Archive for category Azure
Building and Integrating LUIS-enabled Chatbots with Slack, using Azure Bot Service, Bot Builder SDK, and Cosmos DB
In this post, we will explore the development of a machine learning-based LUIS-enabled chatbot using the Azure Bot Service and the BotBuilder SDK. We will enhance the chatbot’s functionality with Azure’s Cloud services, including Cosmos DB and Blob Storage. Once built, we will integrate our chatbot across multiple channels, including Web Chat and Slack.
If you want to compare Azure’s current chatbot technologies with those of AWS and Google, in addition to this post, please read my previous two posts in this series, Building Serverless Actions for Google Assistant with Google Cloud Functions, Cloud Datastore, and Cloud Storage and Building Asynchronous, Serverless Alexa Skills with AWS Lambda, DynamoDB, S3, and Node.js. All three of the article’s demonstrations are written in Node.js, all three leverage their cloud platform’s machine learning-based Natural Language Understanding services, and all three take advantage of NoSQL database and storage services available on their respective cloud platforms.
Here is a brief overview of the key Microsoft technologies we will incorporate into our bot’s architecture.
The machine learning-based Language Understanding Intelligent Service (LUIS) is part of Azure’s Cognitive Services, used to build Natural Language Understanding (NLU) into apps, bots, and IoT devices. According to Microsoft, LUIS allows you to quickly create enterprise-ready, custom machine learning models that continuously improve.
Designed to identify valuable information in conversations, Language Understanding interprets user goals (intents) and distills valuable information from sentences (entities), for a high quality, nuanced language model. Language Understanding integrates seamlessly with the Speech service for instant Speech-to-Intent processing, and with the Azure Bot Service, making it easy to create a sophisticated bot. A LUIS bot contains a domain-specific natural language model, which you design.
Azure Bot Service
The Azure Bot Service provides an integrated environment that is purpose-built for bot development, enabling you to build, connect, test, deploy, and manage intelligent bots, all from one place. Bot Service leverages the Bot Builder SDK.
Bot Builder SDK
The Bot Builder SDK allows you to build, connect, deploy and manage bots, which interact with users, across multiple channels, from your app or website to Facebook, Messenger, Kik, Skype, Slack, Microsoft Teams, Telegram, SMS, Twilio, Cortana, and Skype. Currently, the SDK is available for C# and Node.js. For this post, we will use the current Bot Builder Node.js SDK v3 release to write our chatbot.
According to Microsoft, Cosmos DB is a globally distributed, multi-model database-as-a-service, designed for low latency and scalable applications anywhere in the world. Cosmos DB supports multiple data models, including document, columnar, and graph. Cosmos also supports numerous database SDKs, including MongoDB, Cassandra, and Gremlin DB. We will use the MongoDB SDK to store our documents in Cosmos DB, used by our chatbot.
Azure Blob Storage
According to Microsoft, Azure’s storage-as-a-service, Blob Storage, provides massively scalable object storage for any type of unstructured data, images, videos, audio, documents, and more. We will be using Blob Storage to store publically-accessible images, used by our chatbot.
Azure Application Insights
According to Microsoft, Azure’s Application Insights provides comprehensive, actionable insights through application performance management (APM) and instant analytics. Quickly analyze application telemetry, allowing the detection of anomalies, application failure, performance changes. Application Insights will enable us to monitor our chatbot’s key metrics.
A chatbot user interacts with the chatbot through a number of available channels, such as the Web, Slack, and Skype. The channels communicate with the Web App Bot, part of Azure Bot Service, and running on Azure’s App Service, the fully-managed platform for cloud apps. LUIS integration allows the chatbot to learn and understand the user’s intent based on our own domain-specific natural language model.
Through Azure’s App Service platform, our chatbot is able to retrieve data from Cosmos DB and images from Blob Storage. Our chatbot’s telemetry is available through Azure’s Application Insights.
Another way to understand our chatbot architecture is by examining the Azure resources necessary to build the chatbot. Below is an example of all the Azure resources that will be created as a result of building a LUIS-enabled bot, which has been integrated with Cosmos DB, Blob Storage, and Application Insights.
As a demonstration, we will build an informational chatbot, the Azure Tech Facts Chatbot. The bot will respond to the user with interesting facts about Azure, Microsoft’s Cloud computing platform. Note this is not intended to be an official Microsoft bot and is only used for demonstration purposes.
All open-sourced code for this post can be found on GitHub. The code samples in this post are displayed as GitHub Gists, which may not display correctly on some mobile and social media browsers. Links to the gists are also provided.
This post will focus on the development and integration of a chatbot with the LUIS, Azure platform services, and channels, such as Web Chat and Slack. The post is not intended to be a general how-to article on developing Azure chatbots or the use of the Azure Cloud Platform.
Building the chatbot will involve the following steps.
- Design the chatbot’s conversation flow;
- Provision a Cosmos DB instance and import the Azure Facts documents;
- Provision Azure Storage and upload the images as blobs into Azure Storage;
- Create the new LUIS-enabled Web App Bot with Azure’s Bot Service;
- Define the chatbot’s Intents, Entities, and Utterances with LUIS;
- Train and publish the LUIS app;
- Deploy and test the chatbot;
- Integrate the chatbot with Web Chat and Slack Channels;
The post assumes you have an existing Azure account and a working knowledge of Azure. Let’s explore each step in more detail.
Cost of Azure Bots!
Be aware, you will be charged for Azure Cloud services when building this bot. Unlike an Alexa Custom Skill or an Action for Google Assistant, an Azure chatbot is not a serverless application. A common feature of serverless platforms, you only pay for the compute time you consume. There typically is no charge when your code is not running. This means, unlike AWS and Google Cloud Platform, you will pay for Azure resources you provision, whether or not you use them.
Developing this demo’s chatbot on the Azure platform, with little or no activity most of the time, cost me about $5/day. On AWS or GCP, a similar project would cost pennies per day or less (like, $0). Currently, in my opinion, Azure does not have a very competitive model for building bots, or for serverless computing in general, beyond Azure Functions, when compared to Google and AWS.
The first step in developing a chatbot is designing the conversation flow of the between the user and the bot. Defining the conversation flow is essential to developing the bot’s programmatic logic and training the domain-specific natural language model for the machine learning-based services the bot is integrated with, in this case, LUIS. What are all the ways the user might explicitly invoke our chatbot? What are all the ways the user might implicitly invoke our chatbot and provide intent to the bot? Taking the time to map out the possible conversational interactions is essential.
With more advanced bots, like Alexa, Actions for Google Assistant, and Azure Bots, we also have to consider the visual design of the conversational interfaces. In addition to simple voice and text responses, these bots are capable of responding with a rich array of UX elements, including what are generically known as ‘Cards’. Cards come in varying complexity and may contain elements such as text, title, sub-titles, text, video, audio, buttons, and links. Azure Bot Service offers several different cards for specific use cases.
Another layer of complexity with bots is designing for channels into which they integrate. There is a substantial visual difference in a conversational exchange displayed on Facebook Messanger, as compared to Slack, or Skype, Microsoft Teams, GroupMe, or within a web browser. Producing an effective conversational flow presentation across multiple channels a design challenge.
We will be focusing on two channels for delivery of our bot, the Web Chat and Slack channels. We will want to design the conversational flow and visual elements to be effective and engaging across both channels. The added complexity with both channels, they both have mobile and web-based interfaces. We will ensure our design works with the compact real-estate of an average-sized mobile device screen, as well as average-sized laptop’s screen.
Web Chat Channel Design
Below are two views of our chatbot, delivered through the Web Chat channel. To the left, is an example of the bot responding with ThumbnailCard UX elements. The ThumbnailCards contain a title, sub-title, text, small image, and a button with a link. Below and to the right is an example of the bot responding with a HeroCard. The HeroCard contains the same elements as the ThumbnailCard but takes up about twice the space with a significantly larger image.
Slack Channel Design
Below are three views of our chatbot, delivered through the Slack channel, in this case, the mobile iOS version of the Slack app. Even here on a larger iPhone 8s, there is not a lot of real estate. On the right is the same HeroCard as we saw above in the Web Chat channel. In the middle are the same ThumbnailCards. On the right is a simple text-only response. Although the text-only bot responses are not as rich as the cards, you are able to display more of the conversational flow on a single mobile screen.
Lastly, below we see our chatbot delivered through the Slack for Mac desktop app. Within the single view, we see an example of a HeroCard (top), ThumbnailCard (center), and a text-only response (bottom). Notice how the larger UI of the desktop Slack app changes the look and feel of the chatbot conversational flow.
