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Author: Kevin Feasel

Asynchronous Stats Updates

Published 2017-10-26 by Kevin Feasel

Monica Rathbun explains why you might want to turn on asynchronous statistics updates in your OLTP environment:

By default, when Auto Update Statistics is set to True, the SQL Server Query Optimizer will automatically update statistics when data has met a threshold of changes (insert, update, delete, or merge) and the estimated rows are now potentially stale. When statistics are stale, execution plans can become suboptimal which can lead to degradation in performance.

This best practice option ensures your statistics stay up to date as much as possible. Each time a cached query plan is executed the Optimizer checks for data changes and potentially generates new statistics. This behavior is exactly what we want, but there is a catch. The caveat to this is that a cached query plan will be “held” while the statistics are updated and will recompile to use the new values before running. This caveat can slow down the execution process dramatically.

The advice Monica provides is generally sound, though there are rare cases when asynchronous statistics updates end up causing more problems than they solve, so test the change first.

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SQL Server Agent On A Linux Container

Published 2017-10-26 by Kevin Feasel

Andrew Pruski shows how to get the SQL Agent service running on a Linux container:

Now, one of the benefits of attending SQL Saturdays is that you get to pick the brains of a lot of very clever people and luckily for me, Jan Van Humbeek (blog|twitter) was there.

Jan said that he had gotten the SQL Agent running in Linux containers so I asked if he could send on his code and he very kindly obliged.

So, the disclaimer for this blog post is that I didn’t write the code here, Jan did. All I’ve done is drop it into a dockerfile so that an image can be built. Thank you very much Jan!

Click through for Jan’s code and Andrew’s presentation of the process.

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Cleaning Up Foreign Keys

Published 2017-10-25 by Kevin Feasel

Adrian Buckman looks at how to clean up untrusted foreign key constraints:

SQL Server tracks untrusted Foreign keys in sys.Foreign keys with a column called is_not_trusted, there may be a number of reasons why a Foreign key may have become untrusted below are a couple of examples:

  • Foreign key was disabled using the ‘NOCHECK’ option then re-enabled using ‘CHECK’ (not to be confused with ‘WITH CHECK’)
  • Foreign key was disabled using the ‘NOCHECK’ option , Primary key data was Deleted and the Foreign key was Enabled only using ‘CHECK’ (Again not to be confused with ‘WITH CHECK’)

So what happens when you try and enable a Foreign key ‘WITH CHECK’ (Check existing data for referential integrity), if the data is consistent then this is going to succeed however if Rows have been deleted and the Primary key data no longer exists but the Foreign key data does for example then this is going to fail miserably.

What I like about this post is that he does more than just saying “hey, here’s how you get the key constraint to be trusted again;” he goes further and shows you how to figure out if it will work beforehand.

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Valuable Extended Events Sessions

Published 2017-10-25 by Kevin Feasel

Derik Hammer has a few useful extended events sessions that he’s sharing with us:

Extended Events are the all-around smart choice. They take a little bit of time to get used to, however. With thousands of new events and data points, it can be difficult to create an event session in a pinch. That is why it is important to have event sessions pre-scripted or pre-implemented on your SQL Server instances. A little bit of up-front work can save you a lot of time when you need information on the spot. Having them pre-scripted also prevents you from jumping back to Profiler, which has a much heavier footprint on your server.

When I create Extended Event sessions, I tend to use the SQL Server Management Studio wizard to find the events and actions (additional fields) that I want. Then, I will script it out and save it for later.

Below are five Extended Events sessions that I have found particularly useful and recommend you add to your toolbox.

Click through for all of those scripts, as well as queries to shred the resulting XML.

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Automating Cache Cleanup

Published 2017-10-25 by Kevin Feasel

Tracy Boggiano has a process to automate cleaning up different caches in SQL Server:

First, we need to create a table to store our information on the caches we would like to clear on an automated basis and populate it with values.

For example, we clear SQL Plans if we 10,000 plans are Adhoc or Prepared plans that take up 5GBs of memory or Single Used Plans is greater than 10,000 or the memory used for Adhoc or Prepared plans if more than 50% of memory.  We clear Transactions cache if is more than 2 GBs and Lock Manager : Node 0 if it is more than 2 GBs.

Read on for the script.

