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Category: Versions

December 2020 SQL Tools Releases

Drew Skwiers-Koballa gives us an update on where SQL Server tooling is at:

The December releases of Azure Data Studio 1.25 and SQL Server Management Studio (SSMS) 18.8 are now generally available.  Additionally, the mssql extension for Visual Studio Code has recently been updated to version 1.10.0. Read on to learn more about each of these updates and grab the latest versions of SSMS, Azure Data Studio, or the mssql extension for VS Code.

Read on to learn more.

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Apache Flink 1.12.0 Released

Marta Paes and Aljoscha Krettek announce a new release of Apache Flink:

– The community has added support for efficient batch execution in the DataStream API. This is the next major milestone towards achieving a truly unified runtime for both batch and stream processing.

Kubernetes-based High Availability (HA) was implemented as an alternative to ZooKeeper for highly available production setups.

– The Kafka SQL connector has been extended to work in upsert mode, supported by the ability to handle connector metadata in SQL DDL. Temporal table joins can now also be fully expressed in SQL, no longer depending on the Table API.

– Support for the DataStream API in PyFlink expands its usage to more complex scenarios that require fine-grained control over state and time, and it’s now possible to deploy PyFlink jobs natively on Kubernetes.

Read on for more details on these as well as other changes.

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Sampling the Population of SQL Server Instances

Brent Ozar has a breakdown of SQL Server adoption by version:

Ever wonder how fast people are adopting new versions of SQL Server, or what’s “normal” out there for SQL Server adoption rates, hardware sizes, or numbers of databases? Let’s find out in the summer 2020 version of our SQL ConstantCare® population report.

Out of the 3,650 servers sending in data recently, the most popular version of SQL Server is still 2016. 

It’s an interesting survey, but it’s important to remember with any survey what the biases are before drawing conclusions on the general population of all SQL Server instances. This is among ConstantCare users, so if I had a hunch about them, I’d say they’re going to blow out the middle versus gen pop—in other words, I’d expect 2019 and 2017 usage to be a bit higher across the world (and not just among ConstantCare users), but also 2005 (not pictured), 2008, and 2008 R2 to be higher. My hunch here is that the former are shops which have their act together and figure that they don’t need ConstantCare, and the latter are all of those “hidden” instances of SQL Server acting as the back end for small business websites and the like, where nobody even thinks about having a database administrator, much less paying for tooling. Nonetheless, these are interesting trends and even if I think the general population might look different, my data-less guess is that these numbers look reasonable.

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Azure Data Studio November 2020 Release

Alan Yu announces the November 2020 release of Azure Data Studio:

Another feature request was to provide support for parameters in a notebook. Parameterization is the ability to execute the same notebook with different parameters.

With this release of Azure Data Studio, users will now be able to utilize Papermill’s ability to parameterize, execute, and store notebooks. By stating the parameters cell as the first code cell in your notebook, it ensures that the injected parameters in the outputted parameterized notebook will be placed directly after the original parameters cell. That way the parameterized notebook will utilize the newly injected parameters instead of the original parameters cell.

Users can utilize Papermill CLI as well as the Python API  to pass in a new set of parameters quickly and efficiently as shown below.

That does look interesting.

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Keeping .NET Tools Up to Date

Max Trinidad has a public service announcement for us:

Have you installed any of the .NET Tools? Such as “.NET Interactive” and “PowerShell Global“, then you’ll need to remember, to update these tools manually.

These tools give you the ability to use create Jupyter Notebook using Python Kernel but also with C#, F#, and PowerShell 7 kernels.

Read on to see how to check if you need to update either of these.

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Downgrading SQL Server on Linux

Sreekanth Bandarla wants to roll back cumulative updates on Linux:

Of course you can get this info from SQL or several other ways in Linux. Okay, now we know we got SQL Server 2019 CU5 running on this server to work with. Let’s just assume CU5 broke something in my database and I want to go back to CU4. How do I do that?

Click through to see how to do this for Red Hat (or any system using yum). Debian-based don’t have a downgrade option, but you can use apt-get install mssql-server=[version number] instead.

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Finding and Downloading SQL Server Updates

Andy Levy combines dbatools and KBUpdate:

Another of Chrissy LeMaire’s (blog | twitter) projects is KBUpdate. Compared to dbatools it’s a pretty compact module, but it’s incredibly useful – it’ll seek out information about KB updates and even download them for you! She’s also rolled these functions into dbatools for convenience, so we don’t need to install or import that module separately.

Read on to see how Andy ties it all together.

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The Tuple Mover in SQL Server 2019

Taryn Pratt gives us closure on an issue from a few months back:

I suggest reading my other post first, it’ll only take a few minutes. I’ll wait…

However, if you really don’t want to read it, here’s a quick recap on the initial issue.

In early February 2020, a lot of data was deleted from some clustered columnstore indexes in our PRIZM database. Some of the tables were rebuilt, but 11 tables weren’t since we don’t have maintenance windows, and that would involve downtime. The rebuilds would happen once we upgraded to SQL Server 2019, to take advantage of the ability to rebuild those columnstore indexes online.

Taryn now has the full story and I recommend giving it a read.

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Apache Flink 1.1.0 Released

Marta Paes announces Apache Flink version 1.11:

Change Data Capture (CDC) has become a popular pattern to capture committed changes from a database and propagate those changes to downstream consumers, for example to keep multiple datastores in sync and avoid common pitfalls such as dual writes. Being able to easily ingest and interpret these changelogs into the Table API/SQL has been a highly demanded feature in the Flink community — and it’s now possible with Flink 1.11.

Click through for the full list of updates.

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