In my opinion, the ThumbnailCards work well in the Web Chat channel and Slack channel’s desktop app, while the text-only responses seem to work best with the smaller footprint of the Slack channel’s mobile client. To work across a number of channels, our final bot will contain a mix of ThumbnailCards and text-only responses.
As an alternative to Microsoft’s Cognitive Service, QnA Maker, we will use Cosmos DB to house the responses to user’s requests for facts about Azure. When a user asks our informational chatbot for a fact about Azure, the bot will query Cosmos DB, passing a single unique string value, the fact the user is requesting. In response, Cosmos DB will return a JSON Document, containing field-and-value pairs with the fact’s title, image name, and textual information, as shown below.
There are a few ways to create the new Cosmos DB database and collection, which will hold our documents, we will use the Azure CLI. According to Microsoft, the Azure CLI 2.0 is Microsoft’s cross-platform command line interface (CLI) for managing Azure resources. You can use it in your browser with Azure Cloud Shell, or install it on macOS, Linux, or Windows, and run it from the command line. (gist).
There are a few ways for us to get our Azure facts documents into Cosmos DB. Since we are writing our chatbot in Node.js, I also chose to write a Cosmos DB facts import script in Node.js, cosmos-db-data.js. Since we are using Cosmos DB as a MongoDB datastore, all the script requires is the official MongoDB driver for Node.js. Using the MongoDB driver’s db.collection.insertMany() method, we can upload an entire array of Azure fact document objects with one call. For security, we have set the Cosmos DB connection string as an environment variable, which the script expects to find at runtime (gist).
Azure Blob Storage
When a user asks our informational chatbot for a fact about Azure, the bot will query Cosmos DB. One of the values returned is an image name. The image itself is stored on Azure Blob Storage.
The image, actually an Azure icon available from Microsoft, is then displayed in the ThumbnailCard or HeroCard shown earlier.
According to Microsoft, an Azure storage account provides a unique namespace in the cloud to store and access your data objects in Azure Storage. A storage account contains any blobs, files, queues, tables, and disks that you create under that account. A container organizes a set of blobs, similar to a folder in a file system. All blobs reside within a container. Similar to Cosmos DB, there are a few ways to create a new Azure Storage account and a blob storage container, which will hold our images. Once again, we will use the Azure CLI (gist).
Once the storage account and container are created using the Azure CLI, to upload the images, included with the GitHub project, by using the Azure CLI’s
storage blob upload-batch command (gist).
Web App Chatbot
To create the LUIS-enabled chatbot, we can use the Azure Bot Service, available on the Azure Portal. A Web App Bot is one of a variety of bots available from Azure’s Bot Service, which is part of Azure’s larger and quickly growing suite of AI and Machine Learning Cognitive Services. A Web App Bot is an Azure Bot Service Bot deployed to an Azure App Service Web App. An App Service Web App is a fully managed platform that lets you build, deploy, and scale enterprise-grade web apps.
To create a LUIS-enabled chatbot, choose the Language Understanding Bot template, from the Node.js SDK Language options. This will provide a complete project and boilerplate bot template, written in Node.js, for you to start developing with. I chose to use the SDK v3, as v4 is still in preview and subject to change.
Azure Resource Manager
A great DevOps features of the Azure Platform is Azure’s ability to generate Azure Resource Manager (ARM) templates and the associated automation scripts in PowerShell, .NET, Ruby, and the CLI. This allows engineers to programmatically build and provision services on the Azure platform, without having to write the code themselves.
To build our chatbot, you can continue from the Azure Portal as I did, or download the ARM template and scripts, and run them locally. Once you have created the chatbot, you will have the option to download the source code as a ZIP file from the Bot Management Build console. I prefer to use the JetBrains WebStorm IDE to develop my Node.js-based bots, and GitHub to store my source code.
As part of developing the chatbot, you will need to add two additional application settings to the Azure App Service the chatbot is running within. The Cosmos DB connection string (
COSMOS_DB_CONN_STR) and the URL of your blob storage container (
ICON_STORAGE_URL) will both be referenced from within our bot, as an environment variable. You can manually add the key/value pairs from the Azure Portal (shown below), or programmatically.
The chatbot’s code, in the app.js file, is divided into three sections: Constants and Global Variables, Intent Handlers, and Helper Functions. Let’s look at each section and its functionality.
Below is the Constants used by the chatbot. I have preserved Azure’s boilerplate template comments in the app.js file. The comments are helpful in understanding the detailed inner-workings of the chatbot code (gist).
Notice that using the LUIS-enabled Language Understanding template, Azure has provisioned a LUIS.ai app and integrated it with our chatbot. More about LUIS, next.
The next part of our chatbot’s code handles intents. Our chatbot’s intents include Greeting, Help, Cancel, and AzureFacts. The Greeting intent handler defines how the bot handles greeting a new user when they make an explicit invocation of the chatbot (without intent). The Help intent handler defines how the chatbot handles a request for help. The Cancel intent handler defines how the bot handles a user’s desire to quit, or if an unknown error occurs with our bot. The AzureFact intent handler, handles implicit invocations of the chatbot (with intent), by returning the requested Azure fact. We will use LUIS to train the AzureFacts intent in the next part of this post.
Each intent handler can return a different type of response to the user. For this demo, we will have the Greeting, Help, and AzureFacts handlers return a ThumbnailCard, while the Cancel handler simply returns a text message (gist).
The last part of our chatbot’s code are the helper functions the intent handlers call. The functions include a function to return a random fact if the user requests one,
selectRandomFact(). There are two functions, which return a ThumbnailCard or a HeroCard, depending on the request,
createHeroCard(session, botResponse) and
buildFactResponse(factToQuery, callback) function is called by the AzureFacts intent handler. This function passes the fact from the user (i.e. certifications) and a callback to the
findFact(factToQuery, callback) function. The
findFact function handles calling Cosmos DB, using MongoDB Node.JS Driver’s db.collection().findOne method. The function also returns a callback (gist).
We will use LUIS to add a perceived degree of intelligence to our chatbot, helping it understand the domain-specific natural language model of our bot. If you have built Alexa Skills or Actions for Google Assitant, LUIS apps work almost identically. The concepts of Intents, Intent Handlers, Entities, and Utterances are universal to all three platforms.
Intents are how LUIS determines what a user wants to do. LUIS will parse user utterances, understand the user’s intent, and pass that intent onto our chatbot, to be handled by the proper intent handler. The bot will then respond accordingly to that intent — with a greeting, with the requested fact, or by providing help.
LUIS describes an entity as being like a variable, used to capture and pass important information. We will start by defining our AzureFacts Intent’s Facts Entities. The Facts entities represent each type of fact a user might request. The requested fact is extracted from the user’s utterances and passed to the chatbot. LUIS allows us to import entities as JSON. I have included a set of Facts entities to import, in the azure-facts-entities.json file, within the project on GitHub (gist).
Each entity includes a primary canonical form, as well as possible synonyms the user may utter in their invocation. If the user utters a synonym, LUIS will understand the intent and pass the canonical form of the entity to the chatbot. For example, if we said ‘tell me about Azure AKS,’ LUIS understands the phrasing, identifies the correct intent, AzureFacts intent, substitutes the synonym, ‘AKS’, with the canonical form of the Facts entity, ‘kubernetes’, and passes the top scoring intent to be handled and the value ‘kubernetes’ to our bot. The bot would then query for the document associated with ‘kubernetes’ in Cosmos DB, and return a response.
Once we have created and associated our Facts entities with our AzureFacts intent, we need to input a few possible phrases a user may utter to invoke our chatbot. Microsoft actually recommends not coding too many utterance examples, as part of their best practices. Below you see an elementary list of possible phrases associated with the AzureFacts intent. You also see the blue highlighted word, ‘Facts’ in each utterance, a reference to the Facts entities. LUIS understands that this position in the phrasing represents a Facts entity value.
Patterns, according to Microsoft, are designed to improve the accuracy of LUIS, when several utterances are very similar. By providing a pattern for the utterances, LUIS can have higher confidence in the predictions.
The topic of training your LUIS app is well beyond the scope of this post. Microsoft has an excellent series of articles, I strongly suggest reading. They will greatly assist in improving the accuracy of your LUIS chatbot.
Once you have completed building and training your intents, entities, phrases, and other items, you must publish your LUIS app for it to be accessed from your chatbot. Publish allows LUIS to be queried from an HTTP endpoint. The LUIS interface will enable you to publish both a Staging and a Production copy of the app. For brevity, I published directly to Production. If you recall our chatbot’s application settings, earlier, the settings include a
luisAppKey, and a
luisAppIdHostName. Together these form the HTTP endpoint,
LuisModelUrl, through which the chatbot will access the LUIS app.
