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Author: Kevin Feasel

Pareto Efficiency And Mario Kart

Published 2018-06-11 by Kevin Feasel

The folks at Civis Analytics answer one of the more important questions in life:

Mario Kart was a staple of my childhood — my friends and I would spend hours after school as Mario, Luigi, and other characters from the Nintendo universe racing around cartoonish tracks and lobbing pixelated bananas at each other. One thing that always vexed our little group of would-be speedsters was the question of which character was best. Some people swore by zippy Yoshi, others argued that big, heavy Bowser was the best option. Back then there were only eight options to choose from; fast forward to the current iteration of the Mario Kart franchise and the question is even more complicated because you can select different karts and tires to go with your character. My Mario Kart reflexes aren’t what they used to be, but I am better at data science than I was as a fourth grader, so in this post I’ll use data to finally answer the question “Who is the best character in Mario Kart?”

This post also acts as a primer on Pareto Efficiency, an important concept in economics.

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Flattening JSON Data With Databricks

Published 2018-06-11 by Kevin Feasel

Ivan Vazharov gives us a Databricks notebook to parse and flatten JSON using PySpark:

With Databricks you get:

  • An easy way to infer the JSON schema and avoid creating it manually
  • Subtle changes in the JSON schema won’t break things
  • The ability to explode nested lists into rows in a very easy way (see the Notebook below)
  • Speed!

Following is an example Databricks Notebook (Python) demonstrating the above claims. The JSON sample consists of an imaginary JSON result set, which contains a list of car models within a list of car vendors within a list of people. We want to flatten this result into a dataframe.

Click through for the notebook.

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Microsoft R Open 3.5.0 Released

Published 2018-06-11 by Kevin Feasel

David Smith announces that Microsoft R Open 3.5.0 is now available:

Microsoft R Open 3.5.0 is now available for download for Windows, Mac and Linux. This update includes the open-source R 3.5.0 engine, which is a major update with many new capabilities and improvements to R. In particular, it includes a major new framework for handling data in R, with some major behind-the-scenes performance and memory-use benefits (and with further improvements expected in the future).

Microsoft R Open 3.5.0 points to a fixed CRAN snapshot taken on June 1 2018. This provides a reproducible experience when installing CRAN packages by default, but you always change the default CRAN repository or the built-in checkpoint package to access snapshots of packages from an earlier or later date.

It’s nice to see Microsoft keeping pace with R changes; they look like they’re averaging about 6-8 weeks from an R point release to an MRO release.

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Smarter Indexes Based On Column Cardinality

Published 2018-06-11 by Kevin Feasel

Eric Blinn has a function which organizes columns in the missing index DMV by cardinality:

Bryan Rebok and Brent Ozar recently opened my eyes to something I didn’t know.  When SQL Server recommends missing indexes to you it puts the columns in order in which they are found in the table.  That’s it.  I always thought there was more logic into it.  But there isn’t.  Upon reading this I had a terrible realization that I’ve made a lot of awful indexes in my time.  I owe the world an apology.  I hope this post can serve as that apology.

I’ve written a function that accepts the equality column list from dm_db_missing_index_details as a parameter and spits those columns back out in order by their cardinality.  This won’t necessarily be the proper order for the columns in every index, but it is far more likely to be correct than the initial result from the DMV.

I’m amazed that the missing index DMV generates column names in such a simplistic manner.

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Figuring Out Azure Analysis Services Costs

Published 2018-06-11 by Kevin Feasel

Chris Webb explains that Azure Analysis Services might not be quite as expensive as you’d first think:

What does this mean for the cost of Azure Analysis Services? Basically, if you’re taking advantage of these features you won’t pay one of the monthly prices quoted on the pricing page linked to at the top of this post. Instead you may do things like:

  • Scale up for one hour every day when you need to process your SSAS database, just to get the extra memory and QPUs needed, then scale down when processing has finished
  • Scale out only on certain days, or certain times of day, to handle increased numbers of users
  • Pause your instance when you are sure that no-one needs to run queries

How do you then calculate the likely cost? For my Azure Analysis Services precon at SQLBits a few months ago I built an Excel workbook that shows how to go about this.

There are some good questions in the comments section, so check those out as well.

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BCP And Multiple SQL Server Instances

Published 2018-06-11 by Kevin Feasel

Manoj Pandey investigates an interesting issue with BCP:

I observed one thing here with BCP (Bulk Copy Program), when you have 2 versions of SQL Server installed on you PC or Server. I had SQL Server 2014 & 2016 installed on one of my DEV server.
So if you are executing Query from SQL 2016 instance, it was inserting records in SQL 2014 instance:

exec master..xp_cmdshell ‘BCP AdventureWorks2014.Person.Address2 IN d:\PersonAddressByQuery.txt -T -c’

But even if you use BCP 2016 version, it was still inserting in SQL 2014 instance:

Read on for the reason as well as how to specify which instance you want to use.

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Apache Pulsar 2.0 Released

Published 2018-06-08 by Kevin Feasel

George Leopold reports on a new version of Apache Pulsar:

The startup’s Apache Pulsar 2.0 released on Wednesday (June 6) adds new functionality designed to move data users “beyond batch” processing. Among them is a “stream-native” processing capability called Pulsar Functions designed to apply analytics to data as its flows through the Pulsar platform. Processing functions can be written in either Java or Python, the company said.

Debuted earlier this year as a preview feature, Streamlio announced general availability of Functions this week as part of its 2.0 release.

Another is a Pulsar enhancement developed in conjunction with Apache Bookkeeper, a scalable storage system. Streamlio said the new features, called Topic Compaction, delivers streaming data storage designed to improve the performance of applications consuming data from Pulsar. It serves as a “broker” that builds a snapshot of the latest value for each topic key, the startup said.

Read the whole thing.

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Removing Time From A DateTime

Published 2018-06-08 by Kevin Feasel

Wayne Sheffield compares the performance of four methods for removing time from a DateTime data type:

Today, we’ll compare 3 other methods to the DATEADD/DATEDIFF method:

  1. Taking advantage of the fact that a datetime datatype is stored as a float, with the decimal being fractions of a day and the whole numbers being days, we will convert the datetime to float, taking the floor (just the whole numbers), and converting back to datetime.
  2. Using the DATEADD/DATEDIFF routine.
  3. Converting the datetime to DATE and back to datetime.
  4. Converting the datetime to varbinary (which returns just the time), and subtracting that from the datetime value.

While there are other ways of stripping the time (DATETIMEFROMPARTS, string manipulation), those ways are already known as poorly performing. Let’s just concentrate on these four.

Click through for the methods, as well as a performance test to see which is fastest.

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Scatterplot Matrices

Published 2018-06-08 by Kevin Feasel

The Plotly folks show off scatterplot matrices in Python:

The scatterplot matrix, known acronymically as SPLOM, is a relatively uncommon graphical tool that uses multiple scatterplots to determine the correlation (if any) between a series of variables.

These scatterplots are then organized into a matrix, making it easy to look at all the potential correlations in one place.

SPLOMs, invented by John Hartigan in 1975, allow data aficionados to quickly realize any interesting correlations between parameters in the data set.

In this post, we’ll go over how to make SPLOMs in Plotly with Python. For extra insights, check out our SPLOM tutorial in Python and R.

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