Assessing Haskell (blogpost, slightly negative!)

This page summarizes the projects mentioned and recommended in the original post on /r/haskell

InfluxDB high-performance time series database
Collect, organize, and act on massive volumes of high-resolution data to power real-time intelligent systems.
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  1. containers

    Assorted concrete container types (by haskell)

    Calling linked lists Haskell's "primary data structure" seems off-base to me. Yes, there's String, yes, there's built-in syntax for List... but there's also everything in containers, and vector is pretty easy to use in practice, though it would probably be good for more learning material to mention it more prominently.

  2. InfluxDB

    InfluxDB high-performance time series database. Collect, organize, and act on massive volumes of high-resolution data to power real-time intelligent systems.

    InfluxDB logo
  3. relude

    🌀 Safe, performant, user-friendly and lightweight Haskell standard library

    Another good practice to prevent runtime exceptions is to avoid partial functions like head and tail. One way to ensure that one doesn't accidentally slip in is to use an alternative prelude (e.g. relude) that doesn't export them by default. Another alternative is to use hlint to warn about them (and then import their safe counterparts instead).

  4. tracing-newrelic

    A Haskell package to report to NewRelic using the New Relic C SDK

    There's been a 3rd party NewRelic library since the end of 2019 by the great people at NoRedInk: https://github.com/NoRedInk/tracing-newrelic

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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Did you know that Haskell is
the 25th most popular programming language
based on number of references?