717 lines
25 KiB
Python
717 lines
25 KiB
Python
"""
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msghandler - Cross-Platform Bi-Directional Data Bridge
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Python Desktop Implementation
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This module provides functionality for sending and receiving data across network boundaries
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with support for both direct payload transport and URL-based transport for larger payloads.
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@package msghandler
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"""
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import asyncio
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import base64
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import json
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import uuid
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from datetime import datetime
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from typing import Any, Callable, Dict, List, Tuple, Union
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import aiohttp
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try:
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import pyarrow as arrow
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import pyarrow.ipc as ipc
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ARROW_AVAILABLE = True
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except ImportError:
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ARROW_AVAILABLE = False
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# ---------------------------------------------- Constants ---------------------------------------------- #
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"""
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Default size threshold for switching from direct to link transport (0.5MB)
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"""
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DEFAULT_SIZE_THRESHOLD = 500_000
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"""
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Default broker URL
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"""
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DEFAULT_BROKER_URL = "localhost:4222"
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"""
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Default HTTP file server URL for link transport
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"""
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DEFAULT_FILESERVER_URL = "http://localhost:8080"
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# ---------------------------------------------- Utility Functions ---------------------------------------------- #
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def log_trace(correlation_id: str, message: str) -> None:
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"""
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Log a trace message with correlation ID and timestamp.
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Args:
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correlation_id: Correlation ID for tracing
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message: Message content to log
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"""
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timestamp = datetime.utcnow().isoformat() + 'Z'
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print(f"[{timestamp}] [Correlation: {correlation_id}] {message}")
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# ---------------------------------------------- Serialization Functions ---------------------------------------------- #
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def _serialize_data(data: Any, payload_type: str) -> bytes:
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"""
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Serialize data according to specified format.
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Args:
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data: Data to serialize (string for "text", JSON-serializable for "dictionary",
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table-like for "arrowtable"/"jsontable", binary for "image", "audio", "video", "binary")
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payload_type: Target format: "text", "dictionary", "arrowtable", "jsontable",
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"image", "audio", "video", "binary"
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Returns:
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Binary representation of the serialized data
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Raises:
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Error: If payload_type is not one of the supported types
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Error: If payload_type is "image", "audio", or "video" but data is not bytes
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Error: If payload_type is "arrowtable" but data is not a pandas DataFrame or pyarrow Table
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Error: If payload_type is "jsontable" but data is not a list of dicts
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"""
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if payload_type == 'text':
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if isinstance(data, str):
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return data.encode('utf-8')
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else:
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raise ValueError('Text data must be a string')
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elif payload_type == 'dictionary':
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json_str = json.dumps(data)
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return json_str.encode('utf-8')
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elif payload_type == 'arrowtable':
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if not ARROW_AVAILABLE:
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raise RuntimeError('pyarrow not available for arrowtable serialization')
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import io
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buf = io.BytesIO()
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import pandas as pd
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if isinstance(data, pd.DataFrame):
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table = arrow.Table.from_pandas(data)
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sink = ipc.new_file(buf, table.schema)
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ipc.write_table(table, sink)
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sink.close()
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return buf.getvalue()
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elif isinstance(data, arrow.Table):
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sink = ipc.new_file(buf, data.schema)
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ipc.write_table(data, sink)
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sink.close()
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return buf.getvalue()
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else:
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raise ValueError('Arrow table data must be a pandas DataFrame or pyarrow Table')
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elif payload_type == 'jsontable':
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# Serialize list of dicts to JSON format
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if isinstance(data, list) and all(isinstance(row, dict) for row in data):
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json_str = json.dumps(data)
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return json_str.encode('utf-8')
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else:
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raise ValueError('JSON table data must be a list of dicts')
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elif payload_type == 'image':
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if isinstance(data, (bytes, bytearray)):
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return bytes(data)
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else:
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raise ValueError('Image data must be bytes')
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elif payload_type == 'audio':
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if isinstance(data, (bytes, bytearray)):
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return bytes(data)
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else:
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raise ValueError('Audio data must be bytes')
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elif payload_type == 'video':
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if isinstance(data, (bytes, bytearray)):
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return bytes(data)
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else:
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raise ValueError('Video data must be bytes')
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elif payload_type == 'binary':
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if isinstance(data, (bytes, bytearray)):
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return bytes(data)
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else:
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raise ValueError('Binary data must be bytes')
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else:
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raise ValueError(f'Unknown payload_type: {payload_type}')
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def _deserialize_data(data: bytes, payload_type: str, correlation_id: str) -> Any:
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"""
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Deserialize bytes to data based on type.