Using the LUIS API endpoint, we can test our LUIS app, independent of our chatbot. This can be very useful for troubleshooting bot issues. Below, we see an example utterance of ‘tell me about Azure Functions.’ LUIS has correctly deduced the user intent, assigning the AzureFacts intent with the highest prediction score. LUIS also identified the correct Entity, ‘functions,’ which it would typically return to the chatbot.
With our chatbot developed and our LUIS app built, trained, and published, we can deploy our bot to the Azure platform. There are a few ways to deploy our chatbot, depending on your platform and language choice. I chose to use the DevOps practice of Continuous Deployment, offered in the Azure Bot Management console.
With Continuous Deployment, every time we commit a change to GitHub, a webhook fires, and my chatbot is deployed to the Azure platform. If you have high confidence in your changes through testing, you could choose to commit and deploy directly.
Alternately, you might choose a safer approach, using feature branch or PR requests. In which case your chatbot will be deployed upon a successful merge of the feature branch or PR request to master.
Azure provides the ability to test your bot, from the Azure portal. Using the Bot Management Test in Web Chat console, you can test your bot using the Web Chat channel. We will talk about different kinds of channel later in the post. This is an easy and quick way to manually test your chatbot.
For more sophisticated, automated testing of your chatbot, there are a handful of frameworks available, including bot-tester, which integrates with mocha and chai. Stuart Harrison published an excellent article on testing with the bot-tester framework, Building a test-driven chatbot for the Microsoft Bot Framework in Node.js.
As part of testing and troubleshooting our chatbot in Production, we will need to review application logs occasionally. Azure offers their Log streaming feature. To access log streams, you must turn on application logging and chose a log level, in the Azure Monitoring Diagnostic logs console, which is off by default.
Once Application logging is active, you may review logs in the Monitoring Log stream console. Log streaming will automatically be turned off in 12 hours and times our after 30 minutes of inactivity. I personally find application logging and access to logs, more difficult and far less intuitive on the Azure platform, than on AWS or Google Cloud.
As part of testing and eventually monitoring our chatbot in Production, the Azure App Service Overview console provides basic telemetry about the App Service, on which the bot is running. With Azure Application Insights, we can drill down into finer-grain application telemetry.
With our chatbot built, trained, deployed and tested, we can integrate it with multiple channels. Channels represent all how our users might interact with our chatbot, Web Chat, Slack, Skype, Facebook Messager, and so forth. Microsoft describes channels as a connection between the Bot Framework and communication apps. For this post, we will look at two channels, Web Chat and Slack.
Enabling Web Chat is probably the easiest of the channels. When you create a bot with Bot Service, the Web Chat channel is automatically configured for you. You used it to test your bot, earlier in the post. Displaying your chatbot through the Web Chat channel, embedded in your website, requires a secret, for which, you have two options. Option one is to keep your secret hidden, exchange your secret for a token, and generate the secret. Option two, according to Microsoft, is to embed the web chat control in your website using the secret. This method will allow other developers to easily embed your bot into their websites.
Embedding Web Chat in your website allows your website users to interact directly with your chatbot. Shown below, I have embedded our chatbot’s Web Chat channel in my website. It will enable a user to interact with the bot, independent of the website’s primary content. Here, a user could ask for more information on a particular topic they found of interest in the article, such as Azure Kubernetes Service (AKS). The Web Chat window is collapsible when not in use.
To integrate our chatbot with Slack, we need to create a new Bot App for Slack. The Slack App will be configured to interact with our deployed bot on Azure securely. Microsoft has an excellent set of easy-to-follow documentation on connecting a bot to Slack. I am only going to summarize the steps here.
Once your Slack App is created, the Slack App contains a set of credentials.
Those Slack App credentials are then shared with the chatbot, in the Azure Slack Channel integration console. This ensures secure communication between your chatbot and your Slack App.
Part of creating your Slack App, is configuring Event Subscriptions. The Microsoft documentation outlines six bot events that need to be configured. By subscribing to bot events, your app will be notified of user activities at the URL you specify.
You also configure a Bot User. Adding a Bot User allows you to assign a username for your bot and choose whether it is always shown as online. This is the username you will see within your Slack app.
Once the Slack App is built, credentials are exchanged, events are configured, and the Bot User is created, you finish by authorizing your Slack App to interact with users within your Slack Workspace. Below I am authorizing the Azure Tech Facts Bot Slack App to interact with users in my Programmatic Ponderings Slack Workspace.
Below we see the Azure Tech Facts Bot Slack App deployed to my Programmatic Ponderings Slack Workspace. The Slack App is shown in the Slack for Mac desktop app.
Similarly, we see the same Azure Tech Facts Bot Slack App being used in the Slack iOS App.
In this brief post, we saw how to develop a Machine learning-based LUIS-enabled chatbot using the Azure Bot Service and the BotBuilder SDK. We enhanced the bot’s functionality with two of Azure’s Cloud services, Cosmos DB and Blob Storage. Once built, we integrated our chatbot with the Web Chat and Slack channels.
This was a very simple demonstration of a LUIS chatbot. The true power of intelligent bots comes from further integrating bots with Azure AI and Machine Learning Services, such as Azure’s Cognitive Services. Additionally, Azure cloud platform offers other more traditional cloud services, in addition to Cosmos DB and Blob Storage, to extend the feature and functionally of bots, such as messaging, IoT, Azure serverless Functions, and AKS-based microservices.
All opinions expressed in this post are my own and not necessarily the views of my current or past employers, their clients, or Microsoft.
Architecting Cloud-Optimized Apps with AKS (Azure’s Managed Kubernetes), Azure Service Bus, and Cosmos DB
An earlier post, Eventual Consistency: Decoupling Microservices with Spring AMQP and RabbitMQ, demonstrated the use of a message-based, event-driven, decoupled architectural approach for communications between microservices, using Spring AMQP and RabbitMQ. This distributed computing model is known as eventual consistency. To paraphrase microservices.io, ‘using an event-driven, eventually consistent approach, each service publishes an event whenever it updates its data. Other services subscribe to events. When an event is received, a service (subscriber) updates its data.’
That earlier post illustrated a fairly simple example application, the Voter API, consisting of a set of three Spring Boot microservices backed by MongoDB and RabbitMQ, and fronted by an API Gateway built with HAProxy. All API components were containerized using Docker and designed for use with Docker CE for AWS as the Container-as-a-Service (CaaS) platform.
Optimizing for Kubernetes on Azure
This post will demonstrate how a modern application, such as the Voter API, is optimized for Kubernetes in the Cloud (Kubernetes-as-a-Service), in this case, AKS, Azure’s new public preview of Managed Kubernetes for Azure Container Service. According to Microsoft, the goal of AKS is to simplify the deployment, management, and operations of Kubernetes. I wrote about AKS in detail, in my last post, First Impressions of AKS, Azure’s New Managed Kubernetes Container Service.
In addition to migrating to AKS, the Voter API will take advantage of additional enterprise-grade Azure’s resources, including Azure’s Service Bus and Cosmos DB, replacements for the Voter API’s RabbitMQ and MongoDB. There are several architectural options for the Voter API’s messaging and NoSQL data source requirements when moving to Azure.
- Keep Dockerized RabbitMQ and MongoDB – Easy to deploy to Kubernetes, but not easily scalable, highly-available, or manageable. Would require storage optimized Azure VMs for nodes, node affinity, and persistent storage for data.
- Replace with Cloud-based Non-Azure Equivalents – Use SaaS-based equivalents, such as CloudAMQP (RabbitMQ-as-a-Service) and MongoDB Atlas, which will provide scalability, high-availability, and manageability.
- Replace with Azure Service Bus and Cosmos DB – Provides all the advantages of SaaS-based equivalents, and additionally as Azure resources, benefits from being in the Azure Cloud alongside AKS.
The Kubernetes resource files and deployment scripts used in this post are all available on GitHub. This is the only project you need to clone to reproduce the AKS example in this post.
git clone \ --branch master --single-branch --depth 1 --no-tags \ https://github.com/garystafford/azure-aks-sb-cosmosdb-demo.git
The Docker images for the three Spring microservices deployed to AKS, Voter, Candidate, and Election, are available on Docker Hub. Optionally, the source code, including Dockerfiles, for the Voter, Candidate, and Election microservices, as well as the Voter Client are available on GitHub, in the
git clone \ --branch kub-aks --single-branch --depth 1 --no-tags \ https://github.com/garystafford/candidate-service.git git clone \ --branch kub-aks --single-branch --depth 1 --no-tags \ https://github.com/garystafford/election-service.git git clone \ --branch kub-aks --single-branch --depth 1 --no-tags \ https://github.com/garystafford/voter-service.git git clone \ --branch kub-aks --single-branch --depth 1 --no-tags \ https://github.com/garystafford/voter-client.git
Azure Service Bus
To demonstrate the capabilities of Azure’s Service Bus, the Voter API’s Spring microservice’s source code has been re-written to work with Azure Service Bus instead of RabbitMQ. A future post will explore the microservice’s messaging code. It is more likely that a large application, written specifically for a technology that is easily portable such as RabbitMQ or MongoDB, would likely remain on that technology, even if the application was lifted and shifted to the Cloud or moved between Cloud Service Providers (CSPs). Something important to keep in mind when choosing modern technologies – portability.
