Serverless, or Functions-as-a-Service (FaaS), is an increasingly popular paradigm for application development, as it provides implicit elastic scaling and load based billing.
However, the weak execution guarantees and intrinsic compute-storage separation of FaaS create serious challenges when developing applications that require persistent state, reliable progress, or synchronization.
This has motivated a new generation of serverless frameworks that provide stateful abstractions. For instance, Azure's Durable Functions (DF) programming model enhances FaaS with actors, workflows, and critical sections.
As a programming model, DF is interesting because it combines task and actor parallelism, which makes it suitable for a wide range of serverless applications. We describe DF both informally, using examples, and formally, using an idealized high-level model based on the untyped lambda calculus.
Next, we demystify how the DF runtime can (1) execute in a distributed unreliable serverless environment with compute-storage separation, yet still conform to the fault-free high-level model, and (2) persist execution progress without requiring checkpointing support by the language runtime. To this end we define two progressively more complex execution models, which contain the compute-storage separation and the record-replay, and prove that they are equivalent to the high-level model.