A Fine Slice Of SQL Server
Arun Sirpal shows how to use Azure Automation to rebuild indexes on an Azure SQL Database database:
The answer is via Azure Automation.
At a high level this is what I did.
Create an Automation Account.
Create a credential.
Create a PowerShell Runbook which has the code for index rebuilds.
Create a schedule and link it to the above.
Configure parameters within the schedule (if any).
Configure logging level (if desired).
Click through for the detailed steps.
Comments closedThis solution is easier than Solution 1:
8 steps instead of 22!
No extra Project
However, a very small amount of risk was added by overriding the default MSBuild workflow for SSDT. This risk can be eliminated if Microsoft provides a pre-defined Target for the appropriate event. Please upvote my suggestion to have this happen: Add MSBuild predefined Targets for “BeforeSqlBuild” and “BeforePublish” to SSDT SQL Server Data Projects.
ALSO: Even though we did not sign the assembly with a Strong Name Key, it is still probably a good idea to do that.
If you use CLR, this is worth the read.
Comments closedFrank Gill gives us a demo of how much log space it takes to shrink a database file:
Yesterday, I was running a health assessment for a client. They are running a weekly maintenance plan that is shrinking all of their data files. After I picked myself up off the floor, I searched the web for “Paul Randal shrink” and hit on Paul’s excellent post Why you should not shrink your data files. In the post, Paul (b|t) demonstrates the effect of DBCC SHRINKDATABASE on index fragmentation. After the demo script, Paul writes, “As well as introducing index fragmentation, data file shrink also generates a lot of I/O, uses a lot of CPU and generates *loads* (emphasis Paul’s) of transaction log.”
This led me to ask the question, “How much is *loads*?”. To find an answer, I made the following modification to Paul’s script:
Read on for the answer. There are legitimate reasons to shrink data files, but it comes at a very high cost.
Comments closedErik Darling unwraps some of his Christmas presents a bit early:
There are, as of RC2 being released, 194(!) new Events to Extend your mind with. Not all of them are interesting to me, and I haven’t had time to pry into all of the ones that are interesting just yet.
This is a rundown of the new Events with names or descriptions that I found interesting, and will try to spend some time with.
I can’t promise anything
After all, getting some of these to fire is tougher than using a Debugger.
There are some interesting events here.
Comments closedThe ARM API deploys resources to Azure, but doesn’t deploy code onto those resources. For example you can use ARM to deploy a virtual machine with SQL Server already installed but you can’t use ARM to deploy a database from an SSDT DacPac.
To save time when designing solutions, it is important to understand that ARM API is used simply for resources and we need to use some other technology such as DSC or PowerShell to manage the deployments onto the infrastructure once it is deployed.
This is a nice overview of the topic, and because it’s Ed (who is much better about this than most), he goes into how to test before even getting into how to create.
Comments closedPete Warden shows how to install TensorFlow on a Raspberry Pi:
It’s never been easy to get TensorFlow installed on a Pi though. I had created a makefile script that let you build the C++ part from scratch, but it took several hours to complete and didn’t support Python. Sam Abrahams, an external contributor, did an amazing job maintaining a Python pip wheel for major releases, but building it required you to add swap space on a USB device for your Pi, and took even longer to compile than the makefile approach. Snips managed to get TensorFlow cross-compiling for Rust, but it wasn’t clear how to apply this to other languages.
Plenty of people on the team are Pi enthusiasts, and happily Eugene Brevdo dived in to investigate how we could improve the situation. We knew we wanted to have something that could be run as part of TensorFlow’s Jenkins continuous integration system, which meant building a completely automatic solution that would run with no user intervention. Since having a Pi plugged into a machine to run something like the makefile build would be hard to maintain, we did try using a hosted server from Mythic Beasts. Eugene got the makefile built going after a few hiccups, but the Python version required more RAM than was available, and we couldn’t plug in a USB drive remotely!
Read the whole thing, even if for the science experiment aspect.
Comments closedDirk Eddelbuettel compacts the tidyverse:
Of course, there is a third way: just run
strip --strip-debugover all the shared libraries after the build. As the path is standardized, and the shell does proper globbing, we can just do$ strip --strip-debug /usr/local/lib/R/site-library/*/libs/*.sousing a double-wildcard to get all packages (in that R package directory) and all their shared libraries. Users on macOS probably want
.dylibon the end, users on Windows want another computer as usual (just kidding: use.dll). Either may have to adjust the path which is left as an exercise to the reader.
When running this against the tidyverse library, shared library sizes dropped from 78 MB down to 6.6 MB. Not bad for a single command. H/T R-Bloggers
Comments closedNed Otter continues his series on In-Memory OLTP isolation levels:
Why will it fail?
It will fail because the initiation mode of this transaction is not autocommit, which is required for READ COMMITED SNAPSHOT when referencing memory-optimized tables (the initiation mode is explicit, because we explicitly defined a transaction). So to be totally clear, for queries that only reference memory-optimized tables, we can use the READ COMMITTED or READ COMMITTED SNAPSHOT isolation levels, but the transaction initiation mode must be autocommit. Keep this in mind, because in a moment, you’ll be questioning that statement….
There are some interesting implications that Ned teases out, so I recommend giving it a careful read.
Comments closedJohn Mount wants to know how well you understand dplyr:
dplyris one of the most popularRpackages. It is powerful and important. But is it in fact easily comprehensible?
dplyrmakes sense to those of us who use it a lot. And we can teach part timeRusers a lot of the common good use patterns.But, is it an easy task to study and characterize
dplyritself?
Take John’s quiz and find out. He wasn’t kidding about it being an advanced quiz.
Comments closedDevin Knight continues his Power BI custom visuals series:
In this module you will learn how to use the Dot Plot Custom Visual by MAQ Software. The Dot Plot is used to show distribution using dots among multiple categories or attributes.
Dot plots are nice because they tend to be informative while keeping the ink to whitespace ratio low.
Comments closed