I was first made aware of this edition at the MVP Summit earlier this year, and I need to clear some things up for folks who might be confused about the name, and who it’s for.
Firstly, recall that Azure means “hybrid” now, so while we might expect that it refers to cloud computing, it also takes on-premises infrastructure into account.
Secondly, this is the full SQL Server database engine running on a 64-bit ARM CPU. It could run on a Raspberry Pi, or — provided there was support for the other hardware — Android or iOS devices, however it is geared towards edge devices that gather data from IoT sensors and other data points. Think of this as one step up from the IoT devices capturing data in the field, whether it be wine-making, oil and gas, manufacturing, you name it.
Read the whole thing. I’m definitely interested in how they handle time series. With luck, it’s done well and brought over to the main product.
In that demo, the AdventureWorks sample database was initially set to compatibility level of 140 (SQL Server 2017 default compatibility) to execute a scalar UDF. At this point, the estimated execution plan showed that the UDF was given a cost of 0%, and performance was terrible (the expected behaviour). Then the database compatibility level was switched to 150 (which is all that’s required to enable this new optimization feature), the query was executed again, the UDF was inlined, and performance improved dramatically.
This is where it got interesting. As a test, the compatibility level of the database was set back to 140, but the query plan continued to inline the UDF. Curious. Flushing the plan cache didn’t change the outcome (even though we knew it wasn’t necessary). Had we discovered a bug in a preview version of SQL Server 2019? It was CTP 2.2 after all, and since then (at the time of this writing) CTP 2.5 is already available.
Read on for the answer.
We’re excited to announce the monthly release of SQL Server 2019 community technology preview (CTP) 2.5. SQL Server 2019 is the first release of SQL Server to closely integrate Apache Spark™ and the Hadoop Distributed File System (HDFS) with SQL Server in a unified data platform.
This is a big one for me: lots of changes in Big Data Clusters, PolyBase on Linux, and a Java SDK. Looks like I am going to be pretty busy.
One of the recurring questions I see on Stack Overflow is “How do I restore a SQL Server backup to a previous version of SQL Server?” The answer, of course, is you don’t. Upgrading a database to a newer (major) version is a one-way ticket–at least as far as the database files and subsequent backups go. I recently found myself in a similar position as all those hapless Stack Overflow questioners. I had a customer that had migrated to a newer version of SQL and they wanted to roll back to the previous version. What to do?
A couple of thoughts immediately came to mind. There’s the SQL Server Import and Export Wizard and the Generate and Publish Scripts Wizard. Neither of these sounded convenient. In particular, generating a script with both schema and 500 GB of data sounded like a fruitless endeavor. Two other options sounded much more appealing. So I focused on those.
Dave has a couple of creative methods effectively to downgrade a database.
The key highlights to cover this month include:
– March release recap
– Azure Explorer improvements
– Visual Studio code merge process
– Insiders build process
– Viewlet revamp
– Notebook improvements
– Announcing SandDance extension
– Bug fixes
There’s a lot going on with the product, so grab the latest version and give it a try.
I recently took advantage of an opportunity to try Mirosoft’s Data Migration Assistant. It was a good experience and I found the tool quite useful. As the documentation tells us, the DMA “helps you upgrade to a modern data platform by detecting compatibility issues that can impact database functionality in your new version of SQL Server or Azure SQL Database. DMA recommends performance and reliability improvements for your target environment and allows you to move your schema, data, and uncontained objects from your source server to your target server.” For my use case, I wanted to assess a SQL 2008 R2 environment with more than a hundred user databases for an on-premises upgrade to SQL 2017.
Dave takes us through an upgrade on three sample databases and then gives us some more messages from actual production databases.
Although these screenshots show SQL Server 2019 preview CTP 2.3, this also applies to SQL Server 2017 on 18.04.2, because that’s what I had installed before upgrading the SQL Server version. However, as my friend Jay Falck pointed out on Twitter, Microsoft has stated publicly that it is not yet certified for production use:
Important, this does not change the support state of SQL Server 2017 on Ubuntu 18.04. Work to certify Ubuntu 18.04 with SQL Server 2017 is planned and we will announce when it will be supported for production use on this page. Until such as an announcement occurs, SQL Server 2017 on Ubuntu 18.04 should be considered experimental and for non-production use only.
Read on for Randolph’s thoughts on the issue.
The R Core Team announced yesterday the release of R 3.5.3, and updated binaries for Windows and Linux are now available (with Mac sure to follow soon). This update fixes three minor bugs (to the functions
stopifnot), but you might want to upgrade just to avoid the “package built under R 3.5.4” warnings you might get for new CRAN packages in the future.
Click through for more info on this release, including where the name from each R release comes from.
There’s a few things I want to point out in our YAML file. First, we’re using a Deployment Controller. This will implement a Replica Set of the desired number of replicas using the container imaged defined. In this case, we’ll have 1 replica using the SQL Server 2017 CU11 Image. A Replica Set will guarantee that a defined set of Pods are running at any given time, here we’ll have exactly one Pod. We’re using a Deployment Controller, which gives us move between versions of Replica Sets based off different container images in a controlled fashion…more on that in a second.
Read the whole thing.
With the release of CTP 2.3 of SQL Server 2019 today there was big news for Analysis Services Tabular developers: Calculation Groups. You can read all about them in detail in this blog post:
In my opinion this is the most important new feature in DAX since… well, forever. It allows you to create a new type of calculation – which in most cases will be a time intelligence like a year-to-date or a previous period growth – that can be applied to multiple measures; basically the same thing that we have been doing in SSAS Multidimensional for years with the time utility/shell/date tool dimension technique. It’s certainly going to solve a lot of problems for a lot of SSAS Tabular implementations, many of which have hundreds or even thousands of measures for every combination of base measure and calculation type needed.
Click through for more of Chris’s thoughts and how calculation groups will make your life easier.