Service Bus is Azure’s reliable cloud Messaging-as-a-Service (MaaS). Service Bus is an original Azure resource offering, available for several years. The core components of the Service Bus messaging infrastructure are queues, topics, and subscriptions. According to Microsoft, ‘the primary difference is that topics support publish/subscribe capabilities that can be used for sophisticated content-based routing and delivery logic, including sending to multiple recipients.’
Since the three Voter API’s microservices are not required to produce messages for more than one other service consumer, Service Bus queues are sufficient, as opposed to a pub/sub model using Service Bus topics.
Cosmos DB, Microsoft’s globally distributed, multi-model database, offers throughput, latency, availability, and consistency guarantees with comprehensive service level agreements (SLAs). Ideal for the Voter API, Cosmos DB supports MongoDB’s data models through the MongoDB API, a MongoDB database service built on top of Cosmos DB. The MongoDB API is compatible with existing MongoDB libraries, drivers, tools, and applications. Therefore, there are no code changes required to convert the Voter API from MongoDB to Cosmos DB. I simply had to change the database connection string.
NGINX Ingress Controller
Although the Voter API’s HAProxy-based API Gateway could be deployed to AKS, it is not optimal for Kubernetes. Instead, the Voter API will use an NGINX-based Ingress Controller. NGINX will serve as an API Gateway, as HAProxy did, previously.
According to NGINX, ‘an Ingress is a Kubernetes resource that lets you configure an HTTP load balancer for your Kubernetes services. Such a load balancer usually exposes your services to clients outside of your Kubernetes cluster.’
An Ingress resource requires an Ingress Controller to function. Continuing from NGINX, ‘an Ingress Controller is an application that monitors Ingress resources via the Kubernetes API and updates the configuration of a load balancer in case of any changes. Different load balancers require different Ingress controller implementations. In the case of software load balancers, such as NGINX, an Ingress controller is deployed in a pod along with a load balancer.’
There are currently two NGINX-based Ingress Controllers available, one from Kubernetes and one directly from NGINX. Both being equal, for this post, I chose the Kubernetes version, without RBAC (Kubernetes offers a version with and without RBAC). RBAC should always be used for actual cluster security. There are several advantages of using either version of the NGINX Ingress Controller for Kubernetes, including Layer 4 TCP and UDP and Layer 7 HTTP load balancing, reverse proxying, ease of SSL termination, dynamically-configurable path-based rules, and support for multiple hostnames.
Azure Web App
Lastly, the Voter Client application, not really part of the Voter API, but useful for demonstration purposes, will be converted from a containerized application to an Azure Web App. Since it is not part of the Voter API, separating the Client application from AKS makes better architectural sense. Web Apps are a powerful, richly-featured, yet incredibly simple way to host applications and services on Azure. For more information on using Azure Web Apps, read my recent post, Developing Applications for the Cloud with Azure App Services and MongoDB Atlas.
Revised Component Architecture
Below is a simplified component diagram of the new architecture, including Azure Service Bus, Cosmos DB, and the NGINX Ingress Controller. The new architecture looks similar to the previous architecture, but as you will see, it is actually very different.
To understand the role of each API component, let’s look at one of the event-driven, decoupled process flows, the creation of a new election candidate. In the simplified flow diagram below, an API consumer executes an HTTP POST request containing the new candidate object as JSON. The Candidate microservice receives the HTTP request and creates a new document in the Cosmos DB Voter database. A Spring
RepositoryEventHandler within the Candidate microservice responds to the document creation and publishes a Create Candidate event message, containing the new candidate object as JSON, to the Azure Service Bus Candidate Queue.
Independently, the Voter microservice is listening to the Candidate Queue. Whenever a new message is produced by the Candidate microservice, the Voter microservice retrieves the message off the queue. The Voter microservice then transforms the new candidate object contained in the incoming message to its own candidate data model and creates a new document in its own Voter database.
The same process flows exist between the Election and the Candidate microservices. The Candidate microservice maintains current elections in its database, which are retrieved from the Election queue.
It is useful to understand, the Candidate microservice’s candidate domain model is not necessarily identical to the Voter microservice’s candidate domain model. Each microservice may choose to maintain its own representation of a vote, a candidate, and an election. The Voter service transforms the new candidate object in the incoming message based on its own needs. In this case, the Voter microservice is only interested in a subset of the total fields in the Candidate microservice’s model. This is the beauty of decoupling microservices, their domain models, and their datastores.
The versions of the Voter API microservices used for this post only support Election Created events and Candidate Created events. They do not handle Delete or Update events, which would be necessary to be fully functional. For example, if a candidate withdraws from an election, the Voter service would need to be notified so no one places votes for that candidate. This would normally happen through a Candidate Delete or Candidate Update event.
Provisioning Azure Service Bus
First, the Azure Service Bus is provisioned. Provisioning the Service Bus may be accomplished using several different methods, including manually using the Azure Portal or programmatically using Azure Resource Manager (ARM) with PowerShell or Terraform. I chose to provision the Azure Service Bus and the two queues using the Azure Portal for expediency. I chose the Basic Service Bus Tier of service, of which there are three tiers, Basic, Standard, and Premium.
The application requires two queues, the
candidate.queue, and the
Provisioning Cosmos DB
Next, Cosmos DB is provisioned. Like Azure Service Bus, Cosmos DB may be provisioned using several methods, including manually using the Azure Portal, programmatically using Azure Resource Manager (ARM) with PowerShell or Terraform, or using the Azure CLI, which was my choice.
az cosmosdb create \ --name cosmosdb_instance_name_goes_here \ --resource-group resource_group_name_goes_here \ --location "East US=0" \ --kind MongoDB
The post’s Cosmos DB instance exists within the single East US Region, with no failover. In a real Production environment, you would configure Cosmos DB with multi-region failover. I chose MongoDB as the type of Cosmos DB database account to create. The allowed values are GlobalDocumentDB, MongoDB, Parse. All other settings were left to the default values.
The three Spring microservices each have their own database. You do not have to create the databases in advance of consuming the Voter API. The databases and the database collections will be automatically created when new documents are first inserted by the microservices. Below, the three databases and their collections have been created and populated with documents.
The GitHub project repository also contains three shell scripts to generate sample vote, candidate, and election documents. The scripts will delete any previous documents from the database collections and generate new sets of sample documents. To use, you will have to update the scripts with your own Voter API URL.
MongoDB Aggregation Pipeline
Each of the three Spring microservices uses Spring Data MongoDB, which takes advantage of MongoDB’s Aggregation Framework. According to MongoDB, ‘the aggregation framework is modeled on the concept of data processing pipelines. Documents enter a multi-stage pipeline that transforms the documents into an aggregated result.’ Below is an example of aggregation from the Candidate microservice’s
Aggregation aggregation = Aggregation.newAggregation( match(Criteria.where("election").is(election)), group("candidate").count().as("votes"), project("votes").and("candidate").previousOperation(), sort(Sort.Direction.DESC, "votes") );
To use MongoDB’s aggregation framework with Cosmos DB, it is currently necessary to activate the MongoDB Aggregation Pipeline Preview Feature of Cosmos DB. The feature can be activated from the Azure Portal, as shown below.
Cosmos DB Emulator
Be warned, Cosmos DB can be very expensive, even without database traffic or any Production-grade bells and whistles. Be careful when spinning up instances on Azure for learning purposes, the cost adds up quickly! In less than ten days, while writing this post, my cost was almost US$100 for the Voter API’s Cosmos DB instance.
I strongly recommend downloading the free Azure Cosmos DB Emulator to develop and test applications from your local machine. Although certainly not as convenient, it will save you the cost of developing for Cosmos DB directly on Azure.
With Cosmos DB, you pay for reserved throughput provisioned and data stored in containers (a collection of documents or a table or a graph). Yes, that’s right, Azure charges you per MongoDB collection, not even per database. Azure Cosmos DB’s current pricing model seems less than ideal for microservice architectures, each with their own database instance.
