A Fine Slice Of SQL Server
Daniel Hutmacher just wants those fractions of pennies:
A number of business processes require you to distribute a value over a date range. However, if your distribution keys and values don’t add up perfectly or aren’t perfectly divisible, it’s very easy to get rounding errors in your distributions.
Read on for an example of the problem and how you can fix it.
Comments closedRyan Booz shares some first thoughts on Amazon’s Babelfish offering:
The imputes for creating the tool is clear for AWS. Provide a way for customers to easily connect a SQL Server app to Aurora Postgres, saving big on licensing fees and reducing total cost of ownership. Assuming the tool is successful at some level, I’m sure it will provide a revenue boost for Amazon and some customers might (initially) feel a win. No harm, no foul on Amazon for leading the effort. Free markets, baby!
No matter how clever Babelfish is, however, I just can’t see how this is ultimately a win for SQL Server or PostgreSQL… or the developers that will ultimately need to support these “hybrid” apps.
I think Ryan makes good points and does hit upon the crux of the problem. I’d also say that there’s a secondary problem which Ryan hints at, but it is that a query may be sufficiently fast in one database variant but perform horridly in another. A classic example of this is a solution built on cursors in Oracle and then bringing that to T-SQL.
Comments closedHugo Kornelis takes us through the concept of aggregate functions:
SQL Server currently supports three operators that can compute aggregations: Hash Match, Stream Aggregate, and Window Aggregate. These operators all use the same basic principle of maintaining internal counters as rows are processed, so that the final value of those internal counters is the expected value.
Read on to see the full list, as well as how they operate.
Comments closedRandolph West hits the archives:
When we look at the several data types for storing LOB data in SQL Server, the ones we might think of first are specifically designed for storing clear text. These are
VARCHAR(MAX)andNVARCHAR(MAX), which you can read up about on Microsoft Docs. They’re variable-length data types that are used to store clear text between 8,000 bytes and 2 GB. The key distinction is thatNVARCHARis for Unicode strings, which use at least two bytes per character.Then we get
VARBINARY(MAX), also for storing between 8,000 bytes and 2 GB. This data type does not make any assumptions about the stored blob’s format. You can store text, zip files, images, public key security certificates, you name it. If it can be persisted to storage, it can be stored inVARBINARY(MAX).
Read on for an example of Randolph using Gzip compression and storing XML data as binary for auditing purposes.
Comments closedSteve Jones takes us through querying straightforward JSON data in SQL Server:
Recently I saw Jason Horner do a presentation on JSON at a user group meeting. I’ve lightly looked at JSON in some detail, and I decided to experiment with this.
All in all, I’ve been pretty happy with the syntax for JSON manipulation in T-SQL. I’m not the biggest user of JSON around, but when I’ve needed to slice or build JSON, even when I needed to build it in a certain way to emulate an old application, it has worked for me.
Comments closedDaniel Hutmacher shows another good use of the APPLY operator:
You can tell by the plan why this is an inefficient query: the SQL expression in the common table expression is executed once for every time that it’s referenced in the code.
Better living through CROSS APPLY
You could store the results of the CTE in a temp table, but where’s the fun in that? Instead, why not use the CTE once, and then return four rows for each row that the CTE spits out? That’s exactly what CROSS APPLY does.
Read the whole thing and appreciate that much more all the nice things you can do with APPLY.
Comments closedArun Sirpal shows off some missing functionality in SQL Server:
Being in the cloud does have many benefits, from lower administration to fast scaling but another “side effect” of operating in Azure SQL Database is the cloud first nature of changes. By this I basically mean new features always get pushed to Azure first before the classic on-premises version so some gems come to light.
Here is one for you. Have you ever wanted MySQL’s functinality to apply LEAST() and GREATEST() argument? Well, you can now, in Azure.
I can’t say that I would use this every day or anything, but I have felt the pain of not having it. There are workarounds, though nothing as convenient as syntax. Hopefully this shows up on-prem in the next version of SQL Server.
Comments closedShane O’Neill has a bandolier of SQL tricks to show off:
Recently the DBA Team Lead and I were reviewing some SQL code, and we came across some SQL that neither of us had frequently encountered before. This led to a brief watercooler moment where we shared some uncommon SQL that we had seen. Perfect blog post material, I think.
I had previously learned about ODBC date functions from Shane and also learned about CURRENT in this post, so check it out.
Comments closedDave Mason uses an external memory:
There are certain T-SQL statements whose syntax I have trouble remembering. When those situations arise, I might look up the syntax online; find the same type of object in SSMS, right-click it, script out the object, and use that as a starting point; or find one of my own scripts that has the syntax I’m looking for. Another option that I often overlook is T-SQL code snippets.
Click through to see how to use code snippets in SQL Server Management Studio. You can also create your own as well.
Comments closedJared Poche is flirting with the dark side:
I work on hundreds of databases with the same schema. They have different data sets and distributions, different sizes, and their statistics are going to update at different times. But if one of them chooses a bad plan, I have to push aside whatever other work to research the high CPU on database xyz.
Consistency is really valuable to me. And in this case, the answer is simple. Yes, I want to scan the fast, small memory-optimized table variable first, and use it to filter the larger, slower table. Adding a join hint or a force order to this query should keeps its plan and performance consistent.
Click through for a few examples of where query hints can be useful, but also where they can fail you in unexpected ways.
Comments closed