A Fine Slice Of SQL Server
Tomaz Kastrun shows off contributions:
Github is collecting various images on their Instagram and Twitter that looks like a contribution graph.
Well, why not create one in R with randomly generated data and the use of the ggplot library.
It’s surprisingly easy to create a contribution graph using R.
Comments closedKevin Sookocheff explains how write-ahead logging protects data in databases:
A central tenet of databases is that any committed data survives a crash or a failure. Write-ahead logging is a fundamental primitive that ensures all changes to data are first written safely to stable storage before being applied. Coupling that with some careful use of sequence numbers and we can guarantee that changes made to a database can survive system crashes.
This is a core feature in pretty much every relational database and Kevin dives into how one of the key algorithms behind it works.
Comments closedRobert Cain proves it’s not the end of the line in his KQL series:
It’s not uncommon to want to use a range of values when creating a Kusto query. This might be a range of numeric values, or perhaps a range of dates.
Kusto provides this ability using the
betweenoperator. In this post we’ll see how to use it when authoring your Kusto queries.
Click through to see how you can use between as well as logical alterations such as not between.
Alberto Ferrari and Marco Russo add some context:
RELATED is one of the most commonly used DAX functions. You use RELATED when you are scanning a table, and within that row context you want to access rows in related tables. RELATEDTABLE is the companion of RELATED, and it is used to traverse relationships in the opposite direction. When learning DAX, it is easy to get confused and use RELATED when it is not necessary, or to forget about RELATEDTABLE. In this article, we describe the most common uses of the two functions, along with common misperceptions.
Click through to learn more about these two functions.
Comments closedJeffrey Wang continues a series on EVALUATEANDLOG():
Last week, we learned how to interpret the output of the EvaluateAndLog function of scalar expressions. Albeit interesting, the new function would be much less useful if it could only return scalar values. After all, Power BI users can always define a scalar expression as a measure and then study its values in various contexts. The real power of EvaluateAndLog is that it can also wrap around any table expressions, the output of which was much harder to visualize until now.
This function exposes a lot of information, as you can see in the post.
Comments closedPaul White dives into latch contention:
This design has its roots in SQL Server 7, where read-only query parallelism was introduced. SQL Server 2000 built on this with parallel index builds, which for the first time allowed multiple threads to cooperate to change a persistent database structure. Many improvements have followed since then, but the fundamental parent-child transaction design remains today.
Though lightweight, a latch can become a point of contention when requested sufficiently frequently in incompatible modes by many different threads. Some contention on shared resources is to be expected; it becomes a problem when latch waits start to affect CPU utilisation and throughput.
Read the whole thing, as Paul dives into the latch design, provides an alternative design, and tests the alternative.
Comments closedChris Webb wants to know the situation, STAT:
So far in this series (see part 1, part 2 and part 3) I’ve looked at how you can create a Power Automate custom connector that uses the Power BI Enhanced Refresh API to kick off a dataset refresh. That’s only half the story though: once the refresh has been started you need to know if it has finished and, if so, whether it finished successfully or not. In this post I’ll show how to do this.
Read on to see how.
Comments closedSoheil Bakhshi grabs some Excel data from SharePoint:
We have a retail company selling products. The company releases the list of products in Excel format, including list price and dealer price, every year. The product list is released on the first day of July when the financial year starts. We have to implement a Power BI solution that keeps the latest product data to analyse the sales transactions. The following image shows the Product list for 2013:
So each year, we receive a similar Excel file to the above image. The files are stored on a SharePoint Online site.
Read on to see how it works. Of course, the data source itself doesn’t affect how you implement slowly-changing dimensions, so the technique Soheil shows applies to a broad number of use cases.
Comments closed