By default the reserved throughput, billed as Request Units (RU) per second or RU/s, is set to 1,000 RU/s per collection. For development and testing, you can reduce each collection to a minimum of 400 RU/s. The Voter API creates five collections at 1,000 RU/s or 5,000 RU/s total. Reducing this to a total of 2,000 RU/s makes Cosmos DB marginally more affordable to explore.
Building the AKS Cluster
An existing Azure Resource Group is required for AKS. I chose to use the latest available version of Kubernetes, 1.8.2.
# login to azure az login \ --username your_username \ --password your_password # create resource group az group create \ --resource-group resource_group_name_goes_here \ --location eastus # create aks cluster az aks create \ --name cluser_name_goes_here \ --resource-group resource_group_name_goes_here \ --ssh-key-value path_to_your_public_key \ --kubernetes-version 1.8.2 # get credentials to access aks cluster az aks get-credentials \ --name cluser_name_goes_here \ --resource-group resource_group_name_goes_here # display cluster's worker nodes kubectl get nodes --output=wide
By default, AKS will provision a three-node Kubernetes cluster using Azure’s Standard D1 v2 Virtual Machines. According to Microsoft, ‘D series VMs are general purpose VM sizes provide balanced CPU-to-memory ratio. They are ideal for testing and development, small to medium databases, and low to medium traffic web servers.’ Azure D1 v2 VM’s are based on Linux OS images, currently Debian 8 (Jessie), with 1 vCPU and 3.5 GB of memory. By default with AKS, each VM receives 30 GiB of Standard HDD attached storage.
You should always select the type and quantity of the cluster’s VMs and their attached storage, optimized for estimated traffic volumes and the specific workloads you are running. This can be done using the
--node-osdisk-size arguments with the
az aks create command.
The Voter API resources are deployed to its own Kubernetes Namespace,
voter-api. The NGINX Ingress Controller resources are deployed to a different namespace,
ingress-nginx. Separate namespaces help organize individual Kubernetes resources and separate different concerns.
voter-api namespace is created. Then, five required Kubernetes Secrets are created within the namespace. These secrets all contain sensitive information, such as passwords, that should not be shared. There is one secret for each of the three Cosmos DB database connection strings, one secret for the Azure Service Bus connection string, and one secret for the Let’s Encrypt SSL/TLS certificate and private key, used for secure HTTPS access to the Voter API.
The Voter API’s secrets are used to populate environment variables within the pod’s containers. The environment variables are then available for use within the containers. Below is a snippet of the Voter pods resource file showing how the Cosmos DB and Service Bus connection strings secrets are used to populate environment variables.
env: - name: AZURE_SERVICE_BUS_CONNECTION_STRING valueFrom: secretKeyRef: name: azure-service-bus key: connection-string - name: SPRING_DATA_MONGODB_URI valueFrom: secretKeyRef: name: azure-cosmosdb-voter key: connection-string
Shown below, the Cosmos DB and Service Bus connection strings secrets have been injected into the Voter container and made available as environment variables to the microservice’s executable JAR file on start-up. As environment variables, the secrets are visible in plain text. Access to containers should be tightly controlled through Kubernetes RBAC and Azure AD, to ensure sensitive information, such as secrets, remain confidential.
Next, the three Kubernetes ReplicaSet resources, corresponding to the three Spring microservices, are created using Deployment controllers. According to Kubernetes, a Deployment that configures a ReplicaSet is now the recommended way to set up replication. The Deployments specify three replicas of each of the three Spring Services, resulting in a total of nine Kubernetes Pods.
Each pod, by default, will be scheduled on a different node if possible. According to Kubernetes, ‘the scheduler will automatically do a reasonable placement (e.g. spread your pods across nodes, not place the pod on a node with insufficient free resources, etc.).’ Note below how each of the three microservice’s three replicas has been scheduled on a different node in the three-node AKS cluster.
Next, the three corresponding Kubernetes ClusterIP-type Services are created. And lastly, the Kubernetes Ingress is created. According to Kubernetes, the Ingress resource is an API object that manages external access to the services in a cluster, typically HTTP. Ingress provides load balancing, SSL termination, and name-based virtual hosting.
The Ingress configuration contains the routing rules used with the NGINX Ingress Controller. Shown below are the routing rules for each of the three microservices within the Voter API. Incoming API requests are routed to the appropriate pod and service port by NGINX.
apiVersion: extensions/v1beta1 kind: Ingress metadata: name: voter-ingress namespace: voter-api annotations: ingress.kubernetes.io/ssl-redirect: "true" spec: tls: - hosts: - api.voter-demo.com secretName: api-voter-demo-secret rules: - http: paths: - path: /candidate backend: serviceName: candidate servicePort: 8080 - path: /election backend: serviceName: election servicePort: 8080 - path: /voter backend: serviceName: voter servicePort: 8080
The screengrab below shows all of the Voter API resources created on AKS.
NGINX Ingress Controller
After completing the deployment of the Voter API, the NGINX Ingress Controller is created. It starts with creating the
ingress-nginx namespace. Next, the NGINX Ingress Controller is created, consisting of the NGINX Ingress Controller, three Kubernetes ConfigMap resources, and a default back-end application. The Controller and backend each have their own Service resources. Like the Voter API, each has three replicas, for a total of six pods. Together, the Ingress resource and NGINX Ingress Controller manage traffic to the Spring microservices.
The screengrab below shows all of the NGINX Ingress Controller resources created on AKS.
The NGINX Ingress Controller Service, shown above, has an external public IP address associated with itself. This is because that Service is of the type, Load Balancer. External requests to the Voter API will be routed through the NGINX Ingress Controller, on this IP address.
kind: Service apiVersion: v1 metadata: name: ingress-nginx namespace: ingress-nginx labels: app: ingress-nginx spec: externalTrafficPolicy: Local type: LoadBalancer selector: app: ingress-nginx ports: - name: http port: 80 targetPort: http - name: https port: 443 targetPort: https
If you are only using HTTPS, not HTTP, then the references to HTTP and port 80 in the Ingress configuration are unnecessary. The NGINX Ingress Controller’s resources are explained in detail in the GitHub documentation, along with further configuration instructions.
To provide convenient access to the Voter API and the Voter Client, my domain,
voter-demo.com, is associated with the public IP address associated with the Voter API Ingress Controller and with the public IP address associated with the Voter Client Azure Web App. DNS configuration is done through Azure’s DNS Zone resource.
TXT type records might not look as familiar as the
A type records. The
TXT records are required to associate the domain entries with the Voter Client Azure Web App. Browsing to http://www.voter-demo.com or simply http://voter-demo.com brings up the Voter Client.
The Client sends and receives data via the Voter API, available securely at https://api.voter-demo.com.
Routing API Requests
With the Pods, Services, Ingress, and NGINX Ingress Controller created and configured, as well as the Azure Layer 4 Load Balancer and DNS Zone, HTTP requests from API consumers are properly and securely routed to the appropriate microservices. In the example below, three back-to-back requests are made to the
voter/info API endpoint. HTTP requests are properly routed to one of the three Voter pod replicas using the default round-robin algorithm, as proven by the observing the different hostnames (pod names) and the IP addresses (private pod IPs) in each HTTP response.
Shown below is the final Voter API Azure architecture. To simplify the diagram, I have deliberately left out the three microservice’s ClusterIP-type Services, the three default back-end application pods, and the default back-end application’s ClusterIP-type Service. All resources shown below are within the single East US Azure region, except DNS, which is a global resource.
Shown below is the new Azure Resource Group created by Azure during the AKS provisioning process. The Resource Group contains the various resources required to support the AKS cluster, NGINX Ingress Controller, and the Voter API. Necessary Azure resources were automatically provisioned when I provisioned AKS and when I created the new Voter API and NGINX resources.
In addition to the Resource Group above, the original Resource Group contains the AKS Container Service resource itself, Service Bus, Cosmos DB, and the DNS Zone resource.
The Voter Client Web App, consisting of the Azure App Service and App Service plan resource, is located in a third, separate Resource Group, not shown here.
Cleaning Up AKS
A nice feature of AKS, running a
az aks delete command will delete all the Azure resources created as part of provisioning AKS, the API, and the Ingress Controller. You will have to delete the Cosmos DB, Service Bus, and DNS Zone resources, separately.
az aks delete \ --name cluser_name_goes_here \ --resource-group resource_group_name_goes_here
Taking advantage of Kubernetes with AKS, and the array of Azure’s enterprise-grade resources, the Voter API was shifted from a simple Docker architecture to a production-ready solution. The Voter API is now easier to manage, horizontally scalable, fault-tolerant, and marginally more secure. It is capable of reliably supporting dozens more microservices, with multiple replicas. The Voter API will handle a high volume of data transactions and event messages.
