/architecture.md`](./architecture.md) - Detailed architecture documentation
- [`docs/implementation.md`](./implementation.md) - Implementation details

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@@ -1,939 +0,0 @@
# NATSBridge.jl Walkthrough: Building a Chat System
A step-by-step guided walkthrough for building a real-time chat system using NATSBridge.jl with mixed content support (text, images, audio, video, and files).
## Prerequisites
- Julia 1.7+
- NATS server running
- HTTP file server (Plik) running
## Step 1: Understanding the Chat System Architecture
### System Components
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ Chat System │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ NATS ┌──────────────┐ │
│ │ Julia │◄───────┬───────► │ JavaScript │ │
│ │ Service │ │ │ Client │ │
│ │ │ │ │ │ │
│ │ - Text │ │ │ - Text │ │
│ │ - Images │ │ │ - Images │ │
│ │ - Audio │ ▼ │ - Audio │ │
│ │ - Video │ NATSBridge.jl │ - Files │ │
│ │ - Files │ │ │ - Tables │ │
│ └──────────────┘ │ └──────────────┘ │
│ │ │
│ ┌───────┴───────┐ │
│ │ NATS │ │
│ │ Server │ │
│ └─────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
For large payloads (> 1MB):
┌─────────────────────────────────────────────────────────────────────────────┐
│ File Server (Plik) │
│ │
│ Julia Service ──► Upload ──► File Server ──► Download ◄── JavaScript Client│
│ │
└─────────────────────────────────────────────────────────────────────────────┘
```
### Message Format
Each chat message is an envelope containing multiple payloads:
```json
{
"correlationId": "uuid4",
"msgId": "uuid4",
"timestamp": "2024-01-15T10:30:00Z",
"sendTo": "/chat/room1",
"msgPurpose": "chat",
"senderName": "user-1",
"senderId": "uuid4",
"receiverName": "user-2",
"receiverId": "uuid4",
"brokerURL": "nats://localhost:4222",
"payloads": [
{
"id": "uuid4",
"dataname": "message_text",
"type": "text",
"transport": "direct",
"encoding": "base64",
"size": 256,
"data": "SGVsbG8gV29ybGQh",
"metadata": {}
},
{
"id": "uuid4",
"dataname": "user_image",
"type": "image",
"transport": "link",
"encoding": "none",
"size": 15433,
"data": "http://localhost:8080/file/UPLOAD_ID/FILE_ID/image.jpg",
"metadata": {}
}
]
}
```
## Step 2: Setting Up the Environment
### 1. Start NATS Server
```bash
# Using Docker
docker run -d -p 4222:4222 -p 8222:8222 --name nats-server nats:latest
# Or download from https://github.com/nats-io/nats-server/releases
./nats-server
```
### 2. Start HTTP File Server (Plik)
```bash
# Using Docker
docker run -d -p 8080:8080 --name plik plik/plik:latest
# Or download from https://github.com/arnaud-lb/plik/releases
./plikd -d
```
### 3. Install Julia Dependencies
```julia
using Pkg
Pkg.add("NATS")
Pkg.add("JSON")
Pkg.add("Arrow")
Pkg.add("HTTP")
Pkg.add("UUIDs")
Pkg.add("Dates")
Pkg.add("Base64")
Pkg.add("PrettyPrinting")
Pkg.add("DataFrames")
```
## Step 3: Basic Text-Only Chat
### Sender (User 1)
```julia
using NATS
using JSON
using UUIDs
using Dates
using PrettyPrinting
using DataFrames
using Arrow
using HTTP
using Base64
# Include the bridge module
include("NATSBridge.jl")
using .NATSBridge
# Configuration
const NATS_URL = "nats://localhost:4222"
const FILESERVER_URL = "http://localhost:8080"
const SUBJECT = "/chat/room1"
# File upload handler for plik server
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
url_getUploadID = "$fileserver_url/upload"
headers = ["Content-Type" => "application/json"]
body = """{ "OneShot" : true }"""
httpResponse = HTTP.request("POST", url_getUploadID, headers, body; body_is_form=false)
responseJson = JSON.parse(String(httpResponse.body))
uploadid = responseJson["id"]
uploadtoken = responseJson["uploadToken"]
file_multipart = HTTP.Multipart(dataname, IOBuffer(data), "application/octet-stream")
url_upload = "$fileserver_url/file/$uploadid"
headers = ["X-UploadToken" => uploadtoken]
form = HTTP.Form(Dict("file" => file_multipart))
httpResponse = HTTP.post(url_upload, headers, form)
responseJson = JSON.parse(String(httpResponse.body))
fileid = responseJson["id"]
url = "$fileserver_url/file/$uploadid/$fileid/$dataname"
return Dict("status" => httpResponse.status, "uploadid" => uploadid, "fileid" => fileid, "url" => url)
end
# Send a simple text message
function send_text_message()
message_text = "Hello, how are you today?"
