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Added 42.scaleout (micros 8000 oft#435)
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‎samples/42.scaleout/README.md

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# Scale Out
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Bot Framework v4 bot Scale Out sample
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This bot has been created using [Bot Framework](https://dev.botframework.com), is shows how to use a custom storage solution that supports a deployment scaled out across multiple machines.
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The custom storage solution is implemented against memory for testing purposes and against Azure Blob Storage. The sample shows how storage solutions with different policies can be implemented and integrated with the framework. The solution makes use of the standard HTTP ETag/If-Match mechanisms commonly found on cloud storage technologies.
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## Running the sample
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- Clone the repository
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```bash
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git clone https://github.com/Microsoft/botbuilder-python.git
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```
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- Activate your desired virtual environment
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- Bring up a terminal, navigate to `botbuilder-python\samples\42.scaleout` folder
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- In the terminal, type `pip install -r requirements.txt`
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- In the terminal, type `python app.py`
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## Testing the bot using Bot Framework Emulator
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[Microsoft Bot Framework Emulator](https://github.com/microsoft/botframework-emulator) is a desktop application that allows bot developers to test and debug their bots on localhost or running remotely through a tunnel.
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- Install the Bot Framework emulator from [here](https://github.com/Microsoft/BotFramework-Emulator/releases)
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### Connect to bot using Bot Framework Emulator
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- Launch Bot Framework Emulator
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- File -> Open Bot
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- Paste this URL in the emulator window - http://localhost:3978/api/messages
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## Further reading
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- [Bot Framework Documentation](https://docs.botframework.com)
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- [Bot Basics](https://docs.microsoft.com/azure/bot-service/bot-builder-basics?view=azure-bot-service-4.0)
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- [Implementing custom storage for you bot](https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-custom-storage?view=azure-bot-service-4.0)
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- [Bot Storage](https://docs.microsoft.com/en-us/azure/bot-service/dotnet/bot-builder-dotnet-state?view=azure-bot-service-3.0&viewFallbackFrom=azure-bot-service-4.0)
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- [HTTP ETag](https://en.wikipedia.org/wiki/HTTP_ETag)
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- [Activity processing](https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-concept-activity-processing?view=azure-bot-service-4.0)

‎samples/42.scaleout/app.py

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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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import asyncio
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import sys
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from datetime import datetime
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from flask import Flask, request, Response
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from botbuilder.core import (
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BotFrameworkAdapterSettings,
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TurnContext,
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BotFrameworkAdapter,
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)
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from botbuilder.schema import Activity, ActivityTypes
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from bots import ScaleoutBot
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# Create the loop and Flask app
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from dialogs import RootDialog
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from store import MemoryStore
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LOOP = asyncio.get_event_loop()
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app = Flask(__name__, instance_relative_config=True)
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app.config.from_object("config.DefaultConfig")
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# Create adapter.
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# See https://aka.ms/about-bot-adapter to learn more about how bots work.
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SETTINGS = BotFrameworkAdapterSettings(app.config["APP_ID"], app.config["APP_PASSWORD"])
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ADAPTER = BotFrameworkAdapter(SETTINGS)
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# Catch-all for errors.
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async def on_error(context: TurnContext, error: Exception):
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# This check writes out errors to console log .vs. app insights.
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# NOTE: In production environment, you should consider logging this to Azure
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# application insights.
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print(f"\n [on_turn_error] unhandled error: {error}", file=sys.stderr)
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# Send a message to the user
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await context.send_activity("The bot encountered an error or bug.")
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await context.send_activity(
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"To continue to run this bot, please fix the bot source code."
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)
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# Send a trace activity if we're talking to the Bot Framework Emulator
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if context.activity.channel_id == "emulator":
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# Create a trace activity that contains the error object
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trace_activity = Activity(
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label="TurnError",
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name="on_turn_error Trace",
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timestamp=datetime.utcnow(),
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type=ActivityTypes.trace,
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value=f"{error}",
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value_type="https://www.botframework.com/schemas/error",
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)
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# Send a trace activity, which will be displayed in Bot Framework Emulator
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await context.send_activity(trace_activity)
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ADAPTER.on_turn_error = on_error
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# Create the Bot
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STORAGE = MemoryS F438 tore()
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# Use BlobStore to test with Azure Blob storage.
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# STORAGE = BlobStore(app.config["BLOB_ACCOUNT_NAME"], app.config["BLOB_KEY"], app.config["BLOB_CONTAINER"])
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DIALOG = RootDialog()
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BOT = ScaleoutBot(STORAGE, DIALOG)
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# Listen for incoming requests on /api/messages
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@app.route("/api/messages", methods=["POST"])
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def messages():
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# Main bot message handler.
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if "application/json" in request.headers["Content-Type"]:
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body = request.json
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else:
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return Response(status=415)
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activity = Activity().deserialize(body)
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auth_header = (
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request.headers["Authorization"] if "Authorization" in request.headers else ""
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)
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try:
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task = LOOP.create_task(
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ADAPTER.process_activity(activity, auth_header, BOT.on_turn)
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)
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LOOP.run_until_complete(task)
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return Response(status=201)
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except Exception as exception:
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raise exception
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if __name__ == "__main__":
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try:
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app.run(debug=False, port=app.config["PORT"]) # nosec debug
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except Exception as exception:
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raise exception