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Gathering Information On Memory-Optimized Objects

Published 2017-10-25 by Kevin Feasel

Ned Otter has a diagnostic script for learning about how your servers are using memory-optimized objects:

Instance level evaluates the following:

  • which databases are memory-optimized
  • if running Enterprise, if there are any resource groups defined, and which databases are bound to them
  • version/edition of SQL server
  • ‘max memory’ setting
  • whether or not instance-level collection of execution statistics has been enabled for all natively compiled stored procedures
  • memory clerks for the buffer pool and In-Memory OLTP
  • the value of the committed_target_kb column from sys.dm_os_sys_info
  • display any event notifications (because they conflict with deploying In-Memory OLTP

Database level evaluates the following:

For each memory-optimized database:

  • database files, including container names, size, and location

  • memory-optimized tables

  • indexes on all memory-optimized tables

  • count of indexes per memory-optimized table

  • natively compiled stored procedures

  • whether or not the collection of execution statistics is enabled for any natively compiled procedures

  • count of natively compiled procedures

  • if using the temporal feature for memory-optimized tables, the amount of memory consumed by hidden temporal internal tables

  • memory structures for LOB columns (off-row)

  • average chain length for HASH indexes

Ned provides the script on his blog, so click through to get that.  This looks great if you’re trying to build up some basic information on how developers in your environment use memory-optimized objects.

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Query Store Capture Modes

Published 2017-10-25 by Kevin Feasel

Arun Sirpal notes an important difference in the default Query Store settings for SQL Server 2017 versus Azure SQL Database:

So just remember the only difference when analyzing settings is the difference in Query Store Capture Mode. For Azure it is set to AUTO whereas with local installed SQL Servers it is set to ALL.

What does this mean? ALL means that it is set to capture all queries but AUTO means infrequent queries and queries with insignificant cost are ignored. Thresholds for execution count, compile and runtime duration are internally determined.

Read on to learn more, including how to change these settings.

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Finding AD Group Members With Powershell

Published 2017-10-25 by Kevin Feasel

Amy Herold has a quick script to find which Active Directory users are in particular AD groups:

There was something that popped up today that called for a PowerShell script and the Get-ADGroupMember cmdlet – get a list of users from a list of groups. Some users are in there more than once so this needs to be a distinct list, unless you are into manually cleaning up things like this, and then I will be sad for you. Because that is kinda sad.

I originally wrote a script with two arrays (one for the initial list and one for the de-duped list of users), but even though this is quick and dirty, that was a little too dirty. Enter the Group-Object cmdlet – it takes this list of names and groups them. No black magic this time. Just a cmdlet, that comes baked into PowerShell giving me what I need.

Click through for the script.

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Build Versus Buy For Hadoop

Published 2017-10-24 by Kevin Feasel

Tom Phelan walks through some thoughts on whether to build versus buy when using big data platforms:

This means you absolutely must sweat the details up front. Big Data project failures are more often than not predicated by the statement: “We will do this bit now, and figure the rest out later”. But you need to begin with the end in mind.

You need to know the performance that you’ll be able to deliver and what your requirements are. You need to know how to integrate with your corporate Active Directory, and LDAP, and Kerberos services. You need to know your network topology and security requirements as well as the required user roles and responsibilities breakdown. You need to know how you’ll handle high availability, QoS, and multi-tenancy. You need to know how you’ll manage upgrades to the latest versions of your Hadoop distribution or other big data tools, and how you’ll respond to requests for new big data frameworks and new data science tools. If not, you’re just asking for trouble.

The motif in his post is building your own car, which makes sense as an extended metaphor.

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The Correct Way To Load Libraries In R

Published 2017-10-24 by Kevin Feasel

Gerald Belton opens a can of worms:

When I was an R newbie, I was taught to load packages by using the command library(package). In my Linear Models class, the instructor likes to use require(package). This made me wonder, are the commands interchangeable? What’s the difference, and which command should I use?

Interchangeable commands . . .

The way most users will use these commands, most of the time, they are actually interchangeable. That is, if you are loading a library that has already been installed, and you are using the command outside of a function definition, then it makes no difference if you use “require” or “library.” They do the same thing.

… Well, almost interchangeable

Read on to understand the differences between the two.  I end up doing something very similar to his code snippet for exactly the reason he describes.  H/T R-Bloggers

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