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Category: Misc Languages

From API Call to ML Services Prediction

Published 2021-08-20 by Kevin Feasel

Tomaz Kastrun continues a series:

From the previous two blog posts:

– Creating REST API for reading data from Microsoft SQL Server in web browser

– Writing Data to Microsoft SQL Server from web browser using REST API and node.js

We have looked into the installation process of Node.js, setup of Microsoft SQL Server and made couple of examples on reading the data from database through REST API and how to insert data back to database.

In this post, we will be looking the R predictions using API calls against a sample dataset.

Click through to see it in action.

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Ways to Filter Data in PostgreSQL

Published 2021-08-19 by Kevin Feasel

Gauri Mahajan shows off several techniques for filtering data in PostgreSQL:

Data is hosted in a variety of data repositories, one of which is relational databases. Out of tens of commercial and open-source relational databases, one of the most popular open-source relational databases is PostgreSQL. This database is offered on the Azure cloud platform through a service named Azure Database for PostgreSQL. One of the most fundamental operations performed on the database is reading and writing data to consume and host data. It goes without saying that when the data is consumed, it must be scoped based on the requirements or criteria specified by the consumer. This translates to filtering the data while querying it. Like every other relational database, Postgres offers different operators and options to filter data while querying. Let’s go ahead and learn some of the most fundamental ways to filter data hosted in PostgreSQL.

Most of them are the same as what you have in T-SQL, but not everything.

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Working around Unparameterized IN Clauses with EF

Published 2021-08-17 by Kevin Feasel

Erik Darling bears the bad news:

If you’re using an Entity Framework, and sending in queries that build up IN clauses, they won’t end up getting parameterized.

Even Forced Parameterization won’t help you if you’re sending in other parameters. One limitation is that it doesn’t kick in for partially parameterized queries.

Even if they did get parameterized, well, what’s one funny thing about IN clauses? You don’t know how many values will be in them.

Read on for a couple of work-arounds for this.

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Creating a REST API on SQL Server Data

Published 2021-08-10 by Kevin Feasel

Tomaz Kastrun needs to get at some data:

REST API (or RESTful API) is a application programming interface (abbreviated as API) that complies with REST architectural style and is created to allow communication, information flow and interaction with REST (Representational State Transfare) web services. API is used as an interface for communication between two or more programs (or machines) and provides content required from one side to another side (known as consumer and producer; call/request and response).

Getting data from and to database using API is great for building integration with application and retrieving to client all the information in JSON format only once, instead of every time an action occurs on frontend. Yes there are multiple ways to push data to frontend, it can be entity framework, it can be Dapper, it can be T-SQL with procedures layer, it can be REST API, and many many others.

Click through to see how Tomaz does it with Node.Js.

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Azure Functions and (Lack of) F# Support

Published 2021-08-04 by Kevin Feasel

Jamie Dixon has a shaggy dog tale:

When Azure Functions first came out, F# had pretty good support – templates, the ability to run a .fsx file, cool examples written by Don… Fast forward to 2021 and we have bupkis. I recently wrote a dictionary of amino acid weights that would be perfect as a service: pass in a kmer length and weight, get all hits from amino acids.

I first attempted to create a function app, select a .fsx template, and write the code in my browser. Alas, only .csx scripting is available.

Not to be too cutesy about it, but it would be nice if the product which allows for the execution of functions in a cloud service would support the .NET language which most explicitly embraces the notion of functions. If you feel similarly, there is an open feedback ticket.

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An Introduction to ML.NET

Published 2021-07-30 by Kevin Feasel

Ivan Matec gives us a walkthrough of the ML.NET library and its Model Builder component:

Before we dive into our example, let’s talk a bit about ML.NET history and its current state.

ML.NET draws its origins from the 2002’s Microsoft Research project named TMSN, which stands for “test mining search and navigation.” Later it was renamed to TLC, “the learning code.” ML.NET war derived from the TLC library. Initially, it was used on internal Microsoft products.

The first publicly available version ML.NET 1.0 was released in 2019. It included the Model Builder add-in and AutoML (Automated Machine Learning) capabilities.

The current version is 1.6.0. More details about all releases can be found on the official ML.NET release page.

ML.NET is not a bad library if you need to do some fairly simple work

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Working with App Secrets in .NET Core

Published 2021-07-30 by Kevin Feasel

Santosh Hari shows us how to use application secrets when building .NET Core applications:

I was writing a sample dotnetcore console application for a talk because why I felt using a sample aspnet core web app was overkill. The app was connecting to a bunch of Azure cloud and 3rd party services (think Twilio API for SMS or LaunchDarkly API for Feature Flags) and I had to deal with connection strings.

Now I have a nasty habit of “accidentally” checking in connection string and secrets into public GitHub repositories, so I wanted to do this right from the get go.

That’s a bad habit to be in, and Santosh shows us how we can avoid doing that via use of application secrets.

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Visualizing Data over Time with F#

Published 2021-07-30 by Kevin Feasel

Codesuji takes us through creating an interesting video:

How is this accomplished? I reach into F#’s bag of tricks to leverage Deedle, Plotly.NET, and ffmpeg in order to transform a series of data files into a singular video showing county-level drought data from 1900-2016. Together these bring static data into a dynamic representation. For reference, the Palmer Drought Severity Index (PDSI) typically ranges from -10 (dry) to 10 (wet). Putting this all together is pretty straight-forward, but I wanted to call out a couple specific parts. For this particular example Deedle is overkill, but pairing it with Plotly.NET can often be useful in more complex situations. Plotly offers some nice customization options, which I take advantage of below. Once all the images are generated with Plotly, F# can shell out to ffmpeg to perform the video assembly. I do this in two parts, creating both an mp4 and webm file.

We’re reading datasets, parsing text files, deserializing JSON contents, building a visual for each point in time, and then creating a video out of it—all in 100 lines of code. Not bad.

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Contrasting Scala and Python wrt Spark

Published 2021-07-20 by Kevin Feasel

Sanjay Rathore contrasts two of the three key Apache Spark languages:

Imagine the first day of a new Apache Spark project. The project manager looks at the team and says: which one to choose, scala or python. So let’s start with “scala vs python for spark”. 

You may wonder if this is a tricky question. What does the enterprise demand say? Is this like asking iOS or Android? Is there a right or wrong answer?

So we are here to inform and provide clarity. Today we’re looking at two popular programming languages, Scala and Python, and comparing them in the context of Apache Spark and Big Data in general.

Read on for the comparison. I’m at a point where I think it’s wise to know both languages and roll with whichever is there. If you’re in a greenfield Spark implementation, pick the one you (or your team) is more comfortable with. If you’re equally comfortable with the two, pick Scala because it’s a functional programming language and those are neat.

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