Dedalus: Datalog in Time and Space

Abstract

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.

Explanations and Transparency in Collaborative Workflows

“…Although not focused explicitly on workflows, Dedalus [7,19] and Webdamlog [4,2] are systems supporting distributed data processing based on condition/action rules. Local-as-view approaches are considered in a number of P2P data management systems, e.g., Piazza [29] that also consider richer mappings to specify views.…”

A graph-based framework for the analysis of access control policies

“…Recent research addressed these limitations by extending Datalog to include notions such as updates and persistency, but these features have an operational semantics outside Datalog, which may cause semantic ambiguities [43,45]. Dedalus [2] is an extension of Datalog with an explicit notion of time: all the predicates have an additional parameter to represent time. In this way, Dedalus can easily express the dynamics of distributed systems.…”

Logic and lattices for distributed programming

“…As in Datalog, certain constraints must be adopted to ensure that programs with recursive statements have a sensible interpretation. For deductive statements (<= operator), we require that programs be syntactically stratified [6]: cycles through negation or aggregation are not allowed (unless they contain a deferred or asynchronous operator) [3].…”

“…Figure 4 shows a simple implementation of the lset lattice using a Ruby array for storage. 3 A lattice class must inherit from the built-in Bud::Lattice class and must also define two methods:…”