Recent research has explored using Datalog-based languages to express a distributed system as a set of logical invariants [2,19]. Two properties of distributed systems proved difficult to model in Datalog. First, the state of any such system evolves with its execution. Second, deductions in these systems may be arbitrarily delayed, dropped, or reordered by the unreliable network links they must traverse. Previous efforts addressed the former by extending Datalog to include updates, key constraints, persistence and events, and the latter by assuming ordered and reliable delivery while ignoring delay. These details have a semantics outside Datalog, which increases the complexity of the language or its interpretation, and forces programmers to think operationally. We argue that the missing component from these previous languages is a notion of time.In this paper we present Dedalus, a foundation language for programming and reasoning about distributed systems. Dedalus reduces to a subset of Datalog [30] with negation, aggregate functions, successor and choice, and admits an explicit representation of time into the logic language. We show that Dedalus provides a declarative foundation for the two signature features of distributed systems: mutable state, and asynchronous processing and communication. Given these two features, we address three important properties of programs in a domain-specific manner: a notion of safety appropriate to non-terminating computations, stratified monotonic reasoning with negation over time, and efficient evaluation over time via a simple execution strategy. We also provide conservative syntactic checks for our temporal notions of safety and stratification. Our experience implementing full-featured systems in variants of Datalog suggests that Dedalus is well-suited to the specification of rich distributed services and protocols, and provides both cleaner semantics and richer tests of correctness.

In recent years there has been interest in achieving applicationlevel consistency criteria without the latency and availability costs of strongly consistent storage infrastructure. A standard technique is to adopt a vocabulary of commutative operations; this avoids the risk of inconsistency due to message reordering. A more powerful approach was recently captured by the CALM theorem, which proves that logically monotonic programs are guaranteed to be eventually consistent.In logic languages such as Bloom, CALM analysis can automatically verify that program modules achieve consistency without coordination.In this paper we present Bloom L , an extension to Bloom that takes inspiration from both these traditions. Bloom L generalizes Bloom to support lattices and extends the power of CALM analysis to whole programs containing arbitrary lattices. We show how the Bloom interpreter can be generalized to support efficient evaluation of lattice-based code using well-known strategies from logic programming. Finally, we use Bloom L to develop several practical distributed programs, including a key-value store similar to Amazon Dynamo, and show how Bloom L encourages the safe composition of small, easy-to-analyze lattices into larger programs.

Recent research has explored using Datalog-based languages to express a distributed system as a set of logical invariants [2,19]. Two properties of distributed systems proved difficult to model in Datalog. First, the state of any such system evolves with its execution. Second, deductions in these systems may be arbitrarily delayed, dropped, or reordered by the unreliable network links they must traverse. Previous efforts addressed the former by extending Datalog to include updates, key constraints, persistence and events, and the latter by assuming ordered and reliable delivery while ignoring delay. These details have a semantics outside Datalog, which increases the complexity of the language or its interpretation, and forces programmers to think operationally. We argue that the missing component from these previous languages is a notion of time.In this paper we present Dedalus, a foundation language for programming and reasoning about distributed systems. Dedalus reduces to a subset of Datalog [30] with negation, aggregate functions, successor and choice, and admits an explicit representation of time into the logic language. We show that Dedalus provides a declarative foundation for the two signature features of distributed systems: mutable state, and asynchronous processing and communication. Given these two features, we address three important properties of programs in a domain-specific manner: a notion of safety appropriate to non-terminating computations, stratified monotonic reasoning with negation over time, and efficient evaluation over time via a simple execution strategy. We also provide conservative syntactic checks for our temporal notions of safety and stratification. Our experience implementing full-featured systems in variants of Datalog suggests that Dedalus is well-suited to the specification of rich distributed services and protocols, and provides both cleaner semantics and richer tests of correctness.

In recent years there has been interest in achieving applicationlevel consistency criteria without the latency and availability costs of strongly consistent storage infrastructure. A standard technique is to adopt a vocabulary of commutative operations; this avoids the risk of inconsistency due to message reordering. A more powerful approach was recently captured by the CALM theorem, which proves that logically monotonic programs are guaranteed to be eventually consistent.In logic languages such as Bloom, CALM analysis can automatically verify that program modules achieve consistency without coordination.In this paper we present Bloom L , an extension to Bloom that takes inspiration from both these traditions. Bloom L generalizes Bloom to support lattices and extends the power of CALM analysis to whole programs containing arbitrary lattices. We show how the Bloom interpreter can be generalized to support efficient evaluation of lattice-based code using well-known strategies from logic programming. Finally, we use Bloom L to develop several practical distributed programs, including a key-value store similar to Amazon Dynamo, and show how Bloom L encourages the safe composition of small, easy-to-analyze lattices into larger programs.