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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 our talk, we will introduce a novel approach to system design— TypeOps — in which the application and infrastructure layers are fused to provide unprecedented safety and productivity for Scala teams.
In this lightning talk, we will discuss 2 interesting IntelliJ IDEA features.
In this talk, I'll walk you through how workflows4s works, how it stands apart from tools like Temporal or Camunda, and why it just might be the better approach for modern, event-driven applications.
In this talk, I'd like to share how the Iron library and features from Scala 3 helped us build a solution which is safer, more robust, and easier to maintain.
