This year, I was lucky enough to be invited to Seattle to present two sessions on Microsoft Build 2018 developer conference. Both sessions were presented by Brian Peek and myself and their focus was on game development using Microsoft Azure.
At the first session, we demonstrated how to scale dedicated multiplayer game servers on Azure Container Instances using various Azure Services like Functions, Event Grid and more. Project we demonstrated is open source, completely written in Node.js and you can find it here on GitHub. Same project got a really cool mention at the Azure Container Instances general availability announcement blog post, check the video here (thanks Justin!).
At the second session, we demonstrated how to use Azure Cognitive Services as well as Functions and Cosmos DB to power up a Unity game. Moreover, we added some PlayFab stuff like leaderboards and events. This project is open source as well and you can find it here.
A customer I’ve been talking to recently asked me to work together to create a reference implementation (more like a proof of concept) for the next generation the backend for a game they currently have. In short, they needed a platform that would accommodate these needs and specifications:
accept incoming messages from game servers
incoming message rate would be a couple of hundred messages per minute
we need to store game session related events, so each piece of data is relevant to a specific game session that can run for minutes (20′-30′)
scalability and high availability (of course)
data is needed to be displayed in real-time (e.g. live leaderboards)
data is needed to be stored for later analysis (e.g. best players of the week)
In the previous blog post, we described some thoughts on how to design a game leaderboard and how to represent it programmatically. In this one, we are going to discuss about a new open source project of mine called AzureFunctionsNodeLeaderboards-Cosmos. As the name implies, this project is about game leaderboards using Azure Functions with Node.js and Cosmos DB.Read More »
In this blog post we are going to write down some thoughts regarding a game leaderboard implementation. They are written as question and answer pairs and cover the leaderboard design process as well as its technical implementation on a high level.
What is a leaderboard?
Leaderboards are a necessary asset for many types of games. A leaderboard provides a means to reward the best users and increase the game’s replay value by allowing the players to compete. It is defined as a collection of high scores achieved in a game session during a specific time segment in a specific portion of game for a specific set of users.
‘time segment’ relates to the lifetime of the leaderboard. Is it permanent or resets every day/week/month?
‘game portion’ relates to the portion/segment of the game the score was achieved in. Is the score relevant to the entire game or in just one of its levels? Is the score relevant to a single round of gameplay (single session) or multiple ones?
‘specific set of users’ relates to the ‘locality’ of the users and means that the leaderboard may contain scores for users that are in single machine or in a specific region (e.g. Europe) or worldwide
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/TLS for a service of yours that is hosted on a Kubernetes cluster, making it accessible via https. 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!
update 12/6/2018: this article has been updated for Kubernetes 1.9.6 on Azure Kubernetes Service
This article will demonstrate how to implement a continuous integration/continuous deployment pipeline for a multi-container app using Azure Kubernetes Service 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 own 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.