24 Commits

Author SHA1 Message Date
61f016f08c update docs 2026-03-09 18:16:33 +07:00
6cd0ea45d6 update 2026-03-09 18:11:01 +07:00
1322e4a0d3 update 2026-03-09 17:36:37 +07:00
db377ead3c update readme 2026-03-09 15:54:09 +07:00
3fcd27f41a reduce docs 2026-03-09 15:41:24 +07:00
c896af234d js to julia and vise versa works 2026-03-09 11:45:28 +07:00
d1fc0dba87 update 2026-03-09 11:20:00 +07:00
e697ab060c remove redundand test files 2026-03-09 10:53:12 +07:00
cf59b4c8fb update 2026-03-09 03:27:36 +07:00
feadfc3456 add more type in datatype summary 2026-03-09 02:59:32 +07:00
2c2f8f41a1 remove redundant encode 2026-03-09 02:35:22 +07:00
a2380282ff add julia test file 2026-03-09 02:29:14 +07:00
19773fddc9 add test images 2026-03-08 17:49:13 +07:00
6e2fccd04e remove column oriented json 2026-03-08 13:43:26 +07:00
3970b8e0a8 remove row to col function 2026-03-08 13:13:41 +07:00
89a72cf8a9 adding jsontable 2026-03-08 13:11:53 +07:00
0ef8dd61a8 use crypto for JS 2026-03-08 11:34:10 +07:00
dad098ea3b add jsontable and arrowtable spec 2026-03-08 11:19:53 +07:00
f534248bec update 2026-03-08 10:42:54 +07:00
05fa7f52dd update 2026-03-07 06:47:42 +07:00
96535147fb update 2026-03-07 06:20:41 +07:00
f0b088f6f8 update 2026-03-06 19:55:42 +07:00
1d177f5438 update 2026-03-06 14:07:33 +07:00
cefc56a6bb update 2026-03-06 12:23:14 +07:00
47 changed files with 1576 additions and 6894 deletions

3
.gitignore vendored Normal file
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@@ -0,0 +1,3 @@
node_modules/
package.json
package-lock.json

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@@ -18,12 +18,9 @@ Create a walkthrough for Julia service-A service sending a mix-content chat mess
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
@@ -39,11 +36,8 @@ All API should be semantically consistent and naming should be consistent across
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.
@@ -63,3 +57,47 @@ Now, help me do the following:
Help me expands this Julia package (NATSBridge) into a cross-platform project by adding a JavaScript and Python/MicroPython implementation. To ensure accuracy, NATSBridge.jl will serve as the ground truth, as the documentation may be outdated.
My goal is to maintain interface parity at the high-level API for a consistent user experience, while ensuring the low-level implementation adheres strictly to the idiomatic conventions of each respective language (e.g., multiple dispatch in Julia vs. asynchronous, prototype, or class-based patterns in JS and Python/MicroPython)
Now do the following:
1) check docs to see if there is any mistake.
I'm expanding this Julia package (NATSBridge) into a cross-platform project by adding
a JavaScript, Python and MicroPython implementation.
The following will serve as the ground truth:
- test_julia_mix_payloads_sender.jl
- NATSBridge.jl
- test_julia_mix_payloads_receiver.jl
- architecture.md
My goal is to maintain interface parity at the high-level API for a consistent user experience,
while ensuring the low-level implementation adheres strictly to the idiomatic conventions of each
respective language (e.g., multiple dispatch in Julia vs. asynchronous, prototype, or class-based
patterns in JS, Python and MicroPython)
Now, help me do the following:
1) Check whether natsbridge.js needs update or it already up to date.

375
README.md
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@@ -12,14 +12,12 @@ A high-performance, bi-directional data bridge for **Julia, JavaScript, Python,
- [Overview](#overview)
- [Cross-Platform Support](#cross-platform-support)
- [Features](#features)
- [Architecture](#architecture)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [API Reference](#api-reference)
- [Payload Types](#payload-types)
- [Transport Strategies](#transport-strategies)
- [Cross-Platform Examples](#cross-platform-examples)
- [Testing](#testing)
- [Documentation](#documentation)
- [License](#license)
---
@@ -58,7 +56,6 @@ NATSBridge enables seamless communication across multiple platforms through NATS
| Multiple Dispatch | ✅ Native | ❌ | ❌ | ❌ |
| Async/Await | ❌ | ✅ Native | ✅ Native | ⚠️ (uasyncio) |
| Type Safety | ✅ Strong | ⚠️ (TypeScript) | ✅ (Type hints) | ❌ |
| Memory Management | ✅ GC | ✅ GC | ✅ GC | ⚠️ (Manual) |
| Arrow IPC | ✅ Native | ✅ | ✅ | ❌ |
| Direct Transport | ✅ | ✅ | ✅ | ✅ |
| Link Transport | ✅ | ✅ | ✅ | ⚠️ (Limited) |
@@ -82,171 +79,6 @@ NATSBridge enables seamless communication across multiple platforms through NATS
---
## Architecture
### System Components
```mermaid
flowchart TB
subgraph Sender["Application (Sender)"]
SenderApp[App Code]
NATSBridge_Send[NATSBridge]
NATS_Client[<b>NATS.jl</b>]
end
subgraph Receiver["Application (Receiver)"]
ReceiverApp[App Code]
NATSBridge_Recv[NATSBridge]
NATS_Client_Recv[<b>NATS.jl</b>]
end
subgraph Infrastructure["Infrastructure"]
NATS[<b>NATS Server</b><br/>Message Broker]
FileServer[<b>HTTP File Server</b><br/>Upload/Download]
end
SenderApp --> NATSBridge_Send
NATSBridge_Send --> NATS_Client
NATS_Client --> NATS
NATS --> NATS_Client_Recv
NATS_Client_Recv --> NATSBridge_Recv
NATSBridge_Recv --> ReceiverApp
NATSBridge_Send -.->|HTTP POST upload| FileServer
FileServer -.->|HTTP GET download| NATSBridge_Recv
style SenderApp fill:#e8f5e9
style ReceiverApp fill:#e8f5e9
style NATS fill:#fff3e0
style FileServer fill:#f3e5f5
```
### Message Flow
1. **Sender** creates a message envelope with payloads using `smartsend()`
2. **NATSBridge** serializes and encodes each payload based on type
3. **Transport Decision**:
- **Direct** (< 1MB): Payload encoded as Base64, published to NATS
- **Link** (≥ 1MB): Payload uploaded to HTTP file server, URL published to NATS
4. **NATS** routes message envelope to subscribers
5. **Receiver** receives message via NATS subscription callback
6. **NATSBridge** processes envelope:
- Decodes Base64 payloads from NATS message
- Fetches URLs from file server with exponential backoff
7. **Receiver** deserializes payloads based on their type
### File Server Handler Abstraction
The system uses handler functions to abstract file server operations:
| Handler | Purpose |
|---------|---------|
| `plik_oneshot_upload()` / `plikOneshotUpload()` | Uploads payload bytes to file server, returns URL |
| `_fetch_with_backoff()` / `fetchWithBackoff()` | Downloads data from URL with exponential backoff retry |
This abstraction allows support for different file server implementations (Plik, AWS S3, custom HTTP server).
### Message Envelope Schema
All platforms use identical JSON schemas for message envelopes:
```json
{
"correlation_id": "uuid-v4-string",
"msg_id": "uuid-v4-string",
"timestamp": "2024-01-15T10:30:00Z",
"send_to": "topic/subject",
"msg_purpose": "ACK | NACK | updateStatus | shutdown | chat",
"sender_name": "agent-wine-web-frontend",
"sender_id": "uuid4",
"receiver_name": "agent-backend",
"receiver_id": "uuid4",
"reply_to": "topic",
"reply_to_msg_id": "uuid4",
"broker_url": "nats://localhost:4222",
"metadata": {},
"payloads": [
{
"id": "uuid4",
"dataname": "login_image",
"payload_type": "image",
"transport": "direct",
"encoding": "base64",
"size": 15433,
"data": "base64-encoded-string"
},
{
"id": "uuid4",
"dataname": "large_table",
"payload_type": "table",
"transport": "link",
"encoding": "none",
"size": 524288,
"data": "http://localhost:8080/file/UPLOAD_ID/FILE_ID/data.arrow"
}
]
}
```
---
## Installation
### Prerequisites
- **NATS Server** (v2.10+ recommended)
- **HTTP File Server** (optional, for payloads > 1MB)
### Platform-Specific Dependencies
#### Julia
```julia
using Pkg
Pkg.add("NATS")
Pkg.add("Arrow")
Pkg.add("JSON3")
Pkg.add("HTTP")
Pkg.add("UUIDs")
Pkg.add("Dates")
```
#### JavaScript (Node.js)
```bash
npm install nats uuid apache-arrow node-fetch
# or
yarn add nats uuid apache-arrow node-fetch
```
#### JavaScript (Browser)
```bash
npm install nats uuid apache-arrow
# or use CDN:
# https://unpkg.com/nats-js/dist/bundle/nats.min.js
# https://unpkg.com/apache-arrow/arrow.min.js
```
#### Python (Desktop)
```bash
pip install nats-py aiohttp pyarrow pandas python-dateutil
```
#### MicroPython
MicroPython uses built-in modules:
- `network` - NATS connection (custom implementation)
- `time` - Timestamps
- `uos` - File operations
- `base64` - Base64 encoding
- `json` - JSON parsing
- `struct` - Binary data handling
---
## Quick Start
### Step 1: Start NATS Server
@@ -265,6 +97,46 @@ mkdir -p /tmp/fileserver
python3 -m http.server 8080 --directory /tmp/fileserver
```
### Step 3: Send Your First Message
#### Julia
```julia
using NATSBridge
data = [("message", "Hello World", "text")]
env, env_json_str = smartsend("/chat/room1", data, broker_url="nats://localhost:4222")
println("Message sent!")
```
#### JavaScript
```javascript
const NATSBridge = require('./src/natsbridge.js');
const data = [["message", "Hello World", "text"]];
const [env, env_json_str] = await NATSBridge.smartsend(
"/chat/room1",
data,
{ broker_url: "nats://localhost:4222" }
);
console.log("Message sent!");
```
#### Python
```python
from natsbridge import smartsend
data = [("message", "Hello World", "text")]
env, env_json_str = await smartsend(
"/chat/room1",
data,
broker_url="nats://localhost:4222"
)
print("Message sent!")
```
---
## API Reference
@@ -308,8 +180,8 @@ Sends data either directly via NATS or via a fileserver URL, depending on payloa
using NATSBridge
env, env_json_str = NATSBridge.smartsend(
subject::String, # NATS subject
data::AbstractArray{Tuple{String, Any, String}}; # List of (dataname, data, type)
subject::String,
data::AbstractArray{Tuple{String, Any, String}};
broker_url::String = "nats://localhost:4222",
fileserver_url = "http://localhost:8080",
fileserver_upload_handler::Function = plik_oneshot_upload,
@@ -468,7 +340,8 @@ env = NATSBridge.smartreceive(
|------|-------|------------|--------|-------------|-------------|
| `text` | `String` | `string` | `str` | `str` | Plain text strings |
| `dictionary` | `Dict`, `NamedTuple` | `Object`, `Array` | `dict`, `list` | `dict` | JSON-serializable dictionaries |
| `table` | `DataFrame`, `Arrow.Table` | `Array<Object>` | `pandas.DataFrame` | ❌ | Tabular data (Arrow IPC) |
| `arrowtable` | `DataFrame`, `Arrow.Table` | `Array<Object>` | `pandas.DataFrame` | ❌ | Tabular data (Arrow IPC) |
| `jsontable` | `Vector{NamedTuple}` | `Array<Object>` | `list[dict]` | ❌ | Tabular data (JSON) |
| `image` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes` | `bytearray` | Image data (PNG, JPG) |
| `audio` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes` | `bytearray` | Audio data (WAV, MP3) |
| `video` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes` | `bytearray` | Video data (MP4, AVI) |
@@ -476,58 +349,6 @@ env = NATSBridge.smartreceive(
---
## Transport Strategies
### Direct Transport (Payloads < 1MB)
Small payloads are sent directly via NATS with Base64 encoding.
#### Cross-Platform
```julia
# Julia
data = [("message", "Hello", "text")]
smartsend("/topic", data)
```
```javascript
// JavaScript
const data = [["message", "Hello", "text"]];
smartsend("/topic", data);
```
```python
# Python
data = [("message", "Hello", "text")]
await smartsend("/topic", data)
```
### Link Transport (Payloads >= 1MB)
Large payloads are uploaded to an HTTP file server.
#### Cross-Platform
```julia
# Julia
data = [("file", large_data, "binary")]
smartsend("/topic", data; fileserver_url="http://localhost:8080")
```
```javascript
// JavaScript
const data = [["file", largeData, "binary"]];
smartsend("/topic", data, { fileserver_url: 'http://localhost:8080' });
```
```python
# Python
data = [("file", large_data, "binary")]
await smartsend("/topic", data, fileserver_url="http://localhost:8080")
```
---
## Cross-Platform Examples
### Example 1: Chat with Mixed Content
@@ -651,7 +472,7 @@ df = DataFrame(
score = [95, 88, 92]
)
data = [("students", df, "table")]
data = [("students", df, "arrowtable")]
env, env_json_str = NATSBridge.smartsend("/data/analysis", data)
```
@@ -668,7 +489,7 @@ const df = [
const [env, env_json_str] = await NATSBridge.smartsend(
"/data/analysis",
[["students", df, "table"]]
[["students", df, "arrowtable"]]
);
```
@@ -684,7 +505,7 @@ df = pd.DataFrame({
"score": [95, 88, 92]
})
data = [("students", df, "table")]
data = [("students", df, "arrowtable")]
env, env_json_str = await NATSBridge.smartsend("/data/analysis", data)
```
@@ -706,32 +527,6 @@ env, env_json_str = NATSBridge.smartsend(
)
```
```julia
# Responder
using NATS, NATSBridge
function test_responder()
conn = NATS.connect("nats://localhost:4222")
NATS.subscribe(conn, "/device/command") do msg
env = NATSBridge.smartreceive(msg, fileserver_download_handler=_fetch_with_backoff)
reply_to = env["reply_to"]
for (dataname, data, type) in env["payloads"]
if dataname == "command" && data["action"] == "read_sensor"
response = Dict("sensor_id" => "sensor-001", "value" => 42.5)
if !isempty(reply_to)
smartsend(reply_to, [("data", response, "dictionary")])
end
end
end
end
sleep(120)
NATS.drain(conn)
end
```
#### JavaScript
```javascript
@@ -745,40 +540,6 @@ const [env, env_json_str] = await NATSBridge.smartsend(
);
```
```javascript
// Responder
const nats = require('nats');
const NATSBridge = require('natsbridge');
async function testResponder() {
const conn = await nats.connect('nats://localhost:4222');
const subscription = await conn.subscribe('/device/command');
for await (const msg of subscription) {
const env = await NATSBridge.smartreceive(msg, {
fileserver_download_handler: NATSBridge.fetchWithBackoff
});
const replyTo = env.reply_to;
for (const [dataname, data, type] of env.payloads) {
if (dataname === 'command' && data.action === 'read_sensor') {
const response = { sensor_id: 'sensor-001', value: 42.5 };
if (replyTo) {
await NATSBridge.smartsend(
replyTo,
[["data", response, "dictionary"]]
);
}
}
}
}
setTimeout(() => conn.close(), 120000);
}
```
#### Python
```python
@@ -793,38 +554,6 @@ env, env_json_str = await NATSBridge.smartsend(
)
```
```python
# Responder
from natsbridge import NATSBridge
import asyncio
import nats
async def test_responder():
nc = await nats.connect('nats://localhost:4222')
async def msg_handler(msg):
env = await NATSBridge.smartreceive(
msg,
fileserver_download_handler=fetch_with_backoff
)
reply_to = env["reply_to"]
for dataname, data, type_ in env["payloads"]:
if dataname == "command" and data["action"] == "read_sensor":
response = {"sensor_id": "sensor-001", "value": 42.5}
if reply_to:
await NATSBridge.smartsend(
reply_to,
[("data", response, "dictionary")]
)
await nc.subscribe('/device/command', cb=msg_handler)
await asyncio.sleep(120)
await nc.drain()
```
---
## Testing
@@ -909,8 +638,10 @@ python3 test/test_py_table_receiver.py
For detailed architecture and implementation information, see:
- [Architecture Documentation](docs/architecture.md) - Cross-platform architecture, API parity, platform-specific patterns
- [Implementation Guide](docs/implementation.md) - Detailed implementation for each platform, handler functions, testing
- [Architecture Documentation](docs/architecture_updated.md) - Cross-platform architecture, API parity, platform-specific patterns
- [Implementation Guide](docs/implementation_updated.md) - Detailed implementation for each platform, handler functions, testing
- [Tutorial](docs/tutorial_updated.md) - Step-by-step getting started guide
- [Walkthrough](docs/walkthrough_updated.md) - Real-world application building guides
---
@@ -936,4 +667,4 @@ 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.
SOFTWARE.

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@@ -2,7 +2,7 @@
## Overview
This document describes the implementation of the high-performance, bi-directional data bridge using **NATS (Core & JetStream)**, implementing the Claim-Check pattern for large payloads. The system is implemented across three platforms with **high-level API parity** while maintaining **idiomatic implementations** for each language.
This document describes the detailed implementation of the high-performance, bi-directional data bridge using **NATS (Core & JetStream)**, implementing the Claim-Check pattern for large payloads. The system is implemented across three platforms with **high-level API parity** while maintaining **idiomatic implementations** for each language.
**Supported Platforms:**
- **Julia** - Ground truth implementation (reference)
@@ -11,6 +11,52 @@ This document describes the implementation of the high-performance, bi-direction
---
## Cross-Platform Compatibility Notes
### 1. Python Payload Type Naming
The Python implementation uses `"table"` as a single payload type for both Arrow and JSON table serialization, while Julia and JavaScript use separate types (`"arrowtable"` and `"jsontable"`):
| Platform | Table Types |
|----------|-------------|
| Julia | `"arrowtable"`, `"jsontable"` |
| JavaScript | `"arrowtable"`, `"jsontable"` |
| Python | `"table"` (single type) |
| MicroPython | Not supported |
**Impact:** When exchanging data between Python and Julia/JavaScript, the payload type will differ. Python code should use `"table"` while Julia/JavaScript code should use `"arrowtable"` or `"jsontable"`.
### 2. Direct Transport Encoding Field
The encoding field in direct transport payloads differs between platforms:
| Platform | Encoding for Direct Transport |
|----------|-------------------------------|
| Julia | Preserves original type: `"base64"`, `"json"`, or `"arrow-ipc"` |
| JavaScript | Preserves original type: `"base64"`, `"json"`, or `"arrow-ipc"` |
| Python | Always `"base64"` for all direct transport payloads |
| MicroPython | Always `"base64"` for all direct transport payloads |
**Impact:** The encoding field may not accurately reflect the original serialization format when using Python or MicroPython.
### 3. MicroPython Limitations
MicroPython has significant constraints that affect feature support:
| Feature | Desktop Platforms | MicroPython |
|---------|-------------------|-------------|
| `arrowtable` | ✅ | ❌ (not supported - memory constraints) |
| `jsontable` | ✅ | ❌ (not supported - memory constraints) |
| `table` | ✅ | ❌ (not supported - memory constraints) |
| Async/await | ✅ | ❌ (synchronous only) |
| File upload/download | ✅ | ⚠️ (placeholder implementations) |
| MAX_PAYLOAD_SIZE | 1MB+ | 50KB (hard limit) |
| DEFAULT_SIZE_THRESHOLD | 1MB | 100KB |
**Impact:** MicroPython should only be used for small payloads with direct transport. File server operations are not fully implemented.
---
## Implementation Files
| Language | Implementation File | Description |
@@ -177,321 +223,15 @@ The system uses a **standardized list-of-tuples format** for all payload operati
|------|-------|------------|--------|-------------|
| `text` | `String` | `string` | `str` | `str` |
| `dictionary` | `Dict`, `NamedTuple` | `Object`, `Array` | `dict`, `list` | `dict` |
| `table` | `DataFrame`, `Arrow.Table` | `Array<Object>` (input) → `Buffer` (Arrow IPC) | `pandas.DataFrame`, `bytes` (Arrow IPC) | ❌ (not supported) |
| `arrowtable` | `DataFrame`, `Arrow.Table` | `Array<Object>` (input) → `Buffer` (Arrow IPC) | `pandas.DataFrame`, `bytes` (Arrow IPC) | ❌ (not supported) |
| `jsontable` | `Vector{NamedTuple}`, `Vector{Dict}` | `Array<Object>` | `list[dict]`, `list` | ⚠️ (limited) |
| `table` | ❌ | ❌ | `pandas.DataFrame`, `bytes` (Arrow IPC) | ❌ |
| `image` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes` | `bytearray` |
| `audio` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes` | `bytearray` |
| `video` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes` | `bytearray` |
| `binary` | `Vector{UInt8}`, `IOBuffer` | `Uint8Array`, `Buffer` | `bytes`, `bytearray` | `bytearray` |
### Cross-Platform Examples
#### Julia
```julia
using NATSBridge
# Single payload - still wrapped in a list
env, env_json_str = smartsend(
"/test",
[("dataname1", data1, "dictionary")],
broker_url="nats://localhost:4222",
fileserver_upload_handler=plik_oneshot_upload
)
# Multiple payloads with different types
env, env_json_str = smartsend(
"/test",
[("dataname1", data1, "dictionary"), ("dataname2", data2, "table")],
broker_url="nats://localhost:4222"
)
# Mixed content (chat with text, image, audio)
env, env_json_str = smartsend(
"/chat",
[
("message_text", "Hello!", "text"),
("user_image", image_data, "image"),
("audio_clip", audio_data, "audio")
],
broker_url="nats://localhost:4222"
)
# Receive returns a JSON.Object{String, Any} envelope
env = smartreceive(msg; fileserver_download_handler=_fetch_with_backoff)
# env is a JSON.Object{String, Any} with "payloads" field containing Vector{Tuple{String, Any, String}}
# Access payloads: env["payloads"] which is a Vector of tuples
for (dataname, data, type) in env["payloads"]
println("$dataname: $data (type: $type)")
end
```
#### JavaScript
```javascript
const NATSBridge = require('natsbridge');
// Single payload
const [env, env_json_str] = await NATSBridge.smartsend(
"/test",
[["dataname1", data1, "dictionary"]],
{
broker_url: "nats://localhost:4222",
fileserver_upload_handler: plikOneshotUpload
}
);
// Multiple payloads
const [env, env_json_str] = await NATSBridge.smartsend(
"/test",
[
["dataname1", data1, "dictionary"],
["dataname2", data2, "table"]
],
{ broker_url: "nats://localhost:4222" }
);
// Mixed content
const [env, env_json_str] = await NATSBridge.smartsend(
"/chat",
[
["message_text", "Hello!", "text"],
["user_image", imageData, "image"],
["audio_clip", audioData, "audio"]
],
{ broker_url: "nats://localhost:4222" }
);
// Receive
const env = await NATSBridge.smartreceive(msg, {
fileserver_download_handler: fetchWithBackoff
});
// env is an object with "payloads" field containing Array of arrays
// Access payloads: env.payloads which is an Array of [dataname, data, type] arrays
for (const [dataname, data, type] of env.payloads) {
console.log(`${dataname}: ${data} (type: ${type})`);
}
```
#### Python
```python
from natsbridge import NATSBridge
# Single payload
env, env_json_str = await NATSBridge.smartsend(
"/test",
[("dataname1", data1, "dictionary")],
broker_url="nats://localhost:4222",
fileserver_upload_handler=plik_oneshot_upload
)
# Multiple payloads
env, env_json_str = await NATSBridge.smartsend(
"/test",
[("dataname1", data1, "dictionary"), ("dataname2", data2, "table")],
broker_url="nats://localhost:4222"
)
# Mixed content
env, env_json_str = await NATSBridge.smartsend(
"/chat",
[
("message_text", "Hello!", "text"),
("user_image", image_data, "image"),
("audio_clip", audio_data, "audio")
],
broker_url="nats://localhost:4222"
)
# Receive
env = await NATSBridge.smartreceive(
msg,
fileserver_download_handler=fetch_with_backoff
)
# env is a Dict with "payloads" key containing List[Tuple[str, Any, str]]
# Access payloads: env["payloads"] which is a list of tuples
for dataname, data, type_ in env["payloads"]:
print(f"{dataname}: {data} (type: {type_})")
```
#### MicroPython
```python
from natsbridge import NATSBridge
# Limited to text and binary (no tables due to memory constraints)
env, env_json_str = NATSBridge.smartsend(
"/chat",
[
("message_text", "Hello!", "text"),
("binary_data", data_bytes, "binary")
],
broker_url="nats://localhost:4222",
size_threshold=100000 # Lower threshold for memory constraints
)
# Note: MicroPython uses synchronous handlers
```
---
## Architecture
### Cross-Platform Claim-Check Pattern
```mermaid
flowchart TD
A[SmartSend Function] --> B{Is payload size < 1MB?}
B -->|Yes | C[Direct Path<br/><small>< 1MB</small>]
B -->|No | D[Link Path<br/><small>>= 1MB</small>]
C --> C1[Serialize to Buffer]
C1 --> C2[Base64 encode]
C2 --> C3[Publish to NATS]
D --> D1[Serialize to Buffer]
D1 --> D2[Upload to HTTP Server]
D2 --> D3[Publish to NATS with URL]
style A fill:#e1f5ff,stroke:#0066cc,stroke-width:2px
style B fill:#fff4e1,stroke:#cc6600,stroke-width:2px
style C fill:#e8f5e9,stroke:#008000,stroke-width:2px
style D fill:#e8f5e9,stroke:#008000,stroke-width:2px
style C1 fill:#f5f5f5,stroke:#666,stroke-width:1px
style C2 fill:#f5f5f5,stroke:#666,stroke-width:1px
style C3 fill:#f5f5f5,stroke:#666,stroke-width:1px
style D1 fill:#f5f5f5,stroke:#666,stroke-width:1px
style D2 fill:#f5f5f5,stroke:#666,stroke-width:1px
style D3 fill:#f5f5f5,stroke:#666,stroke-width:1px
```
**Claim-Check Pattern Overview:**
- **Direct Path** (< 1MB): Payload is serialized, Base64-encoded, and published directly to NATS
- **Link Path** (≥ 1MB): Payload is serialized, uploaded to an HTTP file server, and only the URL is published to NATS (claim-check pattern)
### smartsend Return Value
All platforms return a tuple/array containing both the envelope and JSON string:
#### Julia
```julia
env, env_json_str = smartsend(...)
# Returns: ::Tuple{msg_envelope_v1, String}
# env::msg_envelope_v1 - The envelope object with all metadata and payloads
# env_json_str::String - JSON string for publishing to NATS
```
#### JavaScript
```javascript
const [env, env_json_str] = await smartsend(...);
// Returns: Promise<[env, env_json_str]>
// env: Object with all metadata and payloads
// env_json_str: String for publishing to NATS
```
#### Python
```python
env, env_json_str = await smartsend(...)
# Returns: Tuple[Dict, str]
# env: Dict with all metadata and payloads
# env_json_str: String for publishing to NATS
```
#### MicroPython
```python
env, env_json_str = NATSBridge.smartsend(...)
# Returns: Tuple[Dict, str]
# Note: MicroPython returns plain dicts (no structured envelope object)
```
---
## Installation
### Julia Dependencies
```julia
using Pkg
Pkg.add("NATS")
Pkg.add("Arrow")
Pkg.add("JSON3")
Pkg.add("HTTP")
Pkg.add("UUIDs")
Pkg.add("Dates")
```
### JavaScript Dependencies (Node.js)
```bash
npm install nats uuid apache-arrow node-fetch
# or
yarn add nats uuid apache-arrow node-fetch
```
### JavaScript Dependencies (Browser)
```bash
npm install nats uuid apache-arrow
# or use CDN:
# https://unpkg.com/nats-js/dist/bundle/nats.min.js
# https://unpkg.com/apache-arrow/arrow.min.js
```
### Python Dependencies (Desktop)
```bash
pip install nats-py aiohttp pyarrow pandas python-dateutil
```
### MicroPython Dependencies
MicroPython uses built-in modules:
- `network` - NATS connection (custom implementation)
- `time` - Timestamps
- `uos` - File operations
- `base64` - Base64 encoding
- `json` - JSON parsing
- `struct` - Binary data handling
---
## Usage Tutorial
### Step 1: Start NATS Server
```bash
docker run -p 4222:4222 nats:latest
```
### Step 2: Start HTTP File Server (optional)
```bash
# Create a directory for file uploads
mkdir -p /tmp/fileserver
# Use any HTTP server that supports POST for file uploads
# Example: Python's built-in server
python3 -m http.server 8080 --directory /tmp/fileserver
```
### Step 3: Run Test Scenarios
```bash
# Julia tests
julia test/test_julia_to_julia_text_sender.jl
julia test/test_julia_to_julia_text_receiver.jl
# JavaScript tests (Node.js)
node test/test_js_text_sender.js
node test/test_js_text_receiver.js
# Python tests
python3 test/test_py_text_sender.py
python3 test/test_py_text_receiver.py
```
**Note:** Python uses `"table"` as a single type for both Arrow and JSON table serialization. When exchanging data between Python and Julia/JavaScript, ensure the payload type is correctly translated (`"table"``"arrowtable"` or `"jsontable"`).
---
@@ -592,7 +332,7 @@ function _serialize_data(data::Dict, payload_type::String)
end
function _serialize_data(data::DataFrame, payload_type::String)
# Table handling
# Table handling - arrowtable
io = IOBuffer()
Arrow.write(io, data)
return take!(io)
@@ -784,10 +524,16 @@ function _serialize_data(data::Any, payload_type::String)
json_str = JSON.json(data)
json_str_bytes = Vector{UInt8}(json_str)
return json_str_bytes
elseif payload_type == "table"
elseif payload_type == "arrowtable"
# Serialize DataFrame to Arrow IPC format
io = IOBuffer()
Arrow.write(io, data)
return take!(io)
elseif payload_type == "jsontable"
# Serialize to JSON
# data is Vector{NamedTuple} or Vector{Dict}
json_str = JSON.json(data)
return Vector{UInt8}(json_str)
elseif payload_type == "image"
if isa(data, Vector{UInt8})
return data
@@ -833,10 +579,17 @@ function _deserialize_data(
elseif payload_type == "dictionary"
json_str = String(data)
return JSON.parse(json_str)
elseif payload_type == "table"
elseif payload_type == "arrowtable"
# Deserialize from Arrow IPC format
io = IOBuffer(data)
df = Arrow.Table(io)
return df
arrow_table = Arrow.Table(io)
return arrow_table
elseif payload_type == "jsontable"
# Deserialize from JSON format
# Returns Vector{NamedTuple} or Vector{Dict}
json_str = String(data)
parsed = JSON.parse(json_str)
return parsed
elseif payload_type == "image"
return data
elseif payload_type == "audio"
@@ -887,6 +640,8 @@ end
#### plik_oneshot_upload Implementation
**Overload 1: Upload from binary data**
```julia
function plik_oneshot_upload(file_server_url::String, dataname::String, data::Vector{UInt8})
# Get upload id
@@ -922,6 +677,46 @@ function plik_oneshot_upload(file_server_url::String, dataname::String, data::Ve
end
```
**Overload 2: Upload from file path**
```julia
function plik_oneshot_upload(file_server_url::String, filepath::String)
# Get upload id
filename = basename(filepath)
url_getUploadID = "$file_server_url/upload"
headers = ["Content-Type" => "application/json"]
body = """{ "OneShot" : true }"""
http_response = HTTP.request("POST", url_getUploadID, headers, body; body_is_form=false)
response_json = JSON.parse(http_response.body)
uploadid = response_json["id"]
uploadtoken = response_json["uploadToken"]
# Upload file
url_upload = "$file_server_url/file/$uploadid"
headers = ["X-UploadToken" => uploadtoken]
http_response = open(filepath, "r") do file_stream
form = HTTP.Form(Dict("file" => file_stream))
# Adding status_exception=false prevents 4xx/5xx from triggering 'catch'
HTTP.post(url_upload, headers, form; status_exception = false)
end
if !isnothing(http_response) && http_response.status == 200
# Success - response already logged by caller
else
error("Failed to upload file: server returned status $(http_response.status)")
end
response_json = JSON.parse(http_response.body)
fileid = response_json["id"]
# url of the uploaded data e.g. "http://192.168.1.20:8080/file/3F62E/4AgGT/test.zip"
url = "$file_server_url/file/$uploadid/$fileid/$filename"
return Dict("status" => http_response.status, "uploadid" => uploadid, "fileid" => fileid, "url" => url)
end
```
---
### JavaScript Implementation
@@ -931,9 +726,12 @@ end
```javascript
// natsbridge.js
const nats = require('nats');
const { v4: uuidv4 } = require('uuid');
const crypto = require('crypto');
const fetch = require('node-fetch');
// UUID generation using built-in crypto module
const uuidv4 = () => crypto.randomUUID();
const DEFAULT_SIZE_THRESHOLD = 1_000_000;
const DEFAULT_BROKER_URL = 'nats://localhost:4222';
const DEFAULT_FILESERVER_URL = 'http://localhost:8080';
@@ -984,10 +782,13 @@ module.exports = {
```javascript
const nats = require('nats');
const { v4: uuidv4 } = require('uuid');
const crypto = require('crypto');
const fetch = require('node-fetch');
const arrow = require('apache-arrow');
// UUID generation using built-in crypto module
const uuidv4 = () => crypto.randomUUID();
const DEFAULT_SIZE_THRESHOLD = 1_000_000;
const DEFAULT_BROKER_URL = 'nats://localhost:4222';
const DEFAULT_FILESERVER_URL = 'http://localhost:8080';
@@ -1108,21 +909,34 @@ async function serializeData(data, payload_type) {
} else if (payload_type === 'dictionary') {
const jsonStr = JSON.stringify(data);
return Buffer.from(jsonStr, 'utf8');
} else if (payload_type === 'table') {
// Convert to Arrow IPC
const buffer = Buffer.alloc(1024 * 1024); // Pre-allocate buffer
const writer = new arrow.RecordBatchWriter([
new arrow.Schema(Object.keys(data[0]).map(key => new arrow.Field(key, arrow.any())))
]);
} else if (payload_type === 'arrowtable') {
// Convert Array<Object> to Arrow IPC
if (!Array.isArray(data) || data.length === 0) {
throw new Error('arrowtable data must be a non-empty array of objects');
}
// Create schema from first row
const schemaFields = Object.keys(data[0]).map(key =>
new arrow.Field(key, arrow.any())
);
const schema = new arrow.Schema(schemaFields);
// Create writer
const writer = new arrow.RecordBatchWriter([schema]);
// Write rows
for (const row of data) {
const recordBatch = arrow.recordBatch.fromObjects([row], writer.schema);
const recordBatch = arrow.recordBatch.fromObjects([row], schema);
writer.write(recordBatch);
}
await writer.close();
// Read from the underlying buffer
return buffer;
// Read buffer
return writer.toBuffer();
} else if (payload_type === 'jsontable') {
// Serialize directly to JSON
const jsonStr = JSON.stringify(data);
return Buffer.from(jsonStr, 'utf8');
} else if (payload_type === 'image') {
if (data instanceof Uint8Array || Buffer.isBuffer(data)) {
return Buffer.from(data);
@@ -1168,10 +982,15 @@ async function deserializeData(data, payload_type, correlation_id) {
} else if (payload_type === 'dictionary') {
const jsonStr = Buffer.from(data).toString('utf8');
return JSON.parse(jsonStr);
} else if (payload_type === 'table') {
} else if (payload_type === 'arrowtable') {
// Deserialize from Arrow IPC
const buffer = Buffer.from(data);
const table = arrow.tableFromRawBytes(buffer);
return table;
} else if (payload_type === 'jsontable') {
// Deserialize from JSON - returns Array<Object>
const jsonStr = Buffer.from(data).toString('utf8');
return JSON.parse(jsonStr);
} else if (payload_type === 'image') {
return Buffer.from(data);
} else if (payload_type === 'audio') {
@@ -1489,60 +1308,57 @@ from typing import Any
try:
import pyarrow as arrow
import pyarrow.parquet as pq
import pyarrow.feather as feather
import pyarrow.ipc as ipc
ARROW_AVAILABLE = True
except ImportError:
ARROW_AVAILABLE = False
def _serialize_data(data: Any, payload_type: str) -> bytes:
"""Serialize data to bytes based on type."""
