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Author: Kevin Feasel

Temp Tables In Redshift

Published 2018-02-27 by Kevin Feasel

Derik Hammer has some notes on temporary tables in Amazon Redshift:

One difference between regular tables and temporary tables is how they are typically used. Temporary tables are session scoped which means that adding them into a process or report will probably cause them to be created multiple times. Temporary tables might be very similar to regular tables but most regular tables are not re-written into, every time they are queried.

The disk writes involved in populating the temporary table might be more expensive than the reads would be if you were to modify your query to include the logic into one, larger, query. The frequency of the report or process will be a factor into how much of a performance hit you get by using the temporary tables. If you are using temporary tables to make debugging a procedure easier or to enhance readability, make sure you understand the IO cost of performing writes and then reading that data back into a subsequent query.

Read on for more.

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Query Store And CU4

Published 2018-02-27 by Kevin Feasel

Erin Stellato notes that if you ran SQL Server 2017 CU2 with Query Store, you want to upgrade and run a special script:

There is a Query Store fix in the latest Cumulative Updates of SQL Server 2017, and if you are keeping current with the latest cumulative updates, there’s a script you need to run if you’ve installed CU2 at any point.

First, if you are still on CU2, you might want to consider upgrading to CU4 (released February 17, 2018).

Second, if you previously had CU2 installed (and are now on CU3 or CU4), you want to make sure that you’ve run the script included with CU3 and CU4 release notes.

The script removes plans from Query Store that were captured when running CU2.

Read the whole thing and keep those servers patched.

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Replication Update: Dynamic Parameter Loading

Published 2018-02-27 by Kevin Feasel

Sourabh Agarwal announces a new feature with SQL Server replication:

When a new agent user profile is created and assigned to an agent, relevant entries are added to the [dbo].[MSagent_profiles] and the [dbo].[MSagent_parameters] tables in the MSDB database on the Distributor server. The values in the tables are updated whenever a parameter value is updated for the Profile. These values are reloaded from the tables, whenever a replication agent is restarted. As part of the improvement a new profile parameter “ProfileReloadInterval” with a default value of 300 seconds, was added to the profiles. This parameter dictates the interval at which the agent reloads the values from the tables. If using the default value, the agent will try and reload the values from the MSDB tables every 5 minutes. If it encounters that any of the above-mentioned parameter values have changed, the new values will be used by the agents.

I don’t work with replication enough to really see the benefit, but I’m sure somebody is giddy about this.

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Special Commands In mssql-cli

Published 2018-02-27 by Kevin Feasel

Alan Yu announces an update to mssql-cli:

Special commands are meant to make your life easier. They are shortcuts to perform common tasks and queries. All special commands start with a backslash (\), and you can use the built-in IntelliSense to see a list of special commands you can use. You can also learn more by running the following command:

$ mssql-cli>\?

To start off, let’s say you have a common query you run often, but don’t want to type it out each time or scroll through your history. You can “save” your query by running the following special command:

$ mssql-cli>\sn address select * from "Person"."AddressType"

To execute this query, simply run:

$ mssql-cli>\n address

Click through to learn more and see it in action.

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Kerberos And SQLCMD

Published 2018-02-27 by Kevin Feasel

Kenneth Fisher notes a behavior change for SQLCMD with SQL Server 2014 and later:

This error is seen when using a Windows Authenticated id and NTLM or an untrusted Kerberos connection. From what I understand, with Kerberos the client (SQL in this case) gets a ticket containing your credentials, it can then pass this ticket on to other servers that trust it to have authenticated you to be sure you are who you say you are. Note the mention of trust. This means that even if you are using Kerberos if the two servers aren’t in a trusted relationship then you will still see the error. And NTLM doesn’t have the option to do this at all (again, not sure why).

At this point it’s important to note that this is not a SQL Server error. You will see this error in other places where you bounce across multiple machines and aren’t using Kerberos with a trusted relationship. I’ve seen it when trying to access files before (it was a weird situation admittedly).

Read on to learn about the ramifications of this behavioral change and how you can prevent double-hop problems when running newer versions of SQLCMD.