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Args:
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data: Serialized data as bytes
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payload_type: Data type ("text", "dictionary", "arrowtable", "jsontable",
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"image", "audio", "video", "binary")
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correlation_id: Correlation ID for logging
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Returns:
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Deserialized data (String for "text", DataFrame for "arrowtable",
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Vector{Dict} for "jsontable"/"dictionary", bytes for "image", "audio", "video", "binary")
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Raises:
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Error: If payload_type is not one of the supported types
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"""
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if payload_type == 'text':
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return data.decode('utf-8')
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elif payload_type == 'dictionary':
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json_str = data.decode('utf-8')
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return json.loads(json_str)
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elif payload_type == 'arrowtable':
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if not ARROW_AVAILABLE:
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raise RuntimeError('pyarrow not available for arrowtable deserialization')
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import io
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buf = io.BytesIO(data)
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reader = ipc.open_file(buf)
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return reader.read_all().to_pandas()
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elif payload_type == 'jsontable':
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# Deserialize JSON to list of dicts
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json_str = data.decode('utf-8')
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return json.loads(json_str)
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elif payload_type == 'image':
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return data
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elif payload_type == 'audio':
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return data
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elif payload_type == 'video':
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return data
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elif payload_type == 'binary':
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return data
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else:
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raise ValueError(f'Unknown payload_type: {payload_type}')
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# ---------------------------------------------- File Server Handlers ---------------------------------------------- #
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async def plik_oneshot_upload(
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file_server_url: str,
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dataname: str,
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data: bytes
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) -> Dict[str, Any]:
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"""
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Upload data to plik server in one-shot mode.
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This function uploads a raw byte array to a plik server in one-shot mode (no upload session).
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It first creates a one-shot upload session by sending a POST request with {"OneShot": true},
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retrieves an upload ID and token, then uploads the file data as multipart form data using the token.
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Args:
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file_server_url: Base URL of the plik server (e.g., "http://localhost:8080")
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dataname: Name of the file being uploaded
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data: Raw byte data of the file content
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Returns:
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Dict with keys:
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- "status": HTTP server response status
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- "uploadid": ID of the one-shot upload session
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- "fileid": ID of the uploaded file within the session
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- "url": Full URL to download the uploaded file
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Example:
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>>> fileserver_url = "http://localhost:8080"
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>>> dataname = "test.txt"
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>>> data = b"hello world"
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>>> result = await plik_oneshot_upload(file_server_url, dataname, data)
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>>> result["status"], result["uploadid"], result["fileid"], result["url"]
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"""
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async with aiohttp.ClientSession() as session:
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# Get upload id
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url_getUploadID = f"{file_server_url}/upload"
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headers = {'Content-Type': 'application/json'}
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body = json.dumps({"OneShot": True})
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async with session.post(url_getUploadID, headers=headers, data=body) as response:
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response_json = await response.json()
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uploadid = response_json['id']
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uploadtoken = response_json['uploadToken']
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# Upload file
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url_upload = f"{file_server_url}/file/{uploadid}"
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headers = {'X-UploadToken': uploadtoken}
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form = aiohttp.FormData()
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form.add_field('file', data, filename=dataname, content_type='application/octet-stream')
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async with session.post(url_upload, headers=headers, data=form) as upload_response:
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upload_json = await upload_response.json()
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fileid = upload_json['id']
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url = f"{file_server_url}/file/{uploadid}/{fileid}/{dataname}"
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return {
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'status': upload_response.status,
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'uploadid': uploadid,
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'fileid': fileid,
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'url': url
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}
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async def fetch_with_backoff(
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url: str,
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max_retries: int,
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base_delay: int,
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max_delay: int,
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correlation_id: str
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) -> bytes:
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"""
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Fetch data from URL with exponential backoff.
