John McCormack's blogs

How to find out Azure SQL DB Startup time

10th February 2021 By John McCormack 1 Comment

Alarm clock and cup of tea — Wakey Wakey SQL DB

Azure SQL DB Startup time

The query

SELECT sqlserver_start_time FROM sys.dm_os_sys_info;

Why write a post about this?

The traditional methods used for to find the start up time for SQL Server don’t work in Azure SQL DB.

I searched high and low to find this and thought I’ve got to share, and hopefully make it search engine friendly. A traditional google or bing search wasn’t bringing up the best way to find this out. I saw a lot of complicated queries to pull data, convert it and estimate start up time using functions and all kinds of magic. Actually, it turns out that the start up time is provided for you in sys.dm_os_sys_info (which works in Azure SQL DB). It also work in SQL Server which is a bonus.

Permissions to check Azure SQL DB Startup time

The permissions you need to run this query vary depending on the pricing tier of your Azure SQL DB. I ran this successfully on a P2 and a basic tier database.

On SQL Server, requires VIEW SERVER STATE permission.
On SQL Database Basic, S0, and S1 service objectives, and for databases in elastic pools, the Server admin or an Azure Active Directory admin account is required. On all other SQL Database service objectives, the VIEW DATABASE STATE permission is required in the database.
https://docs.microsoft.com/en-us/sql/relational-databases/system-dynamic-management-views/sys-dm-os-sys-info-transact-sql?view=sql-server-ver15

I hope this one liner t-sql script was useful. Please let me know in the comments if you know a better way to get the start up time of Azure SQL DB, using T-SQL.

While you’re on my site, why not take a look to see if you can save money on your Azure SQL DBs, and other Azure resources by looking at my guide: 7 ways for data teams to save money in Azure.

Featured image by congerdesign from Pixabay

Free SQL Tools to make your life easier

9th February 2021 By John McCormack 2 Comments

I’ve written and spoken about free SQL tools to make your life easier on a few occasions. It was the subject of my presentation at SQLBits 2020 so I was delighted to see Mikey Brownowski (b|t) choose tooling as the subject for February’s T-SQL Tuesday #135. #tsl2sday.

In this month’s t-SQL Tuesday, Mikey asks us to “Write a blog post about the most helpful and effective tools you use or know of.” I use quite a few free tools. I love the way the SQL Community embraces sharing and I know I wouldn’t have achieved a fraction of my career success without free tools. In my SQLBits talk, I discussed the following free tools:

For this post, I will focus on Statistics Parser, written by Richie Rump (b|t). My blog has info on some other free sql tools as well.

Statistics Parser

Legend

Legend has it that Richie Rump wrote it during a Brent Ozar conference session. I asked him about this and he told me:

Well, I started it in one of his training classes. It was in Atlanta. It was the last day and it was mentioned that there was an Excel spreadsheet that parsed out statistics io output. I found that as interesting but odd. So I started out writing out a web based parser right there in class. The core of it was finished on the plane ride home.
Richie Rump 2021

So what does Statistics Parser do?

If you run SET STATISTICS TIME,IO ON before you run a query in SSMS, you will get information back on how much data was accessed and how long it took. Things like logical reads, physical reads, CPU time and elapsed time etc.

If you are only querying one or two tables, it is easy enough to just read this in the messages window. But what about those complex stored procs or queries hitting multiple tables and views that return a very long list of outputs? The output can be long and intimidating and certainly hard to understand at a glance.

Statistics Parser is a web page which allows you to paste in the statistics time,io output from the SSMS messages tab, and it formats into neat tables showing how much IO happens for each table. It is immediately easier to read and you get a handy % column on the right hand side showing you which tables are being read the most. I find this really useful for query tuning because it lets me know where my biggest pain points are. For complex queries, which touch a lot of tables, it just makes it easy to see at a glance where you should initially focus your attention. It also shows worktable and workfile tables which serves as a handy hint that tempdb is in play.

A really handy feature is that you can open multiple browser tabs, but give each tab it’s own name. Then you can paste the output from the original query in to a tab you have named as “before” or “original”, then give a name to each tab as you try something new. e.g. “index_abc_added” or “fixed_cursors”. I like to do this when working through query tuning options against a restored copy of production. I use it for tuning CPU and reads. I quite often find that if I can make a big enough impact on the reads, the CPU will also come down.

via GIPHY

How to use Statistics Parser

Run the following query in the same SSMS window as a query you are about to troubleshoot:
1. SET STATISTICS TIME,IO ON
Run your query
Copy/paste the message from SSMS into statisticsparser.com
Click Parse button
Open more tabs as needed

Thanks Richie

Thanks for making a great tool, free and easy to use. And thanks for answering my questions.

FYI

Attendees of SQLBits 2020 can still login using their personal link and see all the recorded content so my talk is there. In it, I spend a few minutes demonstrating Statistics Parser. I’m not sure if they will make it free to everyone else in future, like they’ve done in previous years.

Featured Image

Image by Lu Lettering from Pixabay

What is AWS Athena and why is it awesome?

