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Curated SQL Posts

Database Snapshots

Published 2016-06-24 by Kevin Feasel

Kenneth Fisher discusses database snapshots:

Here is where it starts getting interesting. A snapshot initially takes up little to no space. As changes are made to the source database the snapshot grows in size. In fact the snapshot is the size of all of the pages changed in the source database since the creation of the snapshot. Basically as a page is changed in the source database a copy of the original page is made and stored in the snapshot, but only the first time. (Note: The files used to store these pages are called sparse files.) This means that if you change the same page over and over again it will only be written to the snapshot once. It then logically follows that the largest a snapshot can get is the size of the source database at the time the snapshot was taken. Since most of the time we change a very small portion of the database at any given point in time this means that snapshots tend to be much smaller than the source database. In fact you could load millions of rows into the source database (assuming they are mostly/all in new pages) and it will have little to no effect on the size of the snapshot.

My favorite use of database snapshots was so developers could test their changes in QA and then revert back to a pre-snapshot environment.  That way, they could preserve data for future runs.

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Polybase Setup Errors

Published 2016-06-23 by Kevin Feasel

Murshed Zaman on the Azure CAT team covers a number of Polybase configuration errors:

SSMS Error:

Any Select query fails with the following error.
Msg 106000, Level 16, State 1, Line 1
Java heap space

Possible Reason:

Illegal input may cause the java out of memory error.  In this particular case the file was not in UTF8 format. DMS tries to read the whole file as one row since it cannot decode the row delimiter and runs into Java heap space error.

Possible Solution:

Convert the file to UTF8 format since PolyBase currently requires UTF8 format for text delimited files.

I imagine that this page will get quite a few hits over the years, as there currently exists limited information on how to solve these issues if you run into them, and some of the error messages (especially the one quoted above) have nothing to do with root causes.

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Query Store On Read-Only Databases

Published 2016-06-23 by Kevin Feasel

Kendra Little wants to know if Query Store works on read-only databases:

At least for now, Query Store can only record query performance in databases that are read-write. Once you go read-only you can review the performance of past queries, but you can’t track the performance of anyone who queried the database after the point it went read-only.

At least for now. Query Store is such an awesome feature that perhaps this will change in the future. (I don’t have any inside info, only optimism.)

That’s a little bit of a letdown, but makes perfect sense.

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Checking Users And Principals

Published 2016-06-23 by Kevin Feasel

Shane O’Neill walks through a permissions issue and cautions against jumping the gun:

All the above is what I did.

Trying to fix the permission error, I granted SELECT permission.
Trying to fix the ownership chain, I transferred ownership.
Mainly in trying to fix the problem, I continually jumped the gun.
Which is why I am still a Junior DBA.

To be fair, I’d argue that if you intended to have replicated objects live in a different schema, the second action was fine.  Regardless, the advice is sound.

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Polybase Statistics

Published 2016-06-23 by Kevin Feasel

I dig into a non-trivial Polybase query:

Polybase offers the ability to create statistics on tables, the same way that you would on normal tables.  There are a few rules about statistics:

  1. Stats are not auto-created.  You need to create all statistics manually.

  2. Stats are not auto-updated.  You will need to update all statistics manually, and currently, the only way you can do that is to drop and re-create the stats.

  3. When you create statistics, SQL Server pulls the data into a temp table, so if you have a billion-row table, you’d better have the tempdb space to pull that off.  To mitigate this, you can run stats on a sample of the data.

Round one did not end on a high note, so we’ll see what round two has to offer.

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Netflix Billing Architecture

Published 2016-06-23 by Kevin Feasel

The Netflix tech blog discusses changing their billing infrastructure to be entirely in the cloud (AWS in this case):

Cleaning up Code: We started chipping away existing code into smaller, efficient modules and first moved some critical dependencies to run from the Cloud. We moved our tax solution to the Cloud first.

Next, we retired serving member billing history from giant tables that were part of  many different code paths. We built a new application to capture billing events, migrated only necessary data into our new Cassandra data store and started serving billing history, globally, from the Cloud.

We spent a good amount of time writing a data migration tool that would transform member  billing attributes spread across many tables in Oracle  into a much simpler Cassandra data structure.

We worked with our DVD engineering counterparts to further simplify our integration and got rid of obsolete code.

Purging Data: We took a hard look at every single table to ensure that we were migrating only what we needed and leaving everything else behind. Historical billing data is valuable to legal and customer service teams. Our goal was to migrate only necessary data into the Cloud. So, we worked with impacted teams  to find out what parts of historical data they really needed. We identified alternative data stores that could serve old data for these teams. After that, we started purging data that was obsolete and was not needed for any function.

All in all, a very interesting read on how to migrate large databases.  Even if you’re moving from one version of a product to another, some of these steps might prove very helpful in your environment.

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R 3.3.1 Available

Published 2016-06-23 by Kevin Feasel

David Smith reports that a new version of R is now available, 3.3.1:

This minor update, codenamed “Bug in Your Hair”, makes a few small fixes to the R 3.3.0 release. Bugs fixed include mostly rarely-encountered cases like generating Gamma random numbers with zero or infinite rate parameters, and correctly matching text (with the matchfunction) that only differed in the encoding.

There are no new features in this update, and all R code and packages should work with R 3.3.1 just as they did with R 3.3.0. For a complete list of the fixes in R 3.3.1, follow the link below.

Even though this is a small update, it might be useful to check out.

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New No-Longer-Features In SQL Server 2016

Published 2016-06-23 by Kevin Feasel

Bob Pusateri acts as SQL grim reaper:

32-bit SQL Server. SQL Server 2016 is 64-bit only. If for whatever reason you’re running on a 32-bit architecture, sadly you’re now out of luck – 2014 is the end of the road. On the bright side, there’s probably some new hardware in your future!

Compatibility Level 90. If you’re using compatibility level for backwards compatibility, the oldest available version in SQL Server 2016 is 100, which corresponds to SQL Server 2008. Compatibility level 90, SQL Server 2005, is no longer an option.

Bob also covers a few deprecated features, none of which (hopefully) are in regular use in your environment.

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Radar Charts

Published 2016-06-23 by Kevin Feasel

Devin Knight continues his custom visuals course:

In this module you will learn how to use the Radar Chart, a Power BI Custom Visual. The Radar Chart is sometimes is also know to some as a web chart, spider chart, or star chart.  Using the Radar Chart allows you to display multiple categories of data on each spoke (like spokes on a bicycle wheel) of the chart. The Radar Chart does support the display of multiple metrics, which allows you to compare and contrast the “pull” that each category has on your metrics.

I still say you should stick with the fish chart for all of your visualization needs.

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