A Fine Slice Of SQL Server
Joe Celko defends the honor of the check constraint:
The problem is that newer SQL programmers (and even some more experienced ones) don’t take advantage of these constraints. Consider this slight rewrite of an actual posting on an SQL forum. In the original, the poster thought that identifiers should be integers and that ISO–11179 naming rules do not apply, so I cleaned it up a little, but this will make my point.
Click through to learn more about check constraints.
Comments closedCary Huang digs into Postgres:
I wrote another blog to briefly explain the responsibility of each query processing stage. You can find it here. In this blog. There is one that focuses primarily on the planner module that you can find here. In this blog, we will focus on the executor part of the query processing, which is the entry point to “communicate” with other PostgreSQL internal modules to collect the correct data for the query.
In fact, executor may not be the best term to describe this stage. In the PostgreSQL source code, it is actually “packaged” in another object called “portal”. Often, and it has 2 potential paths to take, the “executor” path, or the “process utility” path. Very often, we just call this stage as “executor” rather than “portal”, because executor handles most of the DML query types that involve SELECT, INSERT, UPDATE, DELETE…etc and it has to process them according to the query plan created by the “planner” module.
Read on to learn more about the portal and its two component parts.
Comments closedMarco Russo provides an option:
DAX is not like M when it comes to data manipulation, and it is not supposed to do that. However, if you need something in DAX similar to Table.FromList in M, this blog post is for you.
If you have a list of values in a string in DAX and you want to obtain a table with one row for each item in the list, you can do the following:
Definitely an example of “There’s a better way to do this than to use DAX” but sometimes you’re stuck, I suppose.
Comments closedJosephine Bush continues a series on modern data warehousing:
In today’s data-driven world, having the right tools to manage and process large datasets is crucial. That’s where Azure Data Lake Storage (ADLS) and Azure Data Factory (ADF) come in handy, making it easier than ever to store and transform your data. In this post, I’ll show you how to set up ADLS to store your Parquet files and configure ADF to manage your data flows efficiently.
Read on for an overview of both technologies.
Comments closedTom Zika performs an experiment:
In SQL Server, using the
KILLcommand to terminate a session results in an entry being logged in the error log.
This raises the question: Does theALTER INDEX REBUILDcommand with theWAIT_AT_LOW_PRIORITYoption also log its actions in the error log?
Tom does violate Betteridge’s Law of Headlines, but you should read the post regardless.
2 Comments