Conversational Concurrency with Dataspaces and Facets

TL;DR

This paper introduces Syndicate, a language that enhances actor-based concurrency by supporting shared conversational context and actor reflection, enabling more natural and flexible multi-conversational programming.

Contribution

It presents novel language features—dataspaces and facets—that extend actor models to better handle context management and parallel conversations.

Findings

Syndicate's dataspace allows actors to share and query assertions.

Facets enable actors to reflect on ongoing conversations.

Comparison shows Syndicate offers unique support not found in other languages.

Abstract

Context: Developers have come to appreciate the simplicity of message-passing actors for concurrent programming tasks. The actor model of computation is easy to grasp; it is just a conversation among actors with a common goal. Importantly, it eliminates some basic pitfalls of the dominant shared-memory model, most critically data races. Inquiry: A close look at real-world conversations suggests, however, that they are not mere exchanges of messages. Participants must keep in mind conversational context, and participants joining late can and often must acquire some of this context. In addition, some settings call for engaging in several conversations in parallel; in others, participants conduct temporarily limited sub-conversations to clarify a point. Existing actor code exhibits complex design patterns that get around the underlying limitations of the pure message-passing model. Approach: These patterns suggest a number of elements involved in programming conversational computations. Translated into terms of language design, they call for two kinds of facilities: (1) one for sharing conversational context and (2) another one for organizing individual actors around on-going conversations and their contexts. Knowledge: This paper presents Syndicate, a language designed to directly support the programming of conversing actors. Beyond message passing, it supplies (1) a dataspace, which allows actors to make public assertions, to withdraw them, and to query what other actors have asserted; and (2) the facet notation, which enables programmers to express individual actors as a reflection of the on-going conversations. Grounding: A worked example introduces these concepts and illustrates conversational programming in Syndicate. A comparison with other research and industrial concurrent languages demonstrates the unique support Syndicate provides. Importance: Syndicate advances concurrent actor programming with enhancements that address some observed limitations of the underlying model. While message-passing simplifies concurrent programming, it falls short in handling the complexities of actual computational conversations. By introducing a dataspace actor for sharing conversational context and the facet notation for organizing actors around ongoing conversations, Syndicate enables developers to naturally express and manage the nuanced interactions often required in concurrent systems. These innovations reduce the need for complex design patterns and provide unique support for building robust, context-aware concurrent applications.