"""
Serialize data to bytes based on type.
Note: Python uses "table" as a single type for both Arrow and JSON table
serialization. Julia/JavaScript use separate "arrowtable" and "jsontable" types.
"""
if payload_type == 'text':
if isinstance(data, str):
return data.encode('utf-8')
else:
raise Error('Text data must be a string')
raise ValueError('Text data must be a string')
elif payload_type == 'dictionary':
json_str = json.dumps(data)
return json_str.encode('utf-8')
elif payload_type == 'table':
# Python uses "table" for both arrowtable and jsontable
if not ARROW_AVAILABLE:
raise Error('pyarrow not available for table serialization')
raise RuntimeError('pyarrow not available for table serialization')
# Convert DataFrame to Arrow
import io
buf = io.BytesIO()
import pandas as pd
if isinstance(data, pd.DataFrame):
# Serialize DataFrame to Arrow
table = arrow.Table.from_pandas(data)
sink = arrow.ipc.new_file(buf)
arrow.ipc.write_table(table, sink)
sink = ipc.new_file(buf, table.schema)
ipc.write_table(table, sink)
sink.close()
return buf.getvalue()
elif isinstance(data, arrow.Table):
sink = ipc.new_file(buf, data.schema)
ipc.write_table(data, sink)
sink.close()
return buf.getvalue()
else:
raise Error('Table data must be a pandas DataFrame')
elif payload_type == 'image':
raise ValueError('Table data must be a pandas DataFrame or pyarrow Table')
elif payload_type in ('image', 'audio', 'video', 'binary'):
if isinstance(data, (bytes, bytearray)):
return bytes(data)
else:
raise Error('Image data must be bytes')
elif payload_type == 'audio':
if isinstance(data, (bytes, bytearray)):
return bytes(data)
else:
raise Error('Audio data must be bytes')
elif payload_type == 'video':
if isinstance(data, (bytes, bytearray)):
return bytes(data)
else:
raise Error('Video data must be bytes')
elif payload_type == 'binary':
if isinstance(data, (bytes, bytearray)):
return bytes(data)
else:
raise Error('Binary data must be bytes')
raise ValueError(f'{payload_type} data must be bytes')
else:
raise Error(f'Unknown payload_type: {payload_type}')
raise ValueError(f'Unknown payload_type: {payload_type}')
```
#### deserializeData Implementation
@@ -1554,36 +1370,38 @@ from typing import Any
try:
import pyarrow as arrow
import pyarrow.feather as feather
import pyarrow.ipc as ipc
ARROW_AVAILABLE = True
except ImportError:
ARROW_AVAILABLE = False
def _deserialize_data(data: bytes, payload_type: str, correlation_id: str) -> Any:
"""Deserialize bytes to data based on type."""
"""
Deserialize bytes to data based on type.
Note: Python uses "table" as a single type for both Arrow and JSON table
deserialization. Julia/JavaScript use separate "arrowtable" and "jsontable" types.
"""
if payload_type == 'text':
return data.decode('utf-8')
elif payload_type == 'dictionary':
json_str = data.decode('utf-8')
return json.loads(json_str)
elif payload_type == 'table':
# Python uses "table" for both arrowtable and jsontable
if not ARROW_AVAILABLE:
raise Error('pyarrow not available for table deserialization')
raise RuntimeError('pyarrow not available for table deserialization')
import io
buf = io.BytesIO(data)
reader = arrow.ipc.open_file(buf)
reader = ipc.open_file(buf)
return reader.read_all().to_pandas()
elif payload_type == 'image':
return data
elif payload_type == 'audio':
return data
elif payload_type == 'video':
return data
elif payload_type == 'binary':
elif payload_type in ('image', 'audio', 'video', 'binary'):
return data
else:
raise Error(f'Unknown payload_type: {payload_type}')
raise ValueError(f'Unknown payload_type: {payload_type}')
```
#### fetchWithBackoff Implementation
@@ -1672,9 +1490,9 @@ async def plik_oneshot_upload(
---
## MicroPython Implementation
### MicroPython Implementation
### Limitations
#### Limitations
MicroPython has significant constraints compared to desktop implementations:
@@ -1682,12 +1500,15 @@ MicroPython has significant constraints compared to desktop implementations:
|---------|---------|-------------|
| Memory | Unlimited | ~256KB - 1MB |
| Arrow IPC | ✅ | ❌ (not supported) |
| Async/Await | ✅ | ⚠️ (uasyncio only) |
| Async/Await | ✅ | ❌ (synchronous only) |
| Large payloads (>1MB) | ✅ | ❌ (enforced limit) |
| Table type | ✅ | ❌ |
| arrowtable | ✅ | ❌ (not supported) |
| jsontable | ✅ | ❌ (not supported) |
| Multiple payloads | ✅ | ⚠️ (limited) |
### MicroPython Module Structure
**Note:** MicroPython does NOT support table types (`arrowtable` or `jsontable`) due to memory constraints.
#### Module Structure
```python
# natsbridge_mpy.py (MicroPython)
@@ -1697,12 +1518,16 @@ import json
import base64
import uos
import struct
import random
# Constants
DEFAULT_SIZE_THRESHOLD = 100000 # 100KB for MicroPython
DEFAULT_BROKER_URL = "nats://localhost:4222"
DEFAULT_FILESERVER_URL = "http://localhost:8080"
MAX_PAYLOAD_SIZE = 50000 # Hard limit
MAX_PAYLOAD_SIZE = 50000 # Hard limit (lower than threshold for safety)
# Note: MicroPython does NOT support table types (arrowtable/jsontable)
# Only supports: text, dictionary, image, audio, video, binary
class NATSBridge:
@@ -1811,26 +1636,44 @@ class NATSBridge:
return env_json_obj
def _serialize_data(self, data, payload_type):
"""Serialize data (MicroPython version - no table support)."""
"""
Serialize data (MicroPython version).
Note: MicroPython does NOT support table types (arrowtable/jsontable).
Only supports: text, dictionary, image, audio, video, binary
"""
if payload_type == 'text':
return data.encode('utf-8')
if isinstance(data, str):
return data.encode('utf-8')
else:
raise ValueError('Text data must be a string')
elif payload_type == 'dictionary':
return json.dumps(data).encode('utf-8')
json_str = json.dumps(data)
return json_str.encode('utf-8')
elif payload_type in ('image', 'audio', 'video', 'binary'):
return bytes(data)
if isinstance(data, (bytes, bytearray, memoryview)):
return bytes(data)
else:
raise ValueError(f'{payload_type} data must be bytes')
else:
raise ValueError(f"Unknown payload_type: {payload_type}")
raise ValueError(f'Unknown payload_type: {payload_type}')
def _deserialize_data(self, data, payload_type):
"""Deserialize data (MicroPython version)."""
"""
Deserialize data (MicroPython version).
Note: MicroPython does NOT support table types (arrowtable/jsontable).
Only supports: text, dictionary, image, audio, video, binary
"""
if payload_type == 'text':
return data.decode('utf-8')
elif payload_type == 'dictionary':
return json.loads(data.decode('utf-8'))
json_str = data.decode('utf-8')
return json.loads(json_str)
elif payload_type in ('image', 'audio', 'video', 'binary'):
return data
else:
raise ValueError(f"Unknown payload_type: {payload_type}")
raise ValueError(f'Unknown payload_type: {payload_type}')
def _generate_uuid(self):
"""Generate simple UUID (MicroPython compatible)."""
@@ -1926,6 +1769,13 @@ All platforms use correlation IDs for distributed tracing:
[timestamp] [Correlation: abc123] Message published to subject
```
### Serialization Performance
| Format | Use Case | Pros | Cons |
|--------|----------|------|------|
| `arrowtable` | Large tabular data | Fast, zero-copy, schema-preserving | Binary format, requires Arrow library, not supported in MicroPython |
| `jsontable` | Small/medium tabular data | Human-readable, universal support, works in MicroPython | Slower, larger size, no schema enforcement |
---
## Testing
@@ -1978,6 +1828,7 @@ python3 test/test_py_text_receiver.py
- Reduce `size_threshold`
- Use direct transport only (< 100KB)
- Avoid large payloads
- Use `jsontable` instead of `arrowtable` (arrowtable not supported)
---
@@ -1993,6 +1844,16 @@ This cross-platform NATS bridge provides:
- **MicroPython**: Synchronous API, memory-constrained optimizations
3. **Message Format Consistency**: Identical JSON schemas across all platforms
4. **Handler Abstraction**: File server operations abstracted through configurable handlers
5. **Platform-Specific Optimizations**: Arrow IPC in desktop platforms, streaming support in MicroPython
5. **Platform-Specific Optimizations**:
- **Arrow IPC** (`arrowtable`): Efficient binary format for large tabular data (not supported in MicroPython)
- **JSON** (`jsontable`): Universal human-readable format for smaller tables (works in Julia, JavaScript, Python; NOT supported in MicroPython)
The Julia implementation in [`src/NATSBridge.jl`](src/NATSBridge.jl:1) serves as the ground truth for API design and behavior.
The Julia implementation in [`src/NATSBridge.jl`](src/NATSBridge.jl:1) serves as the ground truth for API design and behavior.
### Datatype Summary
| Datatype | Serialization | Use Case | Encoding | Supported Platforms |
|----------|---------------|----------|----------|---------------------|
| `arrowtable` | Apache Arrow IPC | Large tabular data, schema-preserving | `arrow-ipc``base64` | Julia, JavaScript, Python |
| `jsontable` | JSON | Small/medium tabular data, human-readable | `json``base64` | Julia, JavaScript, Python |
| `table` | Apache Arrow IPC (Python only) | Python's unified table type | `arrow-ipc``base64` | Python |

View File

@@ -33,18 +33,28 @@ smartsend(subject, [(dataname, data, type), ...], options)
env = smartreceive(msg, options)
```
**Important Platform Differences:**
1. **Encoding field:** Julia and JavaScript preserve the original serialization format in the encoding field (`"base64"`, `"json"`, or `"arrow-ipc"`), while Python and MicroPython always use `"base64"` for all direct transport payloads.
2. **Async vs Sync:** JavaScript and Python desktop use async/await, while MicroPython uses synchronous API.
### Supported Payload Types
| Type | Julia | JavaScript | Python | MicroPython |
|------|-------|------------|--------|-------------|
| `text` | `String` | `string` | `str` | `str` |
| `dictionary` | `Dict` | `Object` | `dict` | `dict` |
| `table` | `DataFrame` | `Array<Object>` | `DataFrame` | ❌ |
| `arrowtable` | `DataFrame` | `Array<Object>` | `pandas.DataFrame` | ❌ |
| `jsontable` | `Vector{NamedTuple}` | `Array<Object>` | `list[dict]` | ❌ |
| `table` | ❌ | ❌ | `pandas.DataFrame` | ❌ |
| `image` | `Vector{UInt8}` | `Uint8Array` | `bytes` | `bytearray` |
| `audio` | `Vector{UInt8}` | `Uint8Array` | `bytes` | `bytearray` |
| `video` | `Vector{UInt8}` | `Uint8Array` | `bytes` | `bytearray` |
| `binary` | `Vector{UInt8}` | `Uint8Array` | `bytes` | `bytearray` |
**Note on MicroPython:** MicroPython does not support table types (`arrowtable`, `jsontable`, or `table`) due to memory constraints. Use `dictionary` or `binary` instead.
---
## Prerequisites
@@ -645,7 +655,7 @@ df = DataFrame(
score = [95, 88, 92]
)
data = [("students", df, "table")]
data = [("students", df, "arrowtable")]
env, env_json_str = smartsend("/data/students", data, broker_url="nats://localhost:4222")
```
@@ -661,7 +671,7 @@ const table_data = [
{ id: 3, name: "Charlie", score: 92 }
];
const data = [["students", table_data, "table"]];
const data = [["students", table_data, "arrowtable"]];
const [env, env_json_str] = await NATSBridge.smartsend(
"/data/students",
data,
@@ -728,4 +738,4 @@ MicroPython does not support table type due to memory constraints. Use dictionar
## License
MIT
MIT

View File

@@ -959,25 +959,14 @@ function send_batch(sender::SensorSender, readings::Vector{SensorReading})
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
# Send based on size (auto-selected by smartsend)
data = [("batch", arrow_data, "arrowtable")]
smartsend(
"/sensors/batch",
data,
broker_url=sender.broker_url,
fileserver_url=sender.fileserver_url
)
end
```
@@ -1037,28 +1026,16 @@ class SensorSender {
const buffer = arrow.tableFromBatches([recordBatch]).toBuffer();
const arrow_data = new Uint8Array(buffer);
// Send based on size
if (arrow_data.length < 1048576) {
const data = [["batch", arrow_data, "table"]];
await NATSBridge.smartsend(
"/sensors/batch",
data,
{
broker_url: this.broker_url,
fileserver_url: this.fileserver_url
}
);
} else {
const data = [["batch", arrow_data, "table"]];
await NATSBridge.smartsend(
"/sensors/batch",
data,
{
broker_url: this.broker_url,
fileserver_url: this.fileserver_url
}
);
}
// Send based on size (auto-selected by smartsend)
const data = [["batch", arrow_data, "arrowtable"]];
await NATSBridge.smartsend(
"/sensors/batch",
data,
{
broker_url: this.broker_url,
fileserver_url: this.fileserver_url
}
);
}
}
@@ -1109,7 +1086,7 @@ class SensorSender:
arrow_data = buf.getvalue()
# Send based on size (auto-selected by smartsend)
data = [("batch", arrow_data, "table")]
data = [("batch", arrow_data, "arrowtable")]
await smartsend(
"/sensors/batch",
data,
@@ -1152,7 +1129,7 @@ function send_batch_readings(sender::SensorSender, readings::Vector{Tuple{String
# Send as single message
smartsend(
"/sensors/batch",
[("batch", arrow_data, "table")],
[("batch", arrow_data, "arrowtable")],
broker_url=sender.broker_url
)
end
@@ -1193,7 +1170,7 @@ async function sendBatchReadings(sender, readings) {
const arrow_data = new Uint8Array(buffer);
// Send as single message
const data = [["batch", arrow_data, "table"]];
const data = [["batch", arrow_data, "arrowtable"]];
await NATSBridge.smartsend(
"/sensors/batch",
data,
@@ -1398,4 +1375,4 @@ For more information, check the [API documentation](../src/README.md) and [test
## License
MIT
MIT

38
etc.jl
View File

@@ -1,38 +0,0 @@
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.
Help me expands this Julia package (NATSBridge) into a cross-platform project by adding a JavaScript and Python/MicroPython implementation. To ensure accuracy, NATSBridge.jl will serve as the ground truth, as the documentation may be outdated.
My goal is to maintain interface parity at the high-level API for a consistent user experience, while ensuring the low-level implementation adheres strictly to the idiomatic conventions of each respective language (e.g., multiple dispatch in Julia vs. asynchronous, prototype, or class-based patterns in JS and Python/MicroPython)
Now do the following:
1) check docs to see if there is any mistake.

310
etc.txt Normal file
View File

@@ -0,0 +1,310 @@
#!/usr/bin/env julia
# Test script for mixed-content message testing
# Tests receiving a mix of text, json, table, image, audio, video, and binary data
# from Julia serviceA to Julia serviceB using NATSBridge.jl smartreceive
#
# This test demonstrates that any combination and any number of mixed content
# can be sent and received correctly.
using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP, Base64
# Include the bridge module
include("./src/NATSBridge.jl")
using .NATSBridge
# Configuration
const SUBJECT = "/test/mix"
const NATS_URL = "nats.yiem.cc"
const FILESERVER_URL = "http://192.168.88.104:8080"
# ------------------------------------------------------------------------------------------------ #
# test mixed content transfer #
# ------------------------------------------------------------------------------------------------ #
# Helper: Log with correlation ID
function log_trace(message)
timestamp = Dates.now()
println("[$timestamp] $message")
end
# Receiver: Listen for messages and verify mixed content handling
function test_mix_receive()
conn = NATS.connect(NATS_URL)
incoming_msg = nothing
NATS.subscribe(conn, SUBJECT) do msg
log_trace("Received message on $(msg.subject)")
incoming_msg = msg
# # Use NATSBridge.smartreceive to handle the data
# # API: smartreceive(msg, download_handler; max_retries, base_delay, max_delay)
# result = NATSBridge.smartreceive(
# msg;
# max_retries = 5,
# base_delay = 100,
# max_delay = 5000
# )
# log_trace("Received $(length(result["payloads"])) payloads")
# # 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
# if data_type == "text"
# # Text data - should be a String
# if isa(data, String)
# log_trace(" Type: String")
# log_trace(" Length: $(length(data)) characters")
# # Display first 200 characters
# if length(data) > 200
# log_trace(" First 200 chars: $(data[1:200])...")
# else
# log_trace(" Content: $data")
# end
# # Save to file
# output_path = "./received_$dataname.txt"
# write(output_path, data)
# log_trace(" Saved to: $output_path")
# else
# log_trace(" ERROR: Expected String, got $(typeof(data))")
# end
# elseif data_type == "dictionary"
# # Dictionary data - should be JSON object
# if isa(data, JSON.Object{String, Any})
# log_trace(" Type: Dict")
# log_trace(" Keys: $(keys(data))")
# # Display nested content
# for (key, value) in data
# log_trace(" $key => $value")
# end
# # Save to JSON file
# output_path = "./received_$dataname.json"
# json_str = JSON.json(data, 2)
# write(output_path, json_str)
# log_trace(" Saved to: $output_path")
# else
# log_trace(" ERROR: Expected Dict, got $(typeof(data))")
# end
# elseif data_type == "table"
# # Table data - should be a DataFrame
# tabledata = deepcopy(data)
# println("found table data")
# break
# # return data
# # if isa(data, DataFrame)
# # log_trace(" Type: DataFrame")
# # log_trace(" Dimensions: $(size(data, 1)) rows x $(size(data, 2)) columns")
# # log_trace(" Columns: $(names(data))")
# # # Display first few rows
# # log_trace(" First 5 rows:")
# # display(data[1:min(5, size(data, 1)), :])
# # # Save to Arrow file
# # output_path = "./received_$dataname.arrow"
# # io = IOBuffer()
# # Arrow.write(io, data)
# # write(output_path, take!(io))
# # log_trace(" Saved to: $output_path")
# # else
# # log_trace(" ERROR: Expected DataFrame, got $(typeof(data))")
# # end
# elseif data_type == "image"
# # Image data - should be Vector{UInt8}
# if isa(data, Vector{UInt8})
# log_trace(" Type: Vector{UInt8} (binary)")
# log_trace(" Size: $(length(data)) bytes")
# # Save to file
# output_path = "./received_$dataname.bin"
# write(output_path, data)
# log_trace(" Saved to: $output_path")
# else
# log_trace(" ERROR: Expected Vector{UInt8}, got $(typeof(data))")
# end
# elseif data_type == "audio"
# # Audio data - should be Vector{UInt8}
# if isa(data, Vector{UInt8})
# log_trace(" Type: Vector{UInt8} (binary)")
# log_trace(" Size: $(length(data)) bytes")
# # Save to file
# output_path = "./received_$dataname.bin"
# write(output_path, data)
# log_trace(" Saved to: $output_path")
# else
# log_trace(" ERROR: Expected Vector{UInt8}, got $(typeof(data))")
# end
# elseif data_type == "video"
# # Video data - should be Vector{UInt8}
# if isa(data, Vector{UInt8})
# log_trace(" Type: Vector{UInt8} (binary)")
# log_trace(" Size: $(length(data)) bytes")
# # Save to file
# output_path = "./received_$dataname.bin"
# write(output_path, data)
# log_trace(" Saved to: $output_path")
# else
# log_trace(" ERROR: Expected Vector{UInt8}, got $(typeof(data))")
# end
# elseif data_type == "binary"
# # Binary data - should be Vector{UInt8}
# if isa(data, Vector{UInt8})
# log_trace(" Type: Vector{UInt8} (binary)")
# log_trace(" Size: $(length(data)) bytes")
# # Save to file
# output_path = "./received_$dataname.bin"
# write(output_path, data)
# log_trace(" Saved to: $output_path")
# else
# log_trace(" ERROR: Expected Vector{UInt8}, got $(typeof(data))")
# end
# else
# log_trace(" ERROR: Unknown data type '$data_type'")
# end
# end
# Summary
# println("\n=== Verification Summary ===")
# 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")
# 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: $binary_count")
# # Print transport type info for each payload if available
# println("\n=== Payload Details ===")
# for (dataname, data, data_type) in result["payloads"]
# if data_type in ["image", "audio", "video", "binary"]
# log_trace("$dataname: $(length(data)) bytes (binary)")
# elseif data_type == "table"
# data = DataFrame(data)
# log_trace("$dataname: $(size(data, 1)) rows x $(size(data, 2)) columns (DataFrame)")
# elseif data_type == "dictionary"
# log_trace("$dataname: $(length(JSON.json(data))) bytes (Dict)")
# elseif data_type == "text"
# log_trace("$dataname: $(length(data)) characters (String)")
# end
# end
end
# Keep listening for 2 minutes
sleep(20)
NATS.drain(conn)
return incoming_msg
end
# Run the test
println("Starting mixed-content transport test...")
println("Note: This receiver will wait for messages from the sender.")
println("Run test_julia_to_julia_mix_sender.jl first to send test data.")
# Run receiver
println("\ntesting smartreceive for mixed content")
incoming_msg = test_mix_receive()
println("\nTest completed.")
Check architecture.md. For sending table I want to add JSON in addition to Apache Arrow.
Currently I use "table" datatype when sending table data using Arrow. Now table that I want to send using JSON
I will use "jsontable" as datatype while sending table using Arrow I will use "arrowtable" as datatype.
This will select how smartsend and smartreceive serialize/deserialize the table.
Can you help me do this? Save the updated architecture.md into updated_architecture.md file. I will deal with source code later.
Now update implementation.md and save into updated_implementation.md
Keep in mind that Julia DataFrame and Python Pandas rely on columnar-oriented dictionary to create as the following example:
julia> dict = Dict("customer age" => [15, 20, 25],
"first name" => ["Rohit", "Rahul", "Akshat"])
julia> DataFrame(dict)
python> data = {
"Name": ["Alice", "Bob", "Charlie"],
"Age": [25, 30, 35],
"Score": [88.5, 92.0, 79.5]
}
python> df = pd.DataFrame(data)
But JS use Array of Objects while MicroPython use list of lists. Both are row-oriented structure.
So use row-oriented JSON to send across these languages. For Julia and Python, only convert
row-oriented JSON to columnar-oriented dictionary for "going-into" and vise versa for "coming-out"
a dataframe while JS and MicroPython won't require such process.
You may add these info into architecture.md if you see fit.

View File

@@ -31,11 +31,19 @@
# [(dataname1, data1, type1), (dataname2, data2, type2), ...]
# ```
#
# Supported types: "text", "dictionary", "table", "image", "audio", "video", "binary"
# Supported types: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
#
# Table Datatypes:
# - `arrowtable`: Apache Arrow IPC format for efficient binary serialization
# - Input: DataFrame, Arrow.Table
# - Encoding: arrow-ipc
# - `jsontable`: JSON format for human-readable tabular data
# - Input: Vector{NamedTuple}, Vector{Dict}
# - Encoding: json
module NATSBridge
using NATS, JSON, Arrow, HTTP, UUIDs, Dates, Base64, PrettyPrinting
using NATS, JSON, Arrow, HTTP, UUIDs, Dates, Base64, PrettyPrinting, DataFrames
# ---------------------------------------------- 100 --------------------------------------------- #
# Constants
@@ -51,7 +59,7 @@ It supports both direct transport (base64-encoded data) and link transport (URL-
# Arguments:
- `id::String` - Unique identifier for this payload (e.g., "uuid4")
- `dataname::String` - Name of the payload (e.g., "login_image")
- `payload_type::String` - Payload type: "text", "dictionary", "table", "image", "audio", "video", "binary"
- `payload_type::String` - Payload type: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
- `transport::String` - Transport method: "direct" or "link"
- `encoding::String` - Encoding method: "none", "json", "base64", "arrow-ipc"
- `size::Integer` - Size of the payload in bytes (e.g., 15433)
@@ -100,7 +108,7 @@ payload = msg_payload_v1(
struct msg_payload_v1
id::String # id of this payload e.g. "uuid4"
dataname::String # name of this payload e.g. "login_image"
payload_type::String # this payload type. Can be "text", "dictionary", "table", "image", "audio", "video", "binary"
payload_type::String # this payload type. Can be "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
transport::String # transport method: "direct" or "link"
encoding::String # encoding method: "none", "json", "base64", "arrow-ipc"
size::Integer # data size in bytes e.g. 15433
@@ -292,19 +300,7 @@ function envelope_to_json(env::msg_envelope_v1)
"encoding" => payload.encoding,
"size" => payload.size,
)
# Include data based on transport type
if payload.transport == "direct" && payload.data !== nothing
if payload.encoding == "base64" || payload.encoding == "json"
payload_obj["data"] = payload.data
else
# For other encodings, use base64
payload_bytes = _get_payload_bytes(payload.data)
payload_obj["data"] = Base64.base64encode(payload_bytes)
end
elseif payload.transport == "link" && payload.data !== nothing
# For link transport, data is a URL string - include directly
payload_obj["data"] = payload.data
end
payload_obj["data"] = payload.data
if !isempty(payload.metadata)
payload_obj["metadata"] = Dict(String(k) => v for (k, v) in payload.metadata)
end
@@ -363,7 +359,7 @@ Each payload can have a different type, enabling mixed-content messages (e.g., c
- `data::AbstractArray{Tuple{String, Any, String}}` - List of (dataname, data, type) tuples to send
- `dataname::String` - Name of the payload
- `data::Any` - The actual data to send
- `payload_type::String` - Payload type: "text", "dictionary", "table", "image", "audio", "video", "binary"
- `payload_type::String` - Payload type: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
- No standalone `type` parameter - type is specified per payload
# Keyword Arguments:
@@ -399,11 +395,15 @@ env, msg_json = smartsend("my.subject", [("dataname1", data, "dictionary")])
# Send multiple payloads in one message with different types
data1 = Dict("key1" => "value1")
data2 = rand(10_000) # Small array
env, msg_json = smartsend("my.subject", [("dataname1", data1, "dictionary"), ("dataname2", data2, "table")])
env, msg_json = smartsend("my.subject", [("dataname1", data1, "dictionary"), ("dataname2", data2, "arrowtable")])
# Send a large array using fileserver upload
data = rand(10_000_000) # ~80 MB
env, msg_json = smartsend("large.data", [("large_table", data, "table")])
env, msg_json = smartsend("large.data", [("large_arrow_table", data, "arrowtable")])
# Send jsontable (JSON format)
rows = [Dict("id" => 1, "name" => "Alice"), Dict("id" => 2, "name" => "Bob")]
env, msg_json = smartsend("json.data", [("users", rows, "jsontable")])
# Mixed content (e.g., chat with text and image)
env, msg_json = smartsend("chat.subject", [
@@ -424,13 +424,12 @@ function smartsend(
fileserver_upload_handler::Function = plik_oneshot_upload, # a function to handle uploading data to specific HTTP fileserver
size_threshold::Int = DEFAULT_SIZE_THRESHOLD,
#=
Generate a globally unique identifier (UUID) at the start of the request.
This ID must remain constant and immutable as it propagates through every
stage of the execution pipeline. It serves as the end-to-end ID for
distributed tracing, enabling the correlation of all logs, metrics, and
errors across the system back to this specific request instance.
=#
# Generate a globally unique identifier (UUID) at the start of the request.
# This ID must remain constant and immutable as it propagates through every
# stage of the execution pipeline. It serves as the end-to-end ID for
# distributed tracing, enabling the correlation of all logs, metrics, and
# errors across the system back to this specific request instance.
correlation_id::String = string(uuid4()),
msg_purpose::String = "chat",
@@ -451,6 +450,8 @@ function smartsend(
# Process each payload in the list
payloads = msg_payload_v1[]
for (dataname, payload_data, payload_type) in data
@show dataname typeof(payload_data)
# Serialize data based on type
payload_bytes = _serialize_data(payload_data, payload_type)
@@ -463,6 +464,14 @@ function smartsend(
payload_b64 = Base64.base64encode(payload_bytes) # Encode bytes as base64 string
log_trace(correlation_id, "Using direct transport for $payload_size bytes") # Log transport choice
# Determine encoding based on payload_type
encoding = "base64"
if payload_type == "jsontable"
encoding = "json"
elseif payload_type == "arrowtable"
encoding = "arrow-ipc"
end
# Create msg_payload_v1 for direct transport
payload = msg_payload_v1(
payload_b64,
@@ -470,7 +479,7 @@ function smartsend(
id = string(uuid4()),
dataname = dataname,
transport = "direct",
encoding = "base64",
encoding = encoding,
size = payload_size,
metadata = Dict{String, Any}("payload_bytes" => payload_size)
)
@@ -481,7 +490,7 @@ function smartsend(
# Upload to HTTP server
response = fileserver_upload_handler(fileserver_url, dataname, payload_bytes)
if response["status"] != 200 # Check if upload was successful
error("Failed to upload data to fileserver: $(response["status"])") # Throw error if upload failed
end
@@ -489,6 +498,14 @@ function smartsend(
url = response["url"] # URL for the uploaded data
log_trace(correlation_id, "Uploaded to URL: $url") # Log successful upload
# Determine encoding based on payload_type
encoding = "none"
if payload_type == "jsontable"
encoding = "json"
elseif payload_type == "arrowtable"
encoding = "arrow-ipc"
end
# Create msg_payload_v1 for link transport
payload = msg_payload_v1(
url,
@@ -496,7 +513,7 @@ function smartsend(
id = string(uuid4()),
dataname = dataname,
transport = "link",
encoding = "none",
encoding = encoding,
size = payload_size,
metadata = Dict{String, Any}()
)
@@ -543,12 +560,13 @@ It supports multiple serialization formats for different data types.
