Reporting Services Versus Power BI Report Server

John White compares and contrasts SQL Server Reporting Services versus Power BI Report Server:

Power BI Report Server (PBIRS) was first introduced in May 2017. Based on SQL Server Reporting Services (SSRS), it brings the ability to work with Power BI reports completely on premises in addition to all the other capabilities of SSRS. Given this, it would be reasonable to conclude that PBIRS was the next version of, or a replacement for SSRS, but that is not the case. I have heard people state that SSRS is “going away”, but this is simply not the case. SSRS is still a core part of the Microsoft BI stack. So, what are the differences between the two platforms? The differences boil down to features, licensing, and update cadence.

If you’re in the BI/report writing space, you will want to read the whole thing.

Automatic Retry With Optimistic Concurrency

Vladimir Khorikov explains an anti-pattern when dealing with a model using optimistic concurrency (for example, memory-optimized tables):

Alright, back to the original question. So, how to combine optimistic locking and automatic retry? In other words, when the application gets an error from the database saying that the versions of a Product don’t match, how to retry the same operation again?

The short answer is: nohow. You don’t want to do that because it defeats the very purpose of having an optimistic lock in the first place.

Remember that the locking mechanism is a way to ensure that all changes are taken into consideration when changing a record in the database. In other words, someone should review the new version of the record and make an informed decision as to whether they still want to submit the update. And that should be the same client who originated the initial request, you can’t make that decision for them.

Plenty of systems do this sort of data merging automatically, but I get Vladimir’s point:  if someone else pulled the rug out from under you, it might change your decision on what that data should look like.

Max And Min Partition Values

Ken Kaufman explains a major performance problem when trying to get maximum (or minimum) values from a partitioned table:

Now that I rambled a bit you want to know why when using a partitioned table does grabbing the min and max of the primary key take sooooo long, and how do you fix it.  Theoretically you would expect SQL to perform the following steps in grabbing the Max Id

  1.         Grab the Max Id from each partition using a seek
  2.         Hold the results in  temp storage
  3.         Get the Max ID from the  temp storage, and return that result.

 

However SQL doesn’t do that, it actually scans each partition and finds the max id after it has examined every record in each partition.  This is very inefficient, and could kill a query that depends on this value, as well as impact a busy server low on physical resources.    So what we need to do, is manually write the code to perform the steps that SQL Server should actually be doing.

Read on for one workaround Ken uses to deal with this inefficiency.

Schema Comparison With Visual Studio

Arun Sirpal shows off the Schema Compare functionality within Visual Studio:

A very common requirement which can be satisfied by various tools. Personally I like using Visual Studio 2017 Community Edition and I thought I would do a quick overview of it.

First thing, you can find the download from this link: https://www.visualstudio.com/downloads/  and once installed (making sure that you select SQL Server Data Tools)  go find Visual Studio 2017 and you will be presented with your start screen.

Click through for the process.  This tool is nice for one-off jobs, like when you want to synchronize production down to source control or see the differences between two environments.  But if you’re doing these comparisons a lot, I think you’re better off scripting it out using SMO and Powershell.

Generating Fake Company Names

Kevin Feasel

2017-09-19

Naming

Daniel Hutmacher has a great way of generating fake company names:

This query is actually a lot simpler than it first appears. Here’s how it breaks down:

  • Pick 100 words at random (table “a”)

  • For each word in “a”, if possible, pick a single random word (“b”) that doesn’t start or end with the same three letters as the “a” word.

  • For each word in “b”, if possible, pick a single random word (“c”) that doesn’t start or end with the same three letters as the “a” nor the “b” word.

  • The UPPER(), LEFT() and SUBSTRING() stuff is just to turn the names into title case.

  • As before, the ORDER BY NEWID() randomizes the order in which the TOP (1) row is returned.

My favorite name when running this was Disaster Votes, followed closely by Fail Users Vendor and Terminated Enterprise.  Apparently my SQL Server instance has a very negative impression of my made up companies’ leadership skills.

Polar Charts In Power BI With R

Leila Etaati shows how to build a polar chart in Power BI using an R component:

I just add a layer to the above furmula “coord_polar()” this function also has been used for creating pie charts. it gets the “theta” variable, in below example I put theta=y axis, so we have below charts

Normally I don’t much like this type of polar chart, though I’m a big fan of radar charts, which follow a similar concept.

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