Author: Kevin Feasel

KPI Indicators

Published 2016-06-29 by Kevin Feasel

Devin Knight’s Power BI Custom Visuals class continues:

Change the Banding type property to one of the following:

Increasing is better – Increasing is best when you’re measuring things like sales or profit. If you go over your profit target that’s a good thing!
Decreasing is better – Decreasing is probably best when you’re looking at something like budgeting. Staying under budget is usually a good thing. Unless you being too far under budget means you won’t get that money again next year which leads to the last option
Closer is better – This is for when you need your data to land in the middle of a bell curve. Meaning if you go too high or too low that’s a bad thing. This is often useful when looking at medical data. For example, if your blood pressure is too high then that’s a bad thing, but if you’re blood pressure is too low that’s also a bad thing too. You need to land in the middle somewhere, which is what this option allows.

There’s plenty of good advice here, so check out the video.

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Auto-Seeding Availability Groups

Published 2016-06-29 by Kevin Feasel

John Sterrett is the latest smart person to take a look at automatic seeding of Availability Groups:

The 600 GB databases took about 66 minutes to seed across the network from a primary replica to the secondary replica. I noticed 1.4 Gbps of consistent throughput during the seeding process. This makes a lot of sense as it caps out around what the storage system can deliver in this environment.

The first thing I would look at for benchmarking throughput for network activity would be the bytes sent per second from the primary replica and bytes received per second on the secondary replicas.

John also includes an extended event session statement to track seeding. Great read.

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Finding Dates

Published 2016-06-29 by Kevin Feasel

Derik Hammer shows the right way and the wrong way of using date functions in a WHERE clause:

I then changed my thought process to find the age of a 65 year old who’s birth day is today. Then I compare the DateOfBirth column to that static value.
SELECT COUNT(*) FROM dbo.Person p WHERE DateOfBirth <= DATEADD(YEAR,-65,GETDATE())
With the above query I bought myself an index seek and 345 logical reads. That works out to <3% of the cost.

This is true not just for date functions, but rather is applicable to almost all scalar functions.

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Startup Stored Procedures

Published 2016-06-29 by Kevin Feasel

Dave Mason looks at using sp_procoption to execute stored procedures at startup:

If you have more than one task you want to run at startup, you could include code for each task in a single stored procedure. But that’s generally bad coding practice. Go with the modular approach and create a separate stored procedure for each distinct task. With sp_procoption, you can set more than one stored procedure for automatic execution. The MSDN documentation was not clear regarding the order of execution, though. I thought there might be something similar to sp_settriggerorder, but I wasn’t able to find anything like that. I ran a test on SQL 2014 with 3 stored procedures set for automatic execution.

Dave has interesting notes on procedure run order, where these procedures need to live, and even some ideas on what you might put into startup stored procedures.

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CASE Statements In GROUP BY Clauses

Published 2016-06-29 by Kevin Feasel

Grant Fritchey looks at CASE statements within GROUP BY clauses:

The same basic set of structures, scans against both tables, to arrive at the data. Cost estimates between the two plans are very different though, with the targeted queries having a much lower estimated cost.

Performance-wise, interestingly enough, the average execution time of the first query, only returning the 10 rows, is 157ms on average, while the query grouping directly on the SalesPersonID averages about 190ms. Now, the reads tell a slightly different story with 17428 on the generic query and 5721 on the specific query. So, maybe a server under load will see a significant performance increase. However, let’s deal with what we have in front of us and say that, at least for these tests, the catch-all GROUP BY query performs well.

Grant’s recommendation is to split this out into several procedures, and if you’re having performance problems, that’s a solid move. I’m a bit more likely to keep them (especially in warehousing reports), but it’s nice to have options.

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Basic Spark Terminology

Published 2016-06-28 by Kevin Feasel

Denny Lee and Jules Damji explain some of the key terms and concepts around Apache Spark:

At the core of Apache Spark is the notion of data abstraction as distributed collection of objects. This data abstraction, called Resilient Distributed Dataset (RDD), allows you to write programs that transform these distributed datasets.

RDDs are immutable distributed collection of elements of your data that can be stored in memory or disk across a cluster of machines. The data is partitioned across machines in your cluster that can be operated in parallel with a low-level API that offers transformations and actions. RDDs are fault tolerant as they track data lineage information to rebuild lost data automatically on failure.

Some of these concepts are new to Spark 2.0, but all are worth learning.

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Analyzing Real-Time Data

Published 2016-06-28 by Kevin Feasel

Manjeet Chayel connects Spark Streaming to Amazon Kinesis and shows how to analyze the data in real time:

To use this post to play around with streaming data, you need an AWS account and AWS CLI configured on your machine. The entire pattern can be implemented in few simple steps:

Create an Amazon Kinesis stream.
Spin up an EMR cluster with Hadoop, Spark, and Zeppelin applications from advanced options.
Use a Simple Java producer to push random IoT events data into the Amazon Kinesis stream.
Connect to the Zeppelin notebook.
Import the Zeppelin notebook from GitHub.
Analyze and visualize the streaming data.

This is a good way of getting started with streaming data. I’ve grown quite fond of notebooks in the short time that I’ve used them, as they make it very easy for people who know what they’re doing to provide code and information to people who want to know what they’re doing.

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Home Labs

Published 2016-06-28 by Kevin Feasel

Chrissy LeMaire shows off her home lab:

I like to test my scripts against a variety of versions/editions and I don’t like spinning VMs up and down all the time. As for the cost; some people spend their money on golf, Polish pottery and gaming rigs. I spend mine on servers, Belgian beer and travel 😉

As you can see, I also have an old Macbook Pro with 256 SSD, 4TB HDD and 8GB RAM in the mix. It’s for photos and videos, however. And someone gave me an old silver Shuttle from like 2002, but I haven’t had the time to set it up yet.

The “cloud versus local” lab is a tough call, as both sides have their advantages and disadvantages.

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Where Polybase Stats Live

Published 2016-06-28 by Kevin Feasel

I dig into where the statistics against a Polybase table actually live:

Today, we learned that Polybase statistics are stored in the same way as other statistics; as far as SQL Server is concerned, they’re just more statistics built from a table (remembering that the way stats get created involves loading data into a temp table and building stats off of that temp table). We can do most of what you’d expect with these stats, but beware calling sys.dm_db_stats_properties() on Polybase stats, as they may not show up.

Also, remember that you cannot maintain, auto-create, auto-update, or otherwise modify these stats. The only way to modify Polybase stats is to drop and re-create them, and if you’re dealing with a large enough table, you might want to take a sample.

The result isn’t very surprising in retrospect, and it’s good that “stats are stats are stats” is the correct answer.

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Sensible Auto-Growth Settings

Published 2016-06-28 by Kevin Feasel

Ajay Jagannathan notes that SQL Server 2016’s database auto-growth has changed to better default values:

model database: New default data and log file size is 8MB and default auto-growth is 64MB. This ensures that any new database created without explicitly specifying the SIZE/FILEGROWTH parameter will have 8MB initial size for all data and log files and 64MB for auto-growth for both data and log files.

For data files, having a 64MB autogrow, aligns with 1 PFS interval (which covers a range of 8088 pages = 64MB). For log files, having a 64MB autogrow helps with sizing the initial VLFs correctly so that they can be garbage claimed (wrapped-around) without which the log can keep growing.

This is much better than the prior default of 1 MB size and 10% auto-growth. Percentage growth leads to eventual pain.

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