A Fine Slice Of SQL Server
Shivam Gupta explains four key strategies for pushing out deployments:
K8s Deployment has different types of strategies to update the deployment. Each has difference in the way to how the achieve the new desired state.
– Rolling Update
– Recreate
– Canary
– Blue/Green
Read on for a quick summary of each strategy, as well as its pros and cons.
Comments closedI’ve been running a proof of concept for SQL Server on Kubernetes over the last year or so (ok, probably longer than that…hey, I’m a busy guy 🙂 ) and have come across an issue that has been sort of a show stopper.
There are currently no HA solutions for SQL Server running on plain K8s (not discussing Azure Arc here) so my tests have been relying on the in-built HA that Kubernetes provides but there’s a problem.
Be sure to check it out because it is a doozy.
Comments closedJonathan D’Aloia shows how you can use Terraform to work with Azure Policies:
As you may all know Terraform serves a great purpose in deploying resources and infrastructure into your Azure environment, however, Terraform can also be used to automate and consistently deploy Azure Policies which can be defined prior to any resources being generated. In this blog, I will cover how you can import policies into your Terraform State to then deploy into an Azure Resource Group in order to secure your landing zone prior to deploying any resources.
Click through for an example.
Comments closedAnthony Nocentino won’t let this container run amok:
Docker gives you the ability to control a container’s access to CPU, Memory, and network and disk IO using resource constraints, sometimes called Limits. You define limits as parameters when creating containers. In its default configuration, a container will have no resource constraints for accessing resources of the host operating system. This post will look at how to configure resource constraints in Docker and look at how SQL Server sees the resources when CPU and Memory resource constraints are in place.
In this post, we will focus on using CPU and Memory Limits using the parameters
--cpus <value>and--memory <value>. These provide the essential resource controls for access to CPU and Memory available on the host. If you want to dive deep into the various types of resource constraints available in Docker, check out this page https://docs.docker.com/config/containers/resource_constraints/. The goal of this post is to introduce the SQL Server DBA into resource constraints in containers.
Read on to learn how you can use CPU and memory limits to control resource allocation for SQL Server containers—including at runtime.
Comments closedJoy George Kunjikkur enumerates reasons why you might want to use Kubernetes to host applications:
I started writing this post 2-3 years back. Mainly when Apache Spark 2.3 started supporting Kubernetes (K8s) in 2018. It was obvious that Kubernetes is taking over app hosting space the same way virtual machines took over physical machines. All are expected to understand where the industry is moving and adopt. Hence I paused this post as there is nothing I need to endorse. But it’s time to resume this post and publish it.
Click through for a slew of thoughts on the topic.
Comments closedArun Sirpal gives us a brief introduction of Azure Kubernetes Service:
You have the ability to run these on-premises (complex) or in a cloud service, like AWS or Azure. Hence AKS – Azure Kubernetes Service which helps reduce the complexity and operational overhead of managing Kubernetes by offloading much of that responsibility to Microsoft. You may be wondering how does containers relate to this? It was something on my mind when I first entered into this technology. Remember that containers is the next step beyond traditional virtualisation, you can run SQL Server Linux in containers, as an example. I then look at AKS as the “management” layer of the container solution, carrying out tasks such as scheduling, scaling, health, load balancing and host management.
Click through for more information.
Comments closedAnthony Nocentino has a round-up for us:
I want to start by saying thank you to all who submitted, and an amazing collection of people submitted some fantastic content. Also, thanks to Steve for asking me to host and being patient with me for mixing up the dates and the hashtag. It’s #tsql2sday and it’s on Tuesday not Wednesday 😛
Click through for links to the entrants.
Comments closedKevin Chant shows off a good use case for containers:
Anyway, during these sessions we tend to do a pipeline demo like the one below. Done within either Azure Pipelines in Azure DevOps or GitHub Actions. I use a standard set of containers locally for both demos. In order to show how to do show deployments to local SQL Server 2019 instances and databases in Azure within one pipeline.
Read on for more.
Comments closedMark Wilkinson combines Telegraf, InfluxDB, and Grafana:
Lots of folks wonder why I would go through the trouble of building out a system when so many vendors have already solved the problem of collecting baseline metrics. The answer at the time was simple: cost. With my setup I could monitor close to 600 instances (including dev) for $3,000 USD per year. That includes data retention of ~2 years! Are there some administration costs as far as my time is concerned? Of course. In the begining things were a little rough as I learned more about InfluxDB, but once things were configured correctly the most work I’ve had to do is to expand the size of the data drive as we started collecting more metrics.
Click through for more info and check out the GitHub repo.
Comments closedMany of you out there may, like me, find yourselves sharing a home with a miniature Minecraft Addict. I do and in between showing off her latest builds she’s begun pestering me to find a way to play with friends and family. I figured I could maybe save some work and re-use my newfound containerised powers for fun as well as profit and a few quick searches proved me right.
Click through for more.
Comments closed