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This internal function retrieves data from a URL with retry logic using
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exponential backoff to handle transient failures.
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Args:
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url: URL to fetch from
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max_retries: Maximum number of retry attempts
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base_delay: Initial delay in milliseconds
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max_delay: Maximum delay in milliseconds
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correlation_id: Correlation ID for logging
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Returns:
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Fetched data as bytes
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Raises:
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Error: If all retry attempts fail
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Example:
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>>> data = await fetch_with_backoff("http://example.com/file.zip", 5, 100, 5000, "correlation123")
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"""
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delay = base_delay
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for attempt in range(1, max_retries + 1):
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try:
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async with aiohttp.ClientSession() as session:
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async with session.get(url) as response:
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if response.status == 200:
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log_trace(correlation_id, f"Successfully fetched data from {url} on attempt {attempt}")
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return await response.read()
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else:
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raise Exception(f"Failed to fetch: {response.status}")
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except Exception as e:
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log_trace(correlation_id, f"Attempt {attempt} failed: {type(e).__name__}")
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if attempt < max_retries:
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await asyncio.sleep(delay / 1000.0)
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delay = min(delay * 2, max_delay)
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raise Exception(f"Failed to fetch data after {max_retries} attempts")
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# ---------------------------------------------- Core Functions ---------------------------------------------- #
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def _build_envelope(
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subject: str,
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payloads: List[Dict[str, Any]],
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options: Dict[str, Any]
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) -> Dict[str, Any]:
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"""
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Build message envelope from payloads and metadata.
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Args:
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subject: Subject/topic
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payloads: Array of payload objects
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options: Envelope metadata options
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Returns:
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Envelope object
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"""
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return {
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'correlation_id': options['correlation_id'],
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'msg_id': options['msg_id'],
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'timestamp': datetime.utcnow().isoformat() + 'Z',
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'send_to': subject,
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'msg_purpose': options['msg_purpose'],
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'sender_name': options['sender_name'],
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'sender_id': options['sender_id'],
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'receiver_name': options['receiver_name'],
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'receiver_id': options['receiver_id'],
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'reply_to': options['reply_to'],
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'reply_to_msg_id': options['reply_to_msg_id'],
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'broker_url': options['broker_url'],
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'metadata': options.get('metadata', {}),
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'payloads': payloads
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}
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def _build_payload(
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dataname: str,
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payload_type: str,
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payload_bytes: bytes,
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transport: str,
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data: Union[str, bytes]
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) -> Dict[str, Any]:
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"""
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Build payload object from serialized data.
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Args:
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dataname: Name of the payload
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payload_type: Type of the payload
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payload_bytes: Serialized payload bytes
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transport: Transport type ("direct" or "link")
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data: Data (base64 for direct, URL for link)
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Returns:
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Payload object
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"""
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# Determine encoding based on payload type (matching Julia/JS implementation)
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encoding = 'base64'
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if payload_type == 'jsontable':
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encoding = 'json'
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elif payload_type == 'arrowtable':
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encoding = 'arrow-ipc'
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return {
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'id': str(uuid.uuid4()),
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'dataname': dataname,
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'payload_type': payload_type,
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'transport': transport,
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'encoding': encoding,
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'size': len(payload_bytes),
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'data': data,
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'metadata': {'payload_bytes': len(payload_bytes)} if transport == 'direct' else {}
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}
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async def smartpack(
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subject: str,
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data: List[Tuple[str, Any, str]],
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broker_url: str = DEFAULT_BROKER_URL,
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fileserver_url: str = DEFAULT_FILESERVER_URL,
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fileserver_upload_handler: Callable = plik_oneshot_upload,
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size_threshold: int = DEFAULT_SIZE_THRESHOLD,
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correlation_id: str = None,
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msg_purpose: str = "chat",
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sender_name: str = "msghandler",
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receiver_name: str = "",
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receiver_id: str = "",
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reply_to: str = "",
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reply_to_msg_id: str = "",
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msg_id: str = None,
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sender_id: str = None
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) -> Tuple[Dict, str]:
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"""
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Send data with automatic transport selection.
