Interlinked Parameters In Powershell

Rob Sewell shows how to make one parameter’s valid set of values depend upon a previous parameter’s selected value:

But what Sander wanted was to validate the value of the second parameter depending on the value of the first one. So lets say we wanted

  • If word is sun, number must be 1 or 2
  • If word is moon, number must be 3 or 4
  • If word is earth, number must be 5 or 6

We can use the ValidateScriptAttribute  to do this. This requires a script block which returns True or False. You can access the current parameter with $_ so we can use a script block like this

Any Powershell post with a link to June Blender is automatically 5% better in my book.


Drew Furgiuele explains how to use the .NET Stopwatch class in Powershell:

We can see I cleared the history buffer of my session ( Clear-History ), then ran a script. It’s nothing fancy; just connecting to my local instance of SQL Server and outputting a list of tables to a text file. With Get-History , I can see every command I put in the buffer, and using expressions I can calculate how long a command took.

And that’s great and all, but that’s the entire execution time. If there are multiple steps to your function, how long does each step take? If your script execution is 186 seconds, how much of time is spent on a database query? What about a loop? How long does each iteration take? Are you writing to a network share, and want to know what the latency is? Get-History  isn’t the tool of this, but thankfully we have other methods.

I use the stopwatch a lot for similar things; it’s a useful tool.

Error 0x80004005 In SQL Server R Services

I ran into an error in SQL Server R Services:

I recently worked through a strange error (with help from a couple sharp cookies at Microsoft) and wanted to throw together a quick blog post in case anybody else sees it.

I have SQL Server R Services set up, and in the process of running a fairly complex stored procedure, got the following error message:

Msg 39004, Level 16, State 22, Line 0

A ‘R’ script error occurred during execution of ‘sp_execute_external_script’ with HRESULT 0x80004005.

Check those output variable and result set definitions.

Code Beautifier

Dave Mason has a small application for auto-formatting code:

One of the challenges I’ve faced as a blogger is quickly reproducing code that looks good in HTML. I’ve tried a few different online code conversion sites, and even a C# library. But I never quite got the results I wanted. I found myself revising the HTML to fix up key words, comments, and operators that sometimes got missed, were the wrong color, or the wrong font. All that HTML editing was tedious and time consuming.

What I really wanted to do was copy my code from SSMS, Visual Studio, or the PowerShell ISE and paste it into my blog so I could spend less time as a web dev and more time writing. Since none of the other tools I found gave me the results I wanted, I wrote my own. The code for my little app is available on GitHub. (It’s my first project, so if something is missing or wrong, let me know.) It was created with Visual Studio 2015–there’s a compiled exe if you prefer that.

Check it out.

Windows Versus SQL Authentication

Kenneth Fisher looks into why we generally consider Windows authentication more sound than SQL authentication:

A quick search on the internet took me here: Choosing an Authentication Mode. And if you go down to the section Connecting Through Windows Authentication it points out a few important things and then even farther down the section Disadvantages of SQL Server Authentication has a bit more. Then I found a couple of good forum questions here and here. In summary (and only discussing actual security features):

Click through for the answers.  Also read Cristian Satnic’s comments below, as Cristian is correct about wanting to keep passwords hashed instead of encrypted.  Incidentally, Windows passwords aren’t encrypted, either—they’re hashed.

Pausing Online Index Rebuilds

Andrew Pruski demos resumable online index rebuilds in SQL Server 2017:

Pretty cool, huh? Full information on this can be found here: –

I think this is very useful but we do need to be careful. The documentation says that pausing an online index rebuild for a long time may affect query performance and disk utilisation. This is due to the newly rebuild index being created side-by-side to the original one so we’ll need to watch out for that.

It’s a good first look at what might be a very interesting solution for companies tight on maintenance window time.

Default Column Storage

Paul Randal explains how default column values are stored:

And selecting the initial 10 rows can be demonstrated to return the 3rd column using the initial default set in step 3. (It makes no difference if any rows are added between steps 3 and 4.)

This means that there *must* be two default values stored when a new column is added: one for the set of already-existing rows that don’t have the new column and one for any new rows. Initially these two default values will be the same, but the one for new rows can change (e.g. in steps 4 and 5 above) with breaking the old rows. This works because after the new column is added (step 3 above), it’s impossible to add any more rows that *don’t* have the new column.

And this is exactly how it works. Let’s investigate!

In typical Paul Randal fashion, this is both a look at internals and an interesting explanation.

Securing Azure Storage

Christos Matskas has an article on securing Azure blobs and containers:

All communication with the Azure Storage via connection strings and BLOB URLs enforce the use of HTTPS, which provides Encryption in Transit. You can enforce the use of “Always HTTPS” by setting the connection string like this: “DefaultEndpointsProtocol=https;AccountName=myblob1…” or in SAS signatures, as in the example below:

To protect data at rest, the service provides an option to encrypt the data as they are stored in the account. There’s no additional cost associated with encrypting the data at rest and it’s a good idea to switch it on as soon as the account is created. There is a one-click setting at the Storage Account level to enable it, and the encryption is applied on both new and existing storage accounts. The data is encrypted with AES 256 cipher and it’s now generally available to all Azure regions and Azure clouds (public, government etc)

There’s some good information here, making it worth the read.

Survival Analysis

Joseph Rickert explains what survival analysis is and shows an example with R:

Looking at the Task View on a small screen is a bit like standing too close to a brick wall – left-right, up-down, bricks all around. It is a fantastic edifice that gives some idea of the significant contributions R developers have made both to the theory and practice of Survival Analysis. As well-organized as it is, however, I imagine that even survival analysis experts need some time to find their way around this task view. (I would be remiss not to mention that we all owe a great deal of gratitude to Arthur Allignol and Aurielien Latouche, the task view maintainers.) Newcomers, people either new to R or new to survival analysis or both, must find it overwhelming. So, it is with newcomers in mind that I offer the following slim trajectory through the task view that relies on just a few packages: survival, KMsurv, Oisurv and ranger

The survival package, which began life as an S package in the late ’90s, is the cornerstone of the entire R Survival Analysis edifice. Not only is the package itself rich in features, but the object created by the Surv() function, which contains failure time and censoring information, is the basic survival analysis data structure in R.

Survival analysis is an interesting field of study.  In engineering fields, the most common use is calculating mean time to failure, but that’s certainly not the only place you’re liable to see it.

Outliers In Histograms

Edwin Thoen has an interesting solution to a classic problem with histograms:

Two strategies that make the above into something more interpretable are taking the logarithm of the variable, or omitting the outliers. Both do not show the original distribution, however. Another way to go, is to create one bin for all the outlier values. This way we would see the original distribution where the density is the highest, while at the same time getting a feel for the number of outliers. A quick and dirty implementation of this would be

hist_data %>% mutate(x_new = ifelse(x > 10, 10, x)) %>% ggplot(aes(x_new)) + geom_histogram(binwidth = .1, col = "black", fill = "cornflowerblue")

Edwin then shows a nicer solution, so read the whole thing.


April 2017
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