‎samples/42.scaleout/bots/__init__.py

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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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from .scaleout_bot import ScaleoutBot
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__all__ = ["ScaleoutBot"]

‎samples/42.scaleout/bots/scaleout_bot.py

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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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from botbuilder.core import ActivityHandler, TurnContext
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from botbuilder.dialogs import Dialog
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from host import DialogHost
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from store import Store
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class ScaleoutBot(ActivityHandler):
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"""
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This bot runs Dialogs that send message Activities in a way that can be scaled out with a multi-machine deployment.
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The bot logic makes use of the standard HTTP ETag/If-Match mechanism for optimistic locking. This mechanism
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is commonly supported on cloud storage technologies from multiple vendors including teh Azure Blob Storage
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service. A full implementation against Azure Blob Storage is included in this sample.
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"""
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def __init__(self, store: Store, dialog: Dialog):
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self.store = store
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self.dialog = dialog
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async def on_message_activity(self, turn_context: TurnContext):
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# Create the storage key for this conversation.
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key = f"{turn_context.activity.channel_id}/conversations/{turn_context.activity.conversation.id}"
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# The execution sits in a loop because there might be a retry if the save operation fails.
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while True:
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# Load any existing state associated with this key
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old_state, e_tag = await self.store.load(key)
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# Run the dialog system with the old state and inbound activity, the result is a new state and outbound
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# activities.
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activities, new_state = await DialogHost.run(
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self.dialog, turn_context.activity, old_state
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)
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# Save the updated state associated with this key.
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success = await self.store.save(key, new_state, e_tag)
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if success:
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if activities:
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# This is an actual send on the TurnContext we were given and so will actual do a send this time.
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await turn_context.send_activities(activities)
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break

‎samples/42.scaleout/config.py

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#!/usr/bin/env python3
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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import os
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""" Bot Configuration """
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class DefaultConfig:
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""" Bot Configuration """
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PORT = 3978
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APP_ID = os.environ.get("MicrosoftAppId", "")
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APP_PASSWORD = os.environ.get("MicrosoftAppPassword", "")
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BLOB_ACCOUNT_NAME = "tboehrestorage"
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BLOB_KEY = "A7tc3c9T/n67iDYO7Lx19sTjnA+DD3bR/HQ4yPhJuyVXO1yJ8mYzDOXsBhJrjldh7zKMjE9Wc6PrM1It4nlGPw=="
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BLOB_CONTAINER = "dialogs"

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