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Using Telegraf To Display SQL Server Metrics In Grafana

Published 2018-02-26 by Kevin Feasel

Tracy Boggiano has a writeup showing how to use Telegraf + InfluxDB + Grafana to view SQL Server metrics:

We have in the middle an open source time series database called InfluxDBis designed for collecting data that is timestamped such as performance metrics. Into that, we feed data from an open source project called Telegraf which can feed in more than just SQL Server statistics. And to be able to show us the data in nice pretty graphs that we can manipulate, drill-down on, and even set up alerts we display it using Grafana. Links to all of these products you find as we go through the setup of the solution.

Tracy’s post is dedicated to installation and configuration more than defining metrics, but it does get you on the road to custom metrics visualization.

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Parsing CSVs With Logstash

Published 2018-02-26 by Kevin Feasel

Mike Hillwig continues his Logstash series by reading in a CSV:

As I was writing this, I thought I’d play with the autodetect_column_namessetting. Unfortunately, it wasn’t an option for this particular file. Logstash threw an error :exception=>java.lang.ArrayIndexOutOfBoundsException: -1which leads me to guess that my file is too wide for this setting. This file is staggeringly wide with 75 columns. If you have a more narrow file, this could be a really cool option. If your file format changes by someone adding or removing a column from the CSV, it’ll be a lot easier to maintain. Alas, it’s not an option in this situation.

Check out the script.

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Hockey Analytics With Power BI

Published 2018-02-26 by Kevin Feasel

Stacia Varga shows off some of Power BI’s filtering and data processing capabilities by looking at hockey stats:

Right now, the data is not ideal for analysis. Keeping in mind how I want to use the data, I need to perform some cleansing and transformation tasks. Any time I work with a new data source, I look to see if I need to do any of the following:

  • Remove unneeded rows or columns. Power BI stores all my data in memory when I have the PBIX file open. For optimal performance when it comes time to calculate something in a report and to minimize the overhead required for my reports, I need to get rid of anything I don’t need.

  • Expand lists or records. Whether I need to perform this step depends on my data source. I’ve noticed it more commonly in JSON data sources whenever there are multiple levels of nesting.

  • Rename columns. I prefer column names to be as short, sweet, and user friendly as possible. Short and sweet because the length of the name affects the width of the column in a report, and it drives me crazy when the name is ten miles long, but the value is an inch long—relatively speaking. User friendly is important because a report is pretty much useless if no one understands what a column value represents without consulting a data dictionary.

  • Rearrange columns. This step is mostly for me to look at things logically in the query editor. When the model is built, the fields in the model are listed alphabetically.

  • Set data types. The model uses data types to determine how to display data or how to use the data in calculations. Therefore, it’s important to get the data types set correctly in the Query Editor.

It’s a fun topic to use for learning about Power BI…says the guy wearing a Blue Jackets shirt right now…

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Splitting A Large Script With Powershell

Published 2018-02-26 by Kevin Feasel

Jana Sattainathan shows how to split a file with a large number of independent operations and have Powershell run the job in batches:

Everyday is an interesting day in the life of a DBA. Today, I received a request to run in a HUGE script. This script had 125k INSERT statements each in a separate line. Just opening the script was an issue, leave alone running it into a database. You get this error in SSMS just trying to open – “The operation could not be completed. Not enough storage is available to complete this operation”

This is how I handled it

  1. I split the file into a manageable 1000 line files (total of 125 files)
  2. Looped through each split file and ran it in!

Yes, it was that simple. Thanks to PowerShell!

Read on to see how simple it is.

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Benefits Of Explicit Transactions

Published 2018-02-26 by Kevin Feasel

Kendra Little talks about explicit transactions and when they’re useful for single-statement operations:

If you do not enable implicit transactions, and you don’t start an explicit transaction, you are in the default “autocommit” mode.

This mode means that individual statements are automatically committed or rolled back as whole units. You can’t end up in a place where only half your statement is committed.

Our question is really about whether there are unseen problems with this default mode of autocommit for single-statement units of work.

By force of habit, I wrap data modification operations in an explicit transaction.  They let me test my changes before committing and the time you’re most likely to spot an error seems to be right after hitting F5.

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