env = NATSBridge.smartsend(
SUBJECT,
[("message", message_text, "text")],
nats_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
size_threshold = 1_000_000,
correlation_id = string(uuid4()),
msg_purpose = "chat",
sender_name = "user-1"
)
println("Sent text message with correlation ID: $(env.correlationId)")
end
send_text_message()
```
### Receiver (User 2)
```julia
using NATS
using JSON
using UUIDs
using Dates
using PrettyPrinting
using DataFrames
using Arrow
using HTTP
using Base64
# Include the bridge module
include("NATSBridge.jl")
using .NATSBridge
# Configuration
const NATS_URL = "nats://localhost:4222"
const SUBJECT = "/chat/room1"
# Message handler
function message_handler(msg::NATS.Msg)
payloads = NATSBridge.smartreceive(
msg,
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
# Extract the text message
for (dataname, data, data_type) in payloads
if data_type == "text"
println("Received message: $data")
# Save to file
write("./received_$dataname.txt", data)
end
end
end
# Subscribe to the chat room
NATS.subscribe(SUBJECT) do msg
message_handler(msg)
end
# Keep the program running
while true
sleep(1)
end
```
## Step 4: Adding Image Support
### Sending an Image
```julia
using NATS
using JSON
using UUIDs
using Dates
using PrettyPrinting
using DataFrames
using Arrow
using HTTP
using Base64
include("NATSBridge.jl")
using .NATSBridge
const NATS_URL = "nats://localhost:4222"
const FILESERVER_URL = "http://localhost:8080"
const SUBJECT = "/chat/room1"
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
url_getUploadID = "$fileserver_url/upload"
headers = ["Content-Type" => "application/json"]
body = """{ "OneShot" : true }"""
httpResponse = HTTP.request("POST", url_getUploadID, headers, body; body_is_form=false)
responseJson = JSON.parse(String(httpResponse.body))
uploadid = responseJson["id"]
uploadtoken = responseJson["uploadToken"]
file_multipart = HTTP.Multipart(dataname, IOBuffer(data), "application/octet-stream")
url_upload = "$fileserver_url/file/$uploadid"
headers = ["X-UploadToken" => uploadtoken]
form = HTTP.Form(Dict("file" => file_multipart))
httpResponse = HTTP.post(url_upload, headers, form)
responseJson = JSON.parse(String(httpResponse.body))
fileid = responseJson["id"]
url = "$fileserver_url/file/$uploadid/$fileid/$dataname"
return Dict("status" => httpResponse.status, "uploadid" => uploadid, "fileid" => fileid, "url" => url)
end
function send_image()
# Read image file
image_data = read("screenshot.png", Vector{UInt8})
# Send with text message
env = NATSBridge.smartsend(
SUBJECT,
[
("text", "Check out this screenshot!", "text"),
("screenshot", image_data, "image")
],
nats_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
size_threshold = 1_000_000,
correlation_id = string(uuid4()),
msg_purpose = "chat",
sender_name = "user-1"
)
println("Sent image with correlation ID: $(env.correlationId)")
end
send_image()
```
### Receiving an Image
```julia
using NATS
using JSON
using UUIDs
using Dates
using PrettyPrinting
using DataFrames
using Arrow
using HTTP
using Base64
include("NATSBridge.jl")
using .NATSBridge
const NATS_URL = "nats://localhost:4222"
const SUBJECT = "/chat/room1"
function message_handler(msg::NATS.Msg)
payloads = NATSBridge.smartreceive(
msg,
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
for (dataname, data, data_type) in payloads
