If we were to hit F5 (or however you execute your TSQL statements in SSMS) without highlighting any statement(s) they would all be executed, one batch after the other. Even if one batch were to fail or we had a THROW in that batch it would fail at that point but execution would continue immediately after the next GO until the end. This is where SET NOEXEC ON comes into play. If I add that at the beginning of the script all succeeding code would not be executed. The statements would only be compiled and not actually run. It would look like this:
SET NOEXEC ON;
PRINT ‘Got Here 1’ ;
PRINT ‘Got Here 2’ ;
PRINT ‘Got Here 3’ ;
PRINT ‘Got Here 4’ ;
This is a useful “accidental F5” protection: you can put it at the top of your long script to keep from running the whole thing at once.
SQL Server 2016 and Azure SQL Database enable you to parse JSON text and transform it into tabular format. In this post, you might see that JSON functions can handle very large JSON text – up to 4GB.
First, I would need very large JSON document. I’m using TPCH database so I will export the content of lineitem table in a file. JSON can be exported using the bcp.exe program:
My first draft read “Jovan Popovic has created a monster.” I might go back to that one. On the plus side, the operation took a lot less time than I had expected, though I’d have to imagine that his SQL Express instance had some decent specs.
Ah! An “SSPI” error. I was certain I knew what the problem was. It had to be a failure to properly register the SPN. I was prepared to run a few SetSPN.exe commands to fix the problem and be on my way. To my surprise, the SPN *was* registered correctly, as I discovered in the Log File Viewer:
Click through for the solution.
Last Friday I had the chance to show the Export-DMVInformation module to the Dutch Powershell user group. After the presentation I got a couple of suggestions and wanted to put them in place them into the module.
Possibility to execute the module using the pipeline
Get all the databases in one statement by assigning the value “ALL” to the database parameter.
Replaced messages with verbose
Read on for more information, including where you can get the module and its Export-DMVInformation cmdlet.
Finally, as a first time host, I was obviously hoping that this topic would garner some responses, but you never know until you hit that post button whether you’ve selected something of interest to the community or not. Thankfully, this month’s topic picked up views from over 20 countries and over 20 blog responses. The list (with a brief post-by-post commentary from me) is below. Happy reading and thanks again for reading/writing/participating!
Read on for the full list of respondents.
Which brings us to the matter of getting stuff done. Imagine if everything you do has to be approved by a stakeholder and a manager, every line of code you write is peer-reviewed, then tested in a dev test environment, then in an acceptance test environment (which should both contain reasonably fresh, yet scrambled copies of the production data), then approved for deployment by the stakeholder (who ideally should also take time to verify the results), and finally deployed to production by two other people, under a four-eyes principle where no single person can perform any change in production alone. Sprinkle this with a bunch of project meetings, all while leaving a long and winding trail of tickets and documentation.
This is how most development cycles look. Except, you know, the test environments are rarely fresh, the tests aren’t really that thorough, and the peer-review could probably be called a peer-glance at best.
A lot of this depends upon the industry and the likelihood that an outage will cause direct physical harm to people. I’m personally ambivalent about where the right risk acceptance point is, but Daniel makes a good argument on the “accept more risk” side.
Additive or multiplicative?
It’s important to understand what the difference between a multiplicative time series and an additive one before we go any further.
There are three components to a time series:
– trend how things are overall changing
– seasonality how things change within a given period e.g. a year, month, week, day
– error/residual/irregular activity not explained by the trend or the seasonal value
How these three components interact determines the difference between a multiplicative and an additive time series.
Click through to learn how to spot an additive time series versus a multiplicative. There is a good bit of very important detail here.
This code was run on PowerShell version 5 and will not run on PowerShell version 3 or earlier as it uses the where method
I put all of our jobs that I required on the estate into a variable called $Jobs. (You will need to fill the $Servers variable with the names of your instances, maybe from a database or CMS or a text file
Click through for the script and line-by-line explanation.
Key CTP 1.3 enhancement: Always On Availability Groups on Linux
In SQL Server v.Next, we continue to add new enhancements for greater availability and higher uptime. A key design principle has been to provide customers with the same HA and DR solutions on all platforms supported by SQL Server. On Windows, Always On depends on Windows Server Failover Clustering (WSFC). On Linux, you can now create Always On Availability Groups, which integrate with Linux-based cluster resource managers to enable automatic monitoring, failure detection and automatic failover during unplanned outages. We started with the popular clustering technology, Pacemaker.
In addition, Availability Groups can now work across Windows and Linux as part of the same Distributed Availability Group. This configuration can accomplish cross-platform migrations without downtime. To learn more, you can read our blog titled “SQL Server on Linux: Mission Critical HADR with Always On Availability Groups”.
That’s a big headline. In the Other Enhancements section, I like resumable online index rebuilds as well.