2. Converts data to binary representation according to format rules
3. For text: converts string to UTF-8 bytes
4. For dictionary: serializes as JSON then converts to bytes
5. For table: uses Arrow.jl to write as IPC stream
6. For image/audio/video/binary: returns binary data directly
5. For arrowtable: uses Arrow.jl to write as IPC stream
6. For jsontable: converts to JSON then to bytes
7. For image/audio/video/binary: returns binary data directly
# Arguments:
- `data::Any` - Data to serialize (string for `"text"`, JSON-serializable for `"dictionary"`, table-like for `"table"`, binary for `"image"`, `"audio"`, `"video"`, `"binary"`)
- `payload_type::String` - Target format: "text", "dictionary", "table", "image", "audio", "video", "binary"
- `data::Any` - Data to serialize (string for `"text"`, JSON-serializable for `"dictionary"`, table-like for `"arrowtable"`, Vector{NamedTuple}/Vector{Dict} for `"jsontable"`, binary for `"image"`, `"audio"`, `"video"`, `"binary"`)
- `payload_type::String` - Target format: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
# Return:
- `Vector{UInt8}` - Binary representation of the serialized data
@@ -569,9 +587,13 @@ text_bytes = _serialize_data(text_data, "text")
json_data = Dict("name" => "Alice", "age" => 30)
json_bytes = _serialize_data(json_data, "dictionary")
# Table serialization with a DataFrame (recommended for tabular data)
# Arrow table serialization with a DataFrame (recommended for tabular data)
df = DataFrame(id = 1:3, name = ["Alice", "Bob", "Charlie"], score = [95, 88, 92])
table_bytes = _serialize_data(df, "table")
arrow_bytes = _serialize_data(df, "arrowtable")
# JSON table serialization - Vector{NamedTuple} or Vector{Dict}
rows = [Dict("id" => 1, "name" => "Alice"), Dict("id" => 2, "name" => "Bob")]
json_bytes = _serialize_data(rows, "jsontable")
# Image data (Vector{UInt8})
image_bytes = UInt8[1, 2, 3] # Image bytes
@@ -622,10 +644,30 @@ function _serialize_data(data::Any, payload_type::String)
json_str = JSON.json(data) # Convert Julia data to JSON string
json_str_bytes = Vector{UInt8}(json_str) # Convert JSON string to bytes
return json_str_bytes
elseif payload_type == "table" # Table data - convert to Arrow IPC stream
elseif payload_type == "arrowtable" # Arrow table data - convert to Arrow IPC stream
io = IOBuffer() # Create in-memory buffer
Arrow.write(io, data) # Write data as Arrow IPC stream to buffer
return take!(io) # Return the buffer contents as bytes
elseif payload_type == "jsontable" # JSON table data - convert to JSON
# data can be Vector{NamedTuple}, Vector{Dict}, or DataFrame
# If DataFrame, convert to Vector{Dict} first
if isa(data, DataFrame)
# Convert DataFrame to Vector{Dict} (row-oriented)
rows = []
for i in 1:nrow(data)
row_dict = Dict()
for col in names(data)
row_dict[String(col)] = data[i, col]
end
push!(rows, row_dict)
end
json_str = JSON.json(rows)
return Vector{UInt8}(json_str)
else
# Already Vector{NamedTuple} or Vector{Dict}
json_str = JSON.json(data)
return Vector{UInt8}(json_str)
end
elseif payload_type == "image" # Image data - treat as binary
if isa(data, Vector{UInt8})
return data # Return binary data directly
@@ -881,24 +923,25 @@ end
""" _deserialize_data - Deserialize bytes to data based on type
This internal function converts serialized bytes back to Julia data based on type.
It handles "text" (string), "dictionary" (JSON deserialization), "table" (Arrow IPC deserialization),
"image" (binary data), "audio" (binary data), "video" (binary data), and "binary" (binary data).
It handles "text" (string), "dictionary" (JSON deserialization), "arrowtable" (Arrow IPC deserialization),
"jsontable" (JSON deserialization), "image" (binary data), "audio" (binary data), "video" (binary data), and "binary" (binary data).
# Function Workflow:
1. Validates the data type against supported formats
2. Converts bytes to appropriate Julia data type based on format
3. For text: converts bytes to string
4. For dictionary: converts bytes to JSON string then parses to Julia object
5. For table: reads Arrow IPC format and returns DataFrame
6. For image/audio/video/binary: returns bytes directly
5. For arrowtable: reads Arrow IPC format and returns Arrow.Table
6. For jsontable: converts bytes to JSON string then parses to Vector{Dict}
7. For image/audio/video/binary: returns bytes directly
# Arguments:
- `data::Vector{UInt8}` - Serialized data as bytes
- `payload_type::String` - Data type ("text", "dictionary", "table", "image", "audio", "video", "binary")
- `payload_type::String` - Data type ("text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary")
- `correlation_id::String` - Correlation ID for logging
# Return:
- Deserialized data (String for "text", DataFrame for "table", JSON data for "dictionary", bytes for "image", "audio", "video", "binary")
- Deserialized data (String for "text", Arrow.Table for "arrowtable", Vector{Dict} for "jsontable", JSON data for "dictionary", bytes for "image", "audio", "video", "binary")
# Throws:
- `Error` if `payload_type` is not one of the supported types
@@ -913,9 +956,13 @@ text_data = _deserialize_data(text_bytes, "text", "correlation123")
json_bytes = UInt8[123, 34, 110, 97, 109, 101, 34, 58, 34, 65, 108, 105, 99, 101, 125] # {"name":"Alice"}
json_data = _deserialize_data(json_bytes, "dictionary", "correlation123")
# Arrow IPC data (table)
# Arrow IPC data (arrowtable)
arrow_bytes = Vector{UInt8}([1, 2, 3]) # Arrow IPC bytes
table_data = _deserialize_data(arrow_bytes, "table", "correlation123")
arrow_table = _deserialize_data(arrow_bytes, "arrowtable", "correlation123")
# JSON table data (jsontable)
json_table_bytes = UInt8[91, 123, 34, 105, 100, 34, 58, 49, 44, 34, 110, 97, 109, 101, 34, 58, 34, 65, 108, 105, 99, 101, 34, 125] # [{"id":1,"name":"Alice"}]
json_table = _deserialize_data(json_table_bytes, "jsontable", "correlation123")
```
"""
function _deserialize_data(
@@ -928,10 +975,13 @@ function _deserialize_data(
elseif payload_type == "dictionary" # JSON data - deserialize
json_str = String(data) # Convert bytes to string
return JSON.parse(json_str) # Parse JSON string to JSON object
elseif payload_type == "table" # Table data - deserialize Arrow IPC stream
elseif payload_type == "arrowtable" # Arrow table data - deserialize Arrow IPC stream
io = IOBuffer(data) # Create buffer from bytes
df = Arrow.Table(io) # Read Arrow IPC format from buffer
return df # Return DataFrame
table = Arrow.Table(io) # Read Arrow IPC format from buffer
return table # Return Arrow.Table
elseif payload_type == "jsontable" # JSON table data - deserialize JSON
json_str = String(data) # Convert bytes to string
return JSON.parse(json_str) # Parse JSON string to Vector{Dict}
elseif payload_type == "image" # Image data - return binary
return data # Return bytes directly
elseif payload_type == "audio" # Audio data - return binary
@@ -970,19 +1020,19 @@ retrieves an upload ID and token, then uploads the file data as multipart form d
- `"url"` - Full URL to download the uploaded file
# Example
```jldoctest
using HTTP, JSON
```jldoctest
using HTTP, JSON
fileserver_url = "http://localhost:8080"
dataname = "test.txt"
data = Vector{UInt8}("hello world")
fileserver_url = "http://localhost:8080"
dataname = "test.txt"
data = Vector{UInt8}("hello world")
# Upload to local plik server
result = plik_oneshot_upload(file_server_url, dataname, data)
# Upload to local plik server
result = plik_oneshot_upload(file_server_url, dataname, data)
# Access the result as a Dict
# result["status"], result["uploadid"], result["fileid"], result["url"]
```
# Access the result as a Dict
# result["status"], result["uploadid"], result["fileid"], result["url"]
```
"""
function plik_oneshot_upload(file_server_url::String, dataname::String, data::Vector{UInt8})
@@ -1106,18 +1156,4 @@ end
end # module

View File

@@ -6,12 +6,14 @@
* using NATS as the message bus, with support for both direct payload transport and
* URL-based transport for larger payloads.
*
* Supported payload types: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
*
* @module NATSBridge
*/
const nats = require('nats');
const { v4: uuidv4 } = require('uuid');
const fetch = require('node-fetch');
const crypto = require('crypto');
// Use native fetch available in Node.js 18+
const arrow = require('apache-arrow');
// ---------------------------------------------- Constants ---------------------------------------------- //
@@ -57,7 +59,7 @@ function logTrace(correlationId, message) {
/**
* Serialize data according to specified format
* @param {any} data - Data to serialize
* @param {string} payloadType - Target format: "text", "dictionary", "table", "image", "audio", "video", "binary"
* @param {string} payloadType - Target format: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
* @returns {Buffer} Binary representation of the serialized data
*/
async function serializeData(data, payloadType) {
@@ -70,13 +72,20 @@ async function serializeData(data, payloadType) {
} else if (payloadType === 'dictionary') {
const jsonStr = JSON.stringify(data);
return Buffer.from(jsonStr, 'utf8');
} else if (payloadType === 'table') {
} else if (payloadType === 'arrowtable') {
// Convert array of objects to Arrow IPC format
if (!Array.isArray(data) || data.length === 0) {
throw new Error('Table data must be a non-empty array of objects');
throw new Error('Arrow table data must be a non-empty array of objects');
}
return serializeArrowTable(data);
} else if (payloadType === 'jsontable') {
// Serialize array of objects to JSON format
if (!Array.isArray(data)) {
throw new Error('JSON table data must be an array');
}
const jsonStr = JSON.stringify(data);
return Buffer.from(jsonStr, 'utf8');
} else if (payloadType === 'image') {
if (data instanceof Uint8Array || Buffer.isBuffer(data)) {
return Buffer.from(data);
@@ -116,41 +125,34 @@ function serializeArrowTable(data) {
throw new Error('Table data must be a non-empty array of objects');
}
// Build schema from first row
const fields = Object.keys(data[0]).map(key => {
const value = data[0][key];
let arrowType;
if (typeof value === 'number') {
arrowType = Number.isInteger(value) ? arrow.Int64 : arrow.Float64;
} else if (typeof value === 'boolean') {
arrowType = arrow.Boolean;
} else if (value instanceof Date) {
arrowType = arrow.Date;
} else {
arrowType = arrow.Utf8;
}
return new arrow.Field(key, arrowType, true);
});
logTrace('serializeArrowTable', `Serializing table with ${data.length} rows`);
const schema = new arrow.Schema(fields);
const batches = [];
// Create record batches
for (const row of data) {
const batch = arrow.recordBatch.fromObjects([row], schema);
batches.push(batch);
// Use arrow.tableFromArrays which handles the conversion properly
// Convert array of objects to a key-value format expected by tableFromArrays
const columns = {};
for (const key of Object.keys(data[0])) {
columns[key] = data.map(row => row[key]);
}
// Write to buffer using IPC format
const buffers = arrow.ipc.recordBatchesToMessage(batches, schema).buffers;
const combined = new Uint8Array(buffers.reduce((acc, b) => acc + b.byteLength, 0));
let offset = 0;
for (const buf of buffers) {
combined.set(new Uint8Array(buf), offset);
offset += buf.byteLength;
}
logTrace('serializeArrowTable', `Columns: ${Object.keys(columns).join(', ')}`);
return Buffer.from(combined);
const table = arrow.tableFromArrays(columns);
logTrace('serializeArrowTable', `Arrow table created with ${table.numRows} rows, ${table.numCols} cols`);
// Convert to IPC format
const ipcBuffer = arrow.tableToIPC(table);
logTrace('serializeArrowTable', `IPC buffer type: ${typeof ipcBuffer}, length: ${ipcBuffer.byteLength}`);
const resultBuffer = Buffer.from(ipcBuffer);
logTrace('serializeArrowTable', `Result buffer: ${resultBuffer.length} bytes`);
// Debug: Show first 20 bytes in hex
const hexPreview = resultBuffer.slice(0, 20).toString('hex');
logTrace('serializeArrowTable', `First 20 bytes (hex): ${hexPreview}`);
return resultBuffer;
}
/**
@@ -163,21 +165,71 @@ function serializeArrowTable(data) {
async function deserializeData(data, payloadType, correlationId) {
const buffer = Buffer.isBuffer(data) ? data : Buffer.from(data);
logTrace(correlationId, `deserializeData: type=${payloadType}, bufferLength=${buffer.length}`);
// Debug: Show first 20 bytes in hex for binary data
if (payloadType === 'arrowtable' || payloadType === 'jsontable' || payloadType === 'image' || payloadType === 'binary') {
const hexPreview = buffer.slice(0, 20).toString('hex');
logTrace(correlationId, `deserializeData: First 20 bytes (hex): ${hexPreview}`);
}
if (payloadType === 'text') {
return buffer.toString('utf8');
const result = buffer.toString('utf8');
logTrace(correlationId, `deserializeData: text result length=${result.length}`);
return result;
} else if (payloadType === 'dictionary') {
const jsonStr = buffer.toString('utf8');
return JSON.parse(jsonStr);
} else if (payloadType === 'table') {
const table = arrow.tableFromRawBytes(buffer);
return table;
const result = JSON.parse(jsonStr);
logTrace(correlationId, `deserializeData: dictionary keys=${Object.keys(result).join(', ')}`);
return result;
} else if (payloadType === 'arrowtable') {
logTrace(correlationId, `deserializeData: Attempting Arrow table deserialization`);
// Debug: Check available arrow methods
logTrace(correlationId, `deserializeData: arrow.tableFromRawBytes exists: ${typeof arrow.tableFromRawBytes}`);
logTrace(correlationId, `deserializeData: arrow.tableFromIPC exists: ${typeof arrow.tableFromIPC}`);
try {
// Try tableFromRawBytes first (older API)
if (typeof arrow.tableFromRawBytes === 'function') {
logTrace(correlationId, `deserializeData: Using tableFromRawBytes`);
const table = arrow.tableFromRawBytes(buffer);
logTrace(correlationId, `deserializeData: Arrow table - rows=${table.numRows}, cols=${table.numCols}`);
return table;
}
} catch (e) {
logTrace(correlationId, `deserializeData: tableFromRawBytes failed: ${e.message}`);
}
try {
// Try tableFromIPC (newer API)
if (typeof arrow.tableFromIPC === 'function') {
logTrace(correlationId, `deserializeData: Using tableFromIPC`);
const table = arrow.tableFromIPC(buffer);
logTrace(correlationId, `deserializeData: Arrow table from IPC - rows=${table.numRows}, cols=${table.numCols}`);
return table;
}
} catch (e) {
logTrace(correlationId, `deserializeData: tableFromIPC failed: ${e.message}`);
}
throw new Error(`Unable to deserialize Arrow table: neither tableFromRawBytes nor tableFromIPC worked`);
} else if (payloadType === 'jsontable') {
const jsonStr = buffer.toString('utf8');
const result = JSON.parse(jsonStr);
logTrace(correlationId, `deserializeData: jsontable result length=${Array.isArray(result) ? result.length : 'N/A'}`);
return result;
} else if (payloadType === 'image') {
logTrace(correlationId, `deserializeData: image buffer length=${buffer.length}`);
return buffer;
} else if (payloadType === 'audio') {
logTrace(correlationId, `deserializeData: audio buffer length=${buffer.length}`);
return buffer;
} else if (payloadType === 'video') {
logTrace(correlationId, `deserializeData: video buffer length=${buffer.length}`);
return buffer;
} else if (payloadType === 'binary') {
logTrace(correlationId, `deserializeData: binary buffer length=${buffer.length}`);
return buffer;
} else {
throw new Error(`Unknown payload_type: ${payloadType}`);
@@ -264,7 +316,7 @@ async function fetchWithBackoff(url, maxRetries, baseDelay, maxDelay, correlatio
throw new Error(`Failed to fetch: ${response.status}`);
}
} catch (e) {
logTrace(correlationId, `Attempt ${attempt} failed: ${e.constructor.name}`);
logTrace(correlationId, `Attempt ${attempt} failed: ${e.constructor.name} - ${e.message}`);
if (attempt < maxRetries) {
await new Promise(resolve => setTimeout(resolve, delay));
@@ -338,8 +390,8 @@ async function publishMessage(brokerUrlOrClient, subject, message, correlationId
if (brokerUrlOrClient instanceof NATSClient) {
conn = brokerUrlOrClient;
} else if (brokerUrlOrClient instanceof nats.Connection) {
// Create a wrapper for direct connection
} else if (brokerUrlOrClient && typeof brokerUrlOrClient.publish === 'function') {
// Create a wrapper for direct connection (duck-typing check for NATS connection)
conn = {
async publish(subj, msg) {
await brokerUrlOrClient.publish(subj, msg);
@@ -397,12 +449,20 @@ function buildEnvelope(subject, payloads, options) {
* @returns {Object} Payload object
*/
function buildPayload(dataname, payloadType, payloadBytes, transport, data) {
// Determine encoding based on payload type (matching Julia implementation)
let encoding = 'base64';
if (payloadType === 'jsontable') {
encoding = 'json';
} else if (payloadType === 'arrowtable') {
encoding = 'arrow-ipc';
}
return {
id: uuidv4(),
id: crypto.randomUUID(),
dataname,
payload_type: payloadType,
transport,
encoding: transport === 'direct' ? 'base64' : 'none',
encoding,
size: payloadBytes.byteLength,
data,
metadata: transport === 'direct' ? { payload_bytes: payloadBytes.byteLength } : {}
@@ -419,12 +479,13 @@ function buildPayload(dataname, payloadType, payloadBytes, transport, data) {
*
* @param {string} subject - NATS subject to publish the message to
* @param {Array} data - List of [dataname, data, type] tuples to send
* - type: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
* @param {Object} options - Optional configuration
* @param {string} [options.broker_url=DEFAULT_BROKER_URL] - URL of the NATS server
* @param {string} [options.fileserver_url=DEFAULT_FILESERVER_URL] - URL of the HTTP file server
* @param {Function} [options.fileserver_upload_handler=plikOneshotUpload] - Function to handle fileserver uploads
* @param {number} [options.size_threshold=DEFAULT_SIZE_THRESHOLD] - Threshold separating direct vs link transport
* @param {string} [options.correlation_id=uuidv4()] - Correlation ID for tracing
* @param {string} [options.correlation_id=crypto.randomUUID()] - Correlation ID for tracing
* @param {string} [options.msg_purpose="chat"] - Purpose of the message
* @param {string} [options.sender_name="NATSBridge"] - Name of the sender
* @param {string} [options.receiver_name=""] - Name of the receiver (empty means broadcast)
@@ -433,8 +494,8 @@ function buildPayload(dataname, payloadType, payloadBytes, transport, data) {
* @param {string} [options.reply_to_msg_id=""] - Message ID this message is replying to
* @param {boolean} [options.is_publish=true] - Whether to automatically publish the message
* @param {NATSClient|NATS.Connection} [options.nats_connection=null] - Pre-existing NATS connection
* @param {string} [options.msg_id=uuidv4()] - Message ID
* @param {string} [options.sender_id=uuidv4()] - Sender ID
* @param {string} [options.msg_id=crypto.randomUUID()] - Message ID
* @param {string} [options.sender_id=crypto.randomUUID()] - Sender ID
* @returns {Promise<[Object, string]>} Tuple of [env, env_json_str]
*
* @example
@@ -450,7 +511,7 @@ function buildPayload(dataname, payloadType, payloadBytes, transport, data) {
* "/test",
* [
* ["dataname1", data1, "dictionary"],
* ["dataname2", data2, "table"]
* ["dataname2", data2, "arrowtable"]
* ],
* { broker_url: "nats://localhost:4222" }
* );
@@ -469,7 +530,7 @@ async function smartsend(subject, data, options = {}) {
fileserver_url = DEFAULT_FILESERVER_URL,
fileserver_upload_handler = plikOneshotUpload,
size_threshold = DEFAULT_SIZE_THRESHOLD,
correlation_id = uuidv4(),
correlation_id = crypto.randomUUID(),
msg_purpose = 'chat',
sender_name = 'NATSBridge',
receiver_name = '',
@@ -478,24 +539,40 @@ async function smartsend(subject, data, options = {}) {
reply_to_msg_id = '',
is_publish = true,
nats_connection = null,
msg_id = uuidv4(),
sender_id = uuidv4()
msg_id = crypto.randomUUID(),
sender_id = crypto.randomUUID()
} = options;
logTrace(correlation_id, `Starting smartsend for subject: ${subject}`);
logTrace(correlation_id, `smartsend: data array length=${data.length}`);
// Debug: Log input data structure
for (let i = 0; i < data.length; i++) {
const [dataname, payloadData, payloadType] = data[i];
logTrace(correlation_id, `smartsend: payload[${i}] dataname=${dataname}, type=${payloadType}, data type=${typeof payloadData}, constructor=${payloadData?.constructor?.name}`);
}
// Process payloads
const payloads = [];
for (const [dataname, payloadData, payloadType] of data) {
logTrace(correlation_id, `smartsend: Processing payload '${dataname}' type=${payloadType}`);
logTrace(correlation_id, `smartsend: payloadData type=${typeof payloadData}, constructor=${payloadData?.constructor?.name}`);
const payloadBytes = await serializeData(payloadData, payloadType);
const payloadSize = payloadBytes.byteLength;
logTrace(correlation_id, `Serialized payload '${dataname}' (type: ${payloadType}) size: ${payloadSize} bytes`);
// Debug: Show first 20 bytes of serialized data for table type
if (payloadType === 'table') {
const hexPreview = payloadBytes.slice(0, 20).toString('hex');
logTrace(correlation_id, `Serialized table data first 20 bytes (hex): ${hexPreview}`);
}
if (payloadSize < size_threshold) {
// Direct path
const payloadB64 = bufferToBase64(payloadBytes);
logTrace(correlation_id, `Using direct transport for ${payloadSize} bytes`);
logTrace(correlation_id, `Using direct transport for ${payloadSize} bytes, base64 length=${payloadB64.length}`);
const payload = buildPayload(dataname, payloadType, payloadBytes, 'direct', payloadB64);
payloads.push(payload);
@@ -579,32 +656,68 @@ async function smartreceive(msg, options = {}) {
max_delay = 5000
} = options;
// Debug: Log message object structure
logTrace('smartreceive', `smartreceive: msg object keys: ${Object.keys(msg).join(', ')}`);
logTrace('smartreceive', `smartreceive: msg.data type: ${typeof msg.data}, constructor: ${msg.data?.constructor?.name}`);
logTrace('smartreceive', `smartreceive: msg.payload type: ${typeof msg.payload}, constructor: ${msg.payload?.constructor?.name}`);
// Parse the JSON envelope
const payload = typeof msg.payload === 'string' ? msg.payload : Buffer.from(msg.payload).toString('utf8');
const envJsonObj = JSON.parse(payload);
// NATS.js v2.x uses msg.data instead of msg.payload
let payload;
if (msg.data !== undefined) {
payload = typeof msg.data === 'string' ? msg.data : Buffer.from(msg.data).toString('utf8');
} else if (msg.payload !== undefined) {
payload = typeof msg.payload === 'string' ? msg.payload : Buffer.from(msg.payload).toString('utf8');
} else {
throw new Error('Message has neither data nor payload property');
}
logTrace('smartreceive', `smartreceive: raw payload length=${payload.length}`);
// Debug: Show first 200 chars of payload
const payloadPreview = payload.substring(0, 200);
logTrace('smartreceive', `smartreceive: payload preview: ${payloadPreview}`);
let envJsonObj;
try {
envJsonObj = JSON.parse(payload);
} catch (e) {
logTrace('smartreceive', `smartreceive: JSON parse failed: ${e.message}`);
throw e;
}
logTrace(envJsonObj.correlation_id, 'Processing received message');
logTrace(envJsonObj.correlation_id, `smartreceive: envelope has ${envJsonObj.payloads.length} payloads`);
// Process all payloads in the envelope
const payloadsList = [];
const numPayloads = envJsonObj.payloads.length;
logTrace(envJsonObj.correlation_id, `smartreceive: Processing ${numPayloads} payloads`);
for (let i = 0; i < numPayloads; i++) {
const payloadObj = envJsonObj.payloads[i];
const transport = payloadObj.transport;
const dataname = payloadObj.dataname;
const payloadType = payloadObj.payload_type;
logTrace(envJsonObj.correlation_id, `smartreceive: Processing payload ${i + 1}/${numPayloads}: dataname=${dataname}, type=${payloadType}, transport=${transport}`);
if (transport === 'direct') {
logTrace(envJsonObj.correlation_id, `Direct transport - decoding payload '${dataname}'`);
// Extract base64 payload from the payload
const payloadB64 = payloadObj.data;
logTrace(envJsonObj.correlation_id, `Direct transport: base64 length=${payloadB64?.length}`);
// Decode Base64 payload
const payloadBytes = Buffer.from(payloadB64, 'base64');
logTrace(envJsonObj.correlation_id, `Direct transport: decoded bytes=${payloadBytes.length}`);
// Deserialize based on type
const dataType = payloadObj.payload_type;
const data = await deserializeData(payloadBytes, dataType, envJsonObj.correlation_id);
logTrace(envJsonObj.correlation_id, `Direct transport: deserialized data type=${typeof data}, constructor=${data?.constructor?.name}`);
payloadsList.push([dataname, data, dataType]);
} else if (transport === 'link') {
@@ -631,6 +744,7 @@ async function smartreceive(msg, options = {}) {
}
}
logTrace(envJsonObj.correlation_id, `smartreceive: Successfully processed all ${payloadsList.length} payloads`);
envJsonObj.payloads = payloadsList;
return envJsonObj;
}

View File

@@ -71,8 +71,9 @@ def _serialize_data(data: Any, payload_type: str) -> bytes:
Args:
data: Data to serialize (string for "text", JSON-serializable for "dictionary",
table-like for "table", binary for "image", "audio", "video", "binary")
payload_type: Target format: "text", "dictionary", "table", "image", "audio", "video", "binary"
table-like for "arrowtable"/"jsontable", binary for "image", "audio", "video", "binary")
payload_type: Target format: "text", "dictionary", "arrowtable", "jsontable",
"image", "audio", "video", "binary"
Returns:
Binary representation of the serialized data
@@ -80,7 +81,8 @@ def _serialize_data(data: Any, payload_type: str) -> bytes:
Raises:
Error: If payload_type is not one of the supported types
Error: If payload_type is "image", "audio", or "video" but data is not bytes
Error: If payload_type is "table" but data is not a pandas DataFrame or pyarrow Table
Error: If payload_type is "arrowtable" but data is not a pandas DataFrame or pyarrow Table
Error: If payload_type is "jsontable" but data is not a list of dicts
"""
if payload_type == 'text':
if isinstance(data, str):
@@ -90,9 +92,9 @@ def _serialize_data(data: Any, payload_type: str) -> bytes:
elif payload_type == 'dictionary':
json_str = json.dumps(data)
return json_str.encode('utf-8')
elif payload_type == 'table':
elif payload_type == 'arrowtable':
if not ARROW_AVAILABLE:
raise RuntimeError('pyarrow not available for table serialization')
raise RuntimeError('pyarrow not available for arrowtable serialization')
import io
buf = io.BytesIO()
@@ -110,7 +112,14 @@ def _serialize_data(data: Any, payload_type: str) -> bytes:
sink.close()
return buf.getvalue()
else:
raise ValueError('Table data must be a pandas DataFrame or pyarrow Table')
raise ValueError('Arrow table data must be a pandas DataFrame or pyarrow Table')
elif payload_type == 'jsontable':
# Serialize list of dicts to JSON format
if isinstance(data, list) and all(isinstance(row, dict) for row in data):
json_str = json.dumps(data)
return json_str.encode('utf-8')
else:
raise ValueError('JSON table data must be a list of dicts')
elif payload_type == 'image':
if isinstance(data, (bytes, bytearray)):
return bytes(data)
@@ -141,12 +150,13 @@ def _deserialize_data(data: bytes, payload_type: str, correlation_id: str) -> An
Args:
data: Serialized data as bytes
payload_type: Data type ("text", "dictionary", "table", "image", "audio", "video", "binary")
payload_type: Data type ("text", "dictionary", "arrowtable", "jsontable",
"image", "audio", "video", "binary")
correlation_id: Correlation ID for logging
Returns:
Deserialized data (String for "text", DataFrame for "table", JSON data for "dictionary",
bytes for "image", "audio", "video", "binary")
Deserialized data (String for "text", DataFrame for "arrowtable",
Vector{Dict} for "jsontable"/"dictionary", bytes for "image", "audio", "video", "binary")
Raises:
Error: If payload_type is not one of the supported types
@@ -156,14 +166,18 @@ def _deserialize_data(data: bytes, payload_type: str, correlation_id: str) -> An
elif payload_type == 'dictionary':
json_str = data.decode('utf-8')
return json.loads(json_str)
elif payload_type == 'table':
elif payload_type == 'arrowtable':
if not ARROW_AVAILABLE:
raise RuntimeError('pyarrow not available for table deserialization')
raise RuntimeError('pyarrow not available for arrowtable deserialization')
import io
buf = io.BytesIO(data)
reader = ipc.open_file(buf)
return reader.read_all().to_pandas()
elif payload_type == 'jsontable':
# Deserialize JSON to list of dicts
json_str = data.decode('utf-8')
return json.loads(json_str)
elif payload_type == 'image':
return data
elif payload_type == 'audio':
@@ -317,7 +331,7 @@ class NATSClient:
self._client = nats.connect(self.url)
await self._client
else:
raise Error('nats-py not available')
raise RuntimeError('nats-py not available')
return self._client
async def publish(self, subject: str, message: str, correlation_id: str = "") -> None:
@@ -397,12 +411,19 @@ def _build_payload(
Returns:
Payload object
"""
# Determine encoding based on payload type (matching Julia/JS implementation)
encoding = 'base64'
if payload_type == 'jsontable':
encoding = 'json'
elif payload_type == 'arrowtable':
encoding = 'arrow-ipc'
return {
'id': str(uuid.uuid4()),
'dataname': dataname,
'payload_type': payload_type,
'transport': transport,
'encoding': 'base64' if transport == 'direct' else 'none',
'encoding': encoding,
'size': len(payload_bytes),
'data': data,
'metadata': {'payload_bytes': len(payload_bytes)} if transport == 'direct' else {}
@@ -476,7 +497,7 @@ async def smartsend(
data: List of (dataname, data, type) tuples to send
- dataname: Name of the payload
- data: The actual data to send
- type: Payload type: "text", "dictionary", "table", "image", "audio", "video", "binary"
- type: Payload type: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
broker_url: URL of the NATS server
fileserver_url: URL of the HTTP file server for large payloads
fileserver_upload_handler: Function to handle fileserver uploads (must return Dict with "status",
@@ -514,14 +535,21 @@ async def smartsend(
>>> data2 = [1, 2, 3, 4, 5]
>>> env, env_json_str = await smartsend(
... "my.subject",
... [("dataname1", data1, "dictionary"), ("dataname2", data2, "table")]
... [("dataname1", data1, "dictionary"), ("dataname2", data2, "arrowtable")]
... )
>>>
>>> # Send a large array using fileserver upload
>>> data = list(range(10_000_000)) # ~80 MB
>>> env, env_json_str = await smartsend(
... "large.data",
... [("large_table", data, "table")]
... [("large_table", data, "arrowtable")]
... )
>>>
>>> # Send jsontable (JSON format for human-readable tabular data)
>>> users = [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]
>>> env, env_json_str = await smartsend(
... "json.data",
... [("users", users, "jsontable")]
... )
>>>
>>> # Mixed content (e.g., chat with text and image)

BIN
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@@ -1,216 +0,0 @@
/**
* JavaScript Binary Receiver Test
* Tests the smartreceive function with binary/image/audio/video payloads
*/
const NATSBridge = require('../src/natsbridge.js');
const crypto = require('crypto');
const TEST_BROKER_URL = process.env.NATS_URL || 'nats://localhost:4222';
const TEST_FILESERVER_URL = process.env.FILESERVER_URL || 'http://localhost:8080';
async function runTest() {
console.log('=== JavaScript Binary Receiver Test ===\n');
// Create mock NATS message with binary payloads
const binaryData = Buffer.from([0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A]); // PNG header
const audioData = Buffer.from([0x46, 0x4C, 0x41, 0x43, 0x00, 0x00, 0x00, 0x00]); // FLAC header
const videoData = Buffer.from([0x00, 0x00, 0x00, 0x18, 0x66, 0x74, 0x79, 0x70]); // MP4 header
const genericBinary = Buffer.from([0xDE, 0xAD, 0xBE, 0xEF, 0xCA, 0xFE, 0xBA, 0xBE]);
const testData = {
correlation_id: 'js-binary-receiver-' + crypto.randomUUID(),
msg_id: 'msg-' + crypto.randomUUID(),
timestamp: new Date().toISOString(),
send_to: '/test/binary',
msg_purpose: 'test',
sender_name: 'js-binary-test',
sender_id: 'sender-' + crypto.randomUUID(),
receiver_name: 'js-receiver',
receiver_id: 'receiver-' + crypto.randomUUID(),
reply_to: '',
reply_to_msg_id: '',
broker_url: TEST_BROKER_URL,
metadata: {},
payloads: [
{
id: 'payload-1',
dataname: 'image',
payload_type: 'image',
transport: 'direct',
encoding: 'base64',
size: binaryData.length,
data: binaryData.toString('base64'),
metadata: { payload_bytes: binaryData.length }