if data_type == "text"
println("Text: $data")
elseif data_type == "image"
# Save image to file
filename = "received_$dataname.bin"
write(filename, data)
println("Saved image: $filename")
end
end
end
NATS.subscribe(SUBJECT) do msg
message_handler(msg)
end
```
## Step 5: Handling Large Files with Link Transport
### Automatic Transport Selection
```julia
using NATS
using JSON
using UUIDs
using Dates
using PrettyPrinting
using DataFrames
using Arrow
using HTTP
using Base64
include("NATSBridge.jl")
using .NATSBridge
const NATS_URL = "nats://localhost:4222"
const FILESERVER_URL = "http://localhost:8080"
const SUBJECT = "/chat/room1"
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
url_getUploadID = "$fileserver_url/upload"
headers = ["Content-Type" => "application/json"]
body = """{ "OneShot" : true }"""
httpResponse = HTTP.request("POST", url_getUploadID, headers, body; body_is_form=false)
responseJson = JSON.parse(String(httpResponse.body))
uploadid = responseJson["id"]
uploadtoken = responseJson["uploadToken"]
file_multipart = HTTP.Multipart(dataname, IOBuffer(data), "application/octet-stream")
url_upload = "$fileserver_url/file/$uploadid"
headers = ["X-UploadToken" => uploadtoken]
form = HTTP.Form(Dict("file" => file_multipart))
httpResponse = HTTP.post(url_upload, headers, form)
responseJson = JSON.parse(String(httpResponse.body))
fileid = responseJson["id"]
url = "$fileserver_url/file/$uploadid/$fileid/$dataname"
return Dict("status" => httpResponse.status, "uploadid" => uploadid, "fileid" => fileid, "url" => url)
end
function send_large_file()
# Create a large file (> 1MB triggers link transport)
large_data = rand(10_000_000) # ~80MB
env = NATSBridge.smartsend(
SUBJECT,
[("large_file", large_data, "binary")],
nats_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
size_threshold = 1_000_000,
correlation_id = string(uuid4()),
msg_purpose = "chat",
sender_name = "user-1"
)
println("Uploaded large file to: $(env.payloads[1].data)")
println("Correlation ID: $(env.correlationId)")
end
send_large_file()
```
## Step 6: Audio and Video Support
### Sending Audio
```julia
using NATS
using JSON
using UUIDs
using Dates
using PrettyPrinting
using DataFrames
using Arrow
using HTTP
using Base64
include("NATSBridge.jl")
using .NATSBridge
const NATS_URL = "nats://localhost:4222"
const FILESERVER_URL = "http://localhost:8080"
const SUBJECT = "/chat/room1"
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
url_getUploadID = "$fileserver_url/upload"
headers = ["Content-Type" => "application/json"]
body = """{ "OneShot" : true }"""
httpResponse = HTTP.request("POST", url_getUploadID, headers, body; body_is_form=false)
responseJson = JSON.parse(String(httpResponse.body))
uploadid = responseJson["id"]
uploadtoken = responseJson["uploadToken"]
file_multipart = HTTP.Multipart(dataname, IOBuffer(data), "application/octet-stream")
url_upload = "$fileserver_url/file/$uploadid"
headers = ["X-UploadToken" => uploadtoken]
form = HTTP.Form(Dict("file" => file_multipart))
httpResponse = HTTP.post(url_upload, headers, form)
responseJson = JSON.parse(String(httpResponse.body))
fileid = responseJson["id"]
url = "$fileserver_url/file/$uploadid/$fileid/$dataname"
return Dict("status" => httpResponse.status, "uploadid" => uploadid, "fileid" => fileid, "url" => url)
end
function send_audio()
