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Category: Containers

Deploying a Container Instance in Azure

Published 2019-10-24 by Kevin Feasel

Anibal Kolker takes us through container deployment in Azure:

As derived from the title, the objective of this post is to help you deploy a container instance inside Azure.

However, we’ll extend the typical scenario and make a slightly more extensive use of networking capabilities, by placing the container group inside a private subnet.

Note: For this example, and for simplicity only, we’ll use NGINX as our container of choice. Of course, you’re welcome to try with any other image.

There are a few pieces in play, but Anibal does a good job putting it all together.

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Storing Container Images in GitHub Package Registry

Published 2019-10-24 by Kevin Feasel

Andrew Pruski shows how we can use GitHub Package Registry to store private container images:

The GitHub Package Registry is available for beta testing and allows us to store container images in it, basically giving us the same functionality as the Docker Hub.

However the Docker Hub only allows for one private repository per free account whereas the Github package registry is completely private! Let’s run through a simple demo to create a registry and upload an image.

It’s pretty easy to set up, so check it out.

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Installing Kubernetes

Published 2019-10-08 by Kevin Feasel

Anthony Nocentino has an updated version of his Kubernetes installation guide:

Kubernetes is a distributed system, you will be creating a cluster which will have a master node that is in charge of all operations in your cluster. In this walkthrough we’ll create three workers which will run our applications. This cluster topology is, by no means, production ready. If you’re looking for production cluster builds check out Kubernetes documentation. Here and here. The primary components that need high availability in a Kubernetes cluster are the API Server which controls the state of the cluster and the etcd database which persists the state of the cluster. You can learn more about Kubernetes cluster components here. If you want to dive into Kubernetes more check out my Pluralsight Courses here! Where I have a dedicated course on Installation and Configuration.

In our demonstration here, the master is where the API Server, etcd, and the other control plan functions will live. The workers/nodes, will be joined to the cluster and run our application workloads. 

Read the whole thing.

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Configuring Memory Limits for SQL Server in Kubernetes

Published 2019-09-30 by Kevin Feasel

Anthony Nocentino doesn’t have all the RAM in the world:

With that Pod deployed, I loaded up a HammerDB TPC-C test with about 10GB of data and drove a workload against our SQL Server. Then while monitoring the workload…boom HammerDB throws connection errors and crashes. Let’s look at why.

First thing’s first, let’s check the Pods status with kubectl get pods. We’ll that’s interesting I have 13 Pods. 1 has a Status of Running and the remainder have are Evicted. 

Anthony does a great job of explaining the problem and showing you the solution.

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Why Root Containers are Troublesome

Published 2019-09-19 by Kevin Feasel

Andrew Pruski explains to us why it can be bad to have a container user running as root:

Recently I noticed that Microsoft uploaded a new dockerfile to the mssql-docker repository on Github. This dockerfile was under the mssql-server-linux-non-root directory and (you guessed it) allows SQL Server containers to run as non-root.

But why is running a container as root bad? Let’s run through an example.

Just as with physical devices and VMs before them, Docker containers can do a lot of damage if you’re logged in as root.

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Non-Root SQL Server 2019 Containers

Published 2019-09-19 by Kevin Feasel

Vin Yu announces a change to Microsoft’s container configuration for SQL Server 2019:

The application process within most Docker containers is running as a root user meaning the process has root privileges within the container user space. The root user within the container is also the same root (uid 0) on the host machine, and if the user can break out of the container, they would have root permissions on the host. Running as root is convenient for development, testing and CI/CD use cases but for production use cases, it is safest to run SQL Server as a non-root process within the container. In this blog, we’re going to share with you how you can preview this upcoming improvement by creating your own non-root SQL Server container.

Vin has a quick demonstration of how it works.

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Upgrading Azure Kubernetes Service

Published 2019-09-18 by Kevin Feasel

Chris Taylor has a point updates to jump in Azure Kubernetes Service:

As it is late at night my brain wasn’t working as it should be but thought I’d put a quick blog out there to say that if you are on v1.11.5 and want to upgrade to >= v1.13.10 then you have to do this in a 2 stage process by upgrading to v1.12.8 first:

Fortunately, upgrading is pretty easy using the Azure command line or even the Azure portal.

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Reading SQL Server Logs from Kubernetes

Published 2019-09-16 by Kevin Feasel

Anthony Nocentino shows us how we can use kubectl logs to read the SQL Server Error Log:

We can use follow flag and that will continuously write the error log to your console, similar to using tail with the -f option. If you remove the follow flag it will write the current log to your console. This can be useful in debugging failed startups or in the case below, monitoring the status of a database restore. When finished you can use CTRL+C to break out and return back to your prompt.

Read on for a brief demonstration.

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Scaling Out Continuous Integration

Published 2019-09-12 by Kevin Feasel

Chris Adkin shows off parallelism in Azure DevOps continuous integration pipelines:

A SQL Server data tools project is checked out of GitHub, built into a DacPac, four containerized SQL Server instances are spun up using clones of the ‘Seed’ docker volume. The DacPac is applied to a database running inside each container, which a tSQLt test is then executed against, finally, at the end very end the tSQLt results are aggregate and published.

This is an interesting approach to the problem of lengthy tests: run them on several separate machines concurrently.

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