Everyone knows you can use SHIFT + [Left/Right Arrow] to highlight text. But you can also use ALT + SHIFT + [Up/Down/Left/Right Arrow] to select a block of text or even make a vertical selection to insert a block of text on multiple lines. Or you can use ALT + [Mouse Drag] to make a block selection with your cursor.
Notepad++ works the same way. Every once in a while, I’ll run into a scenario in some tool which doesn’t implement Alt key functionality—especially certain non-Microsoft platform database products—and it will hurt a little bit inside.
Continuing my “classics” series, Erland Sommarskog has a three-part series (with three appendices) on error handling that will take up your entire weekend:
Why do we have error handling in our code? There are many reasons. In a forms application we validate the user input and inform the users of their mistakes. These user mistakes are anticipated errors. But we also need to handle unanticipated errors. That is, errors that occur because we overlooked something when we wrote our code. A simple strategy is to abort execution or at least revert to a point where we know that we have full control. Whatever we do, simply ignoring an unanticipated error is something we should never permit us. This can have grave consequences and cause the application to present incorrect information to the user or even worse to persist incorrect data in the database. It is also important to communicate that an error has occurred, lest that the user thinks that the operation went fine, when your code in fact performed nothing at all.
Error handling is a crucial part of development. And given that SQL Server has…peculiarities…when it comes to error handling, I highly recommend reading this series.
Your choice(s) here will drive what equipment you need to buy in order to produce a high-quality video on a reasonable budget. You don’t want to overspend on lighting and camera equipment if you’re never going to be shown bigger than a 240×180 pocket in the corner. Likewise, you may not need a USB microphone if you’re going to shoot mostly studio video.
As someone thinking about getting into podcasting and webcasting, this is a top-notch set of advice.
I’ve been determined to produce a SQL Expression that was able to tell you when all the feasts and saints days are. In the following example, I’ve only put the major feast days that were generally celebrated in Britain before the reformation, but it is very easy to add or take away what I’ve given you to taste.
A fitting topic for America’s premier feast day.
Last week I demonstrated how temporal tables in SQL Server 2016 work. If you have implemented a history table—populating it with triggers or stored procedures—it works the same way.
This week, we are going to look at how to modify a temporal table that already has data in the history table.
If you’re using temporal tables for auditing, it’s important to know that yes, data can be modified.
Temporal tables are new type of database tables introduced in SQL Server 2016, these tables are system-versioned and keep history of changes (insert, delete, update) of everything happened on data rows. Retrieving change log from these tables are easy. These tables can simply tell you what was the data at specific point of the time in the table. These tables works with datetime2 columns to keep FROM DATE and TO DATE information of each change. This means these tables can be used for implementing changes in dimensions, yes you know what it called; Slowly Changing Dimension!
Slowly-changing dimensions in an OLTP scenario was the first use I could think of for temporal tables, so I’m glad Reza put this article together.
TDS — Tabular Data Stream — is the protocol that SQL Server talks with its clients. This is a proprietary protocol, owned by Microsoft (and Sybase, who have their version). Nevertheless there is exists FreeTDS which originally was a reverse-engineering effort of TDS. Now when Microsoft has published the TDS specification, they should be able to repair any cracks they may have. Check out the FreeTDS home page for further details. There appears to be a DBD::FreeTDS that goes along with it.
I used FreeTDS to connect to SQL Server from RStudio, so I endorse that method.
Curated SQL will not be taking Thanksgiving off. To compensate for the pace of blog posting activity usually drops around Thanksgiving, I’ll link to a few classic articles. That way, even if you’re in the office, you’ll have something to occupy that extra time.
Rolf Tesmer has a nice series on partitioning going. His latest entry involves calculating partition sizes in advance:
Sometimes (just sometimes) you need to calculate the size your table partitions upfrontbefore you actually go to the pain and effort of partitioning (or repartition) a table. Doing this helps with pre-sizing the database files in advance instead of having them auto-grow many many times over in small increments as you cut data over into the partitions.
Check out the entire series.
Open a document with a table valued function or procedure, click tools–> SSDT Dev Pack –> QuickDeploy or use the keyboard options in SSDT to make Tools.QuickDeploy to a key combination (I use Ctrl+Q, Ctrl+D but it is a little awkward) and your code will be deployed, any messages are added to the standard output window.
If the active document has anything other than stored procedures or table valued functions then they will not be deployed and also if you have more than one procedure of function, all of them in the active document will be deployed.
Ed is quickly becoming one of my favorite bloggers. It seems like every other day, he has a new tool available for us.
Instead of single JSON object you can organize your data in this “collection”. If you do not want to explicitly check structure of each JSON column, you don’t need to add JSON check constraint on every column (in this example I have added CHECK constraint only on EmailAddresses column).
If you compare this structure to the standard NoSQL collection, you might notice that you will have faster access to strongly typed data (FirstName and LastName). Therefore, this solution is good choice for hybrid models where you can identify some information that are repeated across all objects, and other variable information can be stored as JSON. This way, you can combine flexibility and performance.
Okay, we’ve hit my first major problem with JSON support: rampant violation of first normal form. You can create check constraints on JSON code, and that’s pretty snazzy I guess, but I know a better way to store relational data in a relational database system. JSON support is great when you ask SQL Server to be a holder of text blobs, but this is begging for bad design decisions.