Consistency Types for Safe and Efficient Distributed Programming

Margara, Alessandro; Salvaneschi, Guido

doi:10.1145/3103111.3104044

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…weakly consistent values never flow into strongly consistent operations). ConSysT [17] provides consistency specifications at the type level (i.e. values are typed with the desired consistency level).…”

Section: Related Workmentioning

confidence: 99%

A CAPable distributed programming model

Myter

Scholliers

Meuter

2018

Proceedings of the 2018 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Sof

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Developers of modern distributed systems continuously face the impossibility result proved by the CAP theorem. In a nutshell, the theorem states that a partition-tolerant system can either guarantee consistency or availability. Most distributed programming languages implicitly make the choice between consistency or availability in their designs and implementations. Concretely, distributed programming languages can be roughly divided into two categories. A first category of languages provide abstractions to implement the consistent parts of a distributed system. A second category of languages provide abstractions to implement the available parts of a distributed system. However, real-world distributed systems often require consistency for some parts while requiring availability for others. Programmers are therefore forced to implement the abstractions missing from their chosen distributed programming language themselves or rely on external libraries. In this paper we present a novel distributed programming model. This model introduces two object-oriented abstractions: consistents and availables. The former guarantees strong consistency by sacrificing availability. The latter guarantees availability, but only provides eventual consistency. Through these constructs programmers are able to implement the entirety of their distributed system within the same language. We present a prototypical implementation of the model as a TypeScript library called CAPtain.js. To showcase the usefulness of our approach we implement a non-trivial example application. Moreover, we highlight both the functional as well as the performance characteristics of both language abstractions. CCS Concepts • Software and its engineering → Distributed programming languages;

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Section: Related Workmentioning

confidence: 99%

A CAPable distributed programming model

Myter

Scholliers

Meuter

2018

Proceedings of the 2018 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Sof

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“…The idea is to keep track of the consistency level of the data and prevent unexpected information flow from an inconsistent object to a consistent object. ConSysT [Margara and Salvaneschi 2017] is another programming language that supports heterogeneous consistency specifications at the type level. However, the work is still incomplete: the authors only present the syntax of a core calculus without references, as well as subtyping rules.…”

Section: Case Study: Usage Examplesmentioning

confidence: 99%

Consistency types for replicated data in a higher-order distributed programming language

Zhao,

Haller

2019

Preprint

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Distributed systems address an increasing demand for fast access to resources and provide fault tolerance for data. However, due to scalability requirements, software developers need to trade consistency for performance. For certain data, consistency guarantees may be weakened if application correctness is unaffected. In contrast, data flows from data with weak consistency to data with strong consistency requirements are problematic, since application correctness may be broken.In this paper, we propose LatCalculus, a language and type system for distributed programming languages with replicated data types. There are two principal new features: first, the type system statically enforces noninterference between data types with weak consistency and data types with strong consistency; second, static encapsulation enables replication of object graphs with internal aliasing.

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“…Similarly, DCCT is a data-oriented language that mixes multiple consistency levels [41] where actions -for example, queries -access a distributed storage, and annotations define consistency of values. Developing a language and system that supports multiple consistency levels is ongoing work [42].…”

Section: Integrating Multiple Levels Of Consistencymentioning

confidence: 99%

On the Semantics of Distributed Reactive Programming: The Cost of Consistency

Margara

Salvaneschi

2018

IIEEE Trans. Software Eng.

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The reactive programming paradigm aims to simplify the development of reactive systems. It provides abstractions to define time-changing values that are automatically updated by the runtime according to their dependencies. The benefits of reactive programming in distributed settings have been recognized for long. Yet, existing solutions for distributed reactive programming enforce the same semantics as in single processes, introducing communication and synchronization costs that hamper scalability. Establishing suitable abstractions for distributed reactive programming demands for a deeper investigation of the semantics of change propagation. This paper takes a foundational approach and defines precise propagation semantics in terms of consistency guarantees that constrain the order and isolation of value updates. We study the benefits and costs of these consistency guarantees both theoretically and empirically, using case studies and synthetic benchmarks. We show that different applications require different levels of consistency and that manually implementing the required level on a middleware that provides a lower one annuls the abstraction improvements of reactive programming. This motivates a framework that enables the developers to select the best trade-off between consistency and overhead for the problem at hand. To this end, we present DREAM, a distributed reactive programming middleware with flexible consistency guarantees.

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Consistency Types for Safe and Efficient Distributed Programming

Cited by 6 publications

References 9 publications

A CAPable distributed programming model

A CAPable distributed programming model

Consistency types for replicated data in a higher-order distributed programming language

On the Semantics of Distributed Reactive Programming: The Cost of Consistency

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