A Fine Slice Of SQL Server
Just a quick tip about why you might notice some sizes are not available when you are attempting to change the size/scale level of an Azure virtual machine in the portal.
I wanted to change one of my Development VMs to a DS12_v2, but that choice wasn’t available:
It didn’t immediately dawn on me why it wasn’t available, so I thought I’d try PowerShell:
Read on for the solution, as well as a few other common causes.
Comments closedDavid Klee warns against just throwing memory into servers willy-nilly:
An example of this is if you wish to configure 384GB of RAM on a new server. The server has 24 memory slots. You could populate each of the memory slots with 16GB sticks of memory to get to the 384GB total. Or, you could spend a bit more money to buy 32GB sticks of memory and only fill up half of the memory slots. Your outcome is the same amount of RAM. Your price tag on the memory is slightly higher than the relatively cheaper smaller sticks.
In this configuration, your 16GB DIMM configuration runs the memory 22% slower than if you buy the higher density sticks. Check out page 63 of the server build guide for an HPE Proliant DL380 Gen9 server. The fully populated 16GB stick configuration runs the memory at 1866 MHz. If you only fill in the 32GB sticks on half the slots, the memory runs at 2400 MHz.
Very interesting information.
Comments closedIn previous posts I have explained how having dedicated filegroups for user data can improve our RTO by, in case of disaster, recovering critical data first and then the rest.
The thing is when you deal with databases which were not created this way, you need to move the data from one filegroup to another before you can apply this kind of techniques.
Here is where this post can show you one of the gotchas you can find during this process.
Read on for a demo of this.
Comments closedMonica Rathbun shows how to use the Steps Recorder in Windows:
Windows PSR “Problem Steps Recorder”. It’s a nifty tool that helps you trouble shoot a computer problem by recording step by step what the user is doing.
How to:
Go to Start
Windows Accessories
Steps Recorder or Problem Steps Recorder depending on Windows version
Select Start Record (App will popup)
That’s a new one on me; click through to check it out.
Comments closedErik Darling shows how the NOLOCK hint works with common table expressions:
So, for all you NOLOCKers out there, you can now save yourselves oodles of time by only using the hint in outer references to your CTEs and Views.
Congratulations, I suppose.
(Please stop using NOLOCK.)
Agreed, whenever possible.
Comments closedChris Webb shows how to convert a datetime from UTC to your local time zone using M:
Here’s a brief explanation of what the query does:
First it reads the times from the Excel table and sets the Time column to be datetime data type
It then creates a new column called UTC and then takes the values in the Time column and converts them to datetimezone values, using the DateTime.AddZone() function to add a time zone offset of 0 hours, making them UTC times
Finally it creates a column called Local and converts the UTC times to my PC’s local time zone using the DateTimeZone.ToLocal() function
There are some limitations to what it does, so you can’t convert to just any time zone while still retaining Daylight Savings Time awareness.
Comments closedDevin Knight continues his Power BI custom visuals series:
In this module you will learn how to use the Attribute Slicer Power BI Custom Visual. Using the Attribute Slicer you have the ability to filter your entire report while also being able to visibly see a measure value associated with each attribute.
Click through for the video as well as more details. This looks like a very interesting way of integrating a slicer with some important metric, like maybe including dollar amounts per sales region and then filtering by specific regions to show more detailed analyses.
Comments closedLarry Murdock explains the tuning options available to Kafka and Spark Streams:
Kafka is not the Ferrari of messaging middleware, rather it is the salt flats rocket car. It is fast, but don’t expect to find an AUX jack for your iPhone. Everything is stripped down for speed.
Compared to other messaging middleware, the core is simpler and handles fewer features. It is a transaction log and its job is to take the message you sent asynchronously and write it to disk as soon as possible, returning an acknowledgement once it is committed via an optional callback. You can force a degree of synchronicity by chaining a get to the send call, but that is kind of cheating Kafka’s intention. It does not send it on to a receiver. It only does pub-sub. It does not handle back pressure for you.
I like this as a high-level overview of the different options available. Definitely gets a More Research Is Required tag, but this post helps you figure out where to go next.
Comments closedMatthew Rathbone shows different concurrency options available in Scala:
Scala is a functional programming language that aims to avoid side effects by encouraging you to use immutable variables (called ‘values’), and data structures.
So by default in Scala when you build a list, array, string, or other object, that object is immutable and cannot be changed or updated.
This might seem unrelated, but think about a thread which has been given a list of strings to process, perhaps each string is a website that needs crawling.
In the Java model, this list might be updated by other threads at the same time (adding / removing websites), so you need to make sure you either have a thread-safe list, or you safeguard access to it with the
protectedkeyword or a Mutex.By default in Scala this list is immutable, so you can be sure that the list cannot be modified by other threads, because it cannot be modified at all.
While this does force you to program in different ways to work around the immutability, it does have the tremendous effect of simplifying thread-safety concerns. The value of this cannot be understated, it’s a huge burden to worry about thread safety all the time, but in Scala much of that burden goes away.
Read the whole thing if you’re looking at writing Spark applications in Scala. If you’re thinking about functional programming in .NET languages, F# is there for you.
Comments closedAnanda Das explains how linear Support Vector Machines work in classifying spam messages:
Linear SVM assumes that the two classes are linearly separable that is a hyper-plane can separate out the two classes and the data points from the two classes do not get mixed up. Of course this is not an ideal assumption and how we will discuss it later how linear SVM works out the case of non-linear separability. But for a reader with some experience here I pose a question which is like this Linear SVM creates a discriminant function but so does LDA. Yet, both are different classifiers. Why ? (Hint: LDA is based on Bayes Theorem while Linear SVM is based on the concept of margin. In case of LDA, one has to make an assumption on the distribution of the data per class. For a newbie, please ignore the question. We will discuss this point in details in some other post.)
This is a pretty math-heavy post, so get your coffee first. h/t R-Bloggers.
Comments closed