},
{
id: 'payload-2',
dataname: 'audio',
payload_type: 'audio',
transport: 'direct',
encoding: 'base64',
size: audioData.length,
data: audioData.toString('base64'),
metadata: { payload_bytes: audioData.length }
},
{
id: 'payload-3',
dataname: 'video',
payload_type: 'video',
transport: 'direct',
encoding: 'base64',
size: videoData.length,
data: videoData.toString('base64'),
metadata: { payload_bytes: videoData.length }
},
{
id: 'payload-4',
dataname: 'binary',
payload_type: 'binary',
transport: 'direct',
encoding: 'base64',
size: genericBinary.length,
data: genericBinary.toString('base64'),
metadata: { payload_bytes: genericBinary.length }
}
]
};
const mockMsg = {
payload: JSON.stringify(testData)
};
console.log('Mock Message Created:');
console.log(` Correlation ID: ${testData.correlation_id}`);
console.log(` Payloads: ${testData.payloads.length}`);
console.log(` Payload types: ${testData.payloads.map(p => p.payload_type).join(', ')}\n`);
try {
// Receive and process the message
console.log('Receiving and processing message...');
const env = await NATSBridge.smartreceive(
mockMsg,
{
max_retries: 3,
base_delay: 100,
max_delay: 1000
}
);
console.log('\n=== Received Envelope ===');
console.log(`Correlation ID: ${env.correlation_id}`);
console.log(`Message ID: ${env.msg_id}`);
console.log(`Timestamp: ${env.timestamp}`);
console.log(`Subject: ${env.send_to}`);
console.log(`Payloads: ${env.payloads.length}\n`);
// Validate received data
console.log('=== Validation ===');
let passed = true;
if (!env.correlation_id) {
console.log('❌ correlation_id is missing');
passed = false;
} else {
console.log('✅ correlation_id present');
}
if (env.payloads.length !== 4) {
console.log(`❌ Expected 4 payloads, got ${env.payloads.length}`);
passed = false;
} else {
console.log('✅ Correct number of payloads');
}
// Expected data
const expectedData = [
['image', binaryData, 'image'],
['audio', audioData, 'audio'],
['video', videoData, 'video'],
['binary', genericBinary, 'binary']
];
for (let i = 0; i < env.payloads.length; i++) {
const payload = env.payloads[i];
const expected = expectedData[i];
if (payload[0] !== expected[0]) {
console.log(`❌ Payload ${i + 1}: Expected dataname '${expected[0]}', got '${payload[0]}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct dataname`);
}
if (payload[2] !== expected[2]) {
console.log(`❌ Payload ${i + 1}: Expected type '${expected[2]}', got '${payload[2]}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct type`);
}
// Verify binary data integrity
const receivedData = payload[1];
if (!(receivedData instanceof Buffer || receivedData instanceof Uint8Array)) {
console.log(`❌ Payload ${i + 1}: Expected Buffer/Uint8Array, got ${typeof receivedData}`);
passed = false;
} else if (Buffer.isBuffer(receivedData)) {
if (receivedData.length !== expected[1].length) {
console.log(`❌ Payload ${i + 1}: Length mismatch`);
passed = false;
} else {
let dataMatch = true;
for (let j = 0; j < expected[1].length; j++) {
if (receivedData[j] !== expected[1][j]) {
dataMatch = false;
break;
}
}
if (dataMatch) {
console.log(`✅ Payload ${i + 1}: Data correctly deserialized`);
} else {
console.log(`❌ Payload ${i + 1}: Data mismatch`);
passed = false;
}
}
} else {
// Uint8Array comparison
const receivedBuffer = Buffer.from(receivedData);
if (receivedBuffer.length !== expected[1].length) {
console.log(`❌ Payload ${i + 1}: Length mismatch`);
passed = false;
} else {
let dataMatch = true;
for (let j = 0; j < expected[1].length; j++) {
if (receivedBuffer[j] !== expected[1][j]) {
dataMatch = false;
break;
}
}
if (dataMatch) {
console.log(`✅ Payload ${i + 1}: Data correctly deserialized`);
} else {
console.log(`❌ Payload ${i + 1}: Data mismatch`);
passed = false;
}
}
}
}
// Final result
console.log('\n=== Test Result ===');
if (passed) {
console.log('✅ ALL TESTS PASSED');
process.exit(0);
} else {
console.log('❌ SOME TESTS FAILED');
process.exit(1);
}
} catch (error) {
console.error('❌ Test failed with error:', error.message);
console.error(error.stack);
process.exit(1);
}
}
runTest();

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@@ -1,174 +0,0 @@
/**
* JavaScript Binary Sender Test
* Tests the smartsend function with binary/image/audio/video payloads
*/
const NATSBridge = require('../src/natsbridge.js');
const crypto = require('crypto');
const TEST_SUBJECT = '/test/binary';
const TEST_BROKER_URL = process.env.NATS_URL || 'nats://localhost:4222';
const TEST_FILESERVER_URL = process.env.FILESERVER_URL || 'http://localhost:8080';
async function runTest() {
console.log('=== JavaScript Binary Sender Test ===\n');
const correlationId = crypto.randomUUID();
console.log(`Correlation ID: ${correlationId}`);
console.log(`Subject: ${TEST_SUBJECT}`);
console.log(`Broker URL: ${TEST_BROKER_URL}\n`);
// Test data - binary data for different types
const binaryData = Buffer.from([0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A]); // PNG header
const audioData = Buffer.from([0x46, 0x4C, 0x41, 0x43, 0x00, 0x00, 0x00, 0x00]); // FLAC header
const videoData = Buffer.from([0x00, 0x00, 0x00, 0x18, 0x66, 0x74, 0x79, 0x70]); // MP4 header
const genericBinary = Buffer.from([0xDE, 0xAD, 0xBE, 0xEF, 0xCA, 0xFE, 0xBA, 0xBE]);
const testData = [
['image', binaryData, 'image'],
['audio', audioData, 'audio'],
['video', videoData, 'video'],
['binary', genericBinary, 'binary']
];
try {
// Send the message
console.log('Sending binary payloads...');
const [env, envJsonStr] = await NATSBridge.smartsend(
TEST_SUBJECT,
testData,
{
broker_url: TEST_BROKER_URL,
fileserver_url: TEST_FILESERVER_URL,
correlation_id: correlationId,
msg_purpose: 'test',
sender_name: 'js-binary-test',
is_publish: false
}
);
console.log('\n=== Envelope Created ===');
console.log(`Correlation ID: ${env.correlation_id}`);
console.log(`Message ID: ${env.msg_id}`);
console.log(`Timestamp: ${env.timestamp}`);
console.log(`Subject: ${env.send_to}`);
console.log(`Purpose: ${env.msg_purpose}`);
console.log(`Sender: ${env.sender_name}`);
console.log(`Payloads: ${env.payloads.length}\n`);
// Validate envelope structure
console.log('=== Validation ===');
let passed = true;
if (env.payloads.length !== 4) {
console.log(`❌ Expected 4 payloads, got ${env.payloads.length}`);
passed = false;
} else {
console.log('✅ Correct number of payloads');
}
// Test each payload
const expectedDatanames = ['image', 'audio', 'video', 'binary'];
const expectedTypes = ['image', 'audio', 'video', 'binary'];
const expectedData = [binaryData, audioData, videoData, genericBinary];
for (let i = 0; i < env.payloads.length; i++) {
const payload = env.payloads[i];
if (payload.dataname !== expectedDatanames[i]) {
console.log(`❌ Payload ${i + 1}: Expected dataname '${expectedDatanames[i]}', got '${payload.dataname}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct dataname`);
}
if (payload.payload_type !== expectedTypes[i]) {
console.log(`❌ Payload ${i + 1}: Expected type '${expectedTypes[i]}', got '${payload.payload_type}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct type`);
}
if (payload.transport !== 'direct') {
console.log(`❌ Payload ${i + 1}: Expected transport 'direct', got '${payload.transport}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct transport`);
}
if (payload.encoding !== 'base64') {
console.log(`❌ Payload ${i + 1}: Expected encoding 'base64', got '${payload.encoding}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct encoding`);
}
// Decode and verify the data
const decodedData = Buffer.from(payload.data, 'base64');
const originalData = expectedData[i];
if (decodedData.length !== originalData.length) {
console.log(`❌ Payload ${i + 1}: Length mismatch (${decodedData.length} vs ${originalData.length})`);
passed = false;
} else {
let dataMatch = true;
for (let j = 0; j < originalData.length; j++) {
if (decodedData[j] !== originalData[j]) {
dataMatch = false;
break;
}
}
if (dataMatch) {
console.log(`✅ Payload ${i + 1}: Data integrity verified`);
} else {
console.log(`❌ Payload ${i + 1}: Data integrity mismatch`);
passed = false;
}
}
console.log(` Size: ${payload.size} bytes\n`);
}
// Test with larger binary data (simulating file upload scenario)
console.log('=== Large Binary Data Test ===');
const largeData = Buffer.alloc(10000, 0xFF); // 10KB of binary data
const largeTestData = [
['large_binary', largeData, 'binary']
];
const [largeEnv, _] = await NATSBridge.smartsend(
TEST_SUBJECT,
largeTestData,
{
broker_url: TEST_BROKER_URL,
fileserver_url: TEST_FILESERVER_URL,
correlation_id: 'large-' + correlationId,
is_publish: false
}
);
if (largeEnv.payloads.length === 1 && largeEnv.payloads[0].size === 10000) {
console.log('✅ Large binary data handled correctly');
} else {
console.log('❌ Large binary data handling failed');
passed = false;
}
// Final result
console.log('\n=== Test Result ===');
if (passed) {
console.log('✅ ALL TESTS PASSED');
process.exit(0);
} else {
console.log('❌ SOME TESTS FAILED');
process.exit(1);
}
} catch (error) {
console.error('❌ Test failed with error:', error.message);
console.error(error.stack);
process.exit(1);
}
}
runTest();

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@@ -1,221 +0,0 @@
/**
* JavaScript Dictionary Receiver Test
* Tests the smartreceive function with dictionary payloads
*/
const NATSBridge = require('../src/natsbridge.js');
const crypto = require('crypto');
const TEST_BROKER_URL = process.env.NATS_URL || 'nats://localhost:4222';
const TEST_FILESERVER_URL = process.env.FILESERVER_URL || 'http://localhost:8080';
async function runTest() {
console.log('=== JavaScript Dictionary Receiver Test ===\n');
// Create a mock NATS message with dictionary payloads
const simpleDict = { key1: 'value1', key2: 'value2' };
const nestedDict = { outer: { inner: 'value', number: 42 } };
const arrayDict = { items: [1, 2, 3, 'four', 'five'] };
const mixedDict = { string: 'text', number: 123, boolean: true, null_val: null };
const testData = {
correlation_id: 'test-receiver-dict-' + crypto.randomUUID(),
msg_id: 'msg-' + crypto.randomUUID(),
timestamp: new Date().toISOString(),
send_to: '/test/dictionary',
msg_purpose: 'test',
sender_name: 'js-dict-test',
sender_id: 'sender-' + crypto.randomUUID(),
receiver_name: 'js-receiver',
receiver_id: 'receiver-' + crypto.randomUUID(),
reply_to: '',
reply_to_msg_id: '',
broker_url: TEST_BROKER_URL,
metadata: {},
payloads: [
{
id: 'payload-1',
dataname: 'simple_dict',
payload_type: 'dictionary',
transport: 'direct',
encoding: 'base64',
size: Buffer.from(JSON.stringify(simpleDict)).length,
data: Buffer.from(JSON.stringify(simpleDict)).toString('base64'),
metadata: { payload_bytes: Buffer.from(JSON.stringify(simpleDict)).length }
},
{
id: 'payload-2',
dataname: 'nested_dict',
payload_type: 'dictionary',
transport: 'direct',
encoding: 'base64',
size: Buffer.from(JSON.stringify(nestedDict)).length,
data: Buffer.from(JSON.stringify(nestedDict)).toString('base64'),
metadata: { payload_bytes: Buffer.from(JSON.stringify(nestedDict)).length }
},
{
id: 'payload-3',
dataname: 'array_dict',
payload_type: 'dictionary',
transport: 'direct',
encoding: 'base64',
size: Buffer.from(JSON.stringify(arrayDict)).length,
data: Buffer.from(JSON.stringify(arrayDict)).toString('base64'),
metadata: { payload_bytes: Buffer.from(JSON.stringify(arrayDict)).length }
},
{
id: 'payload-4',
dataname: 'mixed_dict',
payload_type: 'dictionary',
transport: 'direct',
encoding: 'base64',
size: Buffer.from(JSON.stringify(mixedDict)).length,
data: Buffer.from(JSON.stringify(mixedDict)).toString('base64'),
metadata: { payload_bytes: Buffer.from(JSON.stringify(mixedDict)).length }
}
]
};
const mockMsg = {
payload: JSON.stringify(testData)
};
console.log('Mock Message Created:');
console.log(` Correlation ID: ${testData.correlation_id}`);
console.log(` Payloads: ${testData.payloads.length}`);
console.log(` Payload types: ${testData.payloads.map(p => p.payload_type).join(', ')}\n`);
try {
// Receive and process the message
console.log('Receiving and processing message...');
const env = await NATSBridge.smartreceive(
mockMsg,
{
max_retries: 3,
base_delay: 100,
max_delay: 1000
}
);
console.log('\n=== Received Envelope ===');
console.log(`Correlation ID: ${env.correlation_id}`);
console.log(`Message ID: ${env.msg_id}`);
console.log(`Timestamp: ${env.timestamp}`);
console.log(`Subject: ${env.send_to}`);
console.log(`Payloads: ${env.payloads.length}\n`);
// Validate received data
console.log('=== Validation ===');
let passed = true;
if (!env.correlation_id) {
console.log('❌ correlation_id is missing');
passed = false;
} else {
console.log('✅ correlation_id present');
}
if (env.payloads.length !== 4) {
console.log(`❌ Expected 4 payloads, got ${env.payloads.length}`);
passed = false;
} else {
console.log('✅ Correct number of payloads');
}
// Expected data
const expectedData = [
['simple_dict', simpleDict, 'dictionary'],
['nested_dict', nestedDict, 'dictionary'],
['array_dict', arrayDict, 'dictionary'],
['mixed_dict', mixedDict, 'dictionary']
];
for (let i = 0; i < env.payloads.length; i++) {
const payload = env.payloads[i];
const expected = expectedData[i];
if (payload[0] !== expected[0]) {
console.log(`❌ Payload ${i + 1}: Expected dataname '${expected[0]}', got '${payload[0]}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct dataname`);
}
if (payload[2] !== expected[2]) {
console.log(`❌ Payload ${i + 1}: Expected type '${expected[2]}', got '${payload[2]}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct type`);
}
const dataMatch = JSON.stringify(payload[1]) === JSON.stringify(expected[1]);
if (!dataMatch) {
console.log(`❌ Payload ${i + 1}: Data mismatch`);
console.log(` Expected: ${JSON.stringify(expected[1])}`);
console.log(` Got: ${JSON.stringify(payload[1])}`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Data correctly deserialized`);
}
}
// Test round-trip with receive
console.log('\n=== Round-trip Test ===');
const roundTripData = {
correlation_id: 'roundtrip-' + crypto.randomUUID(),
msg_id: 'msg-' + crypto.randomUUID(),
timestamp: new Date().toISOString(),
send_to: '/test/dictionary',
msg_purpose: 'test',
sender_name: 'js-test',
sender_id: 'sender-' + crypto.randomUUID(),
receiver_name: 'js-receiver',
receiver_id: 'receiver-' + crypto.randomUUID(),
reply_to: '',
reply_to_msg_id: '',
broker_url: TEST_BROKER_URL,
metadata: {},
payloads: [
{
id: 'payload-rt',
dataname: 'roundtrip',
payload_type: 'dictionary',
transport: 'direct',
encoding: 'base64',
size: Buffer.from(JSON.stringify({ test: 'data', nested: { a: 1, b: 2 } })).length,
data: Buffer.from(JSON.stringify({ test: 'data', nested: { a: 1, b: 2 } })).toString('base64'),
metadata: { payload_bytes: Buffer.from(JSON.stringify({ test: 'data', nested: { a: 1, b: 2 } })).length }
}
]
};
const mockRtMsg = { payload: JSON.stringify(roundTripData) };
const rtEnv = await NATSBridge.smartreceive(mockRtMsg);
if (rtEnv.payloads.length === 1 &&
rtEnv.payloads[0][0] === 'roundtrip' &&
rtEnv.payloads[0][2] === 'dictionary') {
console.log('✅ Round-trip test successful');
} else {
console.log('❌ Round-trip test failed');
passed = false;
}
// Final result
console.log('\n=== Test Result ===');
if (passed) {
console.log('✅ ALL TESTS PASSED');
process.exit(0);
} else {
console.log('❌ SOME TESTS FAILED');
process.exit(1);
}
} catch (error) {
console.error('❌ Test failed with error:', error.message);
console.error(error.stack);
process.exit(1);
}
}
runTest();

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@@ -1,179 +0,0 @@
/**
* JavaScript Dictionary Sender Test
* Tests the smartsend function with dictionary payloads
*/
const NATSBridge = require('../src/natsbridge.js');
const crypto = require('crypto');
const TEST_SUBJECT = '/test/dictionary';
const TEST_BROKER_URL = process.env.NATS_URL || 'nats://localhost:4222';
const TEST_FILESERVER_URL = process.env.FILESERVER_URL || 'http://localhost:8080';
async function runTest() {
console.log('=== JavaScript Dictionary Sender Test ===\n');
const correlationId = crypto.randomUUID();
console.log(`Correlation ID: ${correlationId}`);
console.log(`Subject: ${TEST_SUBJECT}`);
console.log(`Broker URL: ${TEST_BROKER_URL}\n`);
// Test data - various dictionary structures
const testData = [
['simple_dict', { key1: 'value1', key2: 'value2' }, 'dictionary'],
['nested_dict', { outer: { inner: 'value', number: 42 } }, 'dictionary'],
['array_dict', { items: [1, 2, 3, 'four', 'five'] }, 'dictionary'],
['mixed_dict', { string: 'text', number: 123, boolean: true, null_val: null }, 'dictionary']
];
try {
// Send the message
console.log('Sending dictionary payloads...');
const [env, envJsonStr] = await NATSBridge.smartsend(
TEST_SUBJECT,
testData,
{
broker_url: TEST_BROKER_URL,
fileserver_url: TEST_FILESERVER_URL,
correlation_id: correlationId,
msg_purpose: 'test',
sender_name: 'js-dict-test',
is_publish: false
}
);
console.log('\n=== Envelope Created ===');
console.log(`Correlation ID: ${env.correlation_id}`);
console.log(`Message ID: ${env.msg_id}`);
console.log(`Timestamp: ${env.timestamp}`);
console.log(`Subject: ${env.send_to}`);
console.log(`Purpose: ${env.msg_purpose}`);
console.log(`Sender: ${env.sender_name}`);
console.log(`Payloads: ${env.payloads.length}\n`);
// Validate envelope structure
console.log('=== Validation ===');
let passed = true;
if (env.payloads.length !== 4) {
console.log(`❌ Expected 4 payloads, got ${env.payloads.length}`);
passed = false;
} else {
console.log('✅ Correct number of payloads');
}
// Test each payload
const expectedDatanames = ['simple_dict', 'nested_dict', 'array_dict', 'mixed_dict'];
const expectedTypes = ['dictionary', 'dictionary', 'dictionary', 'dictionary'];
for (let i = 0; i < env.payloads.length; i++) {
const payload = env.payloads[i];
if (payload.dataname !== expectedDatanames[i]) {
console.log(`❌ Payload ${i + 1}: Expected dataname '${expectedDatanames[i]}', got '${payload.dataname}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct dataname`);
}
if (payload.payload_type !== expectedTypes[i]) {
console.log(`❌ Payload ${i + 1}: Expected type '${expectedTypes[i]}', got '${payload.payload_type}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct type`);
}
if (payload.transport !== 'direct') {
console.log(`❌ Payload ${i + 1}: Expected transport 'direct', got '${payload.transport}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct transport`);
}
if (payload.encoding !== 'base64') {
console.log(`❌ Payload ${i + 1}: Expected encoding 'base64', got '${payload.encoding}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct encoding`);
}
// Decode and verify the data
const decodedData = JSON.parse(Buffer.from(payload.data, 'base64').toString('utf8'));
const originalData = testData[i][1];
const originalJson = JSON.stringify(originalData);
const decodedJson = JSON.stringify(decodedData);
if (originalJson !== decodedJson) {
console.log(`❌ Payload ${i + 1}: Data integrity mismatch`);
console.log(` Expected: ${originalJson}`);
console.log(` Got: ${decodedJson}`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Data integrity verified`);
}
console.log(` Size: ${payload.size} bytes\n`);
}
// Test round-trip serialization
console.log('=== Round-trip Serialization Test ===');
const roundTripTestData = [
['roundtrip', { test: 'data', numbers: [1, 2, 3], nested: { a: 1, b: 2 } }, 'dictionary']
];
const [rtEnv, rtEnvJsonStr] = await NATSBridge.smartsend(
TEST_SUBJECT,
roundTripTestData,
{
broker_url: TEST_BROKER_URL,
fileserver_url: TEST_FILESERVER_URL,
correlation_id: 'roundtrip-' + correlationId,
is_publish: false
}
);
const rtPayload = rtEnv.payloads[0];
const rtDecoded = JSON.parse(Buffer.from(rtPayload.data, 'base64').toString('utf8'));
if (JSON.stringify(rtDecoded) === JSON.stringify(roundTripTestData[0][1])) {
console.log('✅ Round-trip serialization successful');
} else {
console.log('❌ Round-trip serialization failed');
passed = false;
}
// Test JSON string output
console.log('\n=== JSON String Output Test ===');
try {
const parsed = JSON.parse(envJsonStr);
if (parsed.correlation_id === env.correlation_id &&
parsed.payloads.length === env.payloads.length) {
console.log('✅ JSON string is valid and matches envelope');
} else {
console.log('❌ JSON string does not match envelope');
passed = false;
}
} catch (e) {
console.log('❌ JSON string is invalid:', e.message);
passed = false;
}
// Final result
console.log('\n=== Test Result ===');
if (passed) {
console.log('✅ ALL TESTS PASSED');
process.exit(0);
} else {
console.log('❌ SOME TESTS FAILED');
process.exit(1);
}
} catch (error) {
console.error('❌ Test failed with error:', error.message);
console.error(error.stack);
process.exit(1);
}
}
runTest();

View File

@@ -1,15 +1,18 @@
/**
* JavaScript Mix Payloads Receiver Test
* Tests the smartreceive function with mixed payload types
*
* This test mirrors test_julia_mix_payloads_receiver.jl and demonstrates that
* any combination and any number of mixed content can be received correctly.
*/
const NATSBridge = require('../src/natsbridge.js');
const nats = require('nats');
const crypto = require('crypto');
const TEST_SUBJECT = '/test/mix';
const TEST_BROKER_URL = process.env.NATS_URL || 'nats://localhost:4222';
const TEST_FILESERVER_URL = process.env.FILESERVER_URL || 'http://localhost:8080';
const TEST_SUBJECT = '/natsbridge';
const TEST_BROKER_URL = process.env.NATS_URL || 'nats.yiem.cc';
const TEST_FILESERVER_URL = process.env.FILESERVER_URL || 'http://192.168.88.104:8080';
async function runTest() {
console.log('=== JavaScript Mix Payloads Receiver Test ===\n');
@@ -17,20 +20,8 @@ async function runTest() {
const correlationId = crypto.randomUUID();
console.log(`Correlation ID: ${correlationId}`);
console.log(`Subject: ${TEST_SUBJECT}`);
console.log(`Broker URL: ${TEST_BROKER_URL}\n`);
// Expected test data - same as sender
const expectedTextData = 'Hello, NATSBridge!';
const expectedDictData = { key1: 'value1', key2: 42, nested: { a: 1, b: 2 } };
const expectedBinaryData = Buffer.from([0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A]); // PNG header
const expectedTableData = [
{ id: 1, name: 'Alice', age: 30 },
{ id: 2, name: 'Bob', age: 25 },
{ id: 3, name: 'Charlie', age: 35 }
];
const expectedDatanames = ['message', 'config', 'image', 'users'];
const expectedTypes = ['text', 'dictionary', 'image', 'table'];
console.log(`Broker URL: ${TEST_BROKER_URL}`);
console.log(`Fileserver URL: ${TEST_FILESERVER_URL}\n`);
let testPassed = true;
let messagesReceived = 0;
@@ -49,14 +40,14 @@ async function runTest() {
const messagePromise = new Promise(async (resolve, reject) => {
const timeout = setTimeout(() => {
resolve('timeout');
}, 10000); // 10 second timeout
}, 180000); // 180 second timeout (matches Julia test)
(async () => {
for await (const msg of subscription) {
clearTimeout(timeout);
messagesReceived++;
console.log(`\n=== Message ${messagesReceived} Received ===`);
console.log(`Raw payload length: ${msg.payload.length} bytes`);
console.log(`Received message on ${msg.subject}`);
try {
// Process the message using smartreceive
@@ -69,6 +60,9 @@ async function runTest() {
console.log(`Correlation ID: ${envelope.correlation_id}`);
console.log(`Message ID: ${envelope.msg_id}`);
console.log(`Timestamp: ${envelope.timestamp}`);
console.log(`Purpose: ${envelope.msg_purpose}`);
console.log(`Sender: ${envelope.sender_name}`);
console.log(`Number of payloads: ${envelope.payloads.length}`);
receivedPayloads.push(envelope);
@@ -76,96 +70,161 @@ async function runTest() {
// Validate envelope structure
console.log('\n=== Envelope Validation ===');
if (envelope.payloads.length < 4) {
console.log(`❌ Expected at least 4 payloads, got ${envelope.payloads.length}`);
if (envelope.payloads.length < 1) {
console.log(`❌ Expected at least 1 payload, got ${envelope.payloads.length}`);
testPassed = false;
} else {
console.log(`✅ Correct number of payloads: ${envelope.payloads.length}`);
}
// Validate each payload
// Process all payloads in the envelope
console.log('\n=== Processing Payloads ===');
for (let i = 0; i < envelope.payloads.length; i++) {
const [dataname, data, dataType] = envelope.payloads[i];
console.log(`\n--- Payload ${i + 1}: ${dataname} (type: ${dataType}) ---`);
// Check dataname
if (i < expectedDatanames.length && dataname !== expectedDatanames[i]) {
console.log(`❌ Expected dataname '${expectedDatanames[i]}', got '${dataname}'`);
testPassed = false;
} else {
console.log(`✅ Correct dataname: ${dataname}`);
}
// Check data type
if (i < expectedTypes.length && dataType !== expectedTypes[i]) {
console.log(`❌ Expected type '${expectedTypes[i]}', got '${dataType}'`);
testPassed = false;
} else {
console.log(`✅ Correct type: ${dataType}`);
}
// Validate data based on type
if (dataType === 'text') {
if (typeof data === 'string' && data === expectedTextData) {
console.log(`✅ Text data verified: "${data}"`);
if (typeof data === 'string') {
console.log(`✅ Text data received (${data.length} chars)`);
console.log(` First 200 chars: "${data.substring(0, 200)}${data.length > 200 ? '...' : ''}"`);
// Save to file
const outputPath = `./received_${dataname}.txt`;
require('fs').writeFileSync(outputPath, data);
console.log(` Saved to: ${outputPath}`);
} else {
console.log(`❌ Text data mismatch. Expected: "${expectedTextData}", Got: "${data}"`);
console.log(`❌ Text data is not a string, got: ${typeof data}`);
testPassed = false;
}
} else if (dataType === 'dictionary') {
if (typeof data === 'object' && JSON.stringify(data) === JSON.stringify(expectedDictData)) {
console.log(`✅ Dictionary data verified`);
if (typeof data === 'object' && data !== null && !Array.isArray(data)) {
console.log(`✅ Dictionary data received`);
console.log(` Keys: ${Object.keys(data).join(', ')}`);
// Save to JSON file
const outputPath = `./received_${dataname}.json`;
require('fs').writeFileSync(outputPath, JSON.stringify(data, null, 2));
console.log(` Saved to: ${outputPath}`);
} else {
console.log(`❌ Dictionary data is not an object, got: ${typeof data}`);
testPassed = false;
}
} else if (dataType === 'arrowtable') {
// Arrow tables have numRows and numCols properties
if (data && typeof data === 'object' &&
(data.numRows !== undefined || data.numRows !== null) &&
(data.numCols !== undefined || data.numCols !== null)) {
console.log(`✅ Arrow table data received`);
console.log(` Rows: ${data.numRows}, Columns: ${data.numCols}`);
// Save to file
const outputPath = `./received_${dataname}.arrow`;
// Note: Actual Arrow IPC serialization would require apache-arrow library
console.log(` Saved to: ${outputPath}`);
} else if (data && typeof data === 'object') {
// Some Arrow implementations may have different properties
console.log(`✅ Arrow table data received (non-standard format)`);
console.log(` Keys: ${Object.keys(data).join(', ')}`);
} else {
console.log(`Dictionary data mismatch`);
console.log(` Expected: ${JSON.stringify(expectedDictData)}`);
console.log(` Got: ${JSON.stringify(data)}`);
console.log(`Arrow table data is not a valid object, got: ${typeof data}`);
testPassed = false;
}
} else if (dataType === 'jsontable') {
if (Array.isArray(data)) {
console.log(`✅ JSON table data received`);
console.log(` Rows: ${data.length}`);
if (data.length > 0) {
console.log(` Columns: ${Object.keys(data[0]).join(', ')}`);
}
// Save to JSON file
const outputPath = `./received_${dataname}.json`;
require('fs').writeFileSync(outputPath, JSON.stringify(data, null, 2));
console.log(` Saved to: ${outputPath}`);
} else {
console.log(`❌ JSON table data is not an array, got: ${typeof data}`);
testPassed = false;
}
} else if (dataType === 'image') {
if (data instanceof Buffer || data instanceof Uint8Array) {
const dataBuffer = Buffer.isBuffer(data) ? data : Buffer.from(data);
if (dataBuffer.length === expectedBinaryData.length) {
let dataMatch = true;
for (let j = 0; j < expectedBinaryData.length; j++) {
if (dataBuffer[j] !== expectedBinaryData[j]) {
dataMatch = false;
break;
}
}
if (dataMatch) {
console.log(`✅ Image data verified (${dataBuffer.length} bytes)`);
} else {
console.log(`❌ Image data mismatch`);
testPassed = false;
}
} else {
console.log(`❌ Image data length mismatch. Expected: ${expectedBinaryData.length}, Got: ${dataBuffer.length}`);
testPassed = false;
}
console.log(`✅ Image data received (${dataBuffer.length} bytes)`);
// Save to file
const outputPath = `./received_${dataname}.bin`;
require('fs').writeFileSync(outputPath, dataBuffer);
console.log(` Saved to: ${outputPath}`);
} else {
console.log(`❌ Image data is not a Buffer or Uint8Array`);
console.log(`❌ Image data is not a Buffer or Uint8Array, got: ${typeof data}`);
testPassed = false;
}
} else if (dataType === 'table') {
// For table data, check if it's an Arrow table-like object
if (data && typeof data === 'object') {
// Arrow tables have specific properties
if (data.numRows !== undefined && data.numCols !== undefined) {
console.log(`✅ Table data verified`);
console.log(` Rows: ${data.numRows}, Columns: ${data.numCols}`);
} else {
console.log(`⚠️ Table data received but not standard Arrow format`);
console.log(` Keys: ${Object.keys(data).join(', ')}`);
}
} else if (dataType === 'audio') {
if (data instanceof Buffer || data instanceof Uint8Array) {
const dataBuffer = Buffer.isBuffer(data) ? data : Buffer.from(data);
console.log(`✅ Audio data received (${dataBuffer.length} bytes)`);
// Save to file
const outputPath = `./received_${dataname}.bin`;
require('fs').writeFileSync(outputPath, dataBuffer);
console.log(` Saved to: ${outputPath}`);
} else {
console.log(`Table data is not a valid object`);
console.log(`Audio data is not a Buffer or Uint8Array, got: ${typeof data}`);
testPassed = false;
}
} else if (dataType === 'video') {
if (data instanceof Buffer || data instanceof Uint8Array) {
const dataBuffer = Buffer.isBuffer(data) ? data : Buffer.from(data);
console.log(`✅ Video data received (${dataBuffer.length} bytes)`);
// Save to file
const outputPath = `./received_${dataname}.bin`;
require('fs').writeFileSync(outputPath, dataBuffer);
console.log(` Saved to: ${outputPath}`);
} else {
console.log(`❌ Video data is not a Buffer or Uint8Array, got: ${typeof data}`);
testPassed = false;
}
} else if (dataType === 'binary') {
if (data instanceof Buffer || data instanceof Uint8Array) {
const dataBuffer = Buffer.isBuffer(data) ? data : Buffer.from(data);
console.log(`✅ Binary data received (${dataBuffer.length} bytes)`);
// Save to file
const outputPath = `./received_${dataname}`;
require('fs').writeFileSync(outputPath, dataBuffer);
console.log(` Saved to: ${outputPath}`);
} else {
console.log(`❌ Binary data is not a Buffer or Uint8Array, got: ${typeof data}`);
testPassed = false;
}
} else {
console.log(`❌ Unknown data type: ${dataType}`);
testPassed = false;
}
}
// Print summary
console.log('\n=== Verification Summary ===');
const textCount = envelope.payloads.filter(p => p[2] === 'text').length;
const dictCount = envelope.payloads.filter(p => p[2] === 'dictionary').length;
const arrowtableCount = envelope.payloads.filter(p => p[2] === 'arrowtable').length;
const jsontableCount = envelope.payloads.filter(p => p[2] === 'jsontable').length;
const imageCount = envelope.payloads.filter(p => p[2] === 'image').length;
const audioCount = envelope.payloads.filter(p => p[2] === 'audio').length;
const videoCount = envelope.payloads.filter(p => p[2] === 'video').length;
const binaryCount = envelope.payloads.filter(p => p[2] === 'binary').length;
console.log(`Text payloads: ${textCount}`);
console.log(`Dictionary payloads: ${dictCount}`);
console.log(`Arrow table payloads: ${arrowtableCount}`);
console.log(`JSON table payloads: ${jsontableCount}`);
console.log(`Image payloads: ${imageCount}`);
console.log(`Audio payloads: ${audioCount}`);
console.log(`Video payloads: ${videoCount}`);
console.log(`Binary payloads: ${binaryCount}`);
// Stop after receiving at least one valid message
if (messagesReceived >= 1) {
resolve('done');
@@ -213,4 +272,4 @@ async function runTest() {
}
}
runTest();
runTest();

View File

@@ -1,14 +1,20 @@
/**
* JavaScript Mix Payloads Sender Test
* Tests the smartsend function with mixed payload types
*
* This test mirrors test_julia_mix_payloads_sender.jl and demonstrates that
* any combination and any number of mixed content can be sent correctly.
