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Month: September 2017

Basics Of Survival Analysis

Published 2017-09-14 by Kevin Feasel

Subhasree Chatterjee explains the basics of survival analysis:

Survival analysis is a set of methods to analyze the ‘time to occurrence’ of an event. The response is often referred to as a failure time, survival time, or event time. These methods are widely used in clinical experiments to analyze the ‘time to death’, but nowadays these methods are being used to predict the ‘when’ and ‘why’ of customer churn or employee turnover as well.

The dependent variables for the analysis are generally two functions:

Read the whole thing.  H/T R-Bloggers

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Log Shipping With dbatools

Published 2017-09-14 by Kevin Feasel

Sander Stad shows off a few log shipping functions he created for dbatools:

The entire log shipping functionality is now separated between 5 functions. Four of them are used internally and are not visible as a public function because you can easily break stuff if it’s not being used correctly.

The main function, Invoke-DbaLogShipping, is available in the dbatools module for anyone to use.

If you open the GUI for the log shipping you have lots of choices but most of them are already supplied in the GUI itself and you can decide whether to use them or not.
The whole idea behind the functionality was that it would allow you to quickly setup log shipping using a lot of defaults like you can in the GUI, but if you’re more experienced you can change any setting to your preferences.

Read on for an example of how to use this.  It looks pretty snazzy.

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Loading From Excel Into SQL Server With Powershell

Published 2017-09-14 by Kevin Feasel

Shane O’Neill tackles one of the all-time important questions, how to get data from Excel into something else:

Now let’s say that we are working in an environment that does not allow us to change the server configurations, meaning that OPENROWSET() is closed for us.

Being completely honest, my spreadsheet is only 8 rows so I would just manually insert the data but what happens if it’s a few thousand rows big? Not a viable option.

So with most of my T-SQL ideas have been exhausted, let’s look at PowerShell!

The other all-time important question, of course, is how to get data from something else into Excel.

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Automatically Restoring Databases To Another Server

Published 2017-09-14 by Kevin Feasel

Bjorn Peters needs to migrate a few databases over to a test server nightly:

My first thoughts about that were creating a SQL Server Agent Job with following steps:

  1. check the availability of Shared-Destination-Folder
  2. delete/clear Destination-Folder-Content
  3. Shrink all Transaction-Logfiles
  4. Backup all Databases from given list
  5. Restore each Backup-File from folder
  6. Check all orphaned user
  7. delete/clear Destination-Folder-Content

A year or two ago, I had built this with a lot of normal T-SQL-Agent-Jobs, now I’m doing this with dbatools which make it very easy and fast (and in one step)

It’s only a few lines of Powershell code, which you can see upon clicking through.

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Homoglyphs And SQL Injection

Published 2017-09-14 by Kevin Feasel

Bert Wagner gives us one more reason why blacklists are bad:

Homoglpyhs can exist within a character set (like the Latin character set examples above) or they can exist between character sets. For example, you may have the unicode apostrophe ʼ, which is a homoglyph to the Latin single quote character '.

How does SQL Server handle unicode homoglyphs?

Funny you should ask. If you pass in a unicode character to a non-unicode datatype (like char), SQL implicitly converts the unicode character to its closest resembling non-unicode homoglyph.

Bert’s examples show failure when converting Unicode data to VARCHAR, so this attack vector may not work if the input parameter is NVARCHAR, but even if that’s the case, it’s still one of many reasons why blacklists are awful for preventing against injection attacks.

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Comparing Server Configurations With Powershell

Published 2017-09-14 by Kevin Feasel

Andy Levy shows how to use a dbatools cmdlet, Get-DbaSpConfigure:

I started with Get-DbaSpConfigure to retrieve the settings available from sp_configureas these were the most important to my comparison. I ran this against production as well as each of my test instances and saved the results of each to a variable. Because accessing my production instance requires either jumping through hoops or using SQL Authentication, I passed -SqlCredential (get-credential -Message "Prod" -UserName MySQLLogin) so I’d be prompted for that password instead of using Windows Authentication.

