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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Transducer: Composition, Abstraction, Performance

Higher-order functions like map, fold, filter are indispensable in any functional program. With their flexibility, they are the tool of choice for operations on collections of all kinds. However, their scope of application is not limited to classic lists or vectors. In this article, we examine more fundamental properties of these operations and take a particular look at so-called transducers in the Clojure programming language.

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This post continues our Clojure introduction (Part 1, Part 2, Part 3). This time we‘re covering compound data.

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