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In this talk, I will discuss why it's hard to use the power of RT to test side-effect-heavy apps.
Retrying failed side-effectful actions is the bread & butter for all programmers. Whether you use Python, Ruby, Java, or Scala, you’ll use the same retry strategies: usually some backoff and randomness.
In functional Scala, we use the powers of referential transparency (RT). If your API call is described as an IO value, you just create a new IO value that adds the retry logic of your choice. Easy, right?
Things get nasty very quickly when an API or a DB you call has more constraints. Imagine a retry strategy that starts with a 5ms delay and uses a Fibonacci backoff, but each individual delay is capped at 5s and you always do a final retry after the timeout passes. How would you make sure it’s working correctly? Is referential transparency helpful?
In this talk, I will discuss why it's hard to use the power of RT to test side-effect-heavy apps. The main problem is that our APIs and library APIs don't use the full power of RT: they focus too much on side effects and not the value representation of these side effects. This in turn makes testing such apps very difficult. I will present some alternative ideas for a better, more RT-friendly design for retries and many more side-effectful APIs.
In this talk, I'll look at the different uses to which tagless final is put to, and see what we can learn about when it is useful and when it just gets in the way.
This talk will be a quick introduction to the Unison "paradigm" and language, from the perspective of a long-standing Scala programmer.
During the talk, we’ll build a small effect system using solely Scala 3 context functions step-by-step.
In this talk I will explain the inner workings of an organisation that goes into releasing, maintaining and developing Scala and core parts of its ecosystem.
In this talk, I'll walk you through coding and design practices I've developed over the years, whilst onboarding new graduates into world of Scala (be it typelevel based API, Spark based ETL, or ML pre and post-processings), and how I made the process easier for people who didn't have much Scala experience beforehand.