# Read audio file (WAV, MP3, etc.)
audio_data = read("voice_message.mp3", Vector{UInt8})
env = NATSBridge.smartsend(
SUBJECT,
[("voice_message", audio_data, "audio")],
nats_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
size_threshold = 1_000_000,
correlation_id = string(uuid4()),
msg_purpose = "chat",
sender_name = "user-1"
)
println("Sent audio message: $(env.correlationId)")
end
send_audio()
```
### Sending Video
```julia
using NATS
using JSON
using UUIDs
using Dates
using PrettyPrinting
using DataFrames
using Arrow
using HTTP
using Base64
include("NATSBridge.jl")
using .NATSBridge
const NATS_URL = "nats://localhost:4222"
const FILESERVER_URL = "http://localhost:8080"
const SUBJECT = "/chat/room1"
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
url_getUploadID = "$fileserver_url/upload"
headers = ["Content-Type" => "application/json"]
body = """{ "OneShot" : true }"""
httpResponse = HTTP.request("POST", url_getUploadID, headers, body; body_is_form=false)
responseJson = JSON.parse(String(httpResponse.body))
uploadid = responseJson["id"]
uploadtoken = responseJson["uploadToken"]
file_multipart = HTTP.Multipart(dataname, IOBuffer(data), "application/octet-stream")
url_upload = "$fileserver_url/file/$uploadid"
headers = ["X-UploadToken" => uploadtoken]
form = HTTP.Form(Dict("file" => file_multipart))
httpResponse = HTTP.post(url_upload, headers, form)
responseJson = JSON.parse(String(httpResponse.body))
fileid = responseJson["id"]
url = "$fileserver_url/file/$uploadid/$fileid/$dataname"
return Dict("status" => httpResponse.status, "uploadid" => uploadid, "fileid" => fileid, "url" => url)
end
function send_video()
# Read video file (MP4, AVI, etc.)
video_data = read("video_message.mp4", Vector{UInt8})
env = NATSBridge.smartsend(
SUBJECT,
[("video_message", video_data, "video")],
nats_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
size_threshold = 1_000_000,
correlation_id = string(uuid4()),
msg_purpose = "chat",
sender_name = "user-1"
)
println("Sent video message: $(env.correlationId)")
end
send_video()
```
## Step 7: Table/Data Exchange
### Sending Tabular Data
```julia
using NATS
using JSON
using UUIDs
using Dates
using PrettyPrinting
using DataFrames
using Arrow
using HTTP
using Base64
include("NATSBridge.jl")
using .NATSBridge
const NATS_URL = "nats://localhost:4222"
const FILESERVER_URL = "http://localhost:8080"
const SUBJECT = "/chat/room1"
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
url_getUploadID = "$fileserver_url/upload"
headers = ["Content-Type" => "application/json"]
body = """{ "OneShot" : true }"""
httpResponse = HTTP.request("POST", url_getUploadID, headers, body; body_is_form=false)
responseJson = JSON.parse(String(httpResponse.body))
uploadid = responseJson["id"]
uploadtoken = responseJson["uploadToken"]
file_multipart = HTTP.Multipart(dataname, IOBuffer(data), "application/octet-stream")
url_upload = "$fileserver_url/file/$uploadid"
headers = ["X-UploadToken" => uploadtoken]
form = HTTP.Form(Dict("file" => file_multipart))
httpResponse = HTTP.post(url_upload, headers, form)
responseJson = JSON.parse(String(httpResponse.body))
fileid = responseJson["id"]
url = "$fileserver_url/file/$uploadid/$fileid/$dataname"
return Dict("status" => httpResponse.status, "uploadid" => uploadid, "fileid" => fileid, "url" => url)
end
function send_table()
# Create a DataFrame
df = DataFrame(
id = 1:5,
name = ["Alice", "Bob", "Charlie", "Diana", "Eve"],
score = [95, 88, 92, 98, 85],
grade = ['A', 'B', 'A', 'B', 'B']
)
