Category: Misc Languages

Mahesh Chand demonstrates how Scala can use lazy evaluation to reduce memory requirements:

In this blog, we will talk about lazy evaluation in Scala. How we can add efficiency to our application?

Efficiency is achieved not just by running things faster, but by avoiding things that shouldn’t be done in the first place.

In functional programming, lazy evaluation means efficiency.  Laziness lets us separate the description of an expression from the evaluation of that expression. This gives us a powerful ability—we may choose to describe a “larger” expression than we need, and then evaluate only a portion of it. There are many ways to achieve lazy evaluation in Scala i.e using lazy keyword, views, streams etc.

The fastest operation is the one that doesn’t have to run at all.

Mark Wilkinson continues his Bash for the Powershell developer series:

An expression is a statement that evaluates to either true or false. In our case we are just checking if the $hour_of_day variable is less than 12 and greater than or equal to 5. When comparing numeric values you must use the same operators you are familiar with from PowerShell: -lt -gt -le -ge -ne. When comparing string values you have to use operators you might be more used to from other languages: == != and both values must be enclosed in double quotes [ "string1" == "string2" ].

With just the things Mark has shown so far, you can begin to build helpful scripts.

Mahesh Chand Kandpal shows how to create XPath statements in Scala:

We called the \() on the XML element and asked it to look for all symbol elements.  It returns an instance of scala.xml.NodeSeq, which represents a collection of XML nodes.

The \() method looks only for the elements that are direct descendants of the target element(i.e symbol).   If we want to search through all the elements in the hierarchy starting from the target element, \\() method is used

Check it out, especially if you’re working with Spark, as you never know when a rogue XML file will head your way.

Kevin Sookocheff explains the concepts of domain, range, and codomain:

That is, a function relates an input to an output. But, not all input values have to work, and not all output values. For example, you can imagine a function that only works for positive numbers, or a function that only returns natural numbers. To more clearly specify the types and values of a functions input and output, we use the terms domain, range, and codomain.

Speaking as simply as possible, we can define what can go into a function, and what can come out:

• domain: what can go into a function

• codomain: what may possibly come out of a function

• range: what actually comes out of a function

Read on for more, including a couple of examples.  These are important concepts for learning functional programming.

Chris Webb shows how to build M queries in Visual Studio:

Writing M in the Advanced Editor in Excel or Power BI can be a frustrating experience unless you’re the kind of masochist who loves writing code in Notepad. There are some options for writing M code outside Excel and Power BI, for example Lars Schreiber’s M extension for Notepad++ (see here for details) or the M extension for Visual Studio Code (available from the Visual Studio Marketplace here; more details on Brett Powell’s blog here), but the trouble with them is that you have to copy the code back into Excel or Power BI to run it. What many people don’t realise, however, is that it is possible to write M code and have IntelliSense, formatting, keyword highlighting and also the ability to execute your own M queries, using the Power Query SDK in Visual Studio.

The Power Query SDK (which you can download here) supports Visual Studio 2015 and 2017 and is intended for people who are writing custom Data Connectors for Power BI. To let you test your Data Connector you can create a .pq file containing M code, and this in fact allows you to run any M query you want whether you’re building a Data Connector or not.

And then, once you get comfortable with M, start learning F#.  That will allow you to laugh haughtily at those poor object-oriented sods out there.

Victoria Holt has some basics on Splunk:

Splunk, a software platform, has the capability to leverage machine data for data management and analytics.  It can be used for

• Data driven decision making
• Alerts for network security threats
• Report on system failures
• Analyse and improve functionality

It enables performance analysis, dashboard creation, monitoring, troubleshooting and investigation of the real-time data collected. A Edureka learning video showed the Splunk components.

Advanced Splunk queries are still a bit like magic to me, but this is a very powerful service once you get a handle on how it works.