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A Mixed-Method Empirical Study of Function-As-A-Service Software Development in Industrial Practice

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A Mixed-Method Empirical Study of Function-As-A-Service Software Development in Industrial Practice A mixed-method empirical study of Function-as-a-Service software development in industrial practice Downloaded from: https://research.chalmers.se, 2021-09-25 15:40 UTC Citation for the original published paper (version of record): Leitner, P., Wittern, J., Spillner, J. et al (2019) A mixed-method empirical study of Function-as-a-Service software development in industrial practice Journal of Systems and Software, 149: 340-359 http://dx.doi.org/10.1016/j.jss.2018.12.013 N.B. When citing this work, cite the original published paper. research.chalmers.se offers the possibility of retrieving research publications produced at Chalmers University of Technology. It covers all kind of research output: articles, dissertations, conference papers, reports etc. since 2004. research.chalmers.se is administrated and maintained by Chalmers Library (article starts on next page) A Mixed-Method Empirical Study of Function-as-a-Service Software Development in Industrial Practice Philipp Leitnera,∗, Erik Witternb, Josef Spillnerc, Waldemar Hummerb aSoftware Engineering Division, Chalmers | University of Gothenburg, Sweden bIBM Research, Yorktown Heights, New York, USA cService Prototyping Lab, Zurich University of Applied Sciences, Switzerland Abstract Function-as-a-Service (FaaS) describes cloud computing services that make infrastructure components transparent to application developers, thus falling in the larger group of “serverless” computing models. When using FaaS offerings, such as AWS Lambda, developers provide atomic and short-running code for their functions, and FaaS providers execute and horizontally scale them on-demand. Currently, there is no systematic research on how developers use serverless, what types of applications lend themselves to this model, or what architectural styles and practices FaaS-based applications are based on. We present results from a mixed-method study, combining interviews with practitioners who develop applications and systems that use FaaS, a systematic analysis of grey literature, and a Web-based survey. We find that successfully adopting FaaS requires a different mental model, where systems are primarily constructed by composing pre-existing services, with FaaS often acting as the “glue” that brings these services together. Tooling availability and maturity, especially related to testing and deployment, remains a major difficulty. Further, we find that current FaaS systems lack systematic support for function reuse, and abstractions and programming models for building non-trivial FaaS applications are limited. We conclude with a discussion of implications for FaaS providers, software developers, and researchers. 1. Introduction still often willing to accept in exchange for built-in elas- ticity and resilience, lower costs of operation, and a relief Since its emergence, the programmable cloud has been from having to manage their own infrastructure [2]. a rapidly growing area of interest for application deploy- In recent years, the term “serverless computing”1 has ment. Various providers, including Amazon Web Services, gained momentum to describe the pinnacle of the cloud- Google Cloud, Microsoft Azure, and IBM Cloud (formerly native model: a “serverless” cloud application is deployed Bluemix), offer services on different levels of the cloud to infrastructure components that are entirely transparent stack, e.g., Infrastructure-as-a-Service (IaaS) or Platform- to the application developer. The fundamental promise as-a-Service (PaaS). IaaS services often lend themselves of serverless is sometimes pointedly described as “NoOps”, to a “lift-and-shift” style migration, where entire applica- a wordplay on the well-known DevOps movement [3] that tions can be migrated without deep changes as mostly self- stresses that “no operations” is, at least in theory, required contained virtual machines (VMs) or containers. However, to maintain a serverless application. PaaS services, which provide a higher level of abstraction, The most prominent implementation of the serverless ask for more concessions in how applications are archi- model are Function-as-a-Service (FaaS) offerings, such as tected, built, and deployed in return for a richer devel- AWS Lambda, Azure Functions or IBM’s and Google’s opment experience and more built-in features [1]. Ulti- Cloud Functions. When using FaaS offerings, application mately, many applications deployed in PaaS clouds are developers provide source code of relatively atomic and “cloud-native”, in the sense that they are specifically built typically short-running functions, and define triggers for for the cloud, or even a specific combination of services, executing