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Day: February 23, 2016

Page Compression

Published 2016-02-23 by Kevin Feasel

Andy Mallory continues his discussion of compression options:

You can think of page compression as doing data deduplication within a page. If there is some value repeated in multiple spots on a page, then page compression can store the repetitive value only once, and save some space.

Page compression is actually a process that combines three different compression algorithms into a bigger algorithm. Page compression applies these three algorithms in order:
1) Row compression
2) Prefix compression
3) Dictionary compression

Page compression is my go-to compression option, typically.  There are some cases in which it doesn’t work well, so check beforehand (start with sp_estimate_data_compression_savings), but I’ve had good luck with page compression.

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Junk Dimensions

Published 2016-02-23 by Kevin Feasel

Jesse Seymour talks about junk dimensions in warehousing:

I think one of the single biggest challenges I face as I attempt to warehouse data that originates as a SharePoint list is the handful of miscellaneous descriptive fields, such as approval status, request status, or something similar.  Typically, this fields are setup as Choice fields in the SharePoint list so they have a known range of values, but its still a pain to have to build a dimension for each one.

Enter the junk dimension.  Ever since I learned about this concept it has made my life so much easier.  What the junk dimension does is perform a cross join against the different fields and creates a row for every possible combination of fields.

Junk dimensions are nice for those low-cardinality attributes which are important but don’t really fit anywhere else.  The important thing to remember about a junk dimension is that you don’t want it to be too large:  if you have 5 attributes, each of which has 8 possible values, you have 8^5 (32,768) rows.  That’s not so bad, but make it 10 attributes and now your table has 1,073,741,824 rows, and that’s a lot of rows for a single dimension.  If you find yourself in that scenario, you might want to create two junk dimensions (bringing you back to 2 dimensions with 32K rows), review your design to see if all those attributes are necessary, or review your design to see if your “junk” dimension is hiding a real dimension.

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Wait Stats

Published 2016-02-23 by Kevin Feasel

Grant Fritchey gives an introduction to wait stats:

Now, you have a meaningful list of wait statistics that will tell you exactly why, if not where, your server is running slow. Unfortunately, these waits still need to be interpreted. If you read further on Paul’s blog, you’ll see he has a number of waits and their causes documented. That’s your best bet to start understanding what’s happening on your system (although, I hear, Paul might be creating a more complete database of wait stats. I’ll update this blog post should that become available).

Wait stats are fantastic tools for figuring out your server resource limitations given the server’s historic query workload.  They’re the first place to look when experiencing server pains.

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Robocopy Is Your Friend

Published 2016-02-23 by Kevin Feasel

Jes Borland wants us to stop copying and pasting files between servers:

IT professionals (and amateurs), it’s time we had a chat. It’s time to stop dragging and dropping (or copying and pasting) files between servers and/or workstations.

It’s clumsy. It’s childish. It uses memory on the server.

Oh, and there’s a really easy tool to copy files built into Windows – Robocopy.

The syntax is pretty easy and robocopy handles small files well.  Check out Nic Cain’s comment, though, if you’re going to copy large files in production.

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Power BI Roundup

Published 2016-02-23 by Kevin Feasel

Jorge Segarra has a roundup of this month’s T-SQL Tuesday:

This month I challenged the blogging community to share their own creations in Power BI. We got a ton of great entries this month, thank you everyone who participated! My overarching goal for this month’s topic was to get folks who may not normally play in the BI space to use this fantastic solution and maybe get some ideas flowing on how they may be able to apply it in their everyday work.

The part I like most about T-SQL Tuesday is that it introduces you to a whole new set of bloggers and a whole new set of perspectives on any particular topic.

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Using OPTION(RECOMPILE)

Published 2016-02-23 by Kevin Feasel

Sheldon Hull walks through some scenarios in which OPTION(RECOMPILE) might be useful:

Only want to use in specific scenarios. Basically, from various sources, I’ve always heard that these explicit query hints should typically be designed for edge cases or specific scenarios that are tested, documented, and known to scale appropriately. Anytime you introduce hints, you are taking control from the query analyzer and indicating you know best…. This might be the case, but test test test!

OPTION(RECOMPILE) is like dynamite:  use it to blow up big problems, but understand beforehand what’s going to happen.

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