*/
const NATSBridge = require('../src/natsbridge.js');
const crypto = require('crypto');
const fs = require('fs');
const path = require('path');
const TEST_SUBJECT = '/test/mix';
const TEST_BROKER_URL = process.env.NATS_URL || 'nats://localhost:4222';
const TEST_FILESERVER_URL = process.env.FILESERVER_URL || 'http://localhost:8080';
const TEST_SUBJECT = '/natsbridge';
const TEST_BROKER_URL = process.env.NATS_URL || 'nats.yiem.cc';
const TEST_FILESERVER_URL = process.env.FILESERVER_URL || 'http://192.168.88.104:8080';
const SIZE_THRESHOLD = 1_000_000; // 1MB threshold
async function runTest() {
console.log('=== JavaScript Mix Payloads Sender Test ===\n');
@@ -16,40 +22,169 @@ async function runTest() {
const correlationId = crypto.randomUUID();
console.log(`Correlation ID: ${correlationId}`);
console.log(`Subject: ${TEST_SUBJECT}`);
console.log(`Broker URL: ${TEST_BROKER_URL}\n`);
console.log(`Broker URL: ${TEST_BROKER_URL}`);
console.log(`Fileserver URL: ${TEST_FILESERVER_URL}`);
console.log(`Size Threshold: ${SIZE_THRESHOLD} bytes (1MB)\n`);
// Test data - mixed payload types
const textData = 'Hello, NATSBridge!';
const dictData = { key1: 'value1', key2: 42, nested: { a: 1, b: 2 } };
const binaryData = Buffer.from([0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A]); // PNG header
// Helper: Log with correlation ID
function logTrace(message) {
const timestamp = new Date().toISOString();
console.log(`[${timestamp}] [Correlation: ${correlationId}] ${message}`);
}
// Table data
const tableData = [
{ id: 1, name: 'Alice', age: 30 },
{ id: 2, name: 'Bob', age: 25 },
{ id: 3, name: 'Charlie', age: 35 }
// Create sample data for each type (mirroring Julia test)
const textData = 'Hello! This is a test chat message. 🎉\nHow are you doing today? 😊';
const dictData = {
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']
}
};
// Arrow table data (small - direct transport)
const arrowTableSmall = [
{ id: 1, name: 'Alice', score: 95, active: true },
{ id: 2, name: 'Bob', score: 88, active: false },
{ id: 3, name: 'Charlie', score: 92, active: true },
{ id: 4, name: 'Diana', score: 78, active: true },
{ id: 5, name: 'Eve', score: 85, active: false },
{ id: 6, name: 'Frank', score: 91, active: true },
{ id: 7, name: 'Grace', score: 89, active: true },
{ id: 8, name: 'Henry', score: 76, active: false },
{ id: 9, name: 'Ivy', score: 94, active: true },
{ id: 10, name: 'Jack', score: 82, active: true }
];
const testData = [
['message', textData, 'text'],
['config', dictData, 'dictionary'],
['image', binaryData, 'image'],
['users', tableData, 'table']
// Json table data (small - direct transport)
const jsonTableSmall = [
{ id: 1, name: 'Alice', score: 95, active: true },
{ id: 2, name: 'Bob', score: 88, active: false },
{ id: 3, name: 'Charlie', score: 92, active: true },
{ id: 4, name: 'Diana', score: 78, active: true },
{ id: 5, name: 'Eve', score: 85, active: false },
{ id: 6, name: 'Frank', score: 91, active: true },
{ id: 7, name: 'Grace', score: 89, active: true },
{ id: 8, name: 'Henry', score: 76, active: false },
{ id: 9, name: 'Ivy', score: 94, active: true },
{ id: 10, name: 'Jack', score: 82, active: true }
];
// Audio data (small binary - direct transport)
const audioData = Buffer.alloc(100);
for (let i = 0; i < 100; i++) {
audioData[i] = Math.floor(Math.random() * 255);
}
// Video data (small binary - direct transport)
const videoData = Buffer.alloc(150);
for (let i = 0; i < 150; i++) {
videoData[i] = Math.floor(Math.random() * 255);
}
// Binary data (small - direct transport)
const binaryData = Buffer.alloc(200);
for (let i = 0; i < 200; i++) {
binaryData[i] = Math.floor(Math.random() * 255);
}
// Large data for link transport testing
const largeArrowTable = [];
for (let i = 1; i <= 20000; i++) {
largeArrowTable.push({
id: i,
name: `user_${i}`,
score: Math.floor(Math.random() * 51) + 50,
active: Math.random() > 0.5,
timestamp: new Date().toISOString()
});
}
const largeJsonTable = [];
for (let i = 1; i <= 50000; i++) {
largeJsonTable.push({
id: i,
name: `user_${i}`,
score: Math.floor(Math.random() * 51) + 50,
active: Math.random() > 0.5
});
}
const largeAudioData = Buffer.alloc(1_500_000);
for (let i = 0; i < 1_500_000; i++) {
largeAudioData[i] = Math.floor(Math.random() * 255);
}
const largeVideoData = Buffer.alloc(1_500_000);
for (let i = 0; i < 1_500_000; i++) {
largeVideoData[i] = Math.floor(Math.random() * 255);
}
const largeBinaryData = Buffer.alloc(1_500_000);
for (let i = 0; i < 1_500_000; i++) {
largeBinaryData[i] = Math.floor(Math.random() * 255);
}
// Read image files from disk (following Julia test pattern)
const file_path_small_image = path.join(__dirname, 'small_image.jpg');
const file_data_small_image = fs.readFileSync(file_path_small_image);
const filename_small_image = path.basename(file_path_small_image);
const file_path_large_image = path.join(__dirname, 'large_image.png');
const file_data_large_image = fs.readFileSync(file_path_large_image);
const filename_large_image = path.basename(file_path_large_image);
logTrace('Creating payloads list with mixed content');
// 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, arrowtable, jsontable, small image
['chat_text', textData, 'text'],
['chat_json', dictData, 'dictionary'],
// ['arrow_table_small', arrowTableSmall, 'arrowtable'],
['json_table_small', jsonTableSmall, 'jsontable'],
[filename_small_image, file_data_small_image, 'binary'],
// Large data (link transport) - large arrowtable, large jsontable, large image, large audio, large video, large binary
// ['arrow_table_large', largeArrowTable, 'arrowtable'],
['json_table_large', largeJsonTable, 'jsontable'],
[filename_large_image, file_data_large_image, 'binary'],
// ['audio_clip_large', largeAudioData, 'audio'],
// ['video_clip_large', largeVideoData, 'video'],
// ['binary_file_large', largeBinaryData, 'binary']
];
logTrace(`Total payloads: ${payloads.length}`);
try {
// Send the message
console.log('Sending mixed payloads...');
console.log('Sending mixed payloads...\n');
const [env, envJsonStr] = await NATSBridge.smartsend(
TEST_SUBJECT,
testData,
payloads,
{
broker_url: TEST_BROKER_URL,
fileserver_url: TEST_FILESERVER_URL,
fileserver_upload_handler: NATSBridge.plikOneshotUpload,
size_threshold: SIZE_THRESHOLD,
correlation_id: correlationId,
msg_purpose: 'test',
msg_purpose: 'chat',
sender_name: 'js-mix-test',
is_publish: false
receiver_name: '',
receiver_id: '',
reply_to: '',
reply_to_msg_id: '',
is_publish: true
}
);
@@ -62,144 +197,11 @@ async function runTest() {
console.log(`Sender: ${env.sender_name}`);
console.log(`Payloads: ${env.payloads.length}\n`);
// Validate envelope structure
console.log('=== Validation ===');
let passed = true;
if (env.payloads.length !== 4) {
console.log(`❌ Expected 4 payloads, got ${env.payloads.length}`);
passed = false;
} else {
console.log('✅ Correct number of payloads');
}
// Test each payload
const expectedDatanames = ['message', 'config', 'image', 'users'];
const expectedTypes = ['text', 'dictionary', 'image', 'table'];
const expectedData = [textData, dictData, binaryData, tableData];
for (let i = 0; i < env.payloads.length; i++) {
const payload = env.payloads[i];
if (payload.dataname !== expectedDatanames[i]) {
console.log(`❌ Payload ${i + 1}: Expected dataname '${expectedDatanames[i]}', got '${payload.dataname}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct dataname`);
}
if (payload.payload_type !== expectedTypes[i]) {
console.log(`❌ Payload ${i + 1}: Expected type '${expectedTypes[i]}', got '${payload.payload_type}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct type`);
}
if (payload.transport !== 'direct') {
console.log(`❌ Payload ${i + 1}: Expected transport 'direct', got '${payload.transport}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct transport`);
}
if (payload.encoding !== 'base64') {
console.log(`❌ Payload ${i + 1}: Expected encoding 'base64', got '${payload.encoding}'`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Correct encoding`);
}
// Verify data integrity based on type
if (expectedTypes[i] === 'text') {
const decodedData = Buffer.from(payload.data, 'base64').toString('utf8');
if (decodedData !== expectedData[i]) {
console.log(`❌ Payload ${i + 1}: Data integrity mismatch`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Data integrity verified`);
}
} else if (expectedTypes[i] === 'dictionary') {
const decodedData = JSON.parse(Buffer.from(payload.data, 'base64').toString('utf8'));
if (JSON.stringify(decodedData) !== JSON.stringify(expectedData[i])) {
console.log(`❌ Payload ${i + 1}: Data integrity mismatch`);
passed = false;
} else {
console.log(`✅ Payload ${i + 1}: Data integrity verified`);
}
} else if (expectedTypes[i] === 'image') {
const decodedData = Buffer.from(payload.data, 'base64');
if (decodedData.length !== expectedData[i].length) {
console.log(`❌ Payload ${i + 1}: Length mismatch`);
passed = false;
} else {
let dataMatch = true;
for (let j = 0; j < expectedData[i].length; j++) {
if (decodedData[j] !== expectedData[i][j]) {
dataMatch = false;
break;
}
}
if (dataMatch) {
console.log(`✅ Payload ${i + 1}: Data integrity verified`);
} else {
console.log(`❌ Payload ${i + 1}: Data integrity mismatch`);
passed = false;
}
}
} else if (expectedTypes[i] === 'table') {
const decodedData = Buffer.from(payload.data, 'base64');
if (decodedData.length > 0) {
console.log(`✅ Payload ${i + 1}: Arrow IPC data present (${decodedData.length} bytes)`);
} else {
console.log(`❌ Payload ${i + 1}: Arrow IPC data is empty`);
passed = false;
}
}
console.log(` Size: ${payload.size} bytes\n`);
}
// Test with chat-like payload (text + image + audio)
console.log('=== Chat-like Payload Test ===');
const chatData = [
['text', 'Hello!', 'text'],
['image', Buffer.from([0xFF, 0xD8, 0xFF, 0xE0]), 'image'],
['audio', Buffer.from([0x46, 0x4C, 0x41, 0x43]), 'audio']
];
const [chatEnv, _] = await NATSBridge.smartsend(
TEST_SUBJECT,
chatData,
{
broker_url: TEST_BROKER_URL,
fileserver_url: TEST_FILESERVER_URL,
correlation_id: 'chat-' + correlationId,
is_publish: false
}
);
if (chatEnv.payloads.length === 3) {
console.log('✅ Chat-like payloads handled correctly');
} else {
console.log('❌ Chat-like payloads handling failed');
passed = false;
}
// Final result
console.log('\n=== Test Result ===');
if (passed) {
console.log('✅ ALL TESTS PASSED');
process.exit(0);
} else {
console.log('❌ SOME TESTS FAILED');
process.exit(1);
}
} catch (error) {
console.error('❌ Test failed with error:', error.message);
console.error('\n❌ Test failed with error:', error.message);
console.error(error.stack);
process.exit(1);
}
}
runTest();
runTest();

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@@ -1,173 +0,0 @@
/**
* JavaScript Table Receiver Test
* Tests the smartreceive function with table (Arrow IPC) payloads
*/
const NATSBridge = require('../src/natsbridge.js');
const crypto = require('crypto');
const TEST_BROKER_URL = process.env.NATS_URL || 'nats://localhost:4222';
const TEST_FILESERVER_URL = process.env.FILESERVER_URL || 'http://localhost:8080';
async function runTest() {
console.log('=== JavaScript Table Receiver Test ===\n');
// Create a mock NATS message with table payload
const tableData = [
{ id: 1, name: 'Alice', age: 30, active: true },
{ id: 2, name: 'Bob', age: 25, active: false },
{ id: 3, name: 'Charlie', age: 35, active: true }
];
// Convert to Arrow IPC format
const arrow = require('apache-arrow');
const fields = [
new arrow.Field('id', arrow.Int64, true),
new arrow.Field('name', arrow.Utf8, true),
new arrow.Field('age', arrow.Int64, true),
new arrow.Field('active', arrow.Boolean, true)
];
const schema = new arrow.Schema(fields);
const batches = [];
for (const row of tableData) {
const batch = arrow.recordBatch.fromObjects([row], schema);
batches.push(batch);
}
const buffers = arrow.ipc.recordBatchesToMessage(batches, schema).buffers;
const combined = new Uint8Array(buffers.reduce((acc, b) => acc + b.byteLength, 0));
let offset = 0;
for (const buf of buffers) {
combined.set(new Uint8Array(buf), offset);
offset += buf.byteLength;
}
const arrowBuffer = Buffer.from(combined);
const testData = {
correlation_id: 'js-table-receiver-' + crypto.randomUUID(),
msg_id: 'msg-' + crypto.randomUUID(),
timestamp: new Date().toISOString(),
send_to: '/test/table',
msg_purpose: 'test',
sender_name: 'js-table-test',
sender_id: 'sender-' + crypto.randomUUID(),
receiver_name: 'js-receiver',
receiver_id: 'receiver-' + crypto.randomUUID(),
reply_to: '',
reply_to_msg_id: '',
broker_url: TEST_BROKER_URL,
metadata: {},
payloads: [
{
id: 'payload-1',
dataname: 'users_table',
payload_type: 'table',
transport: 'direct',
encoding: 'base64',
size: arrowBuffer.length,
data: arrowBuffer.toString('base64'),
metadata: { payload_bytes: arrowBuffer.length }
}
]
};
const mockMsg = {
payload: JSON.stringify(testData)
};
console.log('Mock Message Created:');
console.log(` Correlation ID: ${testData.correlation_id}`);
console.log(` Payloads: ${testData.payloads.length}`);
console.log(` Payload type: ${testData.payloads[0].payload_type}`);
console.log(` Transport: ${testData.payloads[0].transport}\n`);
try {
// Receive and process the message
console.log('Receiving and processing message...');
const env = await NATSBridge.smartreceive(
mockMsg,
{
max_retries: 3,
base_delay: 100,
max_delay: 1000
}
);
console.log('\n=== Received Envelope ===');
console.log(`Correlation ID: ${env.correlation_id}`);
console.log(`Message ID: ${env.msg_id}`);
console.log(`Timestamp: ${env.timestamp}`);
console.log(`Subject: ${env.send_to}`);
console.log(`Payloads: ${env.payloads.length}\n`);
// Validate received data
console.log('=== Validation ===');
let passed = true;
if (!env.correlation_id) {
console.log('❌ correlation_id is missing');
passed = false;
} else {
console.log('✅ correlation_id present');
}
if (env.payloads.length !== 1) {
console.log(`❌ Expected 1 payload, got ${env.payloads.length}`);
passed = false;
} else {
console.log('✅ Correct number of payloads');
}
const payload = env.payloads[0];
if (payload[0] !== 'users_table') {
console.log(`❌ Expected dataname 'users_table', got '${payload[0]}'`);
passed = false;
} else {
console.log('✅ Correct dataname');
}
if (payload[2] !== 'table') {
console.log(`❌ Expected type 'table', got '${payload[2]}'`);
passed = false;
} else {
console.log('✅ Correct type');
}
// Verify table data is a Buffer (Arrow IPC format)
if (payload[1] instanceof Buffer || payload[1] instanceof Uint8Array) {
console.log('✅ Table data is Arrow IPC buffer');
console.log(` Buffer size: ${payload[1].length} bytes`);
} else {
console.log(`❌ Expected Buffer/Uint8Array, got ${typeof payload[1]}`);
passed = false;
}
// Test round-trip with Arrow deserialization
console.log('\n=== Arrow Deserialization Test ===');
try {
const table = arrow.tableFromRawBytes(payload[1]);
console.log(`✅ Arrow table deserialized successfully`);
console.log(` Schema: ${table.schema.fields.map(f => f.name).join(', ')}`);
console.log(` Num rows: ${table.numRows}`);
} catch (e) {
console.log('❌ Arrow deserialization failed:', e.message);
passed = false;
}
// Final result
console.log('\n=== Test Result ===');
if (passed) {
console.log('✅ ALL TESTS PASSED');
process.exit(0);
} else {
console.log('❌ SOME TESTS FAILED');
process.exit(1);
}
} catch (error) {
console.error('❌ Test failed with error:', error.message);
console.error(error.stack);
process.exit(1);
}
}
runTest();

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@@ -1,180 +0,0 @@
/**
* JavaScript Table Sender Test
* Tests the smartsend function with table (Arrow IPC) payloads
*/
const NATSBridge = require('../src/natsbridge.js');
const crypto = require('crypto');
const TEST_SUBJECT = '/test/table';
const TEST_BROKER_URL = process.env.NATS_URL || 'nats://localhost:4222';
const TEST_FILESERVER_URL = process.env.FILESERVER_URL || 'http://localhost:8080';
async function runTest() {
console.log('=== JavaScript Table Sender Test ===\n');
const correlationId = crypto.randomUUID();
console.log(`Correlation ID: ${correlationId}`);
console.log(`Subject: ${TEST_SUBJECT}`);
console.log(`Broker URL: ${TEST_BROKER_URL}\n`);
// Test data - table data as array of objects
const tableData = [
{ id: 1, name: 'Alice', age: 30, active: true },
{ id: 2, name: 'Bob', age: 25, active: false },
{ id: 3, name: 'Charlie', age: 35, active: true },
{ id: 4, name: 'Diana', age: 28, active: true },
{ id: 5, name: 'Eve', age: 32, active: false }
];
const testData = [
['users_table', tableData, 'table']
];
try {
// Send the message
console.log('Sending table payload...');
const [env, envJsonStr] = await NATSBridge.smartsend(
TEST_SUBJECT,
testData,
{
broker_url: TEST_BROKER_URL,
fileserver_url: TEST_FILESERVER_URL,
correlation_id: correlationId,
msg_purpose: 'test',
sender_name: 'js-table-test',
is_publish: false
}
);
console.log('\n=== Envelope Created ===');
console.log(`Correlation ID: ${env.correlation_id}`);
console.log(`Message ID: ${env.msg_id}`);
console.log(`Timestamp: ${env.timestamp}`);
console.log(`Subject: ${env.send_to}`);
console.log(`Purpose: ${env.msg_purpose}`);
console.log(`Sender: ${env.sender_name}`);
console.log(`Payloads: ${env.payloads.length}\n`);
// Validate envelope structure
console.log('=== Validation ===');
let passed = true;
if (env.payloads.length !== 1) {
console.log(`❌ Expected 1 payload, got ${env.payloads.length}`);
passed = false;
} else {
console.log('✅ Correct number of payloads');
}
const payload = env.payloads[0];
if (payload.dataname !== 'users_table') {
console.log(`❌ Expected dataname 'users_table', got '${payload.dataname}'`);
passed = false;
} else {
console.log('✅ Correct dataname');
}
if (payload.payload_type !== 'table') {
console.log(`❌ Expected payload_type 'table', got '${payload.payload_type}'`);
passed = false;
} else {
console.log('✅ Correct payload_type');
}
if (payload.transport !== 'direct') {
console.log(`❌ Expected transport 'direct', got '${payload.transport}'`);
passed = false;
} else {
console.log('✅ Correct transport');
}
if (payload.encoding !== 'base64') {
console.log(`❌ Expected encoding 'base64', got '${payload.encoding}'`);
passed = false;
} else {
console.log('✅ Correct encoding');
}
// Verify Arrow IPC data can be decoded
console.log('\n=== Arrow IPC Verification ===');
const decodedData = Buffer.from(payload.data, 'base64');
console.log(`Arrow IPC buffer size: ${decodedData.length} bytes`);
if (decodedData.length > 0) {
console.log('✅ Arrow IPC data present');
} else {
console.log('❌ Arrow IPC data is empty');
passed = false;
}
// Test with larger table
console.log('\n=== Larger Table Test ===');
const largeTableData = [];
for (let i = 1; i <= 100; i++) {
largeTableData.push({
id: i,
name: `User${i}`,
age: Math.floor(Math.random() * 100),
active: Math.random() > 0.5,
score: Math.random() * 100
});
}
const largeTestData = [
['large_table', largeTableData, 'table']
];
const [largeEnv, _] = await NATSBridge.smartsend(
TEST_SUBJECT,
largeTestData,
{
broker_url: TEST_BROKER_URL,
fileserver_url: TEST_FILESERVER_URL,
correlation_id: 'large-' + correlationId,
is_publish: false
}
);
if (largeEnv.payloads.length === 1) {
console.log('✅ Large table handled correctly');
console.log(` Size: ${largeEnv.payloads[0].size} bytes`);
} else {
console.log('❌ Large table handling failed');
passed = false;
}
// Test JSON string output
console.log('\n=== JSON String Output Test ===');
try {
const parsed = JSON.parse(envJsonStr);
if (parsed.correlation_id === env.correlation_id &&
parsed.payloads.length === env.payloads.length) {
console.log('✅ JSON string is valid and matches envelope');
} else {
console.log('❌ JSON string does not match envelope');
passed = false;
}
} catch (e) {
console.log('❌ JSON string is invalid:', e.message);
passed = false;
}
// Final result
console.log('\n=== Test Result ===');
if (passed) {
console.log('✅ ALL TESTS PASSED');
process.exit(0);
} else {
console.log('❌ SOME TESTS FAILED');
process.exit(1);
}
} catch (error) {
console.error('❌ Test failed with error:', error.message);
console.error(error.stack);
process.exit(1);
}
}
runTest();

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@@ -1,207 +0,0 @@
/**
* JavaScript Text Receiver Test
* Tests the smartreceive function with text payloads
*/
const NATSBridge = require('../src/natsbridge.js');
const crypto = require('crypto');
const TEST_BROKER_URL = process.env.NATS_URL || 'nats://localhost:4222';
const TEST_FILESERVER_URL = process.env.FILESERVER_URL || 'http://localhost:8080';
async function runTest() {
console.log('=== JavaScript Text Receiver Test ===\n');
// Create a mock NATS message with text payload
const testData = {
correlation_id: 'test-receiver-' + crypto.randomUUID(),
msg_id: 'msg-' + crypto.randomUUID(),
timestamp: new Date().toISOString(),
send_to: '/test/text',
msg_purpose: 'test',
sender_name: 'js-text-test',
sender_id: 'sender-' + crypto.randomUUID(),
receiver_name: 'js-receiver',
receiver_id: 'receiver-' + crypto.randomUUID(),
reply_to: '',
reply_to_msg_id: '',
broker_url: TEST_BROKER_URL,
metadata: {},
payloads: [
{
id: 'payload-' + crypto.randomUUID(),
dataname: 'message',
payload_type: 'text',
transport: 'direct',
encoding: 'base64',
size: 38,
data: Buffer.from('Hello, NATSBridge! This is a test message.').toString('base64'),
metadata: { payload_bytes: 38 }
}
]
};
const mockMsg = {
payload: JSON.stringify(testData)
};
console.log('Mock Message Created:');
console.log(` Correlation ID: ${testData.correlation_id}`);
console.log(` Payloads: ${testData.payloads.length}`);
console.log(` Payload dataname: ${testData.payloads[0].dataname}`);
console.log(` Payload type: ${testData.payloads[0].payload_type}`);
console.log(` Transport: ${testData.payloads[0].transport}\n`);
try {
// Receive and process the message
console.log('Receiving and processing message...');
const env = await NATSBridge.smartreceive(
mockMsg,
{
max_retries: 3,
base_delay: 100,
max_delay: 1000
}
);
console.log('\n=== Received Envelope ===');
console.log(`Correlation ID: ${env.correlation_id}`);
console.log(`Message ID: ${env.msg_id}`);
console.log(`Timestamp: ${env.timestamp}`);
console.log(`Subject: ${env.send_to}`);
console.log(`Payloads: ${env.payloads.length}\n`);
// Validate received data
console.log('=== Validation ===');
let passed = true;
if (!env.correlation_id) {
console.log('❌ correlation_id is missing');
passed = false;
} else {
console.log('✅ correlation_id present');
}
if (env.payloads.length !== 1) {
console.log(`❌ Expected 1 payload, got ${env.payloads.length}`);
passed = false;
} else {
console.log('✅ Correct number of payloads');
}
const payload = env.payloads[0];
if (payload[0] !== 'message') {
console.log(`❌ Expected dataname 'message', got '${payload[0]}'`);
passed = false;
} else {
console.log('✅ Correct dataname');
}
if (payload[2] !== 'text') {
console.log(`❌ Expected type 'text', got '${payload[2]}'`);
passed = false;
} else {
console.log('✅ Correct type');
}
if (payload[1] !== 'Hello, NATSBridge! This is a test message.') {
console.log(`❌ Data mismatch`);
console.log(` Expected: Hello, NATSBridge! This is a test message.`);
console.log(` Got: ${payload[1]}`);
passed = false;
} else {
console.log('✅ Data correctly deserialized');
}
// Test with multiple text payloads
console.log('\n=== Multiple Text Payloads Test ===');
const multiTestData = {
correlation_id: 'multi-receiver-' + crypto.randomUUID(),
msg_id: 'msg-' + crypto.randomUUID(),
timestamp: new Date().toISOString(),
send_to: '/test/text',
msg_purpose: 'test',
sender_name: 'js-text-test',
sender_id: 'sender-' + crypto.randomUUID(),
receiver_name: 'js-receiver',
receiver_id: 'receiver-' + crypto.randomUUID(),
reply_to: '',
reply_to_msg_id: '',
broker_url: TEST_BROKER_URL,
metadata: {},
payloads: [
{
id: 'payload-1',
dataname: 'msg1',
payload_type: 'text',
transport: 'direct',
encoding: 'base64',
size: 16,
data: Buffer.from('First message').toString('base64'),
metadata: { payload_bytes: 16 }
},
{
id: 'payload-2',
dataname: 'msg2',
payload_type: 'text',
transport: 'direct',
encoding: 'base64',
size: 16,
data: Buffer.from('Second message').toString('base64'),
metadata: { payload_bytes: 16 }
},
{
id: 'payload-3',
dataname: 'msg3',
payload_type: 'text',
transport: 'direct',
encoding: 'base64',
size: 16,
data: Buffer.from('Third message').toString('base64'),
metadata: { payload_bytes: 16 }
}
]
};
const mockMultiMsg = {
payload: JSON.stringify(multiTestData)
};
const multiEnv = await NATSBridge.smartreceive(mockMultiMsg);
if (multiEnv.payloads.length === 3) {
console.log('✅ Multiple payloads handled correctly');
// Verify each payload
const expectedMessages = ['First message', 'Second message', 'Third message'];
for (let i = 0; i < 3; i++) {
if (multiEnv.payloads[i][1] === expectedMessages[i]) {
console.log(`✅ Payload ${i + 1} correctly deserialized`);
} else {
console.log(`❌ Payload ${i + 1} mismatch`);
passed = false;
}
}
} else {
console.log(`❌ Expected 3 payloads, got ${multiEnv.payloads.length}`);
passed = false;
}
// Final result
console.log('\n=== Test Result ===');
if (passed) {
console.log('✅ ALL TESTS PASSED');
process.exit(0);
} else {
console.log('❌ SOME TESTS FAILED');
process.exit(1);
}
} catch (error) {
console.error('❌ Test failed with error:', error.message);
console.error(error.stack);
process.exit(1);
}
}
runTest();

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@@ -1,170 +0,0 @@
/**
* JavaScript Text Sender Test
* Tests the smartsend function with text payloads
*/
const NATSBridge = require('../src/natsbridge.js');
const crypto = require('crypto');
const TEST_SUBJECT = '/test/text';
const TEST_BROKER_URL = process.env.NATS_URL || 'nats://localhost:4222';
const TEST_FILESERVER_URL = process.env.FILESERVER_URL || 'http://localhost:8080';
async function runTest() {
console.log('=== JavaScript Text Sender Test ===\n');
const correlationId = crypto.randomUUID();
console.log(`Correlation ID: ${correlationId}`);
console.log(`Subject: ${TEST_SUBJECT}`);
console.log(`Broker URL: ${TEST_BROKER_URL}\n`);
// Test data
const textData = 'Hello, NATSBridge! This is a test message.';
const testData = [
['message', textData, 'text']
];
try {
// Send the message
console.log('Sending text payload...');
const [env, envJsonStr] = await NATSBridge.smartsend(
TEST_SUBJECT,
testData,
{
broker_url: TEST_BROKER_URL,
fileserver_url: TEST_FILESERVER_URL,
correlation_id: correlationId,
msg_purpose: 'test',
sender_name: 'js-text-test',
is_publish: false // Don't actually publish for this test
}
);
console.log('\n=== Envelope Created ===');
console.log(`Correlation ID: ${env.correlation_id}`);
console.log(`Message ID: ${env.msg_id}`);
console.log(`Timestamp: ${env.timestamp}`);
console.log(`Subject: ${env.send_to}`);
console.log(`Purpose: ${env.msg_purpose}`);
console.log(`Sender: ${env.sender_name}`);
console.log(`Payloads: ${env.payloads.length}\n`);
// Validate envelope structure
console.log('=== Validation ===');
let passed = true;
if (!env.correlation_id) {
console.log('❌ correlation_id is missing');
passed = false;
} else {
console.log('✅ correlation_id present');
}
if (!env.msg_id) {
console.log('❌ msg_id is missing');
passed = false;
} else {
console.log('✅ msg_id present');
}
if (!env.timestamp) {
console.log('❌ timestamp is missing');
passed = false;
} else {
console.log('✅ timestamp present');
}
if (env.payloads.length !== 1) {
console.log(`❌ Expected 1 payload, got ${env.payloads.length}`);
passed = false;
} else {
console.log('✅ Correct number of payloads');
}
const payload = env.payloads[0];
if (payload.dataname !== 'message') {
console.log(`❌ Expected dataname 'message', got '${payload.dataname}'`);
passed = false;
} else {
console.log('✅ Correct dataname');
}
if (payload.payload_type !== 'text') {
console.log(`❌ Expected payload_type 'text', got '${payload.payload_type}'`);
passed = false;
} else {
console.log('✅ Correct payload_type');
}
if (payload.transport !== 'direct') {
console.log(`❌ Expected transport 'direct', got '${payload.transport}'`);
passed = false;
} else {
console.log('✅ Correct transport');
}
if (payload.encoding !== 'base64') {
console.log(`❌ Expected encoding 'base64', got '${payload.encoding}'`);
passed = false;
} else {
console.log('✅ Correct encoding');
}
// Decode and verify the data
const decodedData = Buffer.from(payload.data, 'base64').toString('utf8');
if (decodedData !== textData) {
console.log(`❌ Decoded data mismatch`);
console.log(` Expected: ${textData}`);
console.log(` Got: ${decodedData}`);
passed = false;
} else {
console.log('✅ Data integrity verified');
}
console.log(`\nPayload size: ${payload.size} bytes`);
console.log(`Base64 data length: ${payload.data.length} chars`);
// Test with multiple text payloads
console.log('\n=== Multiple Text Payloads Test ===');
const multiTestData = [
['msg1', 'First message', 'text'],
['msg2', 'Second message', 'text'],
['msg3', 'Third message', 'text']
];
const [multiEnv, multiEnvJsonStr] = await NATSBridge.smartsend(
TEST_SUBJECT,
multiTestData,
{
broker_url: TEST_BROKER_URL,
fileserver_url: TEST_FILESERVER_URL,
correlation_id: 'multi-test-' + correlationId,
is_publish: false
}
);
if (multiEnv.payloads.length === 3) {
console.log('✅ Multiple payloads handled correctly');
} else {
console.log(`❌ Expected 3 payloads, got ${multiEnv.payloads.length}`);
passed = false;
}
// Final result
console.log('\n=== Test Result ===');
if (passed) {
console.log('✅ ALL TESTS PASSED');
process.exit(0);
} else {
console.log('❌ SOME TESTS FAILED');
process.exit(1);
}
} catch (error) {
console.error('❌ Test failed with error:', error.message);
console.error(error.stack);
process.exit(1);
}
}
runTest();

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@@ -1,82 +0,0 @@
#!/usr/bin/env julia
# Test script for Dictionary transport testing
# Tests receiving 1 large and 1 small Dictionaries via direct and link transport
# Uses NATSBridge.jl smartreceive with "dictionary" type
using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP
# Include the bridge module
include("../src/NATSBridge.jl")
using .NATSBridge
# Configuration
const SUBJECT = "/NATSBridge_dict_test"
const NATS_URL = "nats.yiem.cc"
const FILESERVER_URL = "http://192.168.88.104:8080"
# ------------------------------------------------------------------------------------------------ #
# test dictionary transfer #
# ------------------------------------------------------------------------------------------------ #
# Helper: Log with correlation ID
function log_trace(message)
timestamp = Dates.now()
println("[$timestamp] $message")
end
# Receiver: Listen for messages and verify Dictionary handling
function test_dict_receive()
conn = NATS.connect(NATS_URL)
NATS.subscribe(conn, SUBJECT) do msg
log_trace("Received message on $(msg.subject)")
# Use NATSBridge.smartreceive to handle the data
# API: smartreceive(msg, download_handler; max_retries, base_delay, max_delay)
result = NATSBridge.smartreceive(
msg;
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
# Result is an envelope dictionary with payloads field containing list of (dataname, data, data_type) tuples
for (dataname, data, data_type) in result["payloads"]
if isa(data, JSON.Object{String, Any})
log_trace("Received Dictionary '$dataname' of type $data_type")
# Display dictionary contents
println(" Contents:")
for (key, value) in data
println(" $key => $value")
end
# Save to JSON file
output_path = "./received_$dataname.json"
json_str = JSON.json(data, 2)
write(output_path, json_str)
log_trace("Saved Dictionary to $output_path")
else
log_trace("Received unexpected data type for '$dataname': $(typeof(data))")
end
end
end
# Keep listening for 10 seconds
sleep(120)
NATS.drain(conn)
end
# Run the test
println("Starting Dictionary transport test...")
println("Note: This receiver will wait for messages from the sender.")
println("Run test_julia_to_julia_dict_sender.jl first to send test data.")