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This function intelligently routes data delivery based on payload size.
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If the serialized payload is smaller than size_threshold, it encodes the data as Base64
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into a "direct" payload. Otherwise, it uploads the data to a fileserver
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and creates a "link" payload with the URL.
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Transport publishing is the caller's responsibility. This function returns the
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envelope and its JSON string representation.
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Args:
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subject: Subject/topic to send the message to
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data: List of (dataname, data, type) tuples to send
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- dataname: Name of the payload
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- data: The actual data to send
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- type: Payload type: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
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broker_url: Broker URL (for envelope metadata)
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fileserver_url: URL of the HTTP file server for large payloads
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fileserver_upload_handler: Function to handle fileserver uploads (must return Dict with "status",
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"uploadid", "fileid", "url" keys)
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size_threshold: Threshold in bytes separating direct vs link transport
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correlation_id: Correlation ID for tracing (auto-generated UUID if not provided)
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msg_purpose: Purpose of the message: "ACK", "NACK", "updateStatus", "shutdown", "chat", etc.
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sender_name: Name of the sender
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receiver_name: Name of the receiver (empty string means broadcast)
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receiver_id: UUID of the receiver (empty string means broadcast)
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reply_to: Topic to reply to (empty string if no reply expected)
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reply_to_msg_id: Message ID this message is replying to
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msg_id: Message ID (auto-generated UUID if not provided)
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sender_id: Sender ID (auto-generated UUID if not provided)
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Returns:
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Tuple of (env, env_json_str) where:
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- env: Dict containing all metadata and payloads
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- env_json_str: JSON string for transport
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Example:
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>>> # Send a single payload (still wrapped in a list)
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>>> data = {"key": "value"}
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>>> env, env_json_str = await smartpack(
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... "my.subject",
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... [("dataname1", data, "dictionary")]
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... )
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>>>
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>>> # Publish the JSON string via your preferred transport
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>>> # await my_nats_client.publish("my.subject", env_json_str)
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"""
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if correlation_id is None:
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correlation_id = str(uuid.uuid4())
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if msg_id is None:
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msg_id = str(uuid.uuid4())
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if sender_id is None:
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sender_id = str(uuid.uuid4())
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log_trace(correlation_id, f"Starting smartpack for subject: {subject}")
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# Process payloads
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payloads = []
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for dataname, payload_data, payload_type in data:
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payload_bytes = _serialize_data(payload_data, payload_type)
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payload_size = len(payload_bytes)
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log_trace(correlation_id, f"Serialized payload '{dataname}' (type: {payload_type}) size: {payload_size} bytes")
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if payload_size < size_threshold:
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# Direct path
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payload_b64 = base64.b64encode(payload_bytes).decode('utf-8')
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log_trace(correlation_id, f"Using direct transport for {payload_size} bytes")
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payload = _build_payload(dataname, payload_type, payload_bytes, 'direct', payload_b64)
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payloads.append(payload)
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else:
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# Link path
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log_trace(correlation_id, "Using link transport, uploading to fileserver")
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response = await fileserver_upload_handler(fileserver_url, dataname, payload_bytes)
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if response['status'] != 200:
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raise Exception(f"Failed to upload data to fileserver: {response['status']}")
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log_trace(correlation_id, f"Uploaded to URL: {response['url']}")
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payload = _build_payload(dataname, payload_type, payload_bytes, 'link', response['url'])
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payloads.append(payload)
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# Build envelope
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env = _build_envelope(subject, payloads, {
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'correlation_id': correlation_id,
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'msg_id': msg_id,
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'msg_purpose': msg_purpose,
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'sender_name': sender_name,
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'sender_id': sender_id,
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'receiver_name': receiver_name,
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'receiver_id': receiver_id,
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'reply_to': reply_to,
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'reply_to_msg_id': reply_to_msg_id,
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'broker_url': broker_url
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})
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env_json_str = json.dumps(env)
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return env, env_json_str
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async def smartunpack(
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msg: Any,
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fileserver_download_handler: Callable = fetch_with_backoff,
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max_retries: int = 5,
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base_delay: int = 100,
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max_delay: int = 5000
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) -> Dict[str, Any]:
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"""
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Receive and process messages.