It’s good to have an automated process in place to script comparisons, either against a known good build or across servers which ought to be the same.  Things that ought to be the same often aren’t.

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Using Query Governor

Published 2017-09-14 by Kevin Feasel

Monica Rathbun explains what the query governor is in SQL Server:

It’s simple. This option, available in SQL Server 2008 standard and forward, will prevent long running queries based on run time measured in seconds. If I specify a value of 180 the query governor will not allow any execution of a query that it estimates will exceed that value. Notice it says ESTIMATES which means it will be based on optimizer estimates and not ACTUAL run times. It does NOT KILL an actively running query after designated amount of time.  There is no worries for rollback scenarios or partial data.

This is totally different from Resource Governor, which can throttle resources like CPU and I/O.  I’ve never turned on query governor and I’m not sure I ever would.

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dbatools Now With 287 Commands

Published 2017-09-14 by Kevin Feasel

Chrissy LeMaire reports that dbatools has 287 cmdlets:

Really, I said all that so that you’d hopefully excuse the recent lack of communication 😉 Not only did I have the big ol’ audit, but we are also preparing for two dbatools pre-cons. One with Klaas Vandenberghe and Rob Sewell in Ghent, Belgium at dataminds and then the one in Seattle at PASS Summit.

This means that communication will slow a bit until December, but I wanted to give you an update. First, we’re now at a whopping 287 magical commands! We’ve got three kind folks working on the webpages and once they are all ready, I’ll do a new release. Until then, here’s over 80 newish commands that you can explore using Get-Help -Detailed.

dbatools has been a tremendous community success.

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Understanding Neural Networks: Perceptrons

Published 2017-09-13 by Kevin Feasel

Akash Sethi explains what a perceptron is:

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.
Linear classifier defined that the training data should be classified into corresponding categories i.e. if we are applying classification for the 2 categories then all the training data must be lie in these two categories.
Binary classifier defines that there should be only 2 categories for classification.
Hence, The basic Perceptron algorithm is used for binary classification and all the training example should lie in these categories. The basic unit in the Neuron is called the Perceptron.

Click through to learn more about perceptrons.

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Uses Of kd-trees

Published 2017-09-13 by Kevin Feasel

Sandipan Dey explains what a kd-tree is and how it works:

The prime advantage of a 2d-tree over a BST is that it supports efficient implementation of range search and nearest-neighbor search. Each node corresponds to an axis-aligned rectangle, which encloses all of the points in its subtree. The root corresponds to the entire plane [(−∞, −∞), (+∞, +∞ )]; the left and right children of the root correspond to the two rectangles split by the x-coordinate of the point at the root; and so forth.

  • Range search: To find all points contained in a given query rectangle, start at the root and recursively search for points in both subtrees using the following pruning rule: if the query rectangle does not intersect the rectangle corresponding to a node, there is no need to explore that node (or its subtrees). That is, search a subtree only if it might contain a point contained in the query rectangle.

  • Nearest-neighbor search: To find a closest point to a given query point, start at the root and recursively search in both subtrees using the following pruning rule: if the closest point discovered so far is closer than the distance between the query point and the rectangle corresponding to a node, there is no need to explore that node (or its subtrees). That is, search a node only if it might contain a point that is closer than the best one found so far. The effectiveness of the pruning rule depends on quickly finding a nearby point. To do this, organize the recursive method so that when there are two possible subtrees to go down, you choose first the subtree that is on the same side of the splitting line as the query point; the closest point found while exploring the first subtree may enable pruning of the second subtree.

  • k-nearest neighbors search: This method returns the k points that are closest to the query point (in any order); return all n points in the data structure if n ≤ k. It must do this in an efficient manner, i.e. using the technique from kd-tree nearest neighbor search, not from brute force.

Sandipan implements a fairly classic problem in this space:  the behavior of a group of flocking birds.

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