There is much more that needs to be done to productionalize the Voter API on AKS, including:
- Add multi-region failover of Cosmos DB
- Upgrade to Service Bus Standard or Premium Tier
- Optimized Azure VMs and storage for anticipated traffic volumes and application-specific workloads
- Implement Kubernetes RBAC
- Add Monitoring, logging, and alerting with Envoy or similar
- Secure end-to-end TLS communications with Itsio or similar
- Secure the API with OAuth and Azure AD
- Automate everything with DevOps – AKS provisioning, testing code, creating resources, updating microservices, and managing data
All opinions in this post are my own, and not necessarily the views of my current or past employers or their clients.
Kubernetes as a Service
On October 24, 2017, less than a month prior to writing this post, Microsoft released the public preview of Managed Kubernetes for Azure Container Service (AKS). According to Microsoft, the goal of AKS is to simplify the deployment, management, and operations of Kubernetes. According to Gabe Monroy, PM Lead, Containers @ Microsoft Azure, in a blog post, AKS ‘features an Azure-hosted control plane, automated upgrades, self-healing, easy scaling.’ Monroy goes on to say, ‘with AKS, customers get the benefit of open source Kubernetes without complexity and operational overhead.’
Unquestionably, Kubernetes has become the leading Container-as-a-Service (CaaS) choice, at least for now. Along with the release of AKS by Microsoft, there have been other recent announcements, which reinforce Kubernetes dominance. In late September, Rancher Labs announced the release of Rancher 2.0. According to Rancher, Rancher 2.0 would be based on Kubernetes. In mid-October, at DockerCon Europe 2017, Docker announced they were integrating Kubernetes into the Docker platform. Even AWS seems to be warming up to Kubernetes, despite their own ECS, according to sources. There are rumors AWS will announce a Kubernetes offering at AWS re:Invent 2017, starting a week from now.
Being a big fan of both Azure and Kubernetes, I decided to give AKS a try. I chose to deploy an existing, simple, multi-tier web application, which I had used in several previous posts, including Eventual Consistency: Decoupling Microservices with Spring AMQP and RabbitMQ. All the code used for this post is available on GitHub.
The application, the Voter application, is composed of an AngularJS frontend client-side UI, and two Java Spring Boot microservices, both backed by individual MongoDB databases, and fronted with an HAProxy-based API Gateway. The AngularJS UI calls the API Gateway, which in turn calls the Spring services. The two microservices communicate with each other using HTTP-based inter-process communication (IPC). Although I would prefer event-based service-to-service IPC, HTTP-based IPC was simpler to implement, for this post.
Interestingly, the Voter application was designed using Docker Community Edition for Mac and deployed to AWS using Docker Community Edition for AWS. Not only would this be my chance to preview AKS, but also an opportunity to compare the ease of developing for Docker CE on AWS using a Mac, to developing for Kubernetes with AKS using Docker Community Edition for Windows.
In order to develop for AKS on my Windows 10 Enterprise workstation, I first made sure I had the latest copies of the following software:
- Docker CE for Windows (v17.11.0)
- Azure CLI (v2.0.21)
- kubectl (v1.8.3)
- kompose (v1.4.0)
- Windows PowerShell (v5.1)
If you are following along with the post, make sure you have the latest version of the Azure CLI, minimally 2.0.21, according to the Azure CLI release notes. Also, I happen to be running the latest version of Docker CE from the Edge Channel. However, either channel’s latest release of Docker CE for Windows should work for this post. Using PowerShell is optional. I prefer PowerShell over working from the Windows Command Prompt, if for nothing else than to preserve my command history, by default.
Kubernetes Resources with Kompose
Originally developed for Docker CE, the Voter application stack was defined in a single Docker Compose file.
To work on AKS, the application stack’s configuration needs to be reproduced as Kubernetes configuration files. Instead of writing the configuration files manually, I chose to use kompose. Kompose is described on its website as ‘a conversion tool for Docker Compose to container orchestrators such as Kubernetes.’ Using kompose, I was able to automatically convert the Docker Compose file into analogous Kubernetes resource configuration files.
kompose convert -f docker-compose.yml
For the AngularJS Client Service and the HAProxy API Gateway Service, I had to modify the Service configuration files to switch the Service type to a Load Balancer (
type: LoadBalancer). Being a Load Balancer, Kubernetes will assign a publically accessible IP address to each Service; the reasons for which are explained later in the post.
The MongoDB service requires a persistent storage volume. To accomplish this with Kubernetes, kompose created a PersistentVolumeClaims resource configuration file. I did have to create a corresponding PersistentVolume resource configuration file. It was also necessary to modify the PersistentVolumeClaims resource configuration file, specifying the Storage Class Name as manual, to correspond to the AKS Storage Class configuration (
From the original Docker Compose file, containing five Docker services, I ended up with a dozen individual Kubernetes resource configuration files. Individual configuration files are optimal for fine-grain management of Kubernetes resources. The Docker Compose file and the Kubernetes resource configuration files are included in the GitHub project.
git clone \ --branch master --single-branch --depth 1 --no-tags \ https://github.com/garystafford/azure-aks-demo.git
Creating AKS Resources
New AKS Feature Flag
According to Microsoft, to start with AKS, while still a preview, creating new clusters requires a feature flag on your subscription.
az provider register -n Microsoft.ContainerService
Using a brand new Azure account for this demo, I also needed to activate two additional feature flags.
az provider register -n Microsoft.Network az provider register -n Microsoft.Compute
If you are missing required features flags, you will see errors, similar to. the below error.
Operation failed with status: ’Bad Request’. Details: Required resource provider registrations Microsoft.Compute,Microsoft.Network are missing.
AKS requires an Azure Resource Group for AKS. I chose to create a new Resource Group, using the Azure CLI.
az group create \ --resource-group resource_group_name_goes_here \ --location eastus
New Kubernetes Cluster
aks feature of the Azure CLI version 2.0.21 or later, I provisioned a new Kubernetes cluster. By default, Azure will create a 3-node cluster. You can override the default number of nodes using the
--node-count parameter; I chose one node. The version of Kubernetes you choose is also configurable using the
--kubernetes-version parameter. I selected the latest Kubernetes version available with AKS, 1.8.2.
az aks create \ --name cluser_name_goes_here \ --resource-group resource_group_name_goes_here \ --node-count 1 \ --generate-ssh-keys \ --kubernetes-version 1.8.2
The newly created Azure Resource Group and AKS Kubernetes Cluster were both then visible on the Azure Portal.
In addition to the new Resource Group I created, Azure also created a second Resource Group containing seven Azure resources. These Azure resources include a Virtual Machine (the single Kubernetes node), Network Security Group, Network Interface, Virtual Network, Route Table, Disk, and an Availability Group.
With AKS up and running, I used another Azure CLI command to create a proxy connection to the Kubernetes Dashboard, which was deployed automatically and was running within the new AKS Cluster. The Kubernetes Dashboard is a general purpose, web-based UI for Kubernetes clusters.
az aks browse \ --name cluser_name_goes_here \ --resource-group resource_group_name_goes_here
Although no applications were deployed to AKS, yet, there were several Kubernetes components running within the AKS Cluster. Kubernetes components running within the kube-system Namespace, included heapster, kube-dns, kubernetes-dashboard, kube-proxy, kube-svc-redirect, and tunnelfront.
Deploying the Application
MongoDB should be deployed first. Both the Voter and Candidate microservices depend on MongoDB. MongoDB is composed of four Kubernetes resources, a Deployment resource, Service resource, PersistentVolumeClaim resource, and PersistentVolume resource. I used
kubectl, the command line interface for running commands against Kubernetes clusters, to create the four MongoDB resources, from the configuration files.
kubectl create \ -f voter-data-vol-persistentvolume.yaml \ -f voter-data-vol-persistentvolumeclaim.yaml \ -f mongodb-deployment.yaml \ -f mongodb-service.yaml
After MongoDB was deployed and running, I created the four remaining Deployment resources, Client, Gateway, Voter, and Candidate, from the Deployment resource configuration files. According to Kubernetes, ‘a Deployment controller provides declarative updates for Pods and ReplicaSets. You describe a desired state in a Deployment object, and the Deployment controller changes the actual state to the desired state at a controlled rate.’
Lastly, I created the remaining Service resources from the Service resource configuration files. According to Kubernetes, ‘a Service is an abstraction which defines a logical set of Pods and a policy by which to access them.’
Switching back to the Kubernetes Dashboard, the Voter application components were now visible.
There were five Kubernetes Pods, one for each application component. Since there is only one Node in the Kubernetes Cluster, all five Pods were deployed to the same Node. There were also five corresponding Kubernetes Deployments.