27th January 2021 By John McCormack 3 Comments

AWS Athena

This post answers “What is AWS Athena” and gives an overview of what AWS Athena is and some potential use cases. I discuss in simple terms how to optimize your AWS Athena configuration for cost effectiveness and performance efficiency, both of which are pillars of the AWS Well Architected Framework. My Slides | AWS White Paper.

This post was originally published on March 2018, and has subsequently been updated.

AWS’s own documentation is the best place for full details on the Athena offering, this post hopes to serve as further explanation and also act as an anchor to some more detailed information. As it is a managed service, Athena requires no administration, maintenance or patching. It’s not designed for regular querying of tables in a way that you would with an RDBMS. Performance is geared around querying large data sets which may include structured data or semi-structured data. There are no licensing costs like you may have with some Relational Database Management Systems (RDBMS) such as SQL Server and costs are kept low, as you only pay when you run queries in AWS Athena.

More info on AWS Athena

Athena is a serverless interactive query service provided by AWS to query flat files in S3. It allows users to query static files, such as CSVs (which are stored in AWS S3) using SQL Syntax. The queries are made using ANSI SQL so many existing users of database technologies such as SQL Server or MySQL can adapt quickly to using ANSI. New users can learn the commands easily.

How does it save me money?

“Object based storage” like Amazon S3 is a lot cheaper than “block based storage” such as EBS. This means you can store large data sets as CSV files on Amazon S3 at a fraction of the price it would cost to store the data using EBS or in a relational database. You are then charged for each query (currently $5 per 5TB scanned). Clever use of compression and partitioning can reduce the amount of data scanned, meaning queries will be cheaper. AWS Athena is described as serverless which means the end user doesn’t need to manage or administer any servers, this is all done by AWS.

Save more using compression, partitioning and columnar data formats

If you notice from the previous paragraph that the query cost is $5 per 5TB scanned so the pricing is quite straightforward. Athena uses per megabyte charging, with a 10MB minimum. You can save by compressing, partitioning and/or converting data to a columnar format. The less data that needs to be scanned, the cheaper the query.

Compression
- As Athena natively reads compressed files, the same query that works against a CSV file will also work against data compressed into one of the following formats:
  - Snappy (.snappy)
  - Zlib (.bz2)
  - LZO
  - GZIP (.gz)
- As less data is scanned, the overall cost is lower
Partitioning
- Tables can be partitioned on any key. e.g. OrderDate
- If the query can use the key, there is no need to scan all the other partitions, only the relevant partition needs to be scanned.
- Compression and partitioning can be used together to further reduce the amount of scanned data.
Converting to columnar
- Columnar formats such as ORC and Parquet are supported
- Converting may add complexity to your workload
- However it will save money on querying due to the columnar format, data scanned is reduced and speed is improved
- Here’s a tutorial, it will require an intermediate knowledge of EMR

Use Cases

Apache Web Logs
AWS CloudWatch logs
System error logs
Huge, infrequently accessed data sets which were extracted to a flat file format in S3
Ad hoc querying of CSV files

Why is AWS Athena Awesome?

There is no infrastructure to configure
You only pay for what you scan
If you compress, partition and convert your data into columnar formats, you can save up to 90%
ANSI SQL Language is easy to learn or adapt from a dialect such as T-SQL
Athena integrates with Glue to automate your ETL

Free practice questions to help you pass DP-900

26th January 2021 By John McCormack 3 Comments

Pass DP-900 certification from Azure

These are a series of free practice questions and tests to help you pass DP-900 – Microsoft Azure Data Fundamentals. They are written completely by me based on my review of the publicly available course materials. Unlike some sites, these are not exam dumps, so are a perfectly honest and legitimate way to study towards your Azure DP-900 certification.

These practice questions should be seen as a supplement to Microsoft Learn and not a replacement. My number one tip for passing Azure DP-900 is to complete the free learning paths on Microsoft Learn for the DP-900 course.

This series is a work in progress so please keep coming back for more questions and quizzes. I didn’t want to wait until everything was published before sharing this help with you. I hope that you enjoy the practice questions and good luck with your mission to pass DP-900.

I’d also appreciate any feedback if any of the answers appear to be incorrect or if the questions are too easy or too hard.

More questions, quizzes and links are added almost daily. Bookmark this page and come back for new practice questions to help you pass DP-900.

DP-900 practice question links ⬇

Azure Data Fundamentals Learning Paths

Explore core data concepts
Explore relational data in Azure
Explore non-relational data in Azure
Explore modern data warehouse analytics in Azure

Take a look at my Sessionize speaker’s profile

25th January 2021 By John McCormack

Take a look at my Sessionize speaker’s profile. I talk about data and cloud topics, and have done at some major SQL conferences and local user groups. Please get in touch if you’d like me to speak at your event. My live sessions are listed.

https://sessionize.com/john-mccormack/

Contact me

Sometimes, WordPress mail can be a little flaky. If I don’t reply to you, please send me a DM on twitter.