Lenses are an important concept in functional programming with great practical utility. Several years ago, we already introduced functional lenses here on the blog. Since then, we have continuously expanded our use of lenses in our daily work. Today we want to show how we use lenses as bidirectional transformations and how they help us convert between data representations.

We implement the example for this article with our comprehensive and freely available Clojure library called Active Clojure, which we use in all our Clojure projects. This library includes implementations of lenses and Records, the latter of which is for representing compound data in Clojure. The combination of lenses and records is particularly helpful.

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This post is the beginning of a series about functional software architecture in Kotlin. Its material originally comes from a collaboration between Active Group and Blume2000, where we consulted them on the development of their web shop. I wrote this post together with Benedikt Stemmildt, who was CTO at Blume2000 at the time.

This post is about data validation. We want to ensure that objects in our program are „valid,“ meaning they satisfy arbitrary consistency criteria without which our software won‘t function.

My colleague Marco Schneider already presented abstractions for this in Haskell in an earlier post.

The same ideas can also be transferred to Kotlin — with some compromises. In this post, we‘ll tackle the topic anew, specifically how we can help an object-oriented perspective using functional techniques. So it‘s not necessary to read the Haskell post. (We still warmly recommend the post, as it particularly describes the concept of Applicative, which is impractical in Kotlin.) However, basic Kotlin knowledge is assumed.

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Riemann is a stream processing system that is excellent for collecting and processing events and logs from servers and systems. We successfully use Riemann in production in very large IT systems as a core component for log and metric processing and for monitoring.

We use Riemann to process events and forward them to long-term storage systems like Elasticsearch, where these events can be conveniently searched through user interfaces like Kibana. Additionally, we use Riemann to write metrics to time-series databases like InfluxDB; these time series can then be visualized through user interfaces like Grafana or can also be used to alert on errors and problems.

Today we‘ll show with an application example how to extract metrics from logs using Riemann.

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Many applications use configurations to customize their behavior at runtime. The parameters in these configurations can have default values that are used if nothing else is specified. Other values, such as passwords, have no default values and must therefore always be provided when starting the application.

In this article, we will go through several iterations to model configurations in our programs using higher-kinded data in Haskell.

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Our first article in the series „Functional Programming in Practice“ on the topic of data validation with applicative functors.

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