Similarly, there were five corresponding Kubernetes ReplicaSets, the next-generation Replication Controller. There were also five corresponding Kubernetes Services. Note the Gateway and Client Services have an External Endpoint (External IP) associated with them. The IPs were created as a result of adding the Load Balancer Service type to their Service resource configuration files, mentioned earlier.
Lastly, note the Persistent Disk Claim for MongoDB, which had been successfully bound.
Switching back to the Azure Portal, following the application deployment, there were now three additional resources in the AKS Resource Group, a new Azure Load Balancer and two new Public IP Addresses. The Load Balancer is used to balance the Client and Gateway Services, which both have public IP addresses.
To confirm the Gateway, Voter, and Candidate Services were reachable, using the public IP address of the Gateway Service, I browsed to HAProxy’s Statistics web page. Note the two backends, candidate and voter. The green color means HAProxy was able to successfully connect to both of these Services.
Accessing the Application
The Voter application’s AngularJS UI frontend can be accessed using the Client Service’s public IP address. However, this would not be very user-friendly. Even if I brought up the UI, using the public IP, the UI would be unable to connect to the HAProxy API Gateway, and subsequently, the Voter or Candidate Services. Based on the Client’s current configuration, the Client is expecting to find the Gateway at
To make accessing the Client more user-friendly, and to ensure the Client has access to the Gateway, I provisioned an Azure DNS Zone resource for my domain,
voter-demo.com. I assigned the DNS Zone to the AKS Resource Group.
Within the new DNS Zone, I created three DNS records. The first record, an Alias (A) record, associated
voter-demo.com with the public IP address of the Client Service. I added a second Alias (A) record for the
www subdomain, also associating it with the public IP address of the Client Service. The third Alias (A) record associated the
api subdomain with the public IP address of the Gateway Service.
At a high level, the voter application’s routing architecture looks as follows. The client’s requests to the primary domain or to the
api subdomain are resolved to one of the two public IP addresses configured in the load balancer’s frontend. The requests are passed to the load balancer’s backend pool, containing the single Azure VM, which is the single Kubernetes node, and onto the client or gateway Kubernetes Service. From there, requests are routed to one the appropriate Kubernetes Pods, containing the containerized application components, client or gateway.
Using Chrome’s Developer Tools, observe when a new vote is placed, an HTTP
POST is made to the gateway, on the
http://api.voter-demo.com:8080/voter/votes. The Gateway then proxies this request to the Voter Service, at
http://voter:8080/voter/votes. Since the Gateway and Voter Services both run within the same Cluster, the Gateway is able to use the Voter service’s name to address it, using Kubernetes kube-dns.
In the past, I have developed, deployed, and managed containerized applications, using Rancher, AWS, AWS ECS, native Kubernetes, RedHat OpenShift, Docker Enterprise Edition, and Docker Community Edition. Based on my experience, and given my limited testing with Azure’s public preview of AKS, I am very impressed. Creating the Kubernetes Cluster could not have been easier. Scaling the Cluster, adding Persistent Volumes, and upgrading Kubernetes, is equally as easy. Conveniently, AKS integrates with other Kubernetes tools, like kubectl, kompose, and Helm.
I did run into some minor issues with the AKS Preview, such as being unable to connect to an earlier Cluster, after upgrading from the default Kubernetes version to 1.82. I also experience frequent disconnects when proxying to the Kubernetes Dashboard. I am sure the AKS Preview bugs will be worked out by the time AKS is officially released.
In addition to the many advantages of Kubernetes as a CaaS, a huge advantage of using AKS is the ability to easily integrate Azure’s many enterprise-grade compute, networking, database, caching, storage, and messaging resources. It would require minimal effort to swap out the Voter application’s single containerized version of MongoDB with a highly performant and available instance of Azure Cosmos DB. Similarly, it would be relatively easy to swap out the single containerized version of HAProxy with a fully-featured and secure instance of Azure API Management. The current version of the Voter application replies on RabbitMQ for service-to-service IPC versus this earlier application version’s reliance on HTTP-based IPC. It would be fairly simple to swap RabbitMQ for Azure Service Bus.
Lastly, AKS easily integrates with leading Development and DevOps tooling and processes. Building, managing, and deploying applications to AKS, is possible with Visual Studio, VSTS, Jenkins, Terraform, and Chef, according to Microsoft.
A few good references to get started with AKS:
- Container Orchestration Simplified with Managed Kubernetes in Azure Container Service (AKS)
- Deploy an Azure Container Service (AKS) cluster
- Prepare application for Azure Container Service (AKS)
- Kubernetes Basics
- kubectl Cheat Sheet
All opinions in this post are my own, and not necessarily the views of my current or past employers or their clients.
Shift Left Cloud
The continued growth of compute services by leading Cloud Service Providers (CSPs) like Microsoft, Amazon, and Google are transforming the architecture of modern software applications, as well as the software development lifecycle (SDLC). Self-service access to fully-managed, reasonably-priced, secure compute has significantly increased developer productivity. At the same time, cloud-based access to cutting-edge technologies, like Artificial Intelligence (AI), Internet Of Things (IoT), Machine Learning, and Data Analytics, has accelerated the capabilities of modern applications. Finally, as CSPs become increasingly platform agnostic, Developers are no longer limited to a single technology stack or operating system. Today, Developers are solving complex problems with multi-cloud, multi-OS polyglot solutions.
Developers now leverage the Cloud from the very start of the software development process; shift left Cloud, if you will*. Developers are no longer limited to building and testing software on local workstations or on-premise servers, then throwing it over the wall to Operations for deployment to the Cloud. Developers using Azure, AWS, and GCP, develop, build, test, and deploy their code directly to the Cloud. Existing organizations are rapidly moving development environments from on-premise to the Cloud. New organizations are born in the Cloud, without the burden of legacy on-premise data-centers and servers under desks to manage.
To demonstrate the ease of developing a modern application for the Cloud, let’s explore a simple API-based, NoSQL-backed web application. The application, The .NET Diner, simulates a rudimentary restaurant menu ordering interface. It consists of a single-page application (SPA) and two microservices backed by MongoDB. For simplicity, there is no API Gateway between the UI and the two services, as normally would be in place. An earlier version of this application was used in two previous posts, including Cloud-based Continuous Integration and Deployment for .NET Development.
The original restaurant order application was written with JQuery and RESTful .NET WCF Services. The new application, used in this post, has been completely re-written and modernized. The web-based user interface (UI) is written with Google’s Angular 4 framework using TypeScript. The UI relies on a microservices-based API, built with C# using Microsoft’s Web API 2 and .NET 4.7. The services rely on MongoDB for data persistence.
All code for this project is available on GitHub within two projects, one for the Angular UI and another for the C# services. The entire application can easily be built and run locally on Windows using MongoDB Community Edition. Alternately, to run the application in the Cloud, you will require an Azure and MongoDB Atlas account.
This post is primarily about the development experience. For brevity, the post will not delve into security, DevOps practices for CI/CD, and the complexities of staging and releasing code to Production.
The API, consisting of a set of C# microservices, was developed with Microsoft Visual Studio Community 2017 on Windows 10. Visual Studio touts itself as a full-featured Integrated Development Environment (IDE) for Android, iOS, Windows, web, and cloud. Visual Studio is easily integrated with Azure, AWS, and Google, through the use of Extensions. Visual Studio is an ideal IDE for cloud-centric application development.
Other tools used to develop the application include Git and GitHub for source code, MongoDB Community Edition for local database development, and Postman for API development and testing, both locally and on Azure. All the development tools used in the post are cross-platform. Versions of WebStorm, Visual Studio, MongoDB, Postman, Git, Node.js, npm, and Bash are all available for Mac, Windows, and Linux. Cross-platform flexibility is key when developing modern multi-OS polyglot applications.
Postman was used to build, test, and document the application’s API. Postman is an excellent choice for developing RESTful APIs. With Postman, you define Collections of HTTP requests for each of your APIs. You then define Environments, such as Development, Test, and Production, against which you will execute the Collections of HTTP requests. Each environment consists of environment-specific variables. Those variables can be used to define API URLs and as request parameters.
Postman also allows you to write and run automated API integration tests, as well as perform load testing, as shown below.
Azure App Services
The Angular browser-based UI and the C# microservices will be deployed to Azure using the Azure App Service. Azure App Service is nearly identical to AWS Elastic BeanStalk and Google App Engine. According to Microsoft, Azure App Service allows Developers to quickly build, deploy, and scale enterprise-grade web, mobile, and API apps, running on Windows or Linux, using .NET, .NET Core, Java, Ruby, Node.js, PHP, and Python.
