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Curated SQL Posts

Using seplyr Instead Of dplyr

Published 2017-07-25 by Kevin Feasel

John Mount explains seplyr and why it can be better for certain use cases than dplyr:

seplyr is a dplyr adapter layer that prefers “slightly clunkier” standard interfaces (or referentially transparent interfaces), which are actually very powerful and can be used to some advantage.

The above description and comparisons can come off as needlessly broad and painfully abstract. Things are much clearer if we move away from theory and return to our practical example.

Click through for a great example, and also read John’s comment on the Pascal-style assignment operator he uses.

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Data Lake Analysis With Excel And Power BI

Published 2017-07-25 by Kevin Feasel

Sachin C Sheth announces support for Azure Data Lake Store within Excel and Power BI:

Until now, if you had to analyze data stored in ADLS with Excel, you would have to copy it into a relational data store like Azure SQL Data Warehouse or download the data onto a machine, and then use Excel to analyze that data. This was rather cumbersome involving additional cost and time. With this new support, you can now access files stored in ADLS with Excel in-place, without having to copy them to other stores or locations. You can quickly get advanced insights into raw or prepared data. Models and queries you have created using Excel that ran against local data, can be run seamlessly against data stored in ADLS.

Security capabilities of ADLS allow administrators to control access to the data stored in ADLS in a discretionary manner. With this you can limit the access that Excel users have for the data in ADLS. In this manner, data in the ADLS-based data lake continues to be the single source of truth with no redundant copies and can be analyzed by analytics tools of your own choice .

Click through for a demo video.

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Diving Into The Data Lake

Published 2017-07-25 by Kevin Feasel

Jesse Gorter explains the data lake metaphor:

A data lake is a concept that opposes the idea of a data mart. Where a data mart is a silo with structured and cleansed data, a data lake is a huge data collection that is unstructured and raw. You could also say that a data mart is a bottle of clean water whereas the data lake is the lake with (not so clean) water. 🙂

Now why would you want a data lake? Imagine you are generating huge logfiles, for example in airplanes. Machines that track air pressure, temperature etc. If something goes wrong, you definitely want to be alerted. That is event-driven: “if A and B happen, alert pilot, or do C” and there are tools for dealing with that kind of streaming data. But what if the plane landed safely? What do you do with all that data? You do not need it anymore right?

Well, some people would say: “Wrong”. You might need that data later for reasons you do not know today. Google, Microsoft and Facebook are all hoarding data. Also data they are not sure they might need someday. This data could later prove to be valuable for AI, machine learning or for something else.

Read the whole thing.  The data lake concept is powerful, but it requires at least as much data governance as prior models.  Just because you can dump a bunch of files without thinking about it doesn’t mean you’ll get back something useful later.

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Mixing VARCHAR And NVARCHAR

Published 2017-07-25 by Kevin Feasel

Solomon Rutzky walks through some of the nuance of mixing VARCHAR and NVARCHAR data types with respect to your collation:

There are two types of Collations in SQL Server: SQL Server Collations and Windows Collations:

  • SQL Server Collations (those starting with SQL_) are older Collations that were the only ones available prior to SQL Server 2000. These Collations use simplistic sort orders, and do not handle the great variety of linguistic rules defined by Unicode. In fact, they do not have any Unicode rules defined at all, so N[VAR]CHAR data in these Collations will actually use OS-level Collation rules.

  • Windows Collations (those not starting with SQL_) were introduced in SQL Server 2000. These Collations not only have the Unicode rules defined, but they also apply those same linguistic rules to [VAR]CHAR data. While this does come at a slight cost to performance, it also allows for consistency of behavior. And it is this consistency that helps out greatly when there is a mismatch of datatypes (as we will see in a moment).

The simple advice to avoid mixing NVARCHAR and VARCHAR data types is still sound, but do read the whole thing.

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Reading Drive Files Recursively From T-SQL

Published 2017-07-25 by Kevin Feasel

Slava Murygin shows how to use a common table expression to read a recursive drive listing:

In this blog I’ll use undocumented in SQL Server extended stored procedure “xp_dirtree“.
That procedure is well described in Patrick Keisler’s blog.

Briefly, “xp_dirtree” extended procedure returns tree of sub-directories from given folder and has three parameters:
1. Starting or root folder
2. Depth level – determines how deep SQL Server will dig into a sub-directory structure. If it set to zero or omitted whole tree will be returned.
3. If omitted or set to zero will return only folders .If set to “1” will return files as well.

If you want to be really mean to somebody, create a shortcut to the parent directory within a directory.

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Managing Drives On Linux

Published 2017-07-25 by Kevin Feasel

David Klee walks through some basics of Linux administration with respect to drives and mountpoints:

We see that all four of the drives show up in the list. Because of the nature of how I presented the disks to the VM, the bootable drives (sdd) show up at the end of the chain instead of the beginning, but that’s OK. It doesn’t change how the disks are bootable.

We are going to use the Logical Volume Manager (LVM) to manage the disks for us instead of using regular partitions. On Windows-based servers, Windows has the ability to expand partitions without incurring any downtime, and we want to have the same flexibility from a Linux standpoint. With elevated priviledges (for the rest of the commands), let’s scan the drives to look at what it sees.

Read the whole thing.

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Environmental Considerations With SQL Server

Published 2017-07-25 by Kevin Feasel

Jeff Mlakar wraps up his environmental diagnostics series:

Let’s start by defining terms. By environmental I mean factors that affect database performance outside of the code. The remediation of issues can be accomplished without altering SQL, no code change request, no release management necessary. Poor performance can often be addressed by doing a quick review of the overall system and ensuring there are not horrible defaults in use.

This is the low hanging fruit we can get before deep diving into the code

Before you start banging your head against the hood of the car take a walk around and look at the big picture first. It is a common thing to see junior DBAs go right for the most complex area they can identify and ignore the obvious issues which are plain to the eyes of a veteran DBA.

Read on for some resources to help you learn more.

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R For Apache Impala

Published 2017-07-24 by Kevin Feasel

Ian Cook describes implyr, an R interface for Apache Impala:

dplyr provides a grammar of data manipulation, consisting of set of verbs (including mutate(), select(), filter(), summarise(), and arrange()) that can be used together to perform common data manipulation tasks. The implyr package helps dplyr translate this grammar into Impala-compatible SQL commands. This gives R users access to Impala’s scale and speed on large distributed datasets while using the same familiar dplyr syntax that they are accustomed to using on local data frames and other data sources. R users can also choose to directly write SQL commands and execute them on Impala using implyr.

implyr builds upon recent work from RStudio and other contributors, including major updates to the packages dplyr and DBI, and new packages dbplyr and odbc. implyr together with these packages enables data scientists and data engineers to more easily interact with Impala through self-service data science tools like Cloudera Data Science Workbench.

It looks like this project is off to a good start already.

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Data Cleanup Using Drools

Published 2017-07-24 by Kevin Feasel

Rathnadevi Manivannan gives an example of using Drools to create rule-based data cleansing processes:

The oil well drilling datasets contain raw information about wells and their formation details, drill types, and production dates. The Arkansas dataset has 6,040 records and the Oklahoma dataset has 2,559 records.

The raw data contains invalid values such as null, invalid date, invalid drill type, and duplicate well and invalid well information with modified dates.

This raw data from the source is transformed to MS SQL for further filtering and normalization. To download raw data, look at the Reference section.

This is an example of applying several constraints and rules to a single data set.  Each individual rule would probably be easier to do in T-SQL, but the whole bunch becomes easier to understand with a procedural language.

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