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## 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 |
@@ -178,579 +224,14 @@ 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` |
| `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` | `list` |
| `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, "arrowtable")],
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: $type)")
end
```
#### JavaScript
```javascript
const NATSBridge = require('natsbridge');
// Single payload
const [env, env_json_str] = await NATSBridge.smartsend(
"/test",
[["dataname1", data1, "dictionary"]],
{
broker_url: "nats://localhost:4222",
fileserver_upload_handler: plikOneshotUpload
}
);
// Multiple payloads
const [env, env_json_str] = await NATSBridge.smartsend(
"/test",
[
["dataname1", data1, "dictionary"],
["dataname2", data2, "arrowtable"]
],
{ broker_url: "nats://localhost:4222" }
);
// Mixed content
const [env, env_json_str] = await NATSBridge.smartsend(
"/chat",
[
["message_text", "Hello!", "text"],
["user_image", imageData, "image"],
["audio_clip", audioData, "audio"]
],
{ broker_url: "nats://localhost:4222" }
);
// Receive
const env = await NATSBridge.smartreceive(msg, {
fileserver_download_handler: fetchWithBackoff
});
// env is an object with "payloads" field containing Array of arrays
// Access payloads: env.payloads which is an Array of [dataname, data, type] arrays
for (const [dataname, data, type] of env.payloads) {
console.log(`${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, "arrowtable")],
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
```
---
### Mixed-Content Transport Examples
The NATSBridge.jl library supports sending mixed content types in a single message, with automatic transport selection based on payload size. Small payloads (< 1MB threshold) use direct transport (Base64-encoded in NATS message), while large payloads (≥ 1MB) use link transport (uploaded to file server, URL sent via NATS).
#### Julia Mixed-Content Example
```julia
using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP, Base64
include("../src/NATSBridge.jl")
using .NATSBridge
# Configuration
const SUBJECT = "/natsbridge"
const NATS_URL = "nats://localhost:4222"
const FILESERVER_URL = "http://localhost:8080"
# Create sample data - mix of small and large payloads
text_data = "Hello! This is a test chat message. 🎉"
dict_data = Dict(
"type" => "chat",
"sender" => "serviceA",
"content" => Dict("text" => "JSON-formatted message", "format" => "markdown")
)
# Small arrow table (< 1MB) - direct transport
arrow_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)
)
# Large arrow table (≥ 1MB) - link transport
arrow_table_large = DataFrame(
id = 1:200_000,
name = ["user_$i" for i in 1:200_000],
score = rand(50:100, 200_000),
active = rand([true, false], 200_000)
)
# Small jsontable (< 1MB) - direct transport
json_table_small = DataFrame(
id = 1:10,
name = ["Alice", "Bob", "Charlie", "Diana", "Eve", "Frank", "Grace", "Henry", "Ivy", "Jack"],
score = rand(50:100, 10)
)
# Large jsontable (≥ 1MB) - link transport
json_table_large = DataFrame(
id = 1:1_500_000,
name = ["user_$i" for i in 1:1_500_000],
score = rand(50:100, 1_500_000)
)
# Binary data (small) - direct transport
binary_data_small = UInt8[rand(1:255) for _ in 1:200]
# Binary data (large) - link transport
binary_data_large = UInt8[rand(1:255) for _ in 1:1_500_000]
# Read image file
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)
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
payloads = [
# Small data (direct transport)
("chat_text", text_data, "text"),
("chat_json", dict_data, "dictionary"),
("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)
("arrow_table_large", arrow_table_large, "arrowtable"),
("json_table_large", json_table_large, "jsontable"),
(filename_large_image, file_data_large_image, "binary"),
("binary_file_large", binary_data_large, "binary")
]
# Use smartsend with mixed content
correlation_id = string(uuid4())
sendinfo = NATSBridge.smartsend(
SUBJECT,
payloads;
broker_url = NATS_URL,
fileserver_url = FILESERVER_URL,
fileserver_upload_handler = plik_oneshot_upload,
size_threshold = 1_000_000, # 1MB threshold
correlation_id = correlation_id,
msg_purpose = "chat",
sender_name = "mix_sender",
receiver_name = "",
receiver_id = "",
reply_to = "",
reply_to_msg_id = "",
is_publish = true
)
env, env_json_str = sendinfo
# Log transport type for each payload
for (i, payload) in enumerate(env.payloads)
println("Payload $i ('$payload.dataname'):")
println(" Transport: $(payload.transport)")
println(" Type: $(payload.payload_type)")
println(" Size: $(payload.size) bytes")
println(" Encoding: $(payload.encoding)")
if payload.transport == "link"
println(" URL: $(payload.data)")
end
end
# Summary
direct_count = count(p -> p.transport == "direct", env.payloads)
link_count = count(p -> p.transport == "link", env.payloads)
println("\n--- Transport Summary ---")
println("Direct transport: $direct_count payloads")
println("Link transport: $link_count payloads")
```
**Expected Output:**
```
Payload 1 ('chat_text'):
Transport: direct
Type: text
Size: 38 bytes
Encoding: base64
Payload 2 ('chat_json'):
Transport: direct
Type: dictionary
Size: 156 bytes
Encoding: json
Payload 3 ('arrow_table_small'):
Transport: direct
Type: arrowtable
Size: 1245 bytes
Encoding: arrow-ipc
Payload 4 ('json_table_small'):
Transport: direct
Type: jsontable
Size: 892 bytes
Encoding: json
Payload 5 ('small_image.jpg'):
Transport: direct
Type: binary
Size: 73269 bytes
Encoding: base64
Payload 6 ('arrow_table_large'):
Transport: link
Type: arrowtable
Size: 5242880 bytes
Encoding: arrow-ipc
URL: http://localhost:8080/file/ABC123/DEF456/arrow_table_large.arrow
Payload 7 ('json_table_large'):
Transport: link
Type: jsontable
Size: 45678900 bytes
Encoding: json
URL: http://localhost:8080/file/GHI789/JKL012/json_table_large.json