App Service is a fully-managed, turn-key platform. Azure takes care of infrastructure maintenance and load balancing. App Service easily integrates with other Azure services, such as API Management, Queue Storage, Azure Active Directory (AD), Cosmos DB, and Application Insights. Microsoft suggests evaluating the following four criteria when considering Azure App Services:
- You want to deploy a web application that’s accessible through the Internet.
- You want to automatically scale your web application according to demand without needing to redeploy.
- You don’t want to maintain server infrastructure (including software updates).
- You don’t need any machine-level customizations on the servers that host your web application.
There are currently four types of Azure App Services, which are Web Apps, Web Apps for Containers, Mobile Apps, and API Apps. The application in this post will use the Azure Web Apps for the Angular browser-based UI and Azure API Apps for the C# microservices.
Each of the C# microservices has separate MongoDB database. In the Cloud, the services use MongoDB Atlas, a secure, highly-available, and scalable cloud-hosted MongoDB service. Cloud-based databases, like Atlas, are often referred to as Database as a Service (DBaaS). Atlas is a Cloud-based alternative to traditional on-premise databases, as well as equivalent CSP-based solutions, such as Amazon DynamoDB, GCP Cloud Bigtable, and Azure Cosmos DB.
Atlas is an example of a SaaS who offer a single service or small set of closely related services, as an alternative to the big CSP’s equivalent services. Similar providers in this category include CloudAMQP (RabbitMQ as a Service), ClearDB (MySQL DBaaS), Akamai (Content Delivery Network), and Oracle Database Cloud Service (Oracle Database, RAC, and Exadata as a Service). Many of these providers, such as Atlas, are themselves hosted on AWS or other CSPs.
There are three pricing levels for MongoDB Atlas: Free, Essential, and Professional. To follow along with this post, the Free level is sufficient. According to MongoDB, with the Free account level, you get 512 MB of storage with shared RAM, a highly-available 3-node replica set, end-to-end encryption, secure authentication, fully managed upgrades, monitoring and alerts, and a management API. Atlas provides the ability to upgrade your account and CSP specifics at any time.
Once you register for an Atlas account, you will be able to log into Atlas, set up your users, whitelist your IP addresses for security, and obtain necessary connection information. You will need this connection information in the next section to configure the Azure API Apps.
With the Free Atlas tier, you can view detailed Metrics about database cluster activity. However, with the free tier, you do not get access to Real-Time data insights or the ability to use the Data Explorer to view your data through the Atlas UI.
Azure API Apps
The example application’s API consists of two RESTful microservices built with C#, the
RestaurantMenu service and
RestaurantOrder service. Both services are deployed as Azure API Apps. API Apps is a fully-managed platform. Azure performs OS patching, capacity provisioning, server management, and load balancing.
Microsoft Visual Studio has done an excellent job providing Extensions to make cloud integration a breeze. I will be using Visual Studio Tools for Azure in this post. Similar to how you create a Publish Profile for deploying applications to Internet Information Services (IIS), you create a Publish Profile for Azure App Services. Using the step-by-step user interface, you create a Microsft Azure App Service Web Deploy Publish Profile for each service. To create a new Profile, choose the Microsoft Azure App Service Target.
The App Service Plan defines the Location and Size for your API App container; these will determine the cost of the compute. I suggest putting the two API Apps and the Web App in the same location, in this case, East US.
The Publish Profile is now available for deploying the services to Azure. No command line interaction is required. The services can be built and published to Azure with a single click from within Visual Studio.
Azure App Services is highly configurable. For example, each API App requires a different configuration, in each environment, to connect to different instances of MongoDB Atlas databases. For security, sensitive Atlas credentials are not stored in the source code. The Atlas URL and sensitive credentials are stored in App Settings on Azure. For this post, the settings were input directly into the Azure UI, as shown below. You will need to input your own Atlas URL and credentials.
The compiled C# services expect certain environment variables to be present at runtime to connect to MongoDB Atlas. These are provided through Azure’s App Settings. Access to the App Settings in Azure should be tightly controlled through Azure AD and fine-grained Azure Role-Based Access Control (RBAC) service.
If you want to deploy the application from this post to Azure, there is one code change you will need to make to each service, which deals with Cross-Origin Resource Sharing (CORS). The services are currently configured to only accept traffic from my temporary Angular UI App Service’s URL. You will need to adjust the CORS configuration in the
\App_Start\WebApiConfig.cs file in each service, to match your own App Service’s new URL.
Angular UI Web App
The Angular UI application will be deployed as an Azure Web App, one of four types of Azure App Services, mentioned previously. According to Microsoft, Web Apps allow Developers to program in their favorite languages, including .NET, Java, Node.js, PHP, and Python on Windows or .NET Core, Node.js, PHP or Ruby on Linux. Web Apps is a fully-managed platform. Azure performs OS patching, capacity provisioning, server management, and load balancing.
Using the Azure Portal, setting up a new Web App for the Angular UI is simple.
Provide an App Name, Subscription, Resource Group, OS Type, and select whether or not you want Application Insights enabled for the Web App.
Although an amazing IDE for web development, WebStorm lacks some of the direct integrations with Azure, AWS, and Google, available with other IDE’s, like Visual Studio. Since the Angular application was developed in WebStorm on Mac, we will take advantage of Azure App Service’s Continuous Deployment feature.
Azure Web Apps can be deployed automatically from most common source code management platforms, including Visual Studio Team Services (VSTS), GitHub, Bitbucket, OneDrive, and local Git repositories.
For this post, I chose GitHub. To configure deployment from GitHub, select the GitHub Account, Organization, Project, and Branch from which Azure will deploy the Angular Web App.
Configuring GitHub in the Azure Portal, Azure becomes an Authorized OAuth App on the GitHub account. Azure creates a Webhook, which fires each time files are pushed (
git push) to the
dist branch of the GitHub project’s repository.
ng build command can be run from within WebStorm or from the command line.
--env=prod flag ensures that the Production environment configuration, containing the correct Azure API endpoints, issued transpiled into the build. This configuration is stored in the
\src\environments\environment.prod.ts file, shown below. You will need to update these two endpoints to your own endpoints from the two API Apps you previously deployed to Azure.
Optionally, the code should be optimized for Production, by replacing the
--dev flag with the
--prod flag. Amongst other optimizations, the Production version of the code is uglified using UglifyJS. Note the difference in the build files shown below for Production, as compared to files above for Development.
Since I chose GitHub for deployment to Azure, I used Git to manually push the local build files to the
dist branch on GitHub.
Every time the webhook fires, Azure pulls and deploys the new build, overwriting the previously deployed version, as shown below.
To stage new code and not immediately overwrite running code, Azure has a feature called Deployment slots. According to Microsoft, Deployment slots allow Developers to deploy different versions of Web Apps to different URLs. You can test a certain version and then swap content and configuration between slots. This is likely how you would eventually deploy your code to Production.
Up and Running
Below, the three Azure App Services are shown in the Azure Portal, successfully deployed and running. Note their Status, App Type, Location, and Subscription.
Before exploring the deployed UI, the two Azure API Apps should be tested using Postman. Once the API is confirmed to be working properly, populated by making an HTTP Post request to the
menuitems API, the
RestaurantOrderService Azure API Service. When the HTTP Post request is made, the
RestaurantOrderService stores a set of menu items in the
RestaurantMenu Atlas MongoDB database, in the
The Angular UI, the
RestaurantWeb Azure Web App, is viewed by using the URL provided in the Web App’s
Overview tab. The menu items displayed in the drop-down are supplied by an HTTP GET request to the
menuitems API, provided by the
RestaurantMenuService Azure API Service.
Your order is placed through an HTTP Post request to the
orders API, the
RestaurantOrderService Azure API Service. The
RestaurantOrderService stores the order in the
RestaurantOrder Atlas MongoDB database, in the
orders collection. The order details are returned in the response body and displayed in the UI.
Once you have the development version of the application successfully up and running on Atlas and Azure, you can start to configure, test, and deploy additional application versions, as App Services, into higher Azure environments, such as Test, Performance, and eventually, Production.
Azure provides in-depth monitoring and performance analytics capabilities for your deployed applications with services like Application Insights. With Azure’s monitoring resources, you can monitor the live performance of your application and set up alerts for application errors and performance issues. Real-time monitoring useful when conducting performance tests. Developers can analyze response time of each API method and optimize the application, Azure configuration, and MongoDB databases, before deploying to Production.
This post demonstrated how the Cloud has shifted application development to a Cloud-first model. Future posts will demonstrate how an application, such as the app featured in this post, is secured, and how it is continuously built, tested, and deployed, using DevOps practices.
All opinions in this post are my own, and not necessarily the views of my current or past employers or their clients.