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This function processes incoming messages, handling both direct transport
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(base64 decoded payloads) and link transport (URL-based payloads).
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It deserializes the data based on the transport type and returns the result.
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Args:
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msg: Message to process. Accepts JSON string directly, or an object with
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a `payload` or `data` property containing the JSON string.
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fileserver_download_handler: Function to handle downloading data from file server URLs
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max_retries: Maximum retry attempts for fetching URL
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base_delay: Initial delay for exponential backoff in ms
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max_delay: Maximum delay for exponential backoff in ms
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Returns:
|
|
Dict with envelope metadata and payloads field containing List[Tuple[str, Any, str]]
|
|
|
|
Example:
|
|
>>> # Receive from JSON string directly
|
|
>>> env = await smartunpack(json_string)
|
|
>>>
|
|
>>> # Receive from transport message object (e.g., NATS, MQTT)
|
|
>>> env = await smartunpack(nats_msg, fileserver_download_handler=fetch_with_backoff)
|
|
>>> # env is a Dict with "payloads" key containing List[Tuple[str, Any, str]]
|
|
>>> for dataname, data, type_ in env["payloads"]:
|
|
>>> print(f"{dataname}: {data} (type: {type_})")
|
|
"""
|
|
# Parse the JSON envelope
|
|
if isinstance(msg, dict):
|
|
# Already parsed
|
|
env_json_obj = msg
|
|
elif isinstance(msg, str):
|
|
# Raw JSON string
|
|
env_json_obj = json.loads(msg)
|
|
elif hasattr(msg, 'payload'):
|
|
# Transport message object with payload property
|
|
payload = msg.payload if isinstance(msg.payload, str) else msg.payload.decode('utf-8')
|
|
env_json_obj = json.loads(payload)
|
|
elif hasattr(msg, 'data'):
|
|
# Transport message object with data property
|
|
payload = msg.data if isinstance(msg.data, str) else msg.data.decode('utf-8')
|
|
env_json_obj = json.loads(payload)
|
|
else:
|
|
raise ValueError('Invalid message format: expected JSON string or message object')
|
|
|
|
log_trace(env_json_obj['correlation_id'], "Processing received message")
|
|
|
|
# Process all payloads in the envelope
|
|
payloads_list = []
|
|
num_payloads = len(env_json_obj['payloads'])
|
|
|
|
for i in range(num_payloads):
|
|
payload_obj = env_json_obj['payloads'][i]
|
|
transport = payload_obj['transport']
|
|
dataname = payload_obj['dataname']
|
|
|
|
if transport == 'direct':
|
|
log_trace(env_json_obj['correlation_id'], f"Direct transport - decoding payload '{dataname}'")
|
|
|
|
# Extract base64 payload from the payload
|
|
payload_b64 = payload_obj['data']
|
|
|
|
# Decode Base64 payload
|
|
payload_bytes = base64.b64decode(payload_b64)
|
|
|
|
# Deserialize based on type
|
|
data_type = payload_obj['payload_type']
|
|
data = _deserialize_data(payload_bytes, data_type, env_json_obj['correlation_id'])
|
|
|
|
payloads_list.append((dataname, data, data_type))
|
|
elif transport == 'link':
|
|
# Extract download URL from the payload
|
|
url = payload_obj['data']
|
|
log_trace(env_json_obj['correlation_id'], f"Link transport - fetching '{dataname}' from URL: {url}")
|
|
|
|
# Fetch with exponential backoff using the download handler
|
|
downloaded_data = await fileserver_download_handler(
|
|
url,
|
|
max_retries,
|
|
base_delay,
|
|
max_delay,
|
|
env_json_obj['correlation_id']
|
|
)
|
|
|
|
# Deserialize based on type
|
|
data_type = payload_obj['payload_type']
|
|
data = _deserialize_data(downloaded_data, data_type, env_json_obj['correlation_id'])
|
|
|
|
payloads_list.append((dataname, data, data_type))
|
|
else:
|
|
raise Exception(f"Unknown transport type for payload '{dataname}': {transport}")
|
|
|
|
env_json_obj['payloads'] = payloads_list
|
|
return env_json_obj
|
|
|
|
|
|
# ---------------------------------------------- Module Exports ---------------------------------------------- #
|
|
|
|
class msghandler:
|
|
"""
|
|
Cross-platform message bridge implementation.
