Today, it is time to consider the astonishing next step, that a single socket system is the best choice for a transaction processing systems. First, with proper database architecture and tuning, 12 or so physical cores should be more than sufficient for a very large majority of requirements.
We should also factor in that the second generation hyper-threading (two logical processors per physical core) from Nehalem on has almost linear scaling in transactional workloads (heavy on index seeks involving few rows). This is very different from the first generation HT in Willamette and Northwood which was problematic, and the improved first generation in Prescott which was somewhat better in the positive aspects, and had fewer negatives.
Joe knows a lot more about this than I do, but I’m very hesitant about this for two reasons. First, scale. When we start looking at hundreds of concurrent requests, would a single-socket machine really work? I don’t know to answer to that, but in my simplistic “more is better than fewer” rule of thumb, I’d err on the side of caution, especially if it isn’t my money paying for this.
Second, there are batch processes and large background activities which occur even on extremely transactional OLTP systems. Think about running CHECKDB or ETL processing or troubleshooting/monitoring processes. These are going to be processes which benefit from parallelism, and if you’re seriously limiting core counts (which a single socket would necessarily do), you might end up in a bad way when they run even if your “normal” workload performs a little better.
Fear not! The SSIS team has provided a set of updated links for SSIS 2016 SSDT for RC2. There’s other good information in that post. If you want to tinker with SSIS 2016 RC2, I encourage you to read it.
But Wait, There’s More
Once I’d done all this, I could create an SSIS project and add a Script Task to a package. But I could not open the Visual Studio Tools for Applications (VSTA) code editor. When I clicked the “Edit Script…” button in the Script Task Editor, nothing happened.
I contacted the SSIS Development Team (we hang out), and let them know what I was seeing. They are aware of the issue and sent the following screenshot
Sounds like there are still some kinks to work out before release.
As I’m currently planning to migrate the entire BI architecture of one of my customers to the cloud, this made me think: can we ditch SSAS as we know it already in favor of Power BI? What are the alternatives?
To study that, I’ve put some diagrams together to show the possibilities of moving BI to the cloud. First, I’ll discuss the possible architectures, then the impossible architecture (but maybe the situation I was looking for).
One man’s opinion: there will be SSAS for Azure. I have no proof of this, and the nice part about having no proof is that I can throw out wild speculation without fear of violating NDA…. But to me, Power BI solves a different problem and acts in conjunction with SSAS rather than as its replacement. I also don’t see any technical reasons why SSAS couldn’t live in the cloud, and so that leads me to believe that it will be there eventually. But hey, it turns out wild speculation is occasionally wrong…
Essentially, the problem is that a poor estimate can be made not simply when
SYSUTCDATETIME()) appears, as Erland originally reported, but when any
datetime2expression is involved in the predicate (and perhaps only when
DATEADD()is also used). And it can go both ways – if we swap
<=, the estimate becomes the whole table, so it seems that the optimizer is looking at the
SYSDATETIME()value as a constant, and completely ignoring any operations like
DATEADD()that are performed against it.
Paul shared that the workaround is simply to use a
datetimeequivalent when calculating the date, before converting it to the proper data type. In this case, we can swap out
SYSUTCDATETIME()and change it to
I suppose switching to GETUTCDATE isn’t too much of a loss, but it looks like (according to Paul White in the second linked Connect item) this appears to have been fixed in SQL Server 2014.
The CROSS APPLY and the old-school solutions are by far the best choice for dense indexes, i.e. when the first column has a low degree of uniqueness. The old-school solution is only that fast because the optimizer short-circuits the query plan.
LEAD() and the old school strategy are best for selective indexes, i.e. when the first column is highly unique.
There’s a nice set of options available so if one doesn’t work well with your particular data set, try out some of the others and see if they work for you.
WRITELOG waits are a scalability challenge for OLTP workloads under load. Chris Adkin has a lot of experience tuning SQL Server for high-volume OLTP workloads. So I’m going to follow his advice when he writes we should minimize the amount logging generated. And because I can’t improve something if I can’t measure it, I wonder what’s generating the most logging? OLTP workloads are characterized by frequent tiny transactions so I want to measure that activity without filters, but I want to have as little impact to the system as I can. That’s my challenge.
Check out the entire post, as this is a good exercise in investigating busy transactional systems.
Extended Event (XEvent) feature is available as public preview in Azure SQL Database as announcedhere. XEvent supports 3 types of targets – File Target (writes to Azure Blob Storage), Ring Buffer and Event Counter. Once we’ve created an event session, how do we inspect the event session target properties? This blog post describes how to do this in 2 ways: using the User Interface in SSMS and using T-SQL.
It’s nice to see Extended Events making their way into Azure SQL Database.
We are committed to continuously updating the JDBC driver to bring more feature support for connecting to SQL Server, Azure SQL Database, and Azure SQL DW. Please stay tuned for upcoming releases that will have additional feature support. This applies to our wide range of client drivers including PHP 7.0, Node.js, ODBC, and ADO.NET which are already available.
Don’t forget Hadoop integration (e.g., via Sqoop) while you’re at it…
Fortunately Power Query still is available with Live Connection. This gives you ability to join tables, flatten them if you require, apply data transformation and prepare the data as you want. Power Query can also set the data types in a way that be more familiar for the Power BI model to understand. If you want to learn more about Power Query read Power Query sections of Power BI online book.
Reza shows some techniques and also the negative repercussions to using Live Connection. This is a good read if you’re getting into Power BI.
SQLPackage.exe – Needs to be made into at least 3 cmdlets (and possibly more; we have added ideas for additional cmdlets below). The first 3 cmdlets that need to be made into are:
This seems reasonable and would help maintain databases.