With this combination of external data source, file format, and table, we can create our next table. This is a data set which lives in Spark and contains…well, the same data. What we join together isn’t that interesting, after all.
The end result is you get your data back as though these were all local, on-prem tables.
LSTM is a type of Recurrent Neural Network (RNN) that allows the network to retain long-term dependencies at a given time from many timesteps before. RNNs were designed to that effect using a simple feedback approach for neurons where the output sequence of data serves as one of the inputs. However, long term dependencies can make the network untrainable due to the vanishing gradient problem. LSTM is designed precisely to solve that problem.
Sometimes accurate time series predictions depend on a combination of both bits of old and recent data. We have to efficiently learn even what to pay attention to, accepting that there may be a long history of data to learn from. LSTMs combine simple DNN architectures with clever mechanisms to learn what parts of history to ‘remember’ and what to ‘forget’ over long periods. The ability of LSTM to learn patterns in data over long sequences makes them suitable for time series forecasting.
This is a nice overview and as a bonus, there’s a notebook as well where you can try it on your own.
Before you go too crazy trying to stabilize performance with hundreds of forced plans, here are a couple things worth mentioning about plan forcing.
1. I think of plan forcing as a temporary solution. I do not view it as a “set it and forget it” option.
2. Use T-SQL for forcing and un-forcing (versus the UI) so you can track when it was done, and by whom, in change control. Nothing is entered in the ERRORLOG, or anywhere else, and there is currently no event that captures it.
3. Evaluate both the execution statistics (average and total) and the different plans for the query to determine the “best” plan to force.
Erin makes some great points in this post, so read the whole thing.
When people want to pivot data using T-SQL, they either use a standard solution with a grouped query and CASE expressions, or the proprietary PIVOT table operator. The main benefit of the PIVOT operator is that it tends to result in shorter code. However, this operator has a few shortcomings, among them an inherent design trap that can result in bugs in your code. Here I’ll describe the trap, the potential bug, and a best practice that prevents the bug. I’ll also describe a suggestion to enhance the PIVOT operator’s syntax in a way that helps avoid the bug.
If you use the
PIVOT operator, you definitely want to read this article.
A SQL Server data tools project is checked out of GitHub, built into a DacPac, four containerized SQL Server instances are spun up using clones of the ‘Seed’ docker volume. The DacPac is applied to a database running inside each container, which a tSQLt test is then executed against, finally, at the end very end the tSQLt results are aggregate and published.
This is an interesting approach to the problem of lengthy tests: run them on several separate machines concurrently.
When you are using a matrix like this, it can be difficult to tell which “Year” column is coming from which table, as shown below.
One solution to this problem is to rename all the columns in each table by pre-pending Order or Delivery to the front of the existing column names. Once that is done, it is much clearer which column is which.
Matt describes the concept for you, but also has a video showing how to do this.
I’ve always felt responsible for making such a big deal about the differences between estimated and actual plans. I implied in the first edition of the execution plans book (get the new, vastly improved, 3rd edition in digital form for free here, or you can pay for the print version) that these things were so radically different that the estimated plan was useless. This is false. All plans are estimated plans. However, actual plans have some added runtime metrics. It’s not that we’re going to get a completely different execution plan when we look at an actual plan, it’s just going to have those very valuable runtime metrics. The problem with getting those metrics is, you have to execute the query. However, this is no longer true in SQL Server 2019 (CTP 2.4 and greater) thanks to sys.dm_exec_query_plan_stats
Click through for an example, as well as what you need to do to enable this.
In Shabnam Watson’s recent blog post on a bug she found when trying to create a Live connection from Power BI to Analysis Services she mentioned that the AutoSetDefaultInitialCatalog server property could be used to solve her problem. This piqued my interested because I’d seen this property but had no idea what it did exactly or why it was there. Luckily, now I work for Microsoft, it’s even easier for me to find out about things like this from the dev team and Akshai Mirchandani was able to help.
First of all, what does it do? The documentation on this property has just been added here, and this is what it says:
Chris has connections, and we get to benefit from that.