|
|
|
|
This class provides a convenient interface for msghandler functionality,
|
|
encapsulating the main functions and providing a class-based API.
|
|
"""
|
|
|
|
DEFAULT_SIZE_THRESHOLD = DEFAULT_SIZE_THRESHOLD
|
|
DEFAULT_BROKER_URL = DEFAULT_BROKER_URL
|
|
DEFAULT_FILESERVER_URL = DEFAULT_FILESERVER_URL
|
|
|
|
def __init__(self, broker_url: str = None, fileserver_url: str = None):
|
|
"""
|
|
Initialize msghandler.
|
|
|
|
Args:
|
|
broker_url: Broker URL (defaults to DEFAULT_BROKER_URL)
|
|
fileserver_url: HTTP file server URL (defaults to DEFAULT_FILESERVER_URL)
|
|
"""
|
|
self.broker_url = broker_url or self.DEFAULT_BROKER_URL
|
|
self.fileserver_url = fileserver_url or self.DEFAULT_FILESERVER_URL
|
|
|
|
async def smartpack(
|
|
self,
|
|
subject: str,
|
|
data: List[Tuple[str, Any, str]],
|
|
**kwargs
|
|
) -> Tuple[Dict, str]:
|
|
"""
|
|
Send data.
|
|
|
|
Args:
|
|
subject: Subject/topic to send to
|
|
data: List of (dataname, data, type) tuples
|
|
**kwargs: Additional options passed to smartpack
|
|
|
|
Returns:
|
|
Tuple of (env, env_json_str)
|
|
"""
|
|
kwargs['broker_url'] = kwargs.get('broker_url', self.broker_url)
|
|
kwargs['fileserver_url'] = kwargs.get('fileserver_url', self.fileserver_url)
|
|
return await smartpack(subject, data, **kwargs)
|
|
|
|
async def smartunpack(
|
|
self,
|
|
msg: Any,
|
|
**kwargs
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
Receive and process message.
|
|
|
|
Args:
|
|
msg: Message to process
|
|
**kwargs: Additional options passed to smartunpack
|
|
|
|
Returns:
|
|
Dict with envelope metadata and payloads
|
|
"""
|
|
return await smartunpack(msg, **kwargs)
|
|
|
|
|
|
# Convenience functions for module-level usage
|
|
def send(
|
|
subject: str,
|
|
data: List[Tuple[str, Any, str]],
|
|
**kwargs
|
|
) -> Tuple[Dict, str]:
|
|
"""
|
|
Convenience function for sending data.
|
|
|
|
Args:
|
|
subject: Subject/topic to send to
|
|
data: List of (dataname, data, type) tuples
|
|
**kwargs: Additional options
|
|
|
|
Returns:
|
|
Tuple of (env, env_json_str)
|
|
"""
|
|
return asyncio.run(smartpack(subject, data, **kwargs))
|
|
|
|
|
|
def receive(
|
|
msg: Any,
|
|
**kwargs
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
Convenience function for receiving messages.
|
|
|
|
Args:
|
|
msg: Message to process
|
|
**kwargs: Additional options
|
|
|
|
Returns:
|
|
Dict with envelope metadata and payloads
|
|
"""
|
|
return asyncio.run(smartunpack(msg, **kwargs))
|
|
|
|
|
|
__all__ = [
|
|
'smartpack',
|
|
'smartunpack',
|
|
'plik_oneshot_upload',
|
|
'fetch_with_backoff',
|
|
'msghandler',
|
|
'send',
|
|
'receive',
|
|
'DEFAULT_SIZE_THRESHOLD',
|
|
'DEFAULT_BROKER_URL',
|
|
'DEFAULT_FILESERVER_URL',
|
|
'_serialize_data',
|
|
'_deserialize_data',
|
|
'log_trace'
|
|
]
|