Finally, as a first time host, I was obviously hoping that this topic would garner some responses, but you never know until you hit that post button whether you’ve selected something of interest to the community or not. Thankfully, this month’s topic picked up views from over 20 countries and over 20 blog responses. The list (with a brief post-by-post commentary from me) is below. Happy reading and thanks again for reading/writing/participating!
Read on for the full list of respondents.
Aaron Bertrand and Steve Hughes talk about string splitting, and Aaron also discusses string concatenation. First Aaron:
That may not look like a massive simplification, but don’t forget about all the logic buried behind the table-valued function in the first example. And if you’re like several shops I know, if you look across your codebase and see all the messy uses you have for either of these methods, the benefits should be even more clear – and testing should bear that the performance savings compared to traditional, expensive methods are the sweetest part of the deal.
The STRING_SPLIT function will return a single column result set. The column name is “value”. The datatype will be NVARCHAR for strings that are NCHAR or NVARCHAR. VARCHAR is used for strings that are CHAR or VARCHAR types.
These two functions are small, but come in handy quite frequently.
Because how am I supposed to know whether a particular date was before daylight saving started or after? I might know that an incident occurred at 6:30am in UTC, but is that 4:30pm in Melbourne or 5:30pm? Obviously I can consider which month it’s in, because I know that Melbourne observes daylight saving time from the first Sunday in October to the first Sunday in April, but then if there are customers in Brisbane, and Auckland, and Los Angeles, and Phoenix, and various places within Indiana, things get a lot more complicated.
To get around this, there were very few time zones in which SLAs could be defined for that company. It was just considered too hard to cater for more than that. A report could then be customised to say “Consider that on a particular date the time zone changed from X to Y”. It felt messy, but it worked. There was no need for anything to look up the Windows registry, and it basically just worked.
But these days, I would’ve done it differently.
Now, I would’ve used AT TIME ZONE.
Read on for the scenario.
Microsoft announced many new features in SQL Server 2016 SP1 and the fanfare was mostly centered around the Enterprise features now available in SQL Server 2016 Standard Edition. Many may have missed some hidden gems in the announcement. Two of these are columns added to the existing DMVs, sys.dm_server_services and sys.dm_os_sys_info. The columns provide information for two specific features that previously had to be gathered by opening gpedit.msc and/or scrolling through SQL error logs. I am referring to Lock Pages in Memory and Instant File Initialization (enabled via Perform Volume Maintenance Tasks privilege).
It is now possible to simply query the DMVs to determine if these are being used for the running SQL Server instance.
Click through for the details.
SESSION_CONTEXT() brings two major innovations. Firstly, it replaces a 128-byte scalar payload with a key-value structure that can accommodate 256kB of data. You can really go to town filling this thing up.
The second change is less glamorous, but possibly more significant: it is possible to set an entry to read-only, meaning that it can safely be used for the kind of contextual payload you don’t want tampered with. This makes me happy, not because I currently have a great need for it, but because it neatly ties in with things I have been thinking about a lot lately.
Read on for more.
Working with role playing dimensions, which are found when you have say multiple dates in a table and you want to relate them back to a single date table, have always been problematic in SQL Server Analysis Services Tabular. Tabular models only allow one active relationship to a single column at a time. The picture on the left shows how tabular models represent a role playing dimension, and the model on the right is the recommended method for how to model the relationships in Analysis Services Tabular as then users can filter the data on a number of different date tables.
The big downside to this is one has to import the date table into the model multiple times, meaning the same data is imported again and again. At least that was the case until SQL Server 2016 was released. This weeks TSQL topic Fixing Old Problems with Shiny New Toys is really good reason to describe a better way of handling this problem.
Read on for how to implement calculated dimensions.
There are other issues with the MERGE statement. It has bugs… some of which can cause database corruption.
Here we have a Shiny New Toy (feature), supposed to make life easier, yet it causes problems. Until it can perform better (and the bugs are eliminated), I just don’t use it.
Beware the Shiny New Toys.
Wayne makes a great point. Not all new things are good, even when they’re potentially quite useful. I love shiny new toys a lot, but part of being a database administrator is protecting data, and part of that means being able to trust your tools. Sometimes the tools work really well right out of the gate, and sometimes (like in the case of MERGE) they don’t.
Paul’s process gives you info on every instance of a wait the query experienced and it’s very easy to aggregate those results to see the top waits and their total effect on a query. Quite often though, you don’t need a lot of detail. You don’t need to know every wait, just the top several. If you are already generating the actual query plan to have a in-depth look at the plan, wouldn’t it be nice if the query wait stats were already in there for you?
Now they are. In SQL Server 2016 (I’m told it came in SP1, but I don’t have a non-SP1 instance to verify that), the actual execution plan includes the top waits for the query execution in the plan. You can see them by clicking on the left-most (first) operator in the plan and viewing the Properties (shortcut F4). It will list the top waits right there in the properties dialog for you.
Getting single-query wait stats in the execution plan makes life so much simpler.
So what’s our shiny new permissions toy? Well, over time we have been getting more and more server level permissions that affect database level objects. A lot of these can be really useful and I’m hoping we will see more and more of them.
Click through for a couple helpful tables of permissions.
For now, consider the following set of propositions, given that we’re asking at time Y for all changes since a prior time X.
- INSERT: The key did not exist at time X but does exist at time Y.
- DELETE: The key existed at time X but does not exist at time Y.
- UPDATE: The key existed at both time X and at time Y, and at least one change occurred between time X and time Y.
Given these assumptions, we can begin work on a temporal queries that return the necessary rows. Solving for these conditions will require all rows that were active as of time X, rows that were (or are) active at time Y, and for the final case, all rows that were active in-between times X and Y. Since this is a range-based scenario, our best Temporal predication option will be either FROM or BETWEEN. The difference between these two is subtle: FROM uses an open interval (non-inclusive at both endpoints), whereas BETWEEN uses a half-open interval, inclusive on the end date. Given the choice in the scenario, BETWEEN makes more sense, as we can take advantage of the inclusive endpoint to avoid dropping a badly-timed row. But more on that in a moment.
Adam put a lot of thought into edge cases, making this a must-read.