On June 28th, 2017, Azure Container Service team announced that a new version of the service was deployed in the UK region. This version exposes some new cool features with one of them being the ability to deploy DockerCE (swarm mode) clusters. In this article, you will see how the Azure CLI can be used to deploy a DockerCE cluster in Azure Container Service. Once the cluster is deployed, you can manage it with the docker command-line tool and deploy your Linux container(s).
This tutorial requires the Azure CLI version 2.0.4 or later. Run az --version to find the version. If you need to upgrade, see Install Azure CLI 2.0. You can also use the embedded shell in Azure Portal, called Azure Cloud Shell.
If you don’t have an Azure subscription, create a free account before you begin.
This article will dive into the necessary steps that you need to do in order to use SSL for a service of yours that is hosted on a Kubernetes cluster, making it accessible via htttps. We will use one Microsoft Bot Framework app to demonstrate this. This framework allows you to easily built chatbots that are hosted on the provider of your choice. Its Bot Connector service allows your bot to open “conversation channels” with Messenger, Skype, Slack and other providers. For this purpose, it requires the chatbot’s endpoint to be accessible via SSL/HTTPS, so that makes for a nice proof of concept apt for this article. So, how would you host a chatbot app on a Kubernetes cluster, taking into account the SSL requirement? One option, of course, would be to have the app itself handle the certificate process, like this example. The other option, which you’ll see in this article, is to use the Kubernetes ingress controller to handle all the SSL setup and usage. The only prerequisites from your side is to have a domain name that the certificate will be issued for and, of course, access to a Kubernetes cluster.
Just a week ago, it was announced that Azure Text Analytics API has added 16 more languages that can be parsed for sentiment analysis, Greek language being one of them (currently in preview). So, I thought I could give it a try to see how well it’s working with some random Greek phrases. Well, I have to say that the outcome was pretty neat!
This article will demonstrate how to implement a continuous integration/continuous deployment pipeline for a multi-container app using Azure Container Service, Kubernetes and Visual Studio Team Services (thereafter mentioned as VSTS) Build and Release Management.
You will use a simple application which is available on GitHub (heavily based on this one) and is composed of three components, each of those hosted in its Docker container:
service-a: a Node.js app that serves as a web frontend which connects to service-b and mycache
service-b: a .NET Core app that sends a simple text string to service-a that contains current machine’s hostname
mycache: a Redis cache that holds an integer called “requestCount” which is set and requested by service-a
The C# BotBuilder SDK supports a really cool thing call FormFlow. With this, you can write a C# class which is used as a base for a dynamically generated series of question/answer pairs, in order to fill the properties of this class. For instance, do you want to create a burger ordering bot? Just add a enum for BreadOptions, an enum for Toppings, a choice of SauceOptions etc. This is a really cool feature of the C# BotBuilder SDK which allows you to quickly develop a chatbot with predefined rules and flow. As the Node.js BotBuilder SDK lacks this functionality, I tried to replicate it since I needed it for a simple project I’m building. Meet formflowbotbuilder.
A few days ago, I was pitching Bot Framework to an interested party, when a question came up: “Does the Bot Framework support AIML files? We have a lot of them and we are wondering whether we could use them”. Honestly, I didn’t have a clue what AIML is, so I decided to run a quick search.
Turns out that AIML stands for Artificial Intelligence Markup Language and, as Wikipedia nicely mentions it, it is an XML dialect for creating natural language software agents. AIML was used to power Alice Bot back in 1995 (this was one of the first chat bots available to the public). Moreover, AIML powered a discussion with Captain Kirk of Enterprise (ho there, Star Trek fans!). Alice AIML files are open source, you can find them here.
Azure Table Storage Service is an inexpensive, highly available NoSQL key-value store. It can store petabytes of structured data, supports a flexible data schema and Azure team provides a REST API. So, I thought, why not extending my Azure Services for Unity library with support for access to Azure Table Storage Service from any Unity game? Well, here we are, our latest commit to the repository contains access methods for the service.
Yeah, title is long but nevertheless you get the point of what I’m going to describe. So, to cut a long story short, last weekend I attended a hackathon where my teammates and I built a PoC of a movie quiz chat bot. At the end of the hackathon, we attempted to “dockerize” it and host it on App Service on Linux (currently on preview). This blog post documents the process.
In this blog post we’ll discuss how we built a Botfor ParkAround using Microsoft Bot Framework and hosted it in Azure platform.
ParkAround is a prominent startup in Greece which allows you to book your place in hundreds of car parks in the cities of Athens/Thessaloniki as well as the airports of Barcelona and Malaga. We worked with ParkAround to build a Bot that allows the user to book a parking spot at the airports of Athens and Thessaloniki. You can currently chat with the Bot on Facebook’s Messenger platform, whereas support for other channels (e.g. Skype) will be rolled out in the next few weeks.
Bot has the name of “Mitsaras, the parking assistant” (“Mitsaras” being the folk/friendly name for “Dimitris”) and you can chat with it here: https://www.messenger.com/t/parkaroundbot. Beware, bot currently uses Greek language only since it targets Greek audience for now. So, don’t get confused if it’s all Greek to you!
To develop the bot, we used Microsoft’s Bot Framework which allows you to create a bot that will interact with various conversation channels, such as Messenger, Skype, Slack and other services. Bot Framework supports a REST API and has two SDKs, one for .NET and one for Node.js. As most Microsoft SDKs nowadays, both of them are open source. If you aren’t acquainted with Bot Framework SDK, please take a look at the extensive documentation in order to better understand the code segments listed below. Also, ParkAround is a BizSpark startup, so we naturally chose Azure App Service PaaS platform to host the bot, so we can easily scale up/out if needed.
Last but definitely not least, before we continue with bot’s internals, we should mention that this work is a collaboration between myself, my colleague Sophia Chanialaki and ParkAround’s CEO, John Katsiotis.
During the past few months I’ve pushed some updates to my Azure Services for Unity library. The goal of this library remained the same: use Azure App Service Easy Tables and Easy APIs easily from a single codebase without any additional Unity addins, I really want this to be a plugin free experience. Here, I’ll summarise some of the main updates in the library. For an intro to the library and an intro on Easy Tables and Easy APIs, check out my original blog post here. Moreover, check here for how to access Azure Table Storage Service from within a Unity game.