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Day: May 8, 2019

Syncing Google Calendar and Outlook with Microsoft Flow

Published 2019-05-08 by Kevin Feasel

Eugene Meidinger shows how to keep a couple of calendars in sync:

Back when I worked a normal job, I had two calendars: Office 365 for work and Google for home. Now that I work for myself, that’s a lot more complicated. Sometimes a customer will create an account for me in their network. Sometimes I’ll partner with other consultants and work as part of their team. And of course, I’ve got my own work email at eugene@sqlgene.com.

I need all of these calendars to consolidate to one place. My natural inclination and personal preference is to put it all into Google. Now, there are sync apps available, but this sort of problem is a perfect use case. A calendar event is created in outlook, a flow is triggered, and that information is transferred to Google.

I was just complaining about this yesterday and then I see the post this morning. I’m pleased though simultaneously concerned that Eugene has bugged my hotel room.

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Automating Jupyter Notebooks

Published 2019-05-08 by Kevin Feasel

I have some early thoughts on automating Jupyter notebooks:

In the command above, I included the date of execution. That way, I can script this to run once a day, storing results in an HTML file in some directory. Then, I can compare results over time and see when issues popped up.

I can also parse the resultant HTML if need be. Note that this won’t be trivial: even though the output looks like a simple [1] "PROBLEM ALERT", there’s a more complicated HTML blob. 

At some point I’ll probably have follow-up thoughts on the topic. Probably.

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Deploying SQL Server Containers to Azure with Terraform

Published 2019-05-08 by Kevin Feasel

Andrew Pruski has a post covering deployment of SQL Server containers to Azure using Terraform:

What this is going to do is create an Azure Container Instance Group with one container it in, running SQL Server 2019 CTP 2.5. It’ll be publicly exposed to the internet on port 1433 (I’ll cover fixing that in a future post) so we’ll get a public IP that we can use to connect to.

Notice that the location and resource_group_name are set using variables that retrieve the values of the resource group are going to create.

Cool! We are ready to go!

Fun stuff, and Andrew promises more.

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Azure Open Datasets

Published 2019-05-08 by Kevin Feasel

Jen Stirrup is pleased that Microsoft is bringing back open datasets:

Nearly three years ago, I complained bitterly about the demise of Windows Datamarket, which aimed to provide free, stock datasets for any and every purpose. I was a huge fan of the date dimension and  the geography dimension, since they really helped me to get started with data warehousing.

So I’m glad to say that the concept is back, revamped and rebuilt for the data scientists today. Azure Open Datasets will be useful to anyone who wants data for any reason: perhaps for learning, for demos, for improving machine learning accuracy, perhaps.

Go check it out.

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Creating Temp Staging Tables to Avoid Spooling

Published 2019-05-08 by Kevin Feasel

Bert Wagner shows how you can create your own tables in tempdb to avoid eager or lazy spools:

SQL Server Spool operators are a mixed bag. On one hand, they can negatively impact performance when writing data to disk in tempdb. On the other hand, they allow filtered and transformed result sets to be temporarily staged, making it easier for that data to be reused again during that query execution.

The problem with the latter scenario is that SQL Server doesn’t always decide to use a spool; often it’s happy to re-read (and re-process) the same data repeatedly. When this happens, one option you have is to explicitly create your own temporary staging table that will help SQL Server cache data it needs to reuse.

The other problem with spooling is that the spool doesn’t have indexes and so performance can be awful. When I look at an execution plan, one of my immediate red flags is spooling: if we have that, removing it is one of the first candidates for optimization after the trivial stuff (expected scan/seek behavior, “fat pipes” from excessive row counts, residual I/O, etc.).

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Splitting Arrays with OPENJSON

Published 2019-05-08 by Kevin Feasel

Dave Mason continues a journey into parsing JSON with T-SQL:

Starting with SQL Server 2016, Microsoft provided a STRING_SPLIT function. It is a table-valued function that splits a string into rows of substrings, based on a specified separator character. It’s been a welcome addition that we waited a long time for. It has one shortcoming, though: the order of the output rows is not guaranteed to match the order of the substrings in the input string.

Microsoft also provided support for parsing JSON data starting with SQL Server 2016. I discovered the OPENJSON function can be used to split strings, and it can also return the ordinal position of each substring from the original input string.

There are some limitations which you’d expect, namely around requirements for valid JSON.

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