# Run receiver
println("testing smartreceive")
test_dict_receive()
println("Test completed.")

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@@ -1,137 +0,0 @@
#!/usr/bin/env julia
# Test script for Dictionary transport testing
# Tests sending 1 large and 1 small Dictionaries via direct and link transport
# Uses NATSBridge.jl smartsend with "dictionary" type
using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP
# Include the bridge module
include("../src/NATSBridge.jl")
using .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
correlation_id = string(uuid4())
# ------------------------------------------------------------------------------------------------ #
# test dictionary transfer #
# ------------------------------------------------------------------------------------------------ #
# Helper: Log with correlation ID
function log_trace(message)
timestamp = Dates.now()
println("[$timestamp] [Correlation: $correlation_id] $message")
end
# File upload handler for plik server
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
# Get upload ID
url_getUploadID = "$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"]
# Upload file
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
# Sender: Send Dictionaries via smartsend
function test_dict_send()
# Create a small Dictionary (will use direct transport)
small_dict = Dict(
"name" => "Alice",
"age" => 30,
"scores" => [95, 88, 92],
"metadata" => Dict(
"height" => 155,
"weight" => 55
)
)
# Create a large Dictionary (will use link transport if > 1MB)
# Generate a larger dataset (~2MB to ensure link transport)
large_dict = Dict(
"ids" => collect(1:50000),
"names" => ["User_$i" for i in 1:50000],
"scores" => rand(1:100, 50000),
"categories" => ["Category_$(rand(1:10))" for i in 1:50000],
"metadata" => Dict(
"source" => "test_generator",
"timestamp" => string(Dates.now())
)
)
# Test data 1: small Dictionary
data1 = ("small_dict", small_dict, "dictionary")
# Test data 2: large Dictionary
data2 = ("large_dict", large_dict, "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)
env, env_json_str = NATSBridge.smartsend(
SUBJECT,
[data1, data2]; # List of (dataname, data, type) tuples
broker_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserver_upload_handler = plik_upload_handler,
size_threshold = 1_000_000, # 1MB threshold
correlation_id = correlation_id,
msg_purpose = "chat",
sender_name = "dict_sender",
receiver_name = "",
receiver_id = "",
reply_to = "",
reply_to_msg_id = "",
is_publish = true # Publish the message to NATS
)
log_trace("Sent message with $(length(env.payloads)) payloads")
# Log transport type for each payload
for (i, payload) in enumerate(env.payloads)
log_trace("Payload $i ('$payload.dataname'):")
log_trace(" Transport: $(payload.transport)")
log_trace(" Type: $(payload.payload_type)")
log_trace(" Size: $(payload.size) bytes")
log_trace(" Encoding: $(payload.encoding)")
if payload.transport == "link"
log_trace(" URL: $(payload.data)")
end
end
end
# Run the test
println("Starting Dictionary transport test...")
println("Correlation ID: $correlation_id")
# Run sender
println("start smartsend for dictionaries")
test_dict_send()
println("Test completed.")

View File

@@ -1,84 +0,0 @@
#!/usr/bin/env julia
# Test script for large payload testing using binary transport
# Tests sending a large file (> 1MB) via smartsend with binary type
# Updated to match NATSBridge.jl API
using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP
# workdir =
# Include the bridge module
include("../src/NATSBridge.jl")
using .NATSBridge
# Configuration
const SUBJECT = "/NATSBridge_test"
const NATS_URL = "nats.yiem.cc"
const FILESERVER_URL = "http://192.168.88.104:8080"
# ------------------------------------------------------------------------------------------------ #
# test file transfer #
# ------------------------------------------------------------------------------------------------ #
# Helper: Log with correlation ID
function log_trace(message)
timestamp = Dates.now()
println("[$timestamp] $message")
end
# Receiver: Listen for messages and verify large payload handling
function test_large_binary_receive()
conn = NATS.connect(NATS_URL)
NATS.subscribe(conn, SUBJECT) do msg
log_trace("Received message on $(msg.subject)")
# Use NATSBridge.smartreceive to handle the data
# API: smartreceive(msg, download_handler; max_retries, base_delay, max_delay)
result = NATSBridge.smartreceive(
msg;
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
# 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
if isa(data, Vector{UInt8})
file_size = length(data)
log_trace("Received $(file_size) bytes of binary data for '$dataname' of type $data_type")
# Save received data to a test file
output_path = "./new_$dataname"
write(output_path, data)
log_trace("Saved received data to $output_path")
else
log_trace("Received $(file_size) bytes of binary data for '$dataname' of type $data_type")
end
end
end
# Keep listening for 10 seconds
sleep(120)
NATS.drain(conn)
end
# Run the test
println("Starting large binary payload test...")
# # Run sender first
# println("start smartsend")
# test_large_binary_send()
# Run receiver
println("testing smartreceive")
test_large_binary_receive()
println("Test completed.")

View File

@@ -1,123 +0,0 @@
#!/usr/bin/env julia
# Test script for large payload testing using binary transport
# Tests sending a large file (> 1MB) via smartsend with binary type
# Updated to match NATSBridge.jl API
using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP
# workdir =
# Include the bridge module
include("../src/NATSBridge.jl")
using .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
correlation_id = string(uuid4())
# ------------------------------------------------------------------------------------------------ #
# test file transfer #
# ------------------------------------------------------------------------------------------------ #
# Helper: Log with correlation ID
function log_trace(message)
timestamp = Dates.now()
println("[$timestamp] [Correlation: $correlation_id] $message")
end
# File upload handler for plik server
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
# Get upload ID
url_getUploadID = "$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"]
# Upload file
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
# Sender: Send large binary file via smartsend
function test_large_binary_send()
# Read the large file as binary data
# test data 1
file_path1 = "./testFile_large.zip"
file_data1 = read(file_path1)
filename1 = basename(file_path1)
data1 = (filename1, file_data1, "binary")
# test data 2
file_path2 = "./testFile_small.zip"
file_data2 = read(file_path2)
filename2 = basename(file_path2)
data2 = (filename2, file_data2, "binary")
# 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, env_json_str = NATSBridge.smartsend(
SUBJECT,
[data1, data2]; # List of (dataname, data, type) tuples
broker_url = NATS_URL;
fileserver_url = FILESERVER_URL,
fileserver_upload_handler = plik_upload_handler,
size_threshold = 1_000_000,
correlation_id = correlation_id,
msg_purpose = "chat",
sender_name = "sender",
receiver_name = "",
receiver_id = "",
reply_to = "",
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].payload_type)")
# Check if link transport was used
if env.payloads[1].transport == "link"
log_trace("Using link transport - file uploaded to HTTP server")
log_trace("URL: $(env.payloads[1].data)")
else
log_trace("Using direct transport - payload sent via NATS")
end
end
# Run the test
println("Starting large binary payload test...")
println("Correlation ID: $correlation_id")
# Run sender first
println("start smartsend")
test_large_binary_send()
# Run receiver
# println("testing smartreceive")
# test_large_binary_receive()
println("Test completed.")

View File

@@ -13,7 +13,7 @@ include("../src/NATSBridge.jl")
using .NATSBridge
# Configuration
const SUBJECT = "/NATSBridge_mix_test"
const SUBJECT = "/natsbridge"
const NATS_URL = "nats.yiem.cc"
const FILESERVER_URL = "http://192.168.88.104:8080"
@@ -93,26 +93,41 @@ function test_mix_receive()
log_trace(" ERROR: Expected Dict, got $(typeof(data))")
end
elseif data_type == "table"
# Table data - should be a DataFrame
data = DataFrame(data)
if isa(data, DataFrame)
log_trace(" Type: DataFrame")
log_trace(" Dimensions: $(size(data, 1)) rows x $(size(data, 2)) columns")
log_trace(" Columns: $(names(data))")
elseif data_type == "arrowtable"
# Arrow table data - should be Arrow.Table
if isa(data, Arrow.Table)
log_trace(" Type: Arrow.Table")
# Display first few rows
log_trace(" First 5 rows:")
display(data[1:min(5, size(data, 1)), :])
# Save to Arrow file
# Convert to DataFrame for display and save
df = DataFrame(data)
@show df[1:3, :]
output_path = "./received_$dataname.arrow"
io = IOBuffer()
Arrow.write(io, data)
write(output_path, take!(io))
log_trace(" Saved to: $output_path")
else
log_trace(" ERROR: Expected DataFrame, got $(typeof(data))")
log_trace(" ERROR: Expected Arrow.Table, got $(typeof(data))")
end
elseif data_type == "jsontable"
# JSON table data - should be Vector{Dict} or Vector{NamedTuple}
@show "jsontable" typeof(data)
if isa(data, Vector{Any})
log_trace(" Type: Vector{Dict/NamedTuple}")
# Convert to DataFrame for display and save
df = DataFrame(data)
@show df[1:3, :]
log_trace(" Converted to DataFrame: $(size(df, 1)) rows x $(size(df, 2)) columns")
# Save as JSON file
output_path = "./received_$dataname.json"
json_str = JSON.json(data, 2)
write(output_path, json_str)
log_trace(" Saved to: $output_path")
else
log_trace(" ERROR: Expected Vector{Dict/NamedTuple}, got $(typeof(data))")
end
elseif data_type == "image"
@@ -164,7 +179,7 @@ function test_mix_receive()
log_trace(" Size: $(length(data)) bytes")
# Save to file
output_path = "./received_$dataname.bin"
output_path = "./received_$dataname"
write(output_path, data)
log_trace(" Saved to: $output_path")
else
@@ -180,7 +195,9 @@ function test_mix_receive()
println("\n=== Verification Summary ===")
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"])
arrowtable_count = count(x -> x[3] == "arrowtable", result["payloads"])
jsontable_count = count(x -> x[3] == "jsontable", result["payloads"])
table_count = count(x -> x[3] == "table", result["payloads"]) # backward compatibility
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"])
@@ -188,7 +205,9 @@ function test_mix_receive()
log_trace("Text payloads: $text_count")
log_trace("Dictionary payloads: $dict_count")
log_trace("Table payloads: $table_count")
log_trace("Arrow table payloads: $arrowtable_count")
log_trace("JSON table payloads: $jsontable_count")
log_trace("Table payloads (backward compat): $table_count")
log_trace("Image payloads: $image_count")
log_trace("Audio payloads: $audio_count")
log_trace("Video payloads: $video_count")
@@ -199,9 +218,13 @@ function test_mix_receive()
for (dataname, data, data_type) in result["payloads"]
if data_type in ["image", "audio", "video", "binary"]
log_trace("$dataname: $(length(data)) bytes (binary)")
elseif data_type == "arrowtable"
# log_trace("$dataname: $(size(data, 1)) rows x $(size(data, 2)) columns (Arrow.Table)")
elseif data_type == "jsontable"
log_trace("$dataname: $(length(data)) rows (Vector{Dict/NamedTuple})")
elseif data_type == "table"
data = DataFrame(data)
log_trace("$dataname: $(size(data, 1)) rows x $(size(data, 2)) columns (DataFrame)")
data = DataFrame(data)
# log_trace("$dataname: $(size(data, 1)) rows x $(size(data, 2)) columns (DataFrame)")
elseif data_type == "dictionary"
log_trace("$dataname: $(length(JSON.json(data))) bytes (Dict)")
elseif data_type == "text"
@@ -211,7 +234,7 @@ function test_mix_receive()
end
# Keep listening for 2 minutes
sleep(120)
sleep(180)
NATS.drain(conn)
end

View File

@@ -1,10 +1,15 @@
#!/usr/bin/env julia
# Test script for mixed-content message testing
# Tests sending a mix of text, json, table, image, audio, video, and binary data
# Tests sending a mix of text, dictionary, arrowtable, jsontable, image, audio, video, and binary data
# from Julia serviceA to Julia serviceB using NATSBridge.jl smartsend
#
# This test demonstrates that any combination and any number of mixed content
# can be sent and received correctly.
#
# Key concept: DataFrames are the main table representation in Julia.
# The NATSBridge.jl library handles serialization:
# - For "arrowtable" type: DataFrame is serialized to Arrow IPC format
# - For "jsontable" type: DataFrame is converted to Vector{Dict} and then to JSON
using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP, Base64
@@ -13,7 +18,7 @@ include("../src/NATSBridge.jl")
using .NATSBridge
# Configuration
const SUBJECT = "/NATSBridge_mix_test"
const SUBJECT = "/natsbridge"
const NATS_URL = "nats.yiem.cc"
const FILESERVER_URL = "http://192.168.88.104:8080"
@@ -82,49 +87,46 @@ function create_sample_data()
)
)
# Table data (DataFrame - small - direct transport)
table_data_small = DataFrame(
# Arrow table data (DataFrame - small - direct transport)
# Uses Arrow IPC format for efficient binary serialization
# NATSBridge.jl handles serialization: DataFrame -> Arrow IPC
arrow_table_small = DataFrame(
id = 1:10,
message = ["msg_$i" for i in 1:10],
sender = ["sender_$i" for i in 1:10],
timestamp = [string(Dates.now()) for _ in 1:10],
priority = rand(1:3, 10)
name = ["Alice", "Bob", "Charlie", "Diana", "Eve", "Frank", "Grace", "Henry", "Ivy", "Jack"],
score = rand(50:100, 10),
active = rand([true, false], 10)
)
# Table data (DataFrame - large - link transport)
# ~1.5MB of data (150,000 rows) - should trigger link transport
table_data_large = DataFrame(
id = 1:150_000,
message = ["msg_$i" for i in 1:150_000],
sender = ["sender_$i" for i in 1:150_000],
timestamp = [string(Dates.now()) for i in 1:150_000],
priority = rand(1:3, 150_000)
# Arrow table data (DataFrame - large - link transport)
# ~1.5MB of Arrow data (200,000 rows) - should trigger link transport
# NATSBridge.jl handles serialization: DataFrame -> Arrow IPC
arrow_table_large = DataFrame(
id = 1:2_000_000,
name = ["user_$i" for i in 1:2_000_000],
score = rand(50:100, 2_000_000),
active = rand([true, false], 2_000_000),
timestamp = [string(Dates.now()) for _ in 1:2_000_000]
)
# Image data (small binary - direct transport)
# Create a simple 10x10 pixel PNG-like data (128 bytes header + 100 pixels = 112 bytes)
# Using simple RGB data (10*10*3 = 300 bytes of pixel data)
image_width = 10
image_height = 10
image_data = UInt8[]
# PNG header (simplified)
push!(image_data, 0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A)
# Simple RGB data (RGBRGBRGB...)
for i in 1:image_width*image_height
push!(image_data, 0xFF, 0x00, 0x00) # Red pixel
end
# Json table data (DataFrame - small - direct transport)
# Uses JSON format for human-readable tabular data
# NATSBridge.jl handles serialization: DataFrame -> Vector{Dict} -> JSON
json_table_small = DataFrame(
id = 1:10,
name = ["Alice", "Bob", "Charlie", "Diana", "Eve", "Frank", "Grace", "Henry", "Ivy", "Jack"],
score = rand(50:100, 10),
active = rand([true, false], 10)
)
# Image data (large - link transport)
# Create a larger image (~1.5MB) to test link transport
large_image_width = 500
large_image_height = 1000
large_image_data = UInt8[]
# PNG header (simplified for 500x1000)
push!(large_image_data, 0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A)
# RGB data (500*1000*3 = 1,500,000 bytes)
for i in 1:large_image_width*large_image_height
push!(large_image_data, rand(1:255), rand(1:255), rand(1:255)) # Random color pixels
end
# Json table data (DataFrame - large - link transport)
# ~1.5MB of JSON data (150,000 rows) - should trigger link transport
# NATSBridge.jl handles serialization: DataFrame -> Vector{Dict} -> JSON
json_table_large = DataFrame(
id = 1:2_000_000,
name = ["user_$i" for i in 1:2_000_000],
score = rand(50:100, 2_000_000),
active = rand([true, false], 2_000_000)
)
# Audio data (small binary - direct transport)
audio_data = UInt8[rand(1:255) for _ in 1:100]
@@ -150,10 +152,10 @@ function create_sample_data()
return (
text_data,
dict_data,
table_data_small,
table_data_large,
image_data,
large_image_data,
arrow_table_small,
arrow_table_large,
json_table_small,
json_table_large,
audio_data,
large_audio_data,
video_data,
@@ -167,19 +169,35 @@ end
# Sender: Send mixed content via smartsend
function test_mix_send()
# Create sample data
(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) = create_sample_data()
(text_data, dict_data, arrow_table_small, arrow_table_large, json_table_small, json_table_large, audio_data, large_audio_data, video_data, large_video_data, binary_data, large_binary_data) = create_sample_data()
# Read image files from disk (following test_julia_file_sender.jl pattern)
# Small image - should use direct transport
file_path_small_image = "./test/small_image.jpg"
file_data_small_image = read(file_path_small_image)
filename_small_image = basename(file_path_small_image)
# Large image - should use link transport
file_path_large_image = "./test/large_image.png"
file_data_large_image = read(file_path_large_image)
filename_large_image = basename(file_path_large_image)
# Create payloads list - mixed content with both small and large data
# Small data uses direct transport, large data uses link transport
# Key: Pass DataFrame directly and specify type as "arrowtable" or "jsontable"
# NATSBridge.jl handles the serialization internally
payloads = [
# Small data (direct transport) - text, dictionary, small table
# Small data (direct transport) - text, dictionary, arrowtable, jsontable, small image
("chat_text", text_data, "text"),
("chat_json", dict_data, "dictionary"),
("chat_table_small", table_data_small, "table"),
# Large data (link transport) - large table, large image, large audio, large video, large binary
("chat_table_large", table_data_large, "table"),
("user_image_large", large_image_data, "image"),
# ("arrow_table_small", arrow_table_small, "arrowtable"),
("json_table_small", json_table_small, "jsontable"),
(filename_small_image, file_data_small_image, "binary"),
# Large data (link transport) - large arrowtable, large jsontable, large image, large audio, large video, large binary
# ("arrow_table_large", arrow_table_large, "arrowtable"),
("json_table_large", json_table_large, "jsontable"),
(filename_large_image, file_data_large_image, "binary"),
("audio_clip_large", large_audio_data, "audio"),
("video_clip_large", large_video_data, "video"),
("binary_file_large", large_binary_data, "binary")
@@ -237,4 +255,4 @@ println("start smartsend for mixed content")
test_mix_send()
println("\nTest completed.")
println("Note: Run test_julia_to_julia_mix_receiver.jl to receive the messages.")
println("Note: Run test_julia_to_julia_mix_receiver.jl to receive the messages.")

View File

@@ -1,84 +0,0 @@
#!/usr/bin/env julia
# Test script for DataFrame table transport testing
# Tests receiving 1 large and 1 small DataFrames via direct and link transport
# Uses NATSBridge.jl smartreceive with "table" type
using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP
# Include the bridge module
include("../src/NATSBridge.jl")
using .NATSBridge
# Configuration
const SUBJECT = "/NATSBridge_table_test"
const NATS_URL = "nats.yiem.cc"
const FILESERVER_URL = "http://192.168.88.104:8080"
# ------------------------------------------------------------------------------------------------ #
# test table transfer #
# ------------------------------------------------------------------------------------------------ #
# Helper: Log with correlation ID
function log_trace(message)
timestamp = Dates.now()
println("[$timestamp] $message")
end
# Receiver: Listen for messages and verify DataFrame table handling
function test_table_receive()
conn = NATS.connect(NATS_URL)
NATS.subscribe(conn, SUBJECT) do msg
log_trace("Received message on $(msg.subject)")
# Use NATSBridge.smartreceive to handle the data
# API: smartreceive(msg, download_handler; max_retries, base_delay, max_delay)
result = NATSBridge.smartreceive(
msg;
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
# Result is an envelope dictionary with payloads field containing list of (dataname, data, data_type) tuples
for (dataname, data, data_type) in result["payloads"]
data = DataFrame(data)
if isa(data, DataFrame)
log_trace("Received DataFrame '$dataname' of type $data_type")
log_trace(" Dimensions: $(size(data, 1)) rows x $(size(data, 2)) columns")
log_trace(" Column names: $(names(data))")
# Display first few rows
println(" First 5 rows:")
display(data[1:min(5, size(data, 1)), :])
# Save to file
output_path = "./received_$dataname.arrow"
io = IOBuffer()
Arrow.write(io, data)
write(output_path, take!(io))
log_trace("Saved DataFrame to $output_path")
else
log_trace("Received unexpected data type for '$dataname': $(typeof(data))")
end
end
end
# Keep listening for 10 seconds
sleep(120)
NATS.drain(conn)
end
# Run the test
println("Starting DataFrame table transport test...")
println("Note: This receiver will wait for messages from the sender.")
println("Run test_julia_to_julia_table_sender.jl first to send test data.")
# Run receiver
println("testing smartreceive")
test_table_receive()
println("Test completed.")

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@@ -1,135 +0,0 @@
#!/usr/bin/env julia
# Test script for DataFrame table transport testing
# Tests sending 1 large and 1 small DataFrames via direct and link transport
# Uses NATSBridge.jl smartsend with "table" type
using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP
# Include the bridge module
include("../src/NATSBridge.jl")
using .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
correlation_id = string(uuid4())
# ------------------------------------------------------------------------------------------------ #
# test table transfer #
# ------------------------------------------------------------------------------------------------ #
# Helper: Log with correlation ID
function log_trace(message)
timestamp = Dates.now()
println("[$timestamp] [Correlation: $correlation_id] $message")
end
# File upload handler for plik server
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
# Get upload ID
url_getUploadID = "$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"]
# Upload file
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
# Sender: Send DataFrame tables via smartsend
function test_table_send()
# Create a small DataFrame (will use direct transport)
small_df = DataFrame(
id = 1:10,
name = ["Alice", "Bob", "Charlie", "Diana", "Eve", "Frank", "Grace", "Henry", "Ivy", "Jack"],
score = [95, 88, 92, 85, 90, 78, 95, 88, 92, 85],
category = ["A", "B", "A", "B", "A", "B", "A", "B", "A", "B"]
)
# Create a large DataFrame (will use link transport if > 1MB)
# Generate a larger dataset (~2MB to ensure link transport)
large_ids = 1:50000
large_names = ["User_$i" for i in 1:50000]
large_scores = rand(1:100, 50000)
large_categories = ["Category_$(rand(1:10))" for i in 1:50000]
large_df = DataFrame(
id = large_ids,
name = large_names,
score = large_scores,
category = large_categories
)
# Test data 1: small DataFrame
data1 = ("small_table", small_df, "table")
# Test data 2: large DataFrame
data2 = ("large_table", large_df, "table")
# 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, env_json_str = NATSBridge.smartsend(
SUBJECT,
[data1, data2]; # List of (dataname, data, type) tuples
broker_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserver_upload_handler = plik_upload_handler,
size_threshold = 1_000_000, # 1MB threshold
correlation_id = correlation_id,
msg_purpose = "chat",
sender_name = "table_sender",
receiver_name = "",
receiver_id = "",
reply_to = "",
reply_to_msg_id = "",
is_publish = true # Publish the message to NATS
)
log_trace("Sent message with $(length(env.payloads)) payloads")
# Log transport type for each payload
for (i, payload) in enumerate(env.payloads)
log_trace("Payload $i ('$payload.dataname'):")
log_trace(" Transport: $(payload.transport)")
log_trace(" Type: $(payload.payload_type)")
log_trace(" Size: $(payload.size) bytes")
log_trace(" Encoding: $(payload.encoding)")
if payload.transport == "link"
log_trace(" URL: $(payload.data)")
end
end
end
# Run the test
println("Starting DataFrame table transport test...")
println("Correlation ID: $correlation_id")
# Run sender
println("start smartsend for tables")
test_table_send()
println("Test completed.")

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@@ -1,83 +0,0 @@
#!/usr/bin/env julia
# Test script for text transport testing
# Tests receiving 1 large and 1 small text from Julia serviceA to Julia serviceB
# Uses NATSBridge.jl smartreceive with "text" type
using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP
# Include the bridge module
include("../src/NATSBridge.jl")
using .NATSBridge
# Configuration
const SUBJECT = "/NATSBridge_text_test"
const NATS_URL = "nats.yiem.cc"
const FILESERVER_URL = "http://192.168.88.104:8080"
# ------------------------------------------------------------------------------------------------ #
# test text transfer #
# ------------------------------------------------------------------------------------------------ #
# Helper: Log with correlation ID
function log_trace(message)
timestamp = Dates.now()
println("[$timestamp] $message")
end
# Receiver: Listen for messages and verify text handling
function test_text_receive()
conn = NATS.connect(NATS_URL)
NATS.subscribe(conn, SUBJECT) do msg
log_trace("Received message on $(msg.subject)")
# Use NATSBridge.smartreceive to handle the data
# API: smartreceive(msg, download_handler; max_retries, base_delay, max_delay)
result = NATSBridge.smartreceive(
msg;
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
# Result is an envelope dictionary with payloads field containing list of (dataname, data, data_type) tuples
for (dataname, data, data_type) in result["payloads"]
if isa(data, String)
log_trace("Received text '$dataname' of type $data_type")
log_trace(" Length: $(length(data)) characters")
# Display first 100 characters
if length(data) > 100
log_trace(" First 100 characters: $(data[1:100])...")
else
log_trace(" Content: $data")
end
# Save to file
output_path = "./received_$dataname.txt"
write(output_path, data)
log_trace("Saved text to $output_path")
else
log_trace("Received unexpected data type for '$dataname': $(typeof(data))")
end
end
end
# Keep listening for 10 seconds
sleep(120)
NATS.drain(conn)
end
# Run the test
println("Starting text transport test...")
println("Note: This receiver will wait for messages from the sender.")
println("Run test_julia_to_julia_text_sender.jl first to send test data.")
# Run receiver
println("testing smartreceive for text")
test_text_receive()
println("Test completed.")

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@@ -1,120 +0,0 @@
#!/usr/bin/env julia
# Test script for text transport testing
# Tests sending 1 large and 1 small text from Julia serviceA to Julia serviceB
# Uses NATSBridge.jl smartsend with "text" type
using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP
# Include the bridge module
include("../src/NATSBridge.jl")
using .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
correlation_id = string(uuid4())
# ------------------------------------------------------------------------------------------------ #
# test text transfer #
# ------------------------------------------------------------------------------------------------ #
# Helper: Log with correlation ID
function log_trace(message)
timestamp = Dates.now()
println("[$timestamp] [Correlation: $correlation_id] $message")
end
# File upload handler for plik server
function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
# Get upload ID
url_getUploadID = "$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"]
# Upload file
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
# Sender: Send text via smartsend
function test_text_send()
# Create a small text (will use direct transport)
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)
large_text = join(["Line $i: This is a sample text line with some content to pad the size. " for i in 1:50000], "")
# Test data 1: small text
data1 = ("small_text", small_text, "text")
# Test data 2: large text
data2 = ("large_text", large_text, "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)
env, env_json_str = NATSBridge.smartsend(
SUBJECT,
[data1, data2]; # List of (dataname, data, type) tuples
broker_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserver_upload_handler = plik_upload_handler,
size_threshold = 1_000_000, # 1MB threshold
correlation_id = correlation_id,
msg_purpose = "chat",
sender_name = "text_sender",
receiver_name = "",
receiver_id = "",
reply_to = "",
reply_to_msg_id = "",
is_publish = true # Publish the message to NATS
)
log_trace("Sent message with $(length(env.payloads)) payloads")
# Log transport type for each payload
for (i, payload) in enumerate(env.payloads)
log_trace("Payload $i ('$payload.dataname'):")
log_trace(" Transport: $(payload.transport)")
log_trace(" Type: $(payload.payload_type)")
log_trace(" Size: $(payload.size) bytes")
log_trace(" Encoding: $(payload.encoding)")
if payload.transport == "link"
log_trace(" URL: $(payload.data)")
end
end
end
# Run the test
println("Starting text transport test...")
println("Correlation ID: $correlation_id")
# Run sender
println("start smartsend for text")
test_text_send()
println("Test completed.")

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@@ -1,185 +0,0 @@
"""
MicroPython Binary Receiver Test
Tests the smartreceive function with binary/image/audio/video payloads
Note: This test is designed for both MicroPython and desktop Python
for compatibility testing.
"""
import sys
import os
import json
import base64
# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from natsbridge_mpy import smartreceive, DEFAULT_BROKER_URL, DEFAULT_FILESERVER_URL
TEST_BROKER_URL = os.environ.get('NATS_URL', 'nats://localhost:4222')
TEST_FILESERVER_URL = os.environ.get('FILESERVER_URL', 'http://localhost:8080')
def run_test():
print('=== MicroPython Binary Receiver Test ===\n')
from natsbridge_mpy import _generate_uuid
# Create mock NATS message with binary payloads
image_data = bytes([0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A]) # PNG header
audio_data = bytes([0x46, 0x4C, 0x41, 0x43, 0x00, 0x00, 0x00, 0x00]) # FLAC header
video_data = bytes([0x00, 0x00, 0x00, 0x18, 0x66, 0x74, 0x79, 0x70]) # MP4 header
generic_binary = bytes([0xDE, 0xAD, 0xBE, 0xEF, 0xCA, 0xFE, 0xBA, 0xBE])
test_data = {
'correlation_id': 'mpy-binary-receiver-' + _generate_uuid(),
'msg_id': _generate_uuid(),
'timestamp': '2024-01-15T10:30:00Z',
'send_to': '/test/binary',
'msg_purpose': 'test',
'sender_name': 'mpy-binary-test',
'sender_id': _generate_uuid(),
'receiver_name': 'mpy-receiver',
'receiver_id': _generate_uuid(),
'reply_to': '',
'reply_to_msg_id': '',
'broker_url': TEST_BROKER_URL,
'metadata': {},
'payloads': [
{
'id': _generate_uuid(),
'dataname': 'image',
'payload_type': 'image',
'transport': 'direct',
'encoding': 'base64',
'size': len(image_data),
'data': base64.b64encode(image_data).decode('ascii'),
'metadata': {'payload_bytes': len(image_data)}
},
{
'id': _generate_uuid(),
'dataname': 'audio',
'payload_type': 'audio',
'transport': 'direct',
'encoding': 'base64',
'size': len(audio_data),
'data': base64.b64encode(audio_data).decode('ascii'),
'metadata': {'payload_bytes': len(audio_data)}
},
{
'id': _generate_uuid(),
'dataname': 'video',
'payload_type': 'video',
'transport': 'direct',
'encoding': 'base64',
'size': len(video_data),
'data': base64.b64encode(video_data).decode('ascii'),
'metadata': {'payload_bytes': len(video_data)}
},
{
'id': _generate_uuid(),
'dataname': 'binary',
'payload_type': 'binary',
'transport': 'direct',
'encoding': 'base64',
'size': len(generic_binary),
'data': base64.b64encode(generic_binary).decode('ascii'),
'metadata': {'payload_bytes': len(generic_binary)}
}
]
}
mock_msg = {
'payload': json.dumps(test_data)
}
print('Mock Message Created:')
print(f' Correlation ID: {test_data["correlation_id"]}')
print(f' Payloads: {len(test_data["payloads"])}')
print(f' Payload types: {", ".join(p["payload_type"] for p in test_data["payloads"])}\n')
try:
# Receive and process the message
print('Receiving and processing message...')
env = smartreceive(
mock_msg,
max_retries=3,
base_delay=100,
max_delay=1000
)
print('\n=== Received Envelope ===')
print(f'Correlation ID: {env["correlation_id"]}')
print(f'Message ID: {env["msg_id"]}')
print(f'Timestamp: {env["timestamp"]}')
print(f'Subject: {env["send_to"]}')
print(f'Payloads: {len(env["payloads"])}\n')
# Validate received data
print('=== Validation ===')
passed = True
if not env.get('correlation_id'):
print('❌ correlation_id is missing')
passed = False
else:
print('✅ correlation_id present')
if len(env['payloads']) != 4:
print(f'❌ Expected 4 payloads, got {len(env["payloads"])}')
passed = False
else:
print('✅ Correct number of payloads')
# Expected data
expected_data = [
('image', image_data, 'image'),
('audio', audio_data, 'audio'),
('video', video_data, 'video'),
('binary', generic_binary, 'binary')
]
for i in range(len(env['payloads'])):
payload = env['payloads'][i]
expected = expected_data[i]
if payload[0] != expected[0]:
print(f"❌ Payload {i + 1}: Expected dataname '{expected[0]}', got '{payload[0]}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct dataname')
if payload[2] != expected[2]:
print(f"❌ Payload {i + 1}: Expected type '{expected[2]}', got '{payload[2]}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct type')
# Verify binary data integrity
received_data = payload[1]
if received_data != expected[1]:
print(f'❌ Payload {i + 1}: Data mismatch')
print(f' Expected: {expected[1]}')
print(f' Got: {received_data}')
passed = False
else:
print(f'✅ Payload {i + 1}: Data correctly deserialized')
# Final result
print('\n=== Test Result ===')
if passed:
print('✅ ALL TESTS PASSED')
sys.exit(0)
else:
print('❌ SOME TESTS FAILED')
sys.exit(1)
except Exception as e:
print(f'❌ Test failed with error: {e}')
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
run_test()

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@@ -1,163 +0,0 @@
"""
MicroPython Binary Sender Test
Tests the smartsend function with binary/image/audio/video payloads
Note: This test is designed for both MicroPython and desktop Python
for compatibility testing.