env = NATSBridge.smartsend(
SUBJECT,
[("student_scores", df, "table")],
nats_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
size_threshold = 1_000_000,
correlation_id = string(uuid4()),
msg_purpose = "chat",
sender_name = "user-1"
)
println("Sent table with $(nrow(df)) rows")
end
send_table()
```
### Receiving and Using Tables
```julia
using NATS
using JSON
using UUIDs
using Dates
using PrettyPrinting
using DataFrames
using Arrow
using HTTP
using Base64
include("NATSBridge.jl")
using .NATSBridge
const NATS_URL = "nats://localhost:4222"
const SUBJECT = "/chat/room1"
function message_handler(msg::NATS.Msg)
payloads = NATSBridge.smartreceive(
msg,
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
for (dataname, data, data_type) in payloads
if data_type == "table"
data = DataFrame(data)
println("Received table:")
show(data)
println("\nAverage score: $(mean(data.score))")
end
end
end
NATS.subscribe(SUBJECT) do msg
message_handler(msg)
end
```
## Step 8: Bidirectional Communication
### Request-Response Pattern
```julia
using NATS
using JSON
using UUIDs
using Dates
using PrettyPrinting
using DataFrames
using Arrow
using HTTP
using Base64
include("NATSBridge.jl")
using .NATSBridge
const NATS_URL = "nats://localhost:4222"
const SUBJECT = "/api/query"
const REPLY_SUBJECT = "/api/response"
# Request
function send_request()
query_data = Dict("query" => "SELECT * FROM users")
env = NATSBridge.smartsend(
SUBJECT,
[("sql_query", query_data, "dictionary")],
nats_url = NATS_URL,
fileserver_url = "http://localhost:8080",
fileserverUploadHandler = plik_upload_handler,
size_threshold = 1_000_000,
correlation_id = string(uuid4()),
msg_purpose = "request",
sender_name = "frontend",
receiver_name = "backend",
reply_to = REPLY_SUBJECT,
reply_to_msg_id = string(uuid4())
)
println("Request sent: $(env.correlationId)")
end
# Response handler
function response_handler(msg::NATS.Msg)
payloads = NATSBridge.smartreceive(
msg,
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
for (dataname, data, data_type) in payloads
if data_type == "table"
data = DataFrame(data)
println("Query results:")
show(data)
end
end
end
NATS.subscribe(REPLY_SUBJECT) do msg
response_handler(msg)
end
```
## Step 9: Complete Chat Application
### Full Chat System
```julia
module ChatApp
using NATS
using JSON
using UUIDs
using Dates
using PrettyPrinting
using DataFrames
using Arrow
using HTTP
using Base64
# Include the bridge module
include("../src/NATSBridge.jl")
using .NATSBridge
# Configuration
const NATS_URL = "nats://localhost:4222"
const FILESERVER_URL = "http://localhost:8080"
const SUBJECT = "/chat/room1"
# File upload handler for plik server
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
url_getUploadID = "$fileserver_url/upload"
headers = ["Content-Type" => "application/json"]
body = """{ "OneShot" : true }"""
httpResponse = HTTP.request("POST", url_getUploadID, headers, body; body_is_form=false)
responseJson = JSON.parse(String(httpResponse.body))
uploadid = responseJson["id"]
uploadtoken = responseJson["uploadToken"]
file_multipart = HTTP.Multipart(dataname, IOBuffer(data), "application/octet-stream")
url_upload = "$fileserver_url/file/$uploadid"
headers = ["X-UploadToken" => uploadtoken]
form = HTTP.Form(Dict("file" => file_multipart))
httpResponse = HTTP.post(url_upload, headers, form)
responseJson = JSON.parse(String(httpResponse.body))
fileid = responseJson["id"]
url = "$fileserver_url/file/$uploadid/$fileid/$dataname"
return Dict("status" => httpResponse.status, "uploadid" => uploadid, "fileid" => fileid, "url" => url)
