Category: Misc Languages

Camilo Reyes shows how to use Dapper:

Dapper is a lightweight shim around ADO.NET for data access via extension methods. To keep this relevant to any real application, there is quite a bit of code, which I won’t be able to show, so I recommend downloading the repo from GitHub. The focus here is to walk through the code API and follow best practices for building a DAL. The set of extension methods can feel overwhelming because there is a lot of functionality to cover.

I recommend general familiarity with LINQ and the List<> generic class in C#. This guide can be used as a reference so you can read one section at a time. At each pause, I encourage you to play with the code and allow the information to sink in.

I tend to like Dapper a lot, as it’s basically a lightweight wrapper around ADO. It’s very efficient as a result and you can also use stored procedures quite easily.

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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.

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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.

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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.

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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.

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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.

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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).

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