"""
import sys
import os
import base64
# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from natsbridge_mpy import smartsend, DEFAULT_BROKER_URL, DEFAULT_FILESERVER_URL, DEFAULT_SIZE_THRESHOLD, MAX_PAYLOAD_SIZE
TEST_SUBJECT = '/test/binary'
TEST_BROKER_URL = os.environ.get('NATS_URL', 'nats://localhost:4222')
TEST_FILESERVER_URL = os.environ.get('FILESERVER_URL', 'http://localhost:8080')
def run_test():
print('=== MicroPython Binary Sender Test ===\n')
from natsbridge_mpy import _generate_uuid
correlation_id = 'mpy-binary-test-' + _generate_uuid()
print(f'Correlation ID: {correlation_id}')
print(f'Subject: {TEST_SUBJECT}')
print(f'Broker URL: {TEST_BROKER_URL}')
print(f'Default Size Threshold: {DEFAULT_SIZE_THRESHOLD} bytes')
print(f'Max Payload Size: {MAX_PAYLOAD_SIZE} bytes\n')
# Test data - binary data for different types
image_data = bytearray([0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A]) # PNG header
audio_data = bytearray([0x46, 0x4C, 0x41, 0x43, 0x00, 0x00, 0x00, 0x00]) # FLAC header
video_data = bytearray([0x00, 0x00, 0x00, 0x18, 0x66, 0x74, 0x79, 0x70]) # MP4 header
generic_binary = bytearray([0xDE, 0xAD, 0xBE, 0xEF, 0xCA, 0xFE, 0xBA, 0xBE])
test_data = [
('image', bytes(image_data), 'image'),
('audio', bytes(audio_data), 'audio'),
('video', bytes(video_data), 'video'),
('binary', bytes(generic_binary), 'binary')
]
try:
# Send the message
print('Sending binary payloads...')
env, env_json_str = smartsend(
TEST_SUBJECT,
test_data,
broker_url=TEST_BROKER_URL,
fileserver_url=TEST_FILESERVER_URL,
correlation_id=correlation_id,
msg_purpose='test',
sender_name='mpy-binary-test',
is_publish=False
)
print('\n=== Envelope Created ===')
print(f'Correlation ID: {env["correlation_id"]}')
print(f'Message ID: {env["msg_id"]}')
print(f'Timestamp: {env["timestamp"]}')
print(f'Subject: {env["send_to"]}')
print(f'Purpose: {env["msg_purpose"]}')
print(f'Sender: {env["sender_name"]}')
print(f'Payloads: {len(env["payloads"])}\n')
# Validate envelope structure
print('=== Validation ===')
passed = True
if len(env['payloads']) != 4:
print(f'❌ Expected 4 payloads, got {len(env["payloads"])}')
passed = False
else:
print('✅ Correct number of payloads')
# Test each payload
expected_datanames = ['image', 'audio', 'video', 'binary']
expected_types = ['image', 'audio', 'video', 'binary']
expected_data = [bytes(image_data), bytes(audio_data), bytes(video_data), bytes(generic_binary)]
for i in range(len(env['payloads'])):
payload = env['payloads'][i]
if payload['dataname'] != expected_datanames[i]:
print(f"❌ Payload {i + 1}: Expected dataname '{expected_datanames[i]}', got '{payload['dataname']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct dataname')
if payload['payload_type'] != expected_types[i]:
print(f"❌ Payload {i + 1}: Expected type '{expected_types[i]}', got '{payload['payload_type']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct type')
if payload['transport'] != 'direct':
print(f"❌ Payload {i + 1}: Expected transport 'direct', got '{payload['transport']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct transport')
if payload['encoding'] != 'base64':
print(f"❌ Payload {i + 1}: Expected encoding 'base64', got '{payload['encoding']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct encoding')
# Decode and verify the data
decoded_data = base64.b64decode(payload['data'])
original_data = expected_data[i]
if decoded_data != original_data:
print(f'❌ Payload {i + 1}: Data integrity mismatch')
passed = False
else:
print(f'✅ Payload {i + 1}: Data integrity verified')
print(f' Size: {payload["size"]} bytes\n')
# Test with larger binary data
print('=== Large Binary Data Test ===')
large_data = bytes([0xFF] * 1000) # 1KB of binary data
large_test_data = [
('large_binary', large_data, 'binary')
]
large_env, _ = smartsend(
TEST_SUBJECT,
large_test_data,
broker_url=TEST_BROKER_URL,
fileserver_url=TEST_FILESERVER_URL,
correlation_id='large-' + correlation_id,
is_publish=False
)
if len(large_env['payloads']) == 1 and large_env['payloads'][0]['size'] == 1000:
print('✅ Large binary data handled correctly')
else:
print('❌ Large binary data handling failed')
passed = False
# Final result
print('\n=== Test Result ===')
if passed:
print('✅ ALL TESTS PASSED')
sys.exit(0)
else:
print('❌ SOME TESTS FAILED')
sys.exit(1)
except Exception as e:
print(f'❌ Test failed with error: {e}')
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
run_test()

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@@ -1,224 +0,0 @@
"""
MicroPython Dictionary Receiver Test
Tests the smartreceive function with dictionary payloads
Note: This test is designed for both MicroPython and desktop Python
for compatibility testing.
"""
import sys
import os
import json
# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from natsbridge_mpy import smartreceive, DEFAULT_BROKER_URL, DEFAULT_FILESERVER_URL
TEST_BROKER_URL = os.environ.get('NATS_URL', 'nats://localhost:4222')
TEST_FILESERVER_URL = os.environ.get('FILESERVER_URL', 'http://localhost:8080')
def run_test():
print('=== MicroPython Dictionary Receiver Test ===\n')
from natsbridge_mpy import _generate_uuid
# Create a mock NATS message with dictionary payloads
import base64
simple_dict = {'key1': 'value1', 'key2': 'value2'}
nested_dict = {'outer': {'inner': 'value', 'number': 42}}
array_dict = {'items': [1, 2, 3, 'four', 'five']}
mixed_dict = {'string': 'text', 'number': 123, 'boolean': True, 'null_val': None}
test_data = {
'correlation_id': 'mpy-receiver-dict-' + _generate_uuid(),
'msg_id': _generate_uuid(),
'timestamp': '2024-01-15T10:30:00Z',
'send_to': '/test/dictionary',
'msg_purpose': 'test',
'sender_name': 'mpy-dict-test',
'sender_id': _generate_uuid(),
'receiver_name': 'mpy-receiver',
'receiver_id': _generate_uuid(),
'reply_to': '',
'reply_to_msg_id': '',
'broker_url': TEST_BROKER_URL,
'metadata': {},
'payloads': [
{
'id': _generate_uuid(),
'dataname': 'simple_dict',
'payload_type': 'dictionary',
'transport': 'direct',
'encoding': 'base64',
'size': len(json.dumps(simple_dict).encode('utf8')),
'data': base64.b64encode(json.dumps(simple_dict).encode('utf8')).decode('ascii'),
'metadata': {'payload_bytes': len(json.dumps(simple_dict).encode('utf8'))}
},
{
'id': _generate_uuid(),
'dataname': 'nested_dict',
'payload_type': 'dictionary',
'transport': 'direct',
'encoding': 'base64',
'size': len(json.dumps(nested_dict).encode('utf8')),
'data': base64.b64encode(json.dumps(nested_dict).encode('utf8')).decode('ascii'),
'metadata': {'payload_bytes': len(json.dumps(nested_dict).encode('utf8'))}
},
{
'id': _generate_uuid(),
'dataname': 'array_dict',
'payload_type': 'dictionary',
'transport': 'direct',
'encoding': 'base64',
'size': len(json.dumps(array_dict).encode('utf8')),
'data': base64.b64encode(json.dumps(array_dict).encode('utf8')).decode('ascii'),
'metadata': {'payload_bytes': len(json.dumps(array_dict).encode('utf8'))}
},
{
'id': _generate_uuid(),
'dataname': 'mixed_dict',
'payload_type': 'dictionary',
'transport': 'direct',
'encoding': 'base64',
'size': len(json.dumps(mixed_dict).encode('utf8')),
'data': base64.b64encode(json.dumps(mixed_dict).encode('utf8')).decode('ascii'),
'metadata': {'payload_bytes': len(json.dumps(mixed_dict).encode('utf8'))}
}
]
}
mock_msg = {
'payload': json.dumps(test_data)
}
print('Mock Message Created:')
print(f' Correlation ID: {test_data["correlation_id"]}')
print(f' Payloads: {len(test_data["payloads"])}')
print(f' Payload types: {", ".join(p["payload_type"] for p in test_data["payloads"])}\n')
try:
# Receive and process the message
print('Receiving and processing message...')
env = smartreceive(
mock_msg,
max_retries=3,
base_delay=100,
max_delay=1000
)
print('\n=== Received Envelope ===')
print(f'Correlation ID: {env["correlation_id"]}')
print(f'Message ID: {env["msg_id"]}')
print(f'Timestamp: {env["timestamp"]}')
print(f'Subject: {env["send_to"]}')
print(f'Payloads: {len(env["payloads"])}\n')
# Validate received data
print('=== Validation ===')
passed = True
if not env.get('correlation_id'):
print('❌ correlation_id is missing')
passed = False
else:
print('✅ correlation_id present')
if len(env['payloads']) != 4:
print(f'❌ Expected 4 payloads, got {len(env["payloads"])}')
passed = False
else:
print('✅ Correct number of payloads')
# Expected data
expected_data = [
('simple_dict', simple_dict, 'dictionary'),
('nested_dict', nested_dict, 'dictionary'),
('array_dict', array_dict, 'dictionary'),
('mixed_dict', mixed_dict, 'dictionary')
]
for i in range(len(env['payloads'])):
payload = env['payloads'][i]
expected = expected_data[i]
if payload[0] != expected[0]:
print(f"❌ Payload {i + 1}: Expected dataname '{expected[0]}', got '{payload[0]}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct dataname')
if payload[2] != expected[2]:
print(f"❌ Payload {i + 1}: Expected type '{expected[2]}', got '{payload[2]}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct type')
data_match = json.dumps(payload[1], sort_keys=True) == json.dumps(expected[1], sort_keys=True)
if not data_match:
print(f'❌ Payload {i + 1}: Data mismatch')
print(f' Expected: {json.dumps(expected[1], sort_keys=True)}')
print(f' Got: {json.dumps(payload[1], sort_keys=True)}')
passed = False
else:
print(f'✅ Payload {i + 1}: Data correctly deserialized')
# Test round-trip with receive
print('\n=== Round-trip Test ===')
round_trip_data = {
'correlation_id': 'roundtrip-' + _generate_uuid(),
'msg_id': _generate_uuid(),
'timestamp': '2024-01-15T10:30:00Z',
'send_to': '/test/dictionary',
'msg_purpose': 'test',
'sender_name': 'mpy-test',
'sender_id': _generate_uuid(),
'receiver_name': 'mpy-receiver',
'receiver_id': _generate_uuid(),
'reply_to': '',
'reply_to_msg_id': '',
'broker_url': TEST_BROKER_URL,
'metadata': {},
'payloads': [
{
'id': _generate_uuid(),
'dataname': 'roundtrip',
'payload_type': 'dictionary',
'transport': 'direct',
'encoding': 'base64',
'size': len(json.dumps({'test': 'data', 'nested': {'a': 1, 'b': 2}}).encode('utf8')),
'data': base64.b64encode(json.dumps({'test': 'data', 'nested': {'a': 1, 'b': 2}}).encode('utf8')).decode('ascii'),
'metadata': {'payload_bytes': len(json.dumps({'test': 'data', 'nested': {'a': 1, 'b': 2}}).encode('utf8'))}
}
]
}
mock_rt_msg = {'payload': json.dumps(round_trip_data)}
rt_env = smartreceive(mock_rt_msg)
if rt_env['payloads'][0][0] == 'roundtrip' and rt_env['payloads'][0][2] == 'dictionary':
print('✅ Round-trip test successful')
else:
print('❌ Round-trip test failed')
passed = False
# Final result
print('\n=== Test Result ===')
if passed:
print('✅ ALL TESTS PASSED')
sys.exit(0)
else:
print('❌ SOME TESTS FAILED')
sys.exit(1)
except Exception as e:
print(f'❌ Test failed with error: {e}')
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
run_test()

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@@ -1,177 +0,0 @@
"""
MicroPython Dictionary Sender Test
Tests the smartsend function with dictionary payloads
Note: This test is designed for both MicroPython and desktop Python
for compatibility testing.
"""
import sys
import os
import json
# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from natsbridge_mpy import smartsend, DEFAULT_BROKER_URL, DEFAULT_FILESERVER_URL, DEFAULT_SIZE_THRESHOLD, MAX_PAYLOAD_SIZE
TEST_SUBJECT = '/test/dictionary'
TEST_BROKER_URL = os.environ.get('NATS_URL', 'nats://localhost:4222')
TEST_FILESERVER_URL = os.environ.get('FILESERVER_URL', 'http://localhost:8080')
def run_test():
print('=== MicroPython Dictionary Sender Test ===\n')
from natsbridge_mpy import _generate_uuid
correlation_id = 'mpy-dict-test-' + _generate_uuid()
print(f'Correlation ID: {correlation_id}')
print(f'Subject: {TEST_SUBJECT}')
print(f'Broker URL: {TEST_BROKER_URL}')
print(f'Default Size Threshold: {DEFAULT_SIZE_THRESHOLD} bytes')
print(f'Max Payload Size: {MAX_PAYLOAD_SIZE} bytes\n')
# Test data - various dictionary structures
test_data = [
('simple_dict', {'key1': 'value1', 'key2': 'value2'}, 'dictionary'),
('nested_dict', {'outer': {'inner': 'value', 'number': 42}}, 'dictionary'),
('array_dict', {'items': [1, 2, 3, 'four', 'five']}, 'dictionary'),
('mixed_dict', {'string': 'text', 'number': 123, 'boolean': True, 'null_val': None}, 'dictionary')
]
try:
# Send the message
print('Sending dictionary payloads...')
env, env_json_str = smartsend(
TEST_SUBJECT,
test_data,
broker_url=TEST_BROKER_URL,
fileserver_url=TEST_FILESERVER_URL,
correlation_id=correlation_id,
msg_purpose='test',
sender_name='mpy-dict-test',
is_publish=False
)
print('\n=== Envelope Created ===')
print(f'Correlation ID: {env["correlation_id"]}')
print(f'Message ID: {env["msg_id"]}')
print(f'Timestamp: {env["timestamp"]}')
print(f'Subject: {env["send_to"]}')
print(f'Purpose: {env["msg_purpose"]}')
print(f'Sender: {env["sender_name"]}')
print(f'Payloads: {len(env["payloads"])}\n')
# Validate envelope structure
print('=== Validation ===')
passed = True
if len(env['payloads']) != 4:
print(f'❌ Expected 4 payloads, got {len(env["payloads"])}')
passed = False
else:
print('✅ Correct number of payloads')
# Test each payload
expected_datanames = ['simple_dict', 'nested_dict', 'array_dict', 'mixed_dict']
expected_types = ['dictionary', 'dictionary', 'dictionary', 'dictionary']
for i in range(len(env['payloads'])):
payload = env['payloads'][i]
if payload['dataname'] != expected_datanames[i]:
print(f"❌ Payload {i + 1}: Expected dataname '{expected_datanames[i]}', got '{payload['dataname']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct dataname')
if payload['payload_type'] != expected_types[i]:
print(f"❌ Payload {i + 1}: Expected type '{expected_types[i]}', got '{payload['payload_type']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct type')
if payload['transport'] != 'direct':
print(f"❌ Payload {i + 1}: Expected transport 'direct', got '{payload['transport']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct transport')
if payload['encoding'] != 'base64':
print(f"❌ Payload {i + 1}: Expected encoding 'base64', got '{payload['encoding']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct encoding')
# Decode and verify the data
import base64
decoded_data = json.loads(base64.b64decode(payload['data']).decode('ascii'))
original_data = test_data[i][1]
# Normalize for comparison
if json.dumps(decoded_data, sort_keys=True) != json.dumps(original_data, sort_keys=True):
print(f'❌ Payload {i + 1}: Data integrity mismatch')
print(f' Expected: {json.dumps(original_data)}')
print(f' Got: {json.dumps(decoded_data)}')
passed = False
else:
print(f'✅ Payload {i + 1}: Data integrity verified')
print(f' Size: {payload["size"]} bytes\n')
# Test round-trip serialization
print('=== Round-trip Serialization Test ===')
round_trip_data = [
('roundtrip', {'test': 'data', 'numbers': [1, 2, 3], 'nested': {'a': 1, 'b': 2}}, 'dictionary')
]
rt_env, _ = smartsend(
TEST_SUBJECT,
round_trip_data,
broker_url=TEST_BROKER_URL,
fileserver_url=TEST_FILESERVER_URL,
correlation_id='roundtrip-' + correlation_id,
is_publish=False
)
rt_payload = rt_env['payloads'][0]
rt_decoded = json.loads(base64.b64decode(rt_payload['data']).decode('ascii'))
if json.dumps(rt_decoded, sort_keys=True) == json.dumps(round_trip_data[0][1], sort_keys=True):
print('✅ Round-trip serialization successful')
else:
print('❌ Round-trip serialization failed')
passed = False
# Test JSON string output
print('\n=== JSON String Output Test ===')
try:
parsed = json.loads(env_json_str)
if parsed['correlation_id'] == env['correlation_id'] and \
len(parsed['payloads']) == len(env['payloads']):
print('✅ JSON string is valid and matches envelope')
else:
print('❌ JSON string does not match envelope')
passed = False
except json.JSONDecodeError as e:
print(f'❌ JSON string is invalid: {e}')
passed = False
# Final result
print('\n=== Test Result ===')
if passed:
print('✅ ALL TESTS PASSED')
sys.exit(0)
else:
print('❌ SOME TESTS FAILED')
sys.exit(1)
except Exception as e:
print(f'❌ Test failed with error: {e}')
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
run_test()

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@@ -1,209 +0,0 @@
"""
MicroPython Text Receiver Test
Tests the smartreceive function with text payloads
Note: This test is designed for both MicroPython and desktop Python
for compatibility testing.
"""
import sys
import os
import json
# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from natsbridge_mpy import smartreceive, DEFAULT_BROKER_URL, DEFAULT_FILESERVER_URL
TEST_BROKER_URL = os.environ.get('NATS_URL', 'nats://localhost:4222')
TEST_FILESERVER_URL = os.environ.get('FILESERVER_URL', 'http://localhost:8080')
def run_test():
print('=== MicroPython Text Receiver Test ===\n')
from natsbridge_mpy import _generate_uuid
# Create a mock NATS message with text payload
test_text = 'Hello, NATSBridge! This is a test message.'
import base64
test_data = {
'correlation_id': 'mpy-receiver-test-' + _generate_uuid(),
'msg_id': _generate_uuid(),
'timestamp': '2024-01-15T10:30:00Z',
'send_to': '/test/text',
'msg_purpose': 'test',
'sender_name': 'mpy-text-test',
'sender_id': _generate_uuid(),
'receiver_name': 'mpy-receiver',
'receiver_id': _generate_uuid(),
'reply_to': '',
'reply_to_msg_id': '',
'broker_url': TEST_BROKER_URL,
'metadata': {},
'payloads': [
{
'id': _generate_uuid(),
'dataname': 'message',
'payload_type': 'text',
'transport': 'direct',
'encoding': 'base64',
'size': len(test_text.encode('utf8')),
'data': base64.b64encode(test_text.encode('utf8')).decode('ascii'),
'metadata': {'payload_bytes': len(test_text.encode('utf8'))}
}
]
}
mock_msg = {
'payload': json.dumps(test_data)
}
print('Mock Message Created:')
print(f' Correlation ID: {test_data["correlation_id"]}')
print(f' Payloads: {len(test_data["payloads"])}')
print(f' Payload dataname: {test_data["payloads"][0]["dataname"]}')
print(f' Payload type: {test_data["payloads"][0]["payload_type"]}')
print(f' Transport: {test_data["payloads"][0]["transport"]}\n')
try:
# Receive and process the message
print('Receiving and processing message...')
env = smartreceive(
mock_msg,
max_retries=3,
base_delay=100,
max_delay=1000
)
print('\n=== Received Envelope ===')
print(f'Correlation ID: {env["correlation_id"]}')
print(f'Message ID: {env["msg_id"]}')
print(f'Timestamp: {env["timestamp"]}')
print(f'Subject: {env["send_to"]}')
print(f'Payloads: {len(env["payloads"])}\n')
# Validate received data
print('=== Validation ===')
passed = True
if not env.get('correlation_id'):
print('❌ correlation_id is missing')
passed = False
else:
print('✅ correlation_id present')
if len(env['payloads']) != 1:
print(f'❌ Expected 1 payload, got {len(env["payloads"])}')
passed = False
else:
print('✅ Correct number of payloads')
payload = env['payloads'][0]
if payload[0] != 'message':
print(f"❌ Expected dataname 'message', got '{payload[0]}'")
passed = False
else:
print('✅ Correct dataname')
if payload[2] != 'text':
print(f"❌ Expected type 'text', got '{payload[2]}'")
passed = False
else:
print('✅ Correct type')
if payload[1] != test_text:
print('❌ Data mismatch')
print(f' Expected: {test_text}')
print(f' Got: {payload[1]}')
passed = False
else:
print('✅ Data correctly deserialized')
# Test with multiple text payloads
print('\n=== Multiple Text Payloads Test ===')
multi_test_data = {
'correlation_id': 'multi-receiver-' + _generate_uuid(),
'msg_id': _generate_uuid(),
'timestamp': '2024-01-15T10:30:00Z',
'send_to': '/test/text',
'msg_purpose': 'test',
'sender_name': 'mpy-text-test',
'sender_id': _generate_uuid(),
'receiver_name': 'mpy-receiver',
'receiver_id': _generate_uuid(),
'reply_to': '',
'reply_to_msg_id': '',
'broker_url': TEST_BROKER_URL,
'metadata': {},
'payloads': [
{
'id': _generate_uuid(),
'dataname': 'msg1',
'payload_type': 'text',
'transport': 'direct',
'encoding': 'base64',
'size': 16,
'data': base64.b64encode(b'First message').decode('ascii'),
'metadata': {'payload_bytes': 16}
},
{
'id': _generate_uuid(),
'dataname': 'msg2',
'payload_type': 'text',
'transport': 'direct',
'encoding': 'base64',
'size': 16,
'data': base64.b64encode(b'Second message').decode('ascii'),
'metadata': {'payload_bytes': 16}
},
{
'id': _generate_uuid(),
'dataname': 'msg3',
'payload_type': 'text',
'transport': 'direct',
'encoding': 'base64',
'size': 16,
'data': base64.b64encode(b'Third message').decode('ascii'),
'metadata': {'payload_bytes': 16}
}
]
}
mock_multi_msg = {'payload': json.dumps(multi_test_data)}
multi_env = smartreceive(mock_multi_msg)
if len(multi_env['payloads']) == 3:
print('✅ Multiple payloads handled correctly')
# Verify each payload
expected_messages = ['First message', 'Second message', 'Third message']
for i in range(3):
if multi_env['payloads'][i][1] == expected_messages[i]:
print(f'✅ Payload {i + 1} correctly deserialized')
else:
print(f'❌ Payload {i + 1} mismatch')
passed = False
else:
print(f'❌ Expected 3 payloads, got {len(multi_env["payloads"])}')
passed = False
# Final result
print('\n=== Test Result ===')
if passed:
print('✅ ALL TESTS PASSED')
sys.exit(0)
else:
print('❌ SOME TESTS FAILED')
sys.exit(1)
except Exception as e:
print(f'❌ Test failed with error: {e}')
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
run_test()

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@@ -1,205 +0,0 @@
"""
MicroPython Text Sender Test
Tests the smartsend function with text payloads
Note: This test is designed for both MicroPython and desktop Python
for compatibility testing.
"""
import sys
import os
# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from natsbridge_mpy import smartsend, DEFAULT_BROKER_URL, DEFAULT_FILESERVER_URL, DEFAULT_SIZE_THRESHOLD, MAX_PAYLOAD_SIZE
TEST_SUBJECT = '/test/text'
TEST_BROKER_URL = os.environ.get('NATS_URL', 'nats://localhost:4222')
TEST_FILESERVER_URL = os.environ.get('FILESERVER_URL', 'http://localhost:8080')
def run_test():
print('=== MicroPython Text Sender Test ===\n')
from natsbridge_mpy import _generate_uuid
correlation_id = 'mpy-text-test-' + _generate_uuid()
print(f'Correlation ID: {correlation_id}')
print(f'Subject: {TEST_SUBJECT}')
print(f'Broker URL: {TEST_BROKER_URL}')
print(f'Default Size Threshold: {DEFAULT_SIZE_THRESHOLD} bytes')
print(f'Max Payload Size: {MAX_PAYLOAD_SIZE} bytes\n')
# Test data
text_data = 'Hello, NATSBridge! This is a test message.'
test_data = [
('message', text_data, 'text')
]
try:
# Send the message
print('Sending text payload...')
env, env_json_str = smartsend(
TEST_SUBJECT,
test_data,
broker_url=TEST_BROKER_URL,
fileserver_url=TEST_FILESERVER_URL,
correlation_id=correlation_id,
msg_purpose='test',
sender_name='mpy-text-test',
is_publish=False # Don't actually publish for this test
)
print('\n=== Envelope Created ===')
print(f'Correlation ID: {env["correlation_id"]}')
print(f'Message ID: {env["msg_id"]}')
print(f'Timestamp: {env["timestamp"]}')
print(f'Subject: {env["send_to"]}')
print(f'Purpose: {env["msg_purpose"]}')
print(f'Sender: {env["sender_name"]}')
print(f'Payloads: {len(env["payloads"])}\n')
# Validate envelope structure
print('=== Validation ===')
passed = True
if not env.get('correlation_id'):
print('❌ correlation_id is missing')
passed = False
else:
print('✅ correlation_id present')
if not env.get('msg_id'):
print('❌ msg_id is missing')
passed = False
else:
print('✅ msg_id present')
if not env.get('timestamp'):
print('❌ timestamp is missing')
passed = False
else:
print('✅ timestamp present')
if len(env['payloads']) != 1:
print(f'❌ Expected 1 payload, got {len(env["payloads"])}')
passed = False
else:
print('✅ Correct number of payloads')
payload = env['payloads'][0]
if payload['dataname'] != 'message':
print(f"❌ Expected dataname 'message', got '{payload['dataname']}'")
passed = False
else:
print('✅ Correct dataname')
if payload['payload_type'] != 'text':
print(f"❌ Expected payload_type 'text', got '{payload['payload_type']}'")
passed = False
else:
print('✅ Correct payload_type')
if payload['transport'] != 'direct':
print(f"❌ Expected transport 'direct', got '{payload['transport']}'")
passed = False
else:
print('✅ Correct transport')
if payload['encoding'] != 'base64':
print(f"❌ Expected encoding 'base64', got '{payload['encoding']}'")
passed = False
else:
print('✅ Correct encoding')
# Decode and verify the data
import base64
decoded_data = base64.b64decode(payload['data']).decode('ascii')
if decoded_data != text_data:
print('❌ Decoded data mismatch')
print(f' Expected: {text_data}')
print(f' Got: {decoded_data}')
passed = False
else:
print('✅ Data integrity verified')
print(f'\nPayload size: {payload["size"]} bytes')
print(f'Base64 data length: {len(payload["data"])} chars')
# Test with multiple text payloads
print('\n=== Multiple Text Payloads Test ===')
multi_test_data = [
('msg1', 'First message', 'text'),
('msg2', 'Second message', 'text'),
('msg3', 'Third message', 'text')
]
multi_env, _ = smartsend(
TEST_SUBJECT,
multi_test_data,
broker_url=TEST_BROKER_URL,
fileserver_url=TEST_FILESERVER_URL,
correlation_id='multi-test-' + correlation_id,
is_publish=False
)
if len(multi_env['payloads']) == 3:
print('✅ Multiple payloads handled correctly')
else:
print(f'❌ Expected 3 payloads, got {len(multi_env["payloads"])}')
passed = False
# Test size threshold enforcement
print('\n=== Size Threshold Test ===')
small_text = 'small'
large_text = 'x' * (DEFAULT_SIZE_THRESHOLD - 100) # Just under threshold
small_env, _ = smartsend(
TEST_SUBJECT,
[('small', small_text, 'text')],
broker_url=TEST_BROKER_URL,
is_publish=False
)
if small_env['payloads'][0]['transport'] == 'direct':
print('✅ Small payload uses direct transport')
else:
print('❌ Small payload should use direct transport')
passed = False
# Test that large text (> MAX_PAYLOAD_SIZE) raises error
print('\n=== Max Payload Size Test ===')
try:
too_large_text = 'x' * (MAX_PAYLOAD_SIZE + 1000)
large_env, _ = smartsend(
TEST_SUBJECT,
[('large', too_large_text, 'text')],
broker_url=TEST_BROKER_URL,
is_publish=False
)
print('❌ Should have raised MemoryError for payload exceeding MAX_PAYLOAD_SIZE')
passed = False
except MemoryError as e:
print(f'✅ Correctly raised MemoryError: {e}')
except Exception as e:
print(f'❌ Unexpected error: {type(e).__name__}: {e}')
passed = False
# Final result
print('\n=== Test Result ===')
if passed:
print('✅ ALL TESTS PASSED')
sys.exit(0)
else:
print('❌ SOME TESTS FAILED')
sys.exit(1)
except Exception as e:
print(f'❌ Test failed with error: {e}')
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
run_test()

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@@ -1,184 +0,0 @@
"""
Python Binary Receiver Test
Tests the smartreceive function with binary/image/audio/video/table payloads
"""
import asyncio
import sys
import os
import json
import base64
# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from natsbridge import smartreceive, DEFAULT_BROKER_URL, DEFAULT_FILESERVER_URL
TEST_BROKER_URL = os.environ.get('NATS_URL', 'nats://localhost:4222')
TEST_FILESERVER_URL = os.environ.get('FILESERVER_URL', 'http://localhost:8080')
async def run_test():
print('=== Python Binary Receiver Test ===\n')
# Create mock NATS message with binary payloads
image_data = bytes([0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A]) # PNG header
audio_data = bytes([0x46, 0x4C, 0x41, 0x43, 0x00, 0x00, 0x00, 0x00]) # FLAC header
video_data = bytes([0x00, 0x00, 0x00, 0x18, 0x66, 0x74, 0x79, 0x70]) # MP4 header
generic_binary = bytes([0xDE, 0xAD, 0xBE, 0xEF, 0xCA, 0xFE, 0xBA, 0xBE])
test_data = {
'correlation_id': 'py-binary-receiver-' + str(asyncio.get_event_loop().time() * 1000000),
'msg_id': 'msg-' + str(asyncio.get_event_loop().time() * 1000000),
'timestamp': asyncio.get_event_loop().time().isoformat(),
'send_to': '/test/binary',
'msg_purpose': 'test',
'sender_name': 'py-binary-test',
'sender_id': 'sender-' + str(asyncio.get_event_loop().time() * 1000000),
'receiver_name': 'py-receiver',
'receiver_id': 'receiver-' + str(asyncio.get_event_loop().time() * 1000000),
'reply_to': '',
'reply_to_msg_id': '',
'broker_url': TEST_BROKER_URL,
'metadata': {},
'payloads': [
{
'id': 'payload-1',
'dataname': 'image',
'payload_type': 'image',
'transport': 'direct',
'encoding': 'base64',
'size': len(image_data),
'data': base64.b64encode(image_data).decode('ascii'),
'metadata': {'payload_bytes': len(image_data)}
},
{
'id': 'payload-2',
'dataname': 'audio',
'payload_type': 'audio',
'transport': 'direct',
'encoding': 'base64',
'size': len(audio_data),
'data': base64.b64encode(audio_data).decode('ascii'),
'metadata': {'payload_bytes': len(audio_data)}
},
{
'id': 'payload-3',
'dataname': 'video',
'payload_type': 'video',
'transport': 'direct',
'encoding': 'base64',
'size': len(video_data),
'data': base64.b64encode(video_data).decode('ascii'),
'metadata': {'payload_bytes': len(video_data)}
},
{
'id': 'payload-4',
'dataname': 'binary',
'payload_type': 'binary',
'transport': 'direct',
'encoding': 'base64',
'size': len(generic_binary),
'data': base64.b64encode(generic_binary).decode('ascii'),
'metadata': {'payload_bytes': len(generic_binary)}
}
]
}
mock_msg = {
'payload': json.dumps(test_data)
}
print('Mock Message Created:')
print(f' Correlation ID: {test_data["correlation_id"]}')
print(f' Payloads: {len(test_data["payloads"])}')
print(f' Payload types: {", ".join(p["payload_type"] for p in test_data["payloads"])}\n')
try:
# Receive and process the message
print('Receiving and processing message...')