Payload 8 ('large_image.png'):
Transport: link
Type: binary
Size: 1168437 bytes
Encoding: base64
URL: http://localhost:8080/file/MNO345/PQR678/large_image.png
Payload 9 ('binary_file_large'):
Transport: link
Type: binary
Size: 1500000 bytes
Encoding: base64
URL: http://localhost:8080/file/STU901/VWX234/binary_file_large.bin
--- Transport Summary ---
Direct transport: 5 payloads
Link transport: 4 payloads
```
#### Receiver Example
```julia
using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP, Base64
include("../src/NATSBridge.jl")
using .NATSBridge
const SUBJECT = "/natsbridge"
const NATS_URL = "nats://localhost:4222"
conn = NATS.connect(NATS_URL)
NATS.subscribe(conn, SUBJECT) do msg
# Use NATSBridge.smartreceive to handle the data
result = NATSBridge.smartreceive(
msg;
max_retries = 5,
base_delay = 100,
max_delay = 5000
)
println("Received $(length(result["payloads"])) payloads")
# Process each payload
for (dataname, data, data_type) in result["payloads"]
println("\n=== Payload: $dataname (type: $data_type) ===")
if data_type == "arrowtable"
if isa(data, Arrow.Table)
df = DataFrame(data)
println(" Type: Arrow.Table")
println(" Size: $(size(df, 1)) rows x $(size(df, 2)) columns")
end
elseif data_type == "jsontable"
if isa(data, Vector{Any})
df = DataFrame(data)
println(" Type: Vector{Dict}")
println(" Size: $(length(data)) rows")
end
elseif data_type == "text"
if isa(data, String)
println(" Type: String")
println(" Length: $(length(data)) characters")
end
elseif data_type in ["image", "audio", "video", "binary"]
if isa(data, Vector{UInt8})
println(" Type: Vector{UInt8} (binary)")
println(" Size: $(length(data)) bytes")
end
end
end
end
# Keep listening
sleep(120)
NATS.drain(conn)
```
---
## 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/JSON 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("JSON")
Pkg.add("HTTP")
Pkg.add("UUIDs")
Pkg.add("Dates")
Pkg.add("PrettyPrinting")
Pkg.add("DataFrames")
```
### JavaScript Dependencies (Node.js)
```bash
npm install nats apache-arrow node-fetch
# or
yarn add nats apache-arrow node-fetch
```
**Note:** Node.js has a built-in `crypto` module for UUID generation, so no external `uuid` package is needed.
### JavaScript Dependencies (Browser)
```bash
npm install nats apache-arrow
# or use CDN:
# https://unpkg.com/nats-js/dist/bundle/nats.min.js
# https://unpkg.com/apache-arrow/arrow.min.js
```
**Note:** For browser UUID generation, use the built-in `crypto.randomUUID()` API (available in modern browsers) or a lightweight alternative like `uuidv4` package.
### 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"`).
---
@@ -1236,17 +717,6 @@ function plik_oneshot_upload(file_server_url::String, filepath::String)
end
```
**Usage Examples:**
```julia
# Upload binary data
data = read("./test_image.jpg")
result = plik_oneshot_upload("http://localhost:8080", "my_image.jpg", data)
# Upload file directly from disk
result = plik_oneshot_upload("http://localhost:8080", "./test_image.jpg")
```
---
### JavaScript Implementation
@@ -1846,18 +1316,24 @@ except ImportError:
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 == 'arrowtable':
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')
import io
buf = io.BytesIO()
@@ -1865,38 +1341,24 @@ def _serialize_data(data: Any, payload_type: str) -> bytes:
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('arrowtable data must be a pandas DataFrame')
elif payload_type == 'jsontable':
# Serialize directly to JSON
json_str = json.dumps(data)
return json_str.encode('utf-8')
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
@@ -1916,34 +1378,30 @@ except ImportError:
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 == 'arrowtable':
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 == 'jsontable':
# Deserialize from JSON - returns list[dict]
json_str = data.decode('utf-8')
return json.loads(json_str)
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
@@ -2032,9 +1490,9 @@ async def plik_oneshot_upload(
---
## MicroPython Implementation
### MicroPython Implementation
### Limitations
#### Limitations
MicroPython has significant constraints compared to desktop implementations:
@@ -2042,13 +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) |
| arrowtable | ✅ | ❌ |
| jsontable | ⚠️ (limited) | ⚠️ (limited) |
| 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)
@@ -2058,15 +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 uses list[list] for jsontable
# No DataFrame support
# Note: MicroPython does NOT support table types (arrowtable/jsontable)
# Only supports: text, dictionary, image, audio, video, binary
class NATSBridge:
@@ -2175,32 +1636,44 @@ class NATSBridge:
return env_json_obj
def _serialize_data(self, data, payload_type):
"""Serialize data (MicroPython version - no arrowtable 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')
elif payload_type == 'jsontable':
# Serialize list of lists to JSON
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'))
elif payload_type == 'jsontable':
# Returns list of lists
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)."""
@@ -2372,8 +1845,8 @@ This cross-platform NATS bridge provides:
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** (`arrowtable`): Efficient binary format for large tabular data (not supported in MicroPython)
- **JSON** (`jsontable`): Universal human-readable format for smaller tables (works in all platforms)
- **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.
@@ -2382,4 +1855,5 @@ The Julia implementation in [`src/NATSBridge.jl`](src/NATSBridge.jl:1) serves as
| 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, MicroPython |
| `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 |

18
docs/tutorial.md
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

67
docs/walkthrough.md
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