I appreciate their attempt to improve performance, but this presents a challenge for performance-oriented DBAs in many ways. It now changes expected behavior from the basic configuration without prompting or notifying the administrators in any way that this is happening. It also means that if I have a host cluster with a mixed server CPU topology, I could now have NUMA misalignments if a VM vMotions to another physical server that contains a different CPU configuration, which is sure to cause a performance problem.
Worse yet is that if I were to restart this VM on this new host, the hypervisor could automatically change the vNUMA configuration at boot time based on the new host hardware.
I now have a change in vNUMA inside SQL Server. My MaxDOP settings could now be wrong. I now have a change in expected query behavior.
Definitely read the whole thing if you’re using vmWare.
Stream 2 sends along 27 MB worth of data. It’s packaging everything Polybase needs to perform operations, including JAR files and any internal conversion work that the Polybase engine might need to translate Hadoop results into SQL Server results. For example, the sqlsort at the bottom is a DLL that performs ordering using SQL Server-style collations, as per the Polybase academic paper.
Stream 2 is responsible for almost every packet from 43 through 19811; only 479 packets in that range belonged to some other stream (19811 – 43 – 18864 – 425 = 479). We send all of this data to the data node via port 50010.
If you love looking at Wireshark streams, you’ll love this post.
Instantly it is apparent that the most resource intensive query was the same query across both workload tests and note that the query hash is consistent too. It is also apparent that this query performs worse under the new cardinality estimator model version 120. To investigate and understand why this particular query behaves differently under the different cardinality estimators we’ll need to look at the actual query and the execution plans.
Looking at the information in #TempCEStats and the execution plans, the problematic query below belongs to the SLEV stored procedure.
There’s also a discussion of Query Store in there, but it’s important to understand how to figure this out even if you’re on 2014 and don’t have access to Query Store.
The Synoptic Panel allows you connect areas in an image with attributes in your data model.
Using the Synoptic Designer you can convert an image that you have to one that is compatible with plotting out data points on it.
You can fill each area with a color or a saturation of a color.
The Synoptic Panel is definitely a more advanced visual, but it’s very powerful.
The best way is to run the stored procedure yourself to generate and save an “actual” execution plan, which contains the estimates SQL Server used when it generated the plan as well as actual rowcounts, actual memory granted, etc. It will also contain a green tooltip with a “missing index request” if SQL Server thinks an index would help.
This is an introductory-level post which contains good advice.
And there it is, our image in our repository tagged as v1! The reason that I’ve tagged it as v1 is that if I make any changes to my image, I can push the updated image to my repository as v2, v3, v4 etc…
Still with me? Awesome. Final thing to do then is pull that image down from the repository on a different server. If you don’t have a different server don’t worry. What we’ll do is clean-up our existing server so it looks like a fresh install. If you do have a different server to use (lucky you) you don’t need to do this bit!
Read the whole thing.
This release is built on the latest SQL Server 2016 release, offers additional language surface coverage to aid in migrations from SQL Server and other platforms, adds PolyBase connectivity to the current versions of Hadoop from Hortonworks, additional PolyBase security with Kerberos support and credential support for Azure Storage Blobs, greater indexing and collation support and improvements to the setup and upgrade experience with FQDN support.
The majority of these capabilities have shipped in the monthly releases of Azure SQL Data Warehouse service and/or SQL Server 2016 following the cloud first principle of shipping, getting feedback, and improving rapidly across all of our products.
Click through for the list of enhancements. There are quite a few of them.
But my Backup file is still not visible in the wizard!Permissions. If you drill down into the folders in Linux, we found that the files already present in the /data/ folder are owned by the user mssql. Our recently copied backup file is NOT owned by mssql, and it not accessible to other users. So, our wizard cannot see the file.
The whole process is pretty straightforward.
Varying Levels of Peak Workloads
Let’s say during month-end close the reporting activity spikes much higher than the rest of a typical month. In this situation, it’s a shame to provision hardware that is underutilized a large percentage of the rest of the month. This type of scenario makes a scalable PaaS service more attractive than dedicated hardware. Do note that currently Azure SSAS scales compute, known as the QPU or Query Processing Unit level, along with max data size (which is different than some other Azure services which decouple those two).
Read on for more use cases.
A semi-additive average? What exactly are you trying to calculate? Let me explain first. A semi-additive measure is a measure that can be summed across some dimensions, but not all. Typically it’s the time dimension that isn’t additive. For example, the stock level at various warehouses. You can add all the stock levels of your warehouses together, to get an idea of how much stock you have for your entire company. However, you can’t add the stock level across time. 250 stock yesterday and 240 stock today doesn’t equal 490 stock for the two days. In reality the sum aggregation is replaced with another aggregation when aggregating over the non-additive dimension. In our stock example, we could use the last value known (240) or the average (245). Which aggregation you want depends on the requirements.
In this blog post I’m going to calculate a semi-additive measure, using the average for the non-additive dimension. Quite recently a colleague asked how you could calculate this in DAX. The use case is simple: there are employees that perform hours on specific tasks. The number of hours is our measure. The different tasks (the task dimension) is additive. The employee dimension however is not when we calculate an average. When two employees are selected, the result should not be the average of all the individual hours, but rather the average of the sum of the hours per employee. Let’s illustrate with an example:
That’s really interesting, and a good bit easier to do than the T-SQL equivalent (at least in one step).