A Fine Slice Of SQL Server
Koen Verbeeck hits a concatenation issue:
I was writing some dynamic SQL that generates some SQL statements to load my facts and dimensions into a data warehouse. Some of those SQL statements can become very long, for example if a dimension has a lot of columns. When debugging, I noticed a couple of statements failing with various errors. Turns out, they were truncated after 4000 characters. What was going on?
Read on to see what happened.
Leave a CommentRyan Adams continues a series on querying the serverless SQL pool in Azure Synapse Analytics:
There are two ways to read data inside Data Lake using the Synapse Serverless engine. In this article, we’ll look at the second method which uses an external table to query a path within the lake.
Synapse is a collection of tools with four different analytical engines (Dedicated Pool, Spark Pool, Serverless Pool, Data Explorer Pool). This gives you a lot of options for ingesting, transforming, storing, and querying your data. Here you will use the Synapse Serverless Pool to query the data in your ADLS account.
Read on for a demonstration.
Leave a CommentRyan Adams begins a series on reading data from the data lake:
There are two ways to read data inside Data Lake using the Synapse Serverless engine. In this article, we’ll look at the first method which uses OPENROWSET to query a path within the lake.
Synapse is a collection of tools with four different analytical engines (Dedicated Pool, Spark Pool, Serverless Pool, Data Explorer Pool). This gives you a lot of options for ingesting, transforming, storing, and querying your data. The article will focus on how you can use the Synapse Serverless Pool to query the data in your ADLS account.
Click through for a primer on the topic, as well as a demo video.
Leave a CommentReitse Eskens does a performance comparison:
A few days ago, I heard someone stating that Group By was much quicker than Distinct. Less disk impact, less memory etc.
So, I thought I’d find out if it’s true or not because I found it interesting. I always thought there was no difference. I tested a single small table and found no difference in speed, reads or execution plan. But that’s no real world example. Usually the tables contain a lot of data and are joined to other tables.
Click through for the results of Reitse’s analysis.
Leave a CommentKendal Van Dyke walks us through a few new bits of T-SQL in SQL Server 2022:
Time series data is often used for historical comparisons, anomaly detection and alerting, predictive analysis, and reporting, where time is a meaningful axis for viewing or analyzing data.
Time series capabilities in SQL Server were introduced in Azure SQL Edge, Microsoft’s version of SQL Server for the Internet of Things (IoT) which combines capabilities such as data streaming and time series with built-in machine learning and graph features.
I am happy to see that these operators and functions made the leap from Azure SQL Edge and am hopeful that we’ll see a bit more of what makes databases like influxdb so useful for time series make their way in as well.
Leave a CommentDeborah Melkin has a confession and a defense:
I feel like this is where I should say something like, “Hi, my name is Deborah and I’ve used nolock in production.” I would also have to confess to doing things like using correlated sub queries, not using a foreign key, implemented complicated triggers, etc. I often talk about how the first real SQL script I wrote had cursors running over temp tables in SQL Server 6.5, which I’m fairly certain was one of the first thing I read you were NOT supposed to do. And oh, hello there, denomalized table and dynamic SQL! I’m sure I’ve done more things than this too. These are just the ones I can remember doing, or at least I’m willing to admit in public.
With some of these, the answer is “that’s the best alternative I had at the time.” With correlated sub-queries, I wouldn’t even consider that a bad thing. Granted, I personally prefer a combination of common table expressions and the APPLY operator but that’s usually not for performance reasons.
When someone says, “Find all the rows that have been deleted,” it’s a lot easier when the table has an Id/Identity column. Let’s take the Stack Overflow Users table:
It has Ids -1, 1, 2, 3, 4, 5 … but no 6 or 7. (Or 0.) If someone asks you to find all the Ids that got deleted or skipped, how do we do it?
Click through for two methods, one specific to SQL Server 2022 and one which works for all versions of SQL Server.
Comments closedJose Manuel Jurado Diaz writes a program:
Today, I worked on a service request that our customer is looking for all blocking issues that is happening in their database. We have many articles about it Lesson Learned #22: How to identify blocking issues? – Microsoft Community Hub and in Diagnostics Settings and QDS we can collect this information but all points to that we cannot see the TSQL that is blocking and TSQL command that is blocked in an easy way. In the following script that I share as a script example we could take this one.
First of all, please, remember that a blocking issue is normal and fundamental for any RDBMS. This script is basically when you need to understand what is happening to improve or reduce this.
I was a little surprised the answer wasn’t to use Extended Events, though this does work if you simply need something to run in ad hoc scenarios.
Comments closedBill Fellows finds out whose line (feed) it was, anyway:
I ran into an issue today that I wanted to write about so that maybe I remember the solution. We ran into a case where the source data in a column had an unprintable character. In this case, it was a line feed character, which is ASCII value 10, and they had 7 instances in this one row. “How did that get in there? Surely that’s an edge case and we can just ignore it,” and dear reader, I’ve been around long enough to know that this is likely a systemic situation.
Click through for two ways of answering this against data already in SQL Server, as well as one additional route explained but sans demonstration.
Comments closedChad Callihan compares and contrasts:
We recently looked at looked at comparing schemas using Azure Data Studio. What if we need to compare tables by using a query? For this post we’ll compare using EXCEPT, NOT IN, and NOT EXISTS to find differences between two tables.
Our two tables to compare will be Comic and Comic_Copy. Based on counts, we have 48 more records in Comic than we do in Comic_Copy. Let’s find the differences.
In Chad’s specific query, NOT EXISTS works great. Where I like EXCEPT is when you need to see if any of the non-key columns differ. For example, if you also needed to compare titles for rows with the same ID and ensure those titles matched.