A Fine Slice Of SQL Server
Anthony 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 closedAaron 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 runcommand:
Check it out for a quick walkthrough of how to spin up a container and some good uses for it.
Comments closedTsuyoshi 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.
Comments closedArun 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.
Comments closedAmit 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.
Comments closedTsuyoshi 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.
Comments closedAndrew 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.
Comments closed