env = await smartreceive(
mock_msg,
max_retries=3,
base_delay=100,
max_delay=1000
)
print('\n=== Received Envelope ===')
print(f'Correlation ID: {env["correlation_id"]}')
print(f'Message ID: {env["msg_id"]}')
print(f'Timestamp: {env["timestamp"]}')
print(f'Subject: {env["send_to"]}')
print(f'Payloads: {len(env["payloads"])}\n')
# Validate received data
print('=== Validation ===')
passed = True
if not env.get('correlation_id'):
print('❌ correlation_id is missing')
passed = False
else:
print('✅ correlation_id present')
if len(env['payloads']) != 4:
print(f'❌ Expected 4 payloads, got {len(env["payloads"])}')
passed = False
else:
print('✅ Correct number of payloads')
# Expected data
expected_data = [
('image', image_data, 'image'),
('audio', audio_data, 'audio'),
('video', video_data, 'video'),
('binary', generic_binary, 'binary')
]
for i in range(len(env['payloads'])):
payload = env['payloads'][i]
expected = expected_data[i]
if payload[0] != expected[0]:
print(f"❌ Payload {i + 1}: Expected dataname '{expected[0]}', got '{payload[0]}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct dataname')
if payload[2] != expected[2]:
print(f"❌ Payload {i + 1}: Expected type '{expected[2]}', got '{payload[2]}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct type')
# Verify binary data integrity
received_data = payload[1]
if not isinstance(received_data, (bytes, bytearray)):
print(f'❌ Payload {i + 1}: Expected bytes/bytearray, got {type(received_data)}')
passed = False
elif received_data != expected[1]:
print(f'❌ Payload {i + 1}: Data mismatch')
print(f' Expected: {expected[1]}')
print(f' Got: {received_data}')
passed = False
else:
print(f'✅ Payload {i + 1}: Data correctly deserialized')
# Final result
print('\n=== Test Result ===')
if passed:
print('✅ ALL TESTS PASSED')
sys.exit(0)
else:
print('❌ SOME TESTS FAILED')
sys.exit(1)
except Exception as e:
print(f'❌ Test failed with error: {e}')
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
asyncio.run(run_test())

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@@ -1,183 +0,0 @@
"""
Python Binary Sender Test
Tests the smartsend function with binary/image/audio/video/table payloads
"""
import asyncio
import sys
import os
import base64
# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from natsbridge import smartsend, DEFAULT_BROKER_URL, DEFAULT_FILESERVER_URL
TEST_SUBJECT = '/test/binary'
TEST_BROKER_URL = os.environ.get('NATS_URL', 'nats://localhost:4222')
TEST_FILESERVER_URL = os.environ.get('FILESERVER_URL', 'http://localhost:8080')
async def run_test():
print('=== Python Binary Sender Test ===\n')
correlation_id = 'py-binary-test-' + str(asyncio.get_event_loop().time() * 1000000)
print(f'Correlation ID: {correlation_id}')
print(f'Subject: {TEST_SUBJECT}')
print(f'Broker URL: {TEST_BROKER_URL}\n')
# Test data - binary data for different types
image_data = bytes([0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A]) # PNG header
audio_data = bytes([0x46, 0x4C, 0x41, 0x43, 0x00, 0x00, 0x00, 0x00]) # FLAC header
video_data = bytes([0x00, 0x00, 0x00, 0x18, 0x66, 0x74, 0x79, 0x70]) # MP4 header
generic_binary = bytes([0xDE, 0xAD, 0xBE, 0xEF, 0xCA, 0xFE, 0xBA, 0xBE])
# Test table data
try:
import pandas as pd
table_data = pd.DataFrame({
'id': [1, 2, 3, 4, 5],
'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
'value': [10.5, 20.3, 30.1, 40.9, 50.7]
})
table_available = True
except ImportError:
table_available = False
table_data = None
test_data = [
('image', image_data, 'image'),
('audio', audio_data, 'audio'),
('video', video_data, 'video'),
('binary', generic_binary, 'binary')
]
if table_available:
test_data.append(('table', table_data, 'table'))
try:
# Send the message
print('Sending binary payloads...')
env, env_json_str = await smartsend(
TEST_SUBJECT,
test_data,
broker_url=TEST_BROKER_URL,
fileserver_url=TEST_FILESERVER_URL,
correlation_id=correlation_id,
msg_purpose='test',
sender_name='py-binary-test',
is_publish=False
)
print('\n=== Envelope Created ===')
print(f'Correlation ID: {env["correlation_id"]}')
print(f'Message ID: {env["msg_id"]}')
print(f'Timestamp: {env["timestamp"]}')
print(f'Subject: {env["send_to"]}')
print(f'Purpose: {env["msg_purpose"]}')
print(f'Sender: {env["sender_name"]}')
print(f'Payloads: {len(env["payloads"])}\n')
# Validate envelope structure
print('=== Validation ===')
passed = True
expected_count = 5 if table_available else 4
if len(env['payloads']) != expected_count:
print(f'❌ Expected {expected_count} payloads, got {len(env["payloads"])}')
passed = False
else:
print('✅ Correct number of payloads')
# Test each payload
expected_datanames = ['image', 'audio', 'video', 'binary']
expected_types = ['image', 'audio', 'video', 'binary']
expected_data = [image_data, audio_data, video_data, generic_binary]
if table_available:
expected_datanames.append('table')
expected_types.append('table')
for i in range(len(env['payloads'])):
payload = env['payloads'][i]
if payload['dataname'] != expected_datanames[i]:
print(f"❌ Payload {i + 1}: Expected dataname '{expected_datanames[i]}', got '{payload['dataname']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct dataname')
if payload['payload_type'] != expected_types[i]:
print(f"❌ Payload {i + 1}: Expected type '{expected_types[i]}', got '{payload['payload_type']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct type')
if payload['transport'] != 'direct':
print(f"❌ Payload {i + 1}: Expected transport 'direct', got '{payload['transport']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct transport')
if payload['encoding'] != 'base64':
print(f"❌ Payload {i + 1}: Expected encoding 'base64', got '{payload['encoding']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct encoding')
# Decode and verify the data
decoded_data = base64.b64decode(payload['data'])
if i < len(expected_data):
original_data = expected_data[i]
if decoded_data != original_data:
print(f'❌ Payload {i + 1}: Data integrity mismatch')
passed = False
else:
print(f'✅ Payload {i + 1}: Data integrity verified')
else:
# Table payload - just verify it's present
print(f'✅ Payload {i + 1}: Table data present (size: {payload["size"]} bytes)')
print(f' Size: {payload["size"]} bytes\n')
# Test with larger binary data
print('=== Large Binary Data Test ===')
large_data = bytes([0xFF] * 10000) # 10KB of binary data
large_test_data = [
('large_binary', large_data, 'binary')
]
large_env, _ = await smartsend(
TEST_SUBJECT,
large_test_data,
broker_url=TEST_BROKER_URL,
fileserver_url=TEST_FILESERVER_URL,
correlation_id='large-' + correlation_id,
is_publish=False
)
if len(large_env['payloads']) == 1 and large_env['payloads'][0]['size'] == 10000:
print('✅ Large binary data handled correctly')
else:
print('❌ Large binary data handling failed')
passed = False
# Final result
print('\n=== Test Result ===')
if passed:
print('✅ ALL TESTS PASSED')
sys.exit(0)
else:
print('❌ SOME TESTS FAILED')
sys.exit(1)
except Exception as e:
print(f'❌ Test failed with error: {e}')
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
asyncio.run(run_test())

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@@ -1,220 +0,0 @@
"""
Python Dictionary Receiver Test
Tests the smartreceive function with dictionary payloads
"""
import asyncio
import sys
import os
import json
# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from natsbridge import smartreceive, DEFAULT_BROKER_URL, DEFAULT_FILESERVER_URL
TEST_BROKER_URL = os.environ.get('NATS_URL', 'nats://localhost:4222')
TEST_FILESERVER_URL = os.environ.get('FILESERVER_URL', 'http://localhost:8080')
async def run_test():
print('=== Python Dictionary Receiver Test ===\n')
# Create a mock NATS message with dictionary payloads
import base64
simple_dict = {'key1': 'value1', 'key2': 'value2'}
nested_dict = {'outer': {'inner': 'value', 'number': 42}}
array_dict = {'items': [1, 2, 3, 'four', 'five']}
mixed_dict = {'string': 'text', 'number': 123, 'boolean': True, 'null_val': None}
test_data = {
'correlation_id': 'py-receiver-dict-' + str(asyncio.get_event_loop().time() * 1000000),
'msg_id': 'msg-' + str(asyncio.get_event_loop().time() * 1000000),
'timestamp': asyncio.get_event_loop().time().isoformat(),
'send_to': '/test/dictionary',
'msg_purpose': 'test',
'sender_name': 'py-dict-test',
'sender_id': 'sender-' + str(asyncio.get_event_loop().time() * 1000000),
'receiver_name': 'py-receiver',
'receiver_id': 'receiver-' + str(asyncio.get_event_loop().time() * 1000000),
'reply_to': '',
'reply_to_msg_id': '',
'broker_url': TEST_BROKER_URL,
'metadata': {},
'payloads': [
{
'id': 'payload-1',
'dataname': 'simple_dict',
'payload_type': 'dictionary',
'transport': 'direct',
'encoding': 'base64',
'size': len(json.dumps(simple_dict).encode('utf8')),
'data': base64.b64encode(json.dumps(simple_dict).encode('utf8')).decode('ascii'),
'metadata': {'payload_bytes': len(json.dumps(simple_dict).encode('utf8'))}
},
{
'id': 'payload-2',
'dataname': 'nested_dict',
'payload_type': 'dictionary',
'transport': 'direct',
'encoding': 'base64',
'size': len(json.dumps(nested_dict).encode('utf8')),
'data': base64.b64encode(json.dumps(nested_dict).encode('utf8')).decode('ascii'),
'metadata': {'payload_bytes': len(json.dumps(nested_dict).encode('utf8'))}
},
{
'id': 'payload-3',
'dataname': 'array_dict',
'payload_type': 'dictionary',
'transport': 'direct',
'encoding': 'base64',
'size': len(json.dumps(array_dict).encode('utf8')),
'data': base64.b64encode(json.dumps(array_dict).encode('utf8')).decode('ascii'),
'metadata': {'payload_bytes': len(json.dumps(array_dict).encode('utf8'))}
},
{
'id': 'payload-4',
'dataname': 'mixed_dict',
'payload_type': 'dictionary',
'transport': 'direct',
'encoding': 'base64',
'size': len(json.dumps(mixed_dict).encode('utf8')),
'data': base64.b64encode(json.dumps(mixed_dict).encode('utf8')).decode('ascii'),
'metadata': {'payload_bytes': len(json.dumps(mixed_dict).encode('utf8'))}
}
]
}
mock_msg = {
'payload': json.dumps(test_data)
}
print('Mock Message Created:')
print(f' Correlation ID: {test_data["correlation_id"]}')
print(f' Payloads: {len(test_data["payloads"])}')
print(f' Payload types: {", ".join(p["payload_type"] for p in test_data["payloads"])}\n')
try:
# Receive and process the message
print('Receiving and processing message...')
env = await smartreceive(
mock_msg,
max_retries=3,
base_delay=100,
max_delay=1000
)
print('\n=== Received Envelope ===')
print(f'Correlation ID: {env["correlation_id"]}')
print(f'Message ID: {env["msg_id"]}')
print(f'Timestamp: {env["timestamp"]}')
print(f'Subject: {env["send_to"]}')
print(f'Payloads: {len(env["payloads"])}\n')
# Validate received data
print('=== Validation ===')
passed = True
if not env.get('correlation_id'):
print('❌ correlation_id is missing')
passed = False
else:
print('✅ correlation_id present')
if len(env['payloads']) != 4:
print(f'❌ Expected 4 payloads, got {len(env["payloads"])}')
passed = False
else:
print('✅ Correct number of payloads')
# Expected data
expected_data = [
('simple_dict', simple_dict, 'dictionary'),
('nested_dict', nested_dict, 'dictionary'),
('array_dict', array_dict, 'dictionary'),
('mixed_dict', mixed_dict, 'dictionary')
]
for i in range(len(env['payloads'])):
payload = env['payloads'][i]
expected = expected_data[i]
if payload[0] != expected[0]:
print(f"❌ Payload {i + 1}: Expected dataname '{expected[0]}', got '{payload[0]}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct dataname')
if payload[2] != expected[2]:
print(f"❌ Payload {i + 1}: Expected type '{expected[2]}', got '{payload[2]}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct type')
data_match = json.dumps(payload[1], sort_keys=True) == json.dumps(expected[1], sort_keys=True)
if not data_match:
print(f'❌ Payload {i + 1}: Data mismatch')
print(f' Expected: {json.dumps(expected[1], sort_keys=True)}')
print(f' Got: {json.dumps(payload[1], sort_keys=True)}')
passed = False
else:
print(f'✅ Payload {i + 1}: Data correctly deserialized')
# Test round-trip with receive
print('\n=== Round-trip Test ===')
round_trip_data = {
'correlation_id': 'roundtrip-' + str(asyncio.get_event_loop().time() * 1000000),
'msg_id': 'msg-' + str(asyncio.get_event_loop().time() * 1000000),
'timestamp': asyncio.get_event_loop().time().isoformat(),
'send_to': '/test/dictionary',
'msg_purpose': 'test',
'sender_name': 'py-test',
'sender_id': 'sender-' + str(asyncio.get_event_loop().time() * 1000000),
'receiver_name': 'py-receiver',
'receiver_id': 'receiver-' + str(asyncio.get_event_loop().time() * 1000000),
'reply_to': '',
'reply_to_msg_id': '',
'broker_url': TEST_BROKER_URL,
'metadata': {},
'payloads': [
{
'id': 'payload-rt',
'dataname': 'roundtrip',
'payload_type': 'dictionary',
'transport': 'direct',
'encoding': 'base64',
'size': len(json.dumps({'test': 'data', 'nested': {'a': 1, 'b': 2}}).encode('utf8')),
'data': base64.b64encode(json.dumps({'test': 'data', 'nested': {'a': 1, 'b': 2}}).encode('utf8')).decode('ascii'),
'metadata': {'payload_bytes': len(json.dumps({'test': 'data', 'nested': {'a': 1, 'b': 2}}).encode('utf8'))}
}
]
}
mock_rt_msg = {'payload': json.dumps(round_trip_data)}
rt_env = await smartreceive(mock_rt_msg)
if rt_env['payloads'][0][0] == 'roundtrip' and rt_env['payloads'][0][2] == 'dictionary':
print('✅ Round-trip test successful')
else:
print('❌ Round-trip test failed')
passed = False
# Final result
print('\n=== Test Result ===')
if passed:
print('✅ ALL TESTS PASSED')
sys.exit(0)
else:
print('❌ SOME TESTS FAILED')
sys.exit(1)
except Exception as e:
print(f'❌ Test failed with error: {e}')
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
asyncio.run(run_test())

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@@ -1,172 +0,0 @@
"""
Python Dictionary Sender Test
Tests the smartsend function with dictionary payloads
"""
import asyncio
import sys
import os
import json
# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from natsbridge import smartsend, DEFAULT_BROKER_URL, DEFAULT_FILESERVER_URL
TEST_SUBJECT = '/test/dictionary'
TEST_BROKER_URL = os.environ.get('NATS_URL', 'nats://localhost:4222')
TEST_FILESERVER_URL = os.environ.get('FILESERVER_URL', 'http://localhost:8080')
async def run_test():
print('=== Python Dictionary Sender Test ===\n')
correlation_id = 'py-dict-test-' + str(asyncio.get_event_loop().time() * 1000000)
print(f'Correlation ID: {correlation_id}')
print(f'Subject: {TEST_SUBJECT}')
print(f'Broker URL: {TEST_BROKER_URL}\n')
# Test data - various dictionary structures
test_data = [
('simple_dict', {'key1': 'value1', 'key2': 'value2'}, 'dictionary'),
('nested_dict', {'outer': {'inner': 'value', 'number': 42}}, 'dictionary'),
('array_dict', {'items': [1, 2, 3, 'four', 'five']}, 'dictionary'),
('mixed_dict', {'string': 'text', 'number': 123, 'boolean': True, 'null_val': None}, 'dictionary')
]
try:
# Send the message
print('Sending dictionary payloads...')
env, env_json_str = await smartsend(
TEST_SUBJECT,
test_data,
broker_url=TEST_BROKER_URL,
fileserver_url=TEST_FILESERVER_URL,
correlation_id=correlation_id,
msg_purpose='test',
sender_name='py-dict-test',
is_publish=False
)
print('\n=== Envelope Created ===')
print(f'Correlation ID: {env["correlation_id"]}')
print(f'Message ID: {env["msg_id"]}')
print(f'Timestamp: {env["timestamp"]}')
print(f'Subject: {env["send_to"]}')
print(f'Purpose: {env["msg_purpose"]}')
print(f'Sender: {env["sender_name"]}')
print(f'Payloads: {len(env["payloads"])}\n')
# Validate envelope structure
print('=== Validation ===')
passed = True
if len(env['payloads']) != 4:
print(f'❌ Expected 4 payloads, got {len(env["payloads"])}')
passed = False
else:
print('✅ Correct number of payloads')
# Test each payload
expected_datanames = ['simple_dict', 'nested_dict', 'array_dict', 'mixed_dict']
expected_types = ['dictionary', 'dictionary', 'dictionary', 'dictionary']
for i in range(len(env['payloads'])):
payload = env['payloads'][i]
if payload['dataname'] != expected_datanames[i]:
print(f"❌ Payload {i + 1}: Expected dataname '{expected_datanames[i]}', got '{payload['dataname']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct dataname')
if payload['payload_type'] != expected_types[i]:
print(f"❌ Payload {i + 1}: Expected type '{expected_types[i]}', got '{payload['payload_type']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct type')
if payload['transport'] != 'direct':
print(f"❌ Payload {i + 1}: Expected transport 'direct', got '{payload['transport']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct transport')
if payload['encoding'] != 'base64':
print(f"❌ Payload {i + 1}: Expected encoding 'base64', got '{payload['encoding']}'")
passed = False
else:
print(f'✅ Payload {i + 1}: Correct encoding')
# Decode and verify the data
import base64
decoded_data = json.loads(base64.b64decode(payload['data']).decode('utf8'))
original_data = test_data[i][1]
# Normalize for comparison (None vs null, True vs true, etc.)
if json.dumps(decoded_data, sort_keys=True) != json.dumps(original_data, sort_keys=True):
print(f'❌ Payload {i + 1}: Data integrity mismatch')
print(f' Expected: {json.dumps(original_data)}')
print(f' Got: {json.dumps(decoded_data)}')
passed = False
else:
print(f'✅ Payload {i + 1}: Data integrity verified')
print(f' Size: {payload["size"]} bytes\n')
# Test round-trip serialization
print('=== Round-trip Serialization Test ===')
round_trip_data = [
('roundtrip', {'test': 'data', 'numbers': [1, 2, 3], 'nested': {'a': 1, 'b': 2}}, 'dictionary')
]
rt_env, _ = await smartsend(
TEST_SUBJECT,
round_trip_data,
broker_url=TEST_BROKER_URL,
fileserver_url=TEST_FILESERVER_URL,
correlation_id='roundtrip-' + correlation_id,
is_publish=False
)
rt_payload = rt_env['payloads'][0]
rt_decoded = json.loads(base64.b64decode(rt_payload['data']).decode('utf8'))
if json.dumps(rt_decoded, sort_keys=True) == json.dumps(round_trip_data[0][1], sort_keys=True):
print('✅ Round-trip serialization successful')
else:
print('❌ Round-trip serialization failed')
passed = False
# Test JSON string output
print('\n=== JSON String Output Test ===')
try:
parsed = json.loads(env_json_str)
if parsed['correlation_id'] == env['correlation_id'] and \
len(parsed['payloads']) == len(env['payloads']):
print('✅ JSON string is valid and matches envelope')
else:
print('❌ JSON string does not match envelope')
passed = False
except json.JSONDecodeError as e:
print(f'❌ JSON string is invalid: {e}')
passed = False
# Final result
print('\n=== Test Result ===')
if passed:
print('✅ ALL TESTS PASSED')
sys.exit(0)
else:
print('❌ SOME TESTS FAILED')
sys.exit(1)
except Exception as e:
print(f'❌ Test failed with error: {e}')
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
asyncio.run(run_test())

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@@ -1,167 +0,0 @@
"""
Python Table Sender Test
Tests the smartsend function with table (Arrow IPC) payloads
"""
import asyncio
import sys
import os
# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from natsbridge import smartsend, DEFAULT_BROKER_URL, DEFAULT_FILESERVER_URL
TEST_SUBJECT = '/test/table'
TEST_BROKER_URL = os.environ.get('NATS_URL', 'nats://localhost:4222')
TEST_FILESERVER_URL = os.environ.get('FILESERVER_URL', 'http://localhost:8080')
async def run_test():
print('=== Python Table Sender Test ===\n')
correlation_id = 'py-table-test-' + str(asyncio.get_event_loop().time() * 1000000)
print(f'Correlation ID: {correlation_id}')
print(f'Subject: {TEST_SUBJECT}')
print(f'Broker URL: {TEST_BROKER_URL}\n')
# Test data - pandas DataFrame
try:
import pandas as pd
table_data = pd.DataFrame({
'id': [1, 2, 3, 4, 5],
'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
'age': [30, 25, 35, 28, 32],
'active': [True, False, True, True, False]
})
table_available = True
except ImportError:
print('❌ pandas not available - skipping table tests')
sys.exit(0)
test_data = [
('users_table', table_data, 'table')
]
try:
# Send the message
print('Sending table payload...')
env, env_json_str = await smartsend(
TEST_SUBJECT,
test_data,
broker_url=TEST_BROKER_URL,
fileserver_url=TEST_FILESERVER_URL,
correlation_id=correlation_id,
msg_purpose='test',
sender_name='py-table-test',
is_publish=False
)
print('\n=== Envelope Created ===')
print(f'Correlation ID: {env["correlation_id"]}')
print(f'Message ID: {env["msg_id"]}')
print(f'Timestamp: {env["timestamp"]}')
print(f'Subject: {env["send_to"]}')
print(f'Purpose: {env["msg_purpose"]}')
print(f'Sender: {env["sender_name"]}')
print(f'Payloads: {len(env["payloads"])}\n')
# Validate envelope structure
print('=== Validation ===')
passed = True
if len(env['payloads']) != 1:
print(f'❌ Expected 1 payload, got {len(env["payloads"])}')
passed = False
else:
print('✅ Correct number of payloads')
payload = env['payloads'][0]
if payload['dataname'] != 'users_table':
print(f"❌ Expected dataname 'users_table', got '{payload['dataname']}'")
passed = False
else:
print('✅ Correct dataname')
if payload['payload_type'] != 'table':
print(f"❌ Expected payload_type 'table', got '{payload['payload_type']}'")
passed = False
else:
print('✅ Correct payload_type')
if payload['transport'] != 'direct':
print(f"❌ Expected transport 'direct', got '{payload['transport']}'")
passed = False
else:
print('✅ Correct transport')
if payload['encoding'] != 'base64':
print(f"❌ Expected encoding 'base64', got '{payload['encoding']}'")
passed = False
else:
print('✅ Correct encoding')
print(f'\nPayload size: {payload["size"]} bytes')
# Test with larger table
print('\n=== Larger Table Test ===')
large_table_data = pd.DataFrame({
'id': range(100),
'name': [f'User{i}' for i in range(100)],
'age': [20 + (i % 50) for i in range(100)],
'active': [i % 2 == 0 for i in range(100)]
})
large_test_data = [
('large_table', large_table_data, 'table')
]
large_env, _ = await smartsend(
TEST_SUBJECT,
large_test_data,
broker_url=TEST_BROKER_URL,
fileserver_url=TEST_FILESERVER_URL,
correlation_id='large-' + correlation_id,
is_publish=False
)
if len(large_env['payloads']) == 1:
print('✅ Large table handled correctly')
print(f' Size: {large_env["payloads"][0]["size"]} bytes')
else:
print('❌ Large table handling failed')
passed = False
# Test JSON string output
print('\n=== JSON String Output Test ===')
import json
try:
parsed = json.loads(env_json_str)
if parsed['correlation_id'] == env['correlation_id'] and \
len(parsed['payloads']) == len(env['payloads']):
print('✅ JSON string is valid and matches envelope')
else:
print('❌ JSON string does not match envelope')
passed = False
except json.JSONDecodeError as e:
print(f'❌ JSON string is invalid: {e}')
passed = False
# Final result
print('\n=== Test Result ===')
if passed:
print('✅ ALL TESTS PASSED')
sys.exit(0)
else:
print('❌ SOME TESTS FAILED')
sys.exit(1)
except Exception as e:
print(f'❌ Test failed with error: {e}')
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
asyncio.run(run_test())

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@@ -1,205 +0,0 @@
"""
Python Text Receiver Test
Tests the smartreceive function with text payloads
"""
import asyncio
import sys
import os
import json
# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from natsbridge import smartreceive, DEFAULT_BROKER_URL, DEFAULT_FILESERVER_URL
TEST_BROKER_URL = os.environ.get('NATS_URL', 'nats://localhost:4222')
TEST_FILESERVER_URL = os.environ.get('FILESERVER_URL', 'http://localhost:8080')
async def run_test():
print('=== Python Text Receiver Test ===\n')
# Create a mock NATS message with text payload
test_text = 'Hello, NATSBridge! This is a test message.'
import base64
test_data = {
'correlation_id': 'py-receiver-test-' + str(asyncio.get_event_loop().time() * 1000000),
'msg_id': 'msg-' + str(asyncio.get_event_loop().time() * 1000000),
'timestamp': asyncio.get_event_loop().time().isoformat(),
'send_to': '/test/text',
'msg_purpose': 'test',
'sender_name': 'py-text-test',
'sender_id': 'sender-' + str(asyncio.get_event_loop().time() * 1000000),
'receiver_name': 'py-receiver',
'receiver_id': 'receiver-' + str(asyncio.get_event_loop().time() * 1000000),
'reply_to': '',
'reply_to_msg_id': '',
'broker_url': TEST_BROKER_URL,
'metadata': {},
'payloads': [
{
'id': 'payload-' + str(asyncio.get_event_loop().time() * 1000000),
'dataname': 'message',
'payload_type': 'text',
'transport': 'direct',
'encoding': 'base64',
'size': len(test_text.encode('utf8')),
'data': base64.b64encode(test_text.encode('utf8')).decode('ascii'),
'metadata': {'payload_bytes': len(test_text.encode('utf8'))}
}
]
}
mock_msg = {
'payload': json.dumps(test_data)
}
print('Mock Message Created:')
print(f' Correlation ID: {test_data["correlation_id"]}')
print(f' Payloads: {len(test_data["payloads"])}')
print(f' Payload dataname: {test_data["payloads"][0]["dataname"]}')
print(f' Payload type: {test_data["payloads"][0]["payload_type"]}')
print(f' Transport: {test_data["payloads"][0]["transport"]}\n')
try:
# Receive and process the message
print('Receiving and processing message...')
env = await smartreceive(
mock_msg,
max_retries=3,
base_delay=100,
max_delay=1000
)
print('\n=== Received Envelope ===')
print(f'Correlation ID: {env["correlation_id"]}')
print(f'Message ID: {env["msg_id"]}')
print(f'Timestamp: {env["timestamp"]}')
print(f'Subject: {env["send_to"]}')
print(f'Payloads: {len(env["payloads"])}\n')
# Validate received data
print('=== Validation ===')
passed = True
if not env.get('correlation_id'):
print('❌ correlation_id is missing')
passed = False
else:
print('✅ correlation_id present')
if len(env['payloads']) != 1:
print(f'❌ Expected 1 payload, got {len(env["payloads"])}')
passed = False
else:
print('✅ Correct number of payloads')
payload = env['payloads'][0]
if payload[0] != 'message':
print(f"❌ Expected dataname 'message', got '{payload[0]}'")
passed = False
else:
print('✅ Correct dataname')
if payload[2] != 'text':
print(f"❌ Expected type 'text', got '{payload[2]}'")
passed = False
else:
print('✅ Correct type')
if payload[1] != test_text:
print('❌ Data mismatch')
print(f' Expected: {test_text}')
print(f' Got: {payload[1]}')
passed = False
else:
print('✅ Data correctly deserialized')
# Test with multiple text payloads
print('\n=== Multiple Text Payloads Test ===')
multi_test_data = {
'correlation_id': 'multi-receiver-' + str(asyncio.get_event_loop().time() * 1000000),
'msg_id': 'msg-' + str(asyncio.get_event_loop().time() * 1000000),
'timestamp': asyncio.get_event_loop().time().isoformat(),
'send_to': '/test/text',
'msg_purpose': 'test',
'sender_name': 'py-text-test',
'sender_id': 'sender-' + str(asyncio.get_event_loop().time() * 1000000),
'receiver_name': 'py-receiver',
'receiver_id': 'receiver-' + str(asyncio.get_event_loop().time() * 1000000),
'reply_to': '',
'reply_to_msg_id': '',
'broker_url': TEST_BROKER_URL,
'metadata': {},
'payloads': [
{
'id': 'payload-1',
'dataname': 'msg1',
'payload_type': 'text',
'transport': 'direct',
'encoding': 'base64',
'size': 16,
'data': base64.b64encode(b'First message').decode('ascii'),
'metadata': {'payload_bytes': 16}
},
{
'id': 'payload-2',
'dataname': 'msg2',
'payload_type': 'text',
'transport': 'direct',
'encoding': 'base64',
'size': 16,
'data': base64.b64encode(b'Second message').decode('ascii'),
'metadata': {'payload_bytes': 16}
},
{
'id': 'payload-3',
'dataname': 'msg3',
'payload_type': 'text',
'transport': 'direct',
'encoding': 'base64',
'size': 16,
'data': base64.b64encode(b'Third message').decode('ascii'),
'metadata': {'payload_bytes': 16}
}
]
}
mock_multi_msg = {'payload': json.dumps(multi_test_data)}
multi_env = await smartreceive(mock_multi_msg)
if len(multi_env['payloads']) == 3:
print('✅ Multiple payloads handled correctly')
# Verify each payload
expected_messages = ['First message', 'Second message', 'Third message']
for i in range(3):
if multi_env['payloads'][i][1] == expected_messages[i]:
print(f'✅ Payload {i + 1} correctly deserialized')
else:
print(f'❌ Payload {i + 1} mismatch')
passed = False
else:
print(f"❌ Expected 3 payloads, got {len(multi_env['payloads'])}")
passed = False
# Final result
print('\n=== Test Result ===')
if passed:
print('✅ ALL TESTS PASSED')
sys.exit(0)
else:
print('❌ SOME TESTS FAILED')
sys.exit(1)
except Exception as e:
print(f'❌ Test failed with error: {e}')
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
asyncio.run(run_test())

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@@ -1,164 +0,0 @@
"""
Python Text Sender Test
Tests the smartsend function with text payloads
"""
import asyncio
import sys
import os
# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from natsbridge import smartsend, DEFAULT_BROKER_URL, DEFAULT_FILESERVER_URL
TEST_SUBJECT = '/test/text'
TEST_BROKER_URL = os.environ.get('NATS_URL', 'nats://localhost:4222')
TEST_FILESERVER_URL = os.environ.get('FILESERVER_URL', 'http://localhost:8080')
async def run_test():
print('=== Python Text Sender Test ===\n')
correlation_id = 'py-text-test-' + str(asyncio.get_event_loop().time() * 1000000)
print(f'Correlation ID: {correlation_id}')
print(f'Subject: {TEST_SUBJECT}')
print(f'Broker URL: {TEST_BROKER_URL}\n')
# Test data
text_data = 'Hello, NATSBridge! This is a test message.'
test_data = [
('message', text_data, 'text')
]
try:
# Send the message
print('Sending text payload...')
env, env_json_str = await smartsend(
TEST_SUBJECT,
test_data,
broker_url=TEST_BROKER_URL,
fileserver_url=TEST_FILESERVER_URL,
correlation_id=correlation_id,
msg_purpose='test',
sender_name='py-text-test',
is_publish=False # Don't actually publish for this test
)
print('\n=== Envelope Created ===')
print(f'Correlation ID: {env["correlation_id"]}')
print(f'Message ID: {env["msg_id"]}')
print(f'Timestamp: {env["timestamp"]}')
print(f'Subject: {env["send_to"]}')
print(f'Purpose: {env["msg_purpose"]}')
print(f'Sender: {env["sender_name"]}')
print(f'Payloads: {len(env["payloads"])}\n')
# Validate envelope structure
print('=== Validation ===')
passed = True
if not env.get('correlation_id'):
print('❌ correlation_id is missing')
passed = False
else:
print('✅ correlation_id present')
if not env.get('msg_id'):
print('❌ msg_id is missing')
passed = False
else:
print('✅ msg_id present')
if not env.get('timestamp'):
print('❌ timestamp is missing')
passed = False
else:
print('✅ timestamp present')
if len(env['payloads']) != 1:
print(f'❌ Expected 1 payload, got {len(env["payloads"])}')
passed = False
else:
print('✅ Correct number of payloads')
payload = env['payloads'][0]
if payload['dataname'] != 'message':
print(f"❌ Expected dataname 'message', got '{payload['dataname']}'")
passed = False
else:
print('✅ Correct dataname')
if payload['payload_type'] != 'text':
print(f"❌ Expected payload_type 'text', got '{payload['payload_type']}'")
passed = False
else:
print('✅ Correct payload_type')
if payload['transport'] != 'direct':
print(f"❌ Expected transport 'direct', got '{payload['transport']}'")
passed = False
else:
print('✅ Correct transport')
if payload['encoding'] != 'base64':
print(f"❌ Expected encoding 'base64', got '{payload['encoding']}'")
passed = False
else:
print('✅ Correct encoding')
# Decode and verify the data
import base64
decoded_data = base64.b64decode(payload['data']).decode('utf8')
if decoded_data != text_data:
print('❌ Decoded data mismatch')
print(f' Expected: {text_data}')
print(f' Got: {decoded_data}')
passed = False
else:
print('✅ Data integrity verified')
print(f"\nPayload size: {payload['size']} bytes")
print(f'Base64 data length: {len(payload["data"])} chars')
# Test with multiple text payloads
print('\n=== Multiple Text Payloads Test ===')
multi_test_data = [
('msg1', 'First message', 'text'),
('msg2', 'Second message', 'text'),
('msg3', 'Third message', 'text')
]
multi_env, _ = await smartsend(
TEST_SUBJECT,
multi_test_data,
broker_url=TEST_BROKER_URL,
fileserver_url=TEST_FILESERVER_URL,
correlation_id='multi-test-' + correlation_id,
is_publish=False
)
if len(multi_env['payloads']) == 3:
print('✅ Multiple payloads handled correctly')
else:
print(f"❌ Expected 3 payloads, got {len(multi_env['payloads'])}")
passed = False
# Final result
print('\n=== Test Result ===')
if passed:
print('✅ ALL TESTS PASSED')
sys.exit(0)
else:
print('❌ SOME TESTS FAILED')
sys.exit(1)
except Exception as e:
print(f'❌ Test failed with error: {e}')
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
asyncio.run(run_test())