these functions, for example HTTP requests or and cannot easily be operated anywhere else. This intro- system events. The FaaS provider then, on-demand, ex- duces a certain amount of lock-in, which practitioners are ecutes and bills functions as isolated instances and scales their execution horizontally as needed. Ideally, application ∗Corresponding author developers using FaaS benefit from simple deployments, re- Email addresses: [email protected] (Philipp duced operation efforts, and pay-as-you-go pricing, while Leitner), [email protected] (Erik Wittern), [email protected] (Josef Spillner), [email protected] (Waldemar Hummer) 1https://martinfowler.com/articles/serverless.html Preprint submitted to Journal of Systems and Software December 17, 2018 providers have the chance to efficiently serve large numbers functions and other important concepts in Section 2. In of users and functions with relatively few resources. Section 3, we discuss our study design in detail, followed Unsurprisingly, these advantages of FaaS do not come by an extensive discussion of results and study outcomes for free. FaaS-based applications need to adhere to a mul- in Section 4. Section 5 discusses the main implications of titude of technical and architectural restrictions to ease these results and presents the central lessons learned for their transparent management. For instance, functions FaaS providers, users, and researchers. Section 6 puts our deployed to AWS Lambda cannot, at the time of writing, work in context of the existing body of research. Finally, retain state between invocations, need to finish processing Section 7 summarizes and concludes the paper. in 5 minutes or less, have unpredictable execution time de- viations and cannot make use of more than a single CPU 2. Background thread [4]. More importantly, AWS Lambda and other FaaS services are ultimately intended to host small mi- This section introduces the main concepts and archi- croservices implemented in a few hundred lines of program tecture of Function-as-a-Service (FaaS) offerings. It fur- code. Composing larger applications from such stateless ther introduces selected technology stacks, providers, and microservices, or decomposing an existing monolith into developer tools including their characteristics and limita- them, is still a challenging open issue [5, 3]. tions. In this paper, we present the first systematic study of software development for FaaS-based applications. Our 2.1. FaaS Characteristics study addresses the following research questions: FaaS offerings allow developers to provide pieces of • RQ1: Which types of applications is FaaS-based code that, upon being triggered, are executed in an iso- computing used for in today’s industrial practice? lated environment. This model can be considered an evo- Which types of use cases is this technology valuable lution over previous cloud computing paradigms. Initial for? cloud computing offerings such as Amazon EC2 or Soft- Layer relied on (orchestrated) virtual machines which, along • RQ2: What are the key architectural patterns and with system containers, offer a high degree of isolation and best practices for building FaaS applications? architecture-specific but language-independent encapsula- tion. Platform-as-a-Service offerings such as Heroku raise • RQ3: What are the major advantages and chal- the abstraction by providing language-specific application lenges of using serverless and FaaS in practice? runtimes, which are generally long-running. In compar- ison, FaaS offerings provide more fine-grained scalability Given the immaturity of our study subject, we use an and corresponding pricing. exploratory mixed-method empirical research design to ad- Individual functions typically describe only parts of a dress these questions. We initially conduct a structured larger application. For example, rather than containing a review of multi-vocal (“grey” [6]) literature (e.g., online complete web application with a RESTful interface, a sin- blogs) and semi-structured interviews with 12 practition- gle function may only implement one endpoint of such an ers in the field. We use grounded theory, as well as open interface. Functions are expected to execute in a limited and axial coding, to generate a number of initial findings amount of time, i.e., a few minutes at most. When exceed- and hypotheses related to our research questions, which ing this threshold, the execution will time out. Depending we then validate and refine based on a Web-based survey on configuration, failed executions (due to timeout or any with 182 valid respondents. other reason) may be automatically retried. Hence, it is Our study shows that FaaS is commonly used in back- important that the logic of functions be implemented in end scenarios, where the technology is often used to han- an idempotent manner [7]. Functions can receive input dle batch jobs. Building user-facing
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