end
function send_chat_message(
text::String,
image_path::Union{String, Nothing}=nothing,
audio_path::Union{String, Nothing}=nothing
)
# Build payloads list
payloads = [("message_text", text, "text")]
if image_path !== nothing
image_data = read(image_path, Vector{UInt8})
push!(payloads, ("user_image", image_data, "image"))
end
if audio_path !== nothing
audio_data = read(audio_path, Vector{UInt8})
push!(payloads, ("user_audio", audio_data, "audio"))
end
env = NATSBridge.smartsend(
SUBJECT,
payloads,
nats_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserverUploadHandler = plik_upload_handler,
size_threshold = 1_000_000,
correlation_id = string(uuid4()),
msg_purpose = "chat",
sender_name = "user-1"
)
println("Message sent with correlation ID: $(env.correlationId)")
end
function receive_chat_messages()
function message_handler(msg::NATS.Msg)
payloads = NATSBridge.smartreceive(
msg,
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
println("\n--- New Message ---")
for (dataname, data, data_type) in payloads
if data_type == "text"
println("Text: $data")
elseif data_type == "image"
filename = "received_$dataname.bin"
write(filename, data)
println("Image saved: $filename")
elseif data_type == "audio"
filename = "received_$dataname.bin"
write(filename, data)
println("Audio saved: $filename")
elseif data_type == "table"
println("Table received:")
data = DataFrame(data)
show(data)
end
end
end
NATS.subscribe(SUBJECT) do msg
message_handler(msg)
end
println("Subscribed to: $SUBJECT")
end
function run_interactive_chat()
println("\n=== Interactive Chat ===")
println("1. Send a message")
println("2. Join a chat room")
println("3. Exit")
while true
print("\nSelect option (1-3): ")
choice = readline()
if choice == "1"
print("Enter message text: ")
text = readline()
send_chat_message(text)
elseif choice == "2"
receive_chat_messages()
elseif choice == "3"
break
end
end
end
end # module
# Run the chat app
using .ChatApp
ChatApp.run_interactive_chat()
```
## Step 10: Testing the Chat System
### Test Scenario 1: Text-Only Chat
```bash
# Terminal 1: Start the chat receiver
julia test_julia_to_julia_text_receiver.jl
# Terminal 2: Send a message
julia test_julia_to_julia_text_sender.jl
```
### Test Scenario 2: Image Chat
```bash
# Terminal 1: Receive messages
julia test_julia_to_julia_mix_payloads_receiver.jl
# Terminal 2: Send image
julia test_julia_to_julia_mix_payload_sender.jl
```
### Test Scenario 3: Large File Transfer
```bash
# Terminal 2: Send large file
julia test_julia_to_julia_mix_payload_sender.jl
```
## Conclusion
This walkthrough demonstrated how to build a chat system using NATSBridge.jl with support for:
- Text messages
- Images (direct transport for small, link transport for large)
- Audio files
- Video files
- Tabular data (DataFrames)
- Bidirectional communication
- Mixed-content messages
The key takeaways are:
1. **Always wrap payloads in a list** - Even for single payloads: `[("dataname", data, "type")]`
2. **Use appropriate transport** - NATSBridge automatically handles size-based routing
3. **Support mixed content** - Multiple payloads of different types in one message
4. **Handle errors** - Implement proper error handling for network failures
5. **Use correlation IDs** - Track messages across distributed systems
For more information, see:
- [`docs/architecture.md`](./docs/architecture.md) - Detailed architecture documentation
- [`docs/implementation.md`](./docs/implementation.md) - Implementation details