If you’ve ever looked at sys.sysprocesses or sys.dm_exec_requests (or a number of other DMVs), you’ve noticed there is a column called “sql_handle” that contains some binary gobbledygook. Books Online gives the (un)helpful definition as “Hash map of the SQL text of the request.”
“Binary gobbledygook” is probably the best description of the plan handle available.
I was testing some calculations in my tabular model when I noticed that my Lost Customers calculation wasn’t working as expected. This was rather interesting to me since the calculation I was using was from DAXPatterns.com. After some experimentation, I determined that the difference between my data situation, and the situation that fit the DAX Pattern was that my customer dimension was a Type 2 Slowly Changing Dimension. That meant I couldn’t use the customer key to identify a unique customer, since each customer could have multiple customer keys (surrogate keys) with the same customer number (business key). Specifically, if a customer made a purchase in December 2015, then changed their name, then made a purchase in January and February, my calculation was counting them as lost because it was not recognizing that the customer with the new name and same customer number was actually the same customer.
She ends up with two solutions, each with different trade-offs. Knowing as little DAX as I do, looking at two different ways of solving this problem is great because it gives you more insight into language semantics.
So obviously we brought back internet Sales for all years captured into the cube. So now we need to understand the WHERE clause and how to use it properly in MDX.
The “WHERE” clause in MDX is used to define another slicer and limit the attributes from the defined slicer. The slicer used in the “WHERE” clause cannot be the same slicer used when defining your rows hence why I use the date dimension. The MDX query below depicts the WHERE clause and syntax:
I liked Dan’s introductory-level presentation on learning MDX, and this blog series is following along those same lines.
A fundamental component of SQL Server is locking and locks. Locks within SQL Server are critical to the proper functioning of the database and the integrity of the data within the database. The presence of locks does not inherently mean there is a problem. In no way should locking within SQL Server be considered a monster, though locks may often times be misconstrued in that light.
This is an introductory-level discussion, so it doesn’t include optimistic concurrency or snapshot/RCSI, but if you’re unfamiliar with pessimistic concurrency, this is a good place to start.
Did you know that you can change the password on the SQL Service account that is running your SQL instance without a reboot or restart? Turns out this is true. We have a new round of password requirements and it means that we need to change passwords on servers more often. But, since we need our servers up and reboots have to be heavily planned, we needed a solution that kept us from having to restart an instance after a password change. This lovely msdn article explains all the details, but let me give you the cliffs notes.
This is helpful for those max uptime scenarios where even a momentary service restart requires planned downtime.
You’ll notice that these results are wildly different from those above. What we’re looking is largely a server versus a database, but not completely. I mean that sys.dm_os_wait_stats is showing the waits for the instance on which my primary Azure SQL Database is currently running. Most of those waits are mine, but because it’s part of the management structure of Azure, sys.dm_os_wait_stats shows some information that’s not applicable, directly, to me. The “server” is not really that. It’s a logical container holding your database. There’s a lot more to it under the covers. To get the waits that are absolutely applicable to me and my databases, I have to go to sys.dm_db_wait_stats.
Azure SQL Database is going to behave a bit differently from on-premise SQL Server, so if you’ve got an Azure SQL Database, pay attention to those differences.