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Category: Containers

Uses for SQL Server Containers

Aaron Bertrand explains two good uses for using SQL Server containers:

I used this technique of “stop, drop, repeat” just the other day while testing some behavior around case-sensitive and binary collations at the instance level. In the old days, I would have had to install a second full-on instance of SQL Server in order to test a specific instance-level collation, then repeat for every collation in my set of tests. Yikes! With containers, this is much easier; I just have to add one additional argument to the docker run command:

Check it out for a quick walkthrough of how to spin up a container and some good uses for it.

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Running Azure ML On-Premises via Azure Arc

Tsuyoshi Matsuzaki takes us through running Azure Machine Learning via Azure Arc:

First of all, you must run Azure Arc enabled Kubernetes on-premise or on 3rd party cloud. For running Arc-enabled Machine Learning later, use machines with more than 4 CPUs, since Arc-enabled ML requires enough resources.

In this post, I assume that we run KIND (Kubernetes in Docker) cluster on on-premise Ubuntu server. (For test purpose, I have used Ubuntu 18.04 on a single virtual machine in Azure, Standard D3 v2, which has 4 CPUs and 14 GB memory.)

Click through to see how it’s done.

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SQL Server on Azure Container Instances

Arun Sirpal has a series for us. Part 1 involves spinning up SQL Server on ACI:

This is Microsoft’s serverless technology which allows us to deploy containers without having to worry about managing the underlying hardware. It’s a way to get access to SQL fast (faster than traditional methods like installing a virtual machine) to do things like test code fixes etc.

There a couple of ways of doing this, you can use the portal, PowerShell or Azure CLI, I actually like Azure CLI.

Part 2 gives you an idea of what you get:

In the last post we built an image of SQL server 2019 Linux hosted in Azure Container Instance for fast access to SQL server. So, your next question is probably, lets see some database action?

When you connect to SSMS its not different, the feel and look, is, SQL server. Lets have a tour.

The normal warning with Azure Container Instances is that they’re great for development and testing efforts (in part because of how inexpensive it is compared to alternatives on Azure) but won’t have the same uptime or high availability guarantees that a service like Azure Kubernetes Service will have.

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Goodbye, SQL Server on Windows Containers

Amit Khandelwal shuts the doors:

As you may be aware, the SQL Server on Windows Containers Beta program began in 2017. It has remained in Beta mode meant for only test and development environment until now. Due to the existing ecosystem challenges and usage patterns we have decided to suspend the SQL Server on Windows Containers beta program for foreseeable future. Should the circumstances change, we will revisit the decision at appropriate time and make relevant announcement.

I never heard of many people using Widows containers, but with the differences in available products and features between Windows and Linux versions of SQL Server, I can see why some people would want to use them.

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Running Dask on AKS

Tsuyoshi Matsuzaki sets up Dask as a distributed service:

In my last post, I showed you tutorial for running Apache Spark on managed kubernetes, Azure Kubernetes Service (AKS).
In this post, I’ll show you the tutorial for running distributed workloads of Dask on AKS.

By using Dask, you can run Scikit-Learn compliant functions and jobs for data which cannot fit in memory, or run in distributed manners. For simplicity, here I’ll use built-in Dask ML function (dask_ml.linear_model.LinearRegression) in this tutorial. (With the same manners, you can also run regular sklearn functions.)
Cloud managed kubernetes will make you speed up this large ML workloads.

Click through for the process. I’ve had some positive experiences with Dask as a dashboarding tool. It’s definitely one of the better ones if you’re big into Python.

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Running SQL Server Containers from Scratch

Andrew Pruski tells us there is no spoon:

I’ve been interested (obsessed?) with running SQL Server in containers for a while now, ever since I saw how quick and easy it was to spin one up. That interest has led me down some rabbit holes for the last few years as I’ve been digging into exactly how containers work.

The weirdest concept I had to get my head around was that containers aren’t actually a thing.

Containers are just processes running on a host that implement a set of Linux constructs in order to achieve isolation.

So if we know what constructs are used…shouldn’t we be able to build our own container from scratch?

Read on as Andrew breaks out the three necessary constructs and dives into it.

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Containerizing a Shiny App

Peter Solymos takes us through the process of running a Shiny app in a Docker container:

Docker provides isolation to applications. Images are immutable. Running multiple instances of the same image can serve many users at the same time. All these general advantages of containerized applications apply to Shiny apps too.

All the general advantages of containerized applications apply to Shiny apps. Docker provides isolation to applications. Images are immutable: once build it cannot be changes, and if the app is working, it will work the same in the future. Another important consideration is scaling. Shiny apps are single threaded, but running multiple instances of the same image can serve many users at the same time. Let’s dive into the details of how to achieve this.

Click through for a walkthrough. Containerizing these sorts of apps has been a boon for my team, as it lets us spin up appropriately-sized servers on the cheap. H/T R-Bloggers

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Aligning Kubernetes Nodes to Physical Infrastructure

Frank Denneman has some advice for us:

With the new VM service and the customizable VM classes, you can help the developer align their nodes to the infrastructure. Infrastructure details are not always visible at the Kubernetes layers, and maybe not all developers are keen to learn about the intricacies of your environment. The VM service allows you to publish only the VM classes you see fit for that particular application project. One of the reasons could be the avoidance of monster-VM deployment. Before this update, developers could have deployed a six worker node Kubernetes cluster using the guaranteed 8XLarge class (each worker node equipped with 32 vCPUs, 128Gi all reserved), granted if your hosts config is sufficient. But the restriction is only one angle to this situation. Long-lived relationships are typically symbiotic of nature, and powerplays typically don’t help build relationships between developers and the InfraOps team. What would be better is to align it with the NUMA configuration of the ESXi hosts within the cluster.

Click through for more detail. This is aimed particularly at operations people running Kubernetes clusters over VMware.

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Kubernetes Alone is Insufficient

Chris Adkin does some explaining:

Someone I know had worked at an organization that needed to scale out their OpenShift clusters/footprint, they were constrained by the speed of their procurement department and were wondering if they could get by with vanilla Kubernetes. Following on from this I posted a thread on twitter as to why Kubernetes on its own is not enough, much to my pleasant surprise it generated a lot of interest, as such I wanted to do this subject justice in the form of a blog post.

Read on for the full argument as well as some objection-handling.

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Installing Kubernetes on EC2

Praveen Sripati eschews EKS:

There are tons of ways of setting up K8S on AWS. Today we will see one of the easiest way to get started with K8S on AWS. The good thing is that we would be using t2.micro instance type, which falls under the AWS free tier. This configuration is good enough to get started with K8S and not for production setup. It’s with the assumption that the reader is familiar with the basic concepts of AWS.

Click through for the process.

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