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Curated SQL Posts

Understanding Analysis Services Memory Behavior

Published 2018-12-10 by Kevin Feasel

Shabnam Watson walks us through SQL Server Analysis Services memory settings and application behavior under memory stress:


If memory consumption is below the Low limit everything is fine and it is free to stay in memory. Once the consumption passes the Low limit a cleaner thread wakes up and tries to clean up memory. At this point price of memory is no longer zero. It starts from 2 at the Low limit and goes as high as 1000 when memory consumption reaches the Total limit. The higher the memory pressure the more aggressive the cleaner gets. Once memory consumption reaches the Hard limit all connections/sessions are closed and queries are cancelled with an out of memory error.

This is a thorough explanation with some good demos and terrible queries.  Give it a read.

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Passing Data To Java In SQL Server 2019

Published 2018-12-10 by Kevin Feasel

Niels Berglund continues looking at the SQL Server 2019 Extensibility Framework’s support for Java:

In Java, there are also helper components, (a topic for future posts), but the integration is not as tight, so when we want to pass data into and out of Java we need to code somewhat more explicit to make data passing possible.
In our Java code, we need to represent the data passed in and out as class member column arrays. You define in your classes, one array per column passed in, and one array per column returned. These column arrays are some of the “magic” members I mentioned above, and they are the equivalent to InputDataSet, and OutputDataSet.
The components that are part of the Java extension need to know about these members as the components either populate them when pushing data into Java or read from them when returning data from Java. The way that the components know about the members is based on a naming standard.

It’s definitely easier to pass data in and get data back from R and Python, but I suppose part of that is Java being a static, compiled language.

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The Intuition Behind Principal Component Analysis

Published 2018-12-07 by Kevin Feasel

Holger von Jouanne-Diedrich gives us an intuition behind how principal component analysis (PCA) works:


Principal component analysis (PCA) is a dimension-reduction method that can be used to reduce a large set of (often correlated) variables into a smaller set of (uncorrelated) variables, called principal components, which still contain most of the information.
PCA is a concept that is traditionally hard to grasp so instead of giving you the n’th mathematical derivation I will provide you with some intuition.
Basically PCA is nothing else but a projection of some higher dimensional object into a lower dimension. What sounds complicated is really something we encounter every day: when we watch TV we see a 2D-projection of 3D-objects!

Click through for the rest of the story.

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Thoughts On Exclusive Locks

Published 2018-12-07 by Kevin Feasel

Louis Davidson shares some thoughts on exclusive locks in SQL Server:


You will find that the SELECT statement executes, ignoring the exclusive lock, because it is not a write lock, and the data on the page has not been changed.
The main reason people try to do this is to force access to a row in a single threaded manner. For example, building their own sequence number, either in a row they update, or by trying to do MAX() on all of the data in a table to make sure only one reader gets the same value.
This is generally a bad idea, since locking an entire table is a generally bad idea, but if you needed to block readers, you can couple the XLOCK with a PAGLOCK. So, change the first reader to:

BEGIN TRANSACTION;
SELECT *
FROM   Demo.Test WITH (XLOCK,PAGLOCK);

As Louis points out in the summary, locking is complicated.  Having a good understanding of the locking model will serve you well, though.

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NoSQL? No! MoSQL

Published 2018-12-07 by Kevin Feasel

Steve Jones points out a bit of a shift at Google:


Google is doing more SQL, or at least shifting towards relational SQL databases as a way of storing data. At least, some of their engineers see this as a better way to store data for many problems. Since I’m a relational database advocate, I found this to be interesting.
When Google first started to publish information on BigTable and other new ways of dealing with large amounts of data, I felt that these weren’t solutions I’d use or problems that many people had. The idea of Map Reduce is interesting and certainly applicable to the problem space Google had of a global database of sites, but that’s not a problem I’ve ever encountered. Instead, most of the struggles I’ve had with relational systems are still better addressed in a relational system.

Read the whole thing.  Note that this is slightly different than Feasel’s Law, as Steve is focusing more on the consistency side of things rather than the interface.

Also, just going to leave this here:

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When Azure SQL DB Tells You You’re Hitting Your DTU Limits

Published 2018-12-07 by Kevin Feasel

Brent Ozar shows us how to use sp_BlitzFirst to determine if you’re hitting DTU limits:


You’ve got an Azure SQL DB, and your queries are going slow. You’re wondering, “Am I hitting the performance limits? Is Microsoft throttling my queries?”
There’s an easy way to check: run sp_BlitzFirst. sp_BlitzFirst is our free performance health check stored procedure that analyzes a lot of common performance issues and then gives you a prioritized list of reasons why your server might be slow right now.

Click through for an example.  The same signal can mean “it’s time to move to a higher level in your tier,” “it’s time to move up to the next tier,” or “it’s time to tune that cursor in a cursor in a cursor which performs several scans of a very large table for each operation.”

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Finding The SQL Server Port With T-SQL

Published 2018-12-07 by Kevin Feasel

Jack Vamvas shows us how to find the port SQL Server is listening on using T-SQL:


Question: Without going into the SQL Server Configuration manager via the GUI is there a command oriented method to extract the port number SQL Server is listening on?
 
Answer: There are a few different methods to extract the port number without going into the Configuration interface.
Method 1 – use xp_readererrorlog

Read on for an explanation of this technique as well as links to a couple other methods.  I hadn’t thought about using the error log as a source, but it works.

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Preventing Credential Compromise When Using AWS

Published 2018-12-06 by Kevin Feasel

Will Bengtston walks us through techniques Netflix uses to protect credentials in AWS:

Scope

In this post, we’ll discuss how to prevent or mitigate compromise of credentials due to certain classes of vulnerabilities such as Server Side Request Forgery (SSRF) and XML External Entity (XXE) injection. If an attacker has remote code execution (RCE) or local presence on the AWS server, these methods discussed will not prevent compromise. For more information on how the AWS services mentioned work, see the Background section at the end of this post.

Protecting Your Credentials

There are many ways that you can protect your AWS temporary credentials. The two methods covered here are:

  • Enforcing where API calls are allowed to originate from.

  • Protecting the EC2 Metadata service so that credentials cannot be retrieved via a vulnerability in an application such as Server Side Request Forgery (SSRF).

Read the whole thing if you’re an AWS user.

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Indexed View Matching

Published 2018-12-06 by Kevin Feasel

Erik Darling has a series of posts on indexed views, with the latest covering query matching even when using a keyword in creation of the indexed view itself:

There are a whole bunch of limitations in creating indexed views. One of them is that you can’t base the query on DISTINCT.

Fair enough, but you can do GROUP BY.

And what’s pretty cool is that the optimizer can match a query written to find distinct values to an indexed view with a group by.

Click through for the best example ever.

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