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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 introduce Bazel, exploring its core concepts and the unique aspects that set it apart from other build tools. I'll dive into some typical challenges Scala developers might face when working with Bazel.
This talk will introduce Mill: a newer build tool that does everything SBT does, but better. Faster, simpler, easier, Mill democratizes the build so you don't need to be a build tool expert to work on it.
In this presentation you will learn the source of your issues, and a third way - sanely-automatic derivation which is fast to compile, fast to run, and easy to debug by its users.
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.
I will demonstrate how Pillars can take you from zero to production in record time. By leveraging Pillars’ integration of well-known libraries, you can bypass the usual complexities of setting up observability (traces, metrics, and logs), database access, API calls, and feature flag management.
