Press "Enter" to skip to content

Category: Misc Languages

Pattern Matching in Scala

Kuldeepak Gupta shows off pattern matching in Scala:

Pattern Matching is a mechanism of checking a value against a Pattern. It gives a way of checking the given sequence of tokens for the presence of a specific pattern. Here, we match expressions against a pattern.

Compared to the ‘switch’ in C++, C, JAVA, there’s no fall through to the next alternative in Scala pattern matching. A Match error is thrown when no pattern matches.

This is a powerful part of functional programming.

Comments closed

Lambda Expressions in Scala

Shubham Shrivastava explains how lambda expressions work in Scala:

Lambda expressions in Scala the syntax for these uses a symbol it’s an equals and a greater than and we refer to this as rocket ( => ). When we’re reading the code the idea of a lambda expression is a short literal expression that defines a function and typically these should not be overly long. so for example I could define a function for squaring values that looks something like this.

Lambda expressions are great in cases where you need to perform an operation exactly one time. If you create a separate function with its own name, there’s always a wonder in the back of a developer’s mind if this thing will get used again, and so it takes up a little bit of cognitive load. A lambda expression answers that conclusively: no, we won’t use this code again.

Comments closed

Table-Valued Parameters and Dapper (.NET Core Edition)

Randolph West hits on a timely question:

A customer I’ve been working with for a while now has a monolithic ASP.NET MVC web application which we are porting to .NET Core 3.1 (and then almost immediately to .NET 6). One of our biggest changes was getting rid of Entity Framework and replacing it with Dapper, because performance is a feature.

To deflect the ire of EF Core aficionados out there, the answer is still no.

Dapper is a micro-ORM in that it does not do as much “magic” as Entity Framework. This necessitates more work at the data access layer, but we have the trade-off of speed.

I say this is timely because my team is working through this exact thing right now. For future reference, anticipating what my team is working on and writing a blog post which answers a question we have is an outstanding way of getting noticed here.

Comments closed

Caching Strategies with Redis

Camilo Reyes shares performance data from four Redis caching strategies:

Redis is a cache database that stores documents in memory. The data store has a key-value pair lookup with O(1) time complexity. This makes the cache fast and convenient because it does not have to deal with complex execution plans to get data. The cache service can be trusted to find a cache entry with a value in almost no time.

When datasets in cache begin to grow, it can be surprising to realize that any latency is not a Redis issue. In this take, I will show you several strategies to cache Redis data structures then show what you can do to pick the best approach.

Read on for the contenders and how they do. ProtoBuf’s results on small datasets surprised me.

Comments closed

Lessons Learned in Migrating to .NET 5 or 6

Patrick Smacchia has a few tips for migrating code from .NET Framework to .NET 5 or even 6:

In January 2020 I wrote the post Not planning now to migrate your .NET 4.8 legacy, is certainly a mistake. Hopefully we followed our own advice and have been migrated most of our non-UI code. This way latest NDepend version 2021.2 can now run analysis, reporting, power tools and API against .NET 5 on Windows, Linux and MacOS.

We learn a few things during this migration journey. Let me expose those in five points:

My most positive experiences with this have come in migrating projects with relatively few third party dependencies. The big problem there is that a fair percentage of older libraries never made the leap to Standard, so you may be stuck with a re-write (or just stuck in general) as a result.

Comments closed

Error Handling in Scala

Ashish Chaudhary gives us three different ways of handling errors in Scala:

Error handling is the process of handling the possibility of failure. For example, failing to read a file and then continuing to use that bad input would clearly be problematic. Noticing and explicitly managing these errors saves the rest of the program from various pitfalls.

Exceptions in Scala work the same way as in C++ or Java. When an exception occurs, say an Arithmetic Exception then the current operation is aborted, and the runtime system looks for an exception handler that can accept an Arithmetic Exception. Control resumes with the innermost such handler. If no such handler exists, the program terminates.

Or, another way to put it is, structural programming with try/catch or functional programming via monads (Option and Either).

Comments closed

Using Azure Queue Storage as a Trigger for Function Apps

Aveek Das shows how you can use Azure Queue Storage as a way to trigger an Azure Function App:

In this article, we are going to learn how to trigger Function Apps from Queue Storage in Azure. Function Apps has been one of the most popular cloud services of Microsoft Azure. Function Apps allow users to write code in any language and then execute the code in the cloud. There is no infrastructure to be managed and hence is very flexible for writing and building applications on the go. Every Function App can be triggered in multiple ways, for example, by calling the function URL using an HTTP endpoint or from some other functions in Azure. In this article, we are going to trigger the Function App from Queue Storage in Azure and see how to pass a message from the queue to the Function App.

Queue Storage in Azure is another service in Azure that allows users to store multiple messages in it. Users can use a queue to create a list of items that need to be processed one by one. Messages to Queue Storage in Azure can be added by using the HTTP or HTTPS endpoints. Usually, a queue can store data up to 64 KB in size. We can add millions of messages in a queue if it is supported by the storage account.

Click through to see how. Though now I wonder why I might use Queue Storage instead of an Event Hub or an Event Grid. But I suppose that’s a question for a different article.

Comments closed

Interactive .NET Notebooks in Visual Studio Code

Deborah Melkin tries out .NET Interactive Notebooks in Visual Studio Code:

These days, we tend to think Azure Data Studio when we database developers talk about notebooks, specifically SQL Notebooks. But what Rob used for his demos are a new functionality within VS Code called .NET Interactive Notebooks. It was developed in combination with the Azure Data Studio team and it has support for SQL. But the cool thing that intrigued me was that a notebook could support multiple kernels, unlike Azure Data Studio. Knowing how much we love our SQL and PowerShell and this being a feature that many of us want to see in SQL Notebooks, I decided to try and set this up and poke around.

Click through for Deb’s experiences. And I’ll also point out that .NET Interactive Notebooks supports the best .NET language (and the one which most naturally fits the ethos of notebooks), F#.

Comments closed

.NET 5 C# Language Extension for SQL Server

Nikita Takru makes an announcement:

SQL Server 2019 supports the R, Python, and Java Language Extensions. These language extensions provide many benefits to developers. They provide data security, rapid speed for deployment, and ease of integration.

Previously, we announced the release of JavaR, and Python extensions. Today we are thrilled to share that we are open sourcing the .NET 5 C# Language Extension for SQL Server on GitHub.

My response to this is roughly the same as what Panagiotis Kanavos put in a GitHub issue. F#, being a functional programming language intended for things like data analysis, would be a no-brainer. And the choice of Windows-only support probably helped get it out the door faster, but can be limiting over time.

I guess the short version is, we’ll see what the community does with this. I’d also like to see how it compares to CLR on actions. My guess is that it’ll be slower based on the way the R and Python engines work, but that’s just a guess. Overall, I’m happy this exists and want it to be more useful.

Comments closed

Image Classification with ML.NET

Ivan Matec shows how to use ML.NET’s image classification with an example of vital importance:

One of the best scenes from Silicon Valley is Jian Yang demoing his “Hotdog, not hotdog” application. In this article, we will build our own “Hotdog, not hotdog” solution using ML.NET. After all, who would not want to determine if that dish is, or is not a hot dog? Just take a picture, upload it to the web or desktop application, and get results with almost 90% certainty in a second.

Although some may say this is not a very useful application, it is a fun way to explore another machine learning concept through ML.NET. I covered installing and getting started with ML.NET in Visual Studio in my previous article, so refer to it if you missed it.

Click through for the implementation, which is quite straightforward.

Comments closed