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Function-as-a-Service or FaaS is a popular delivery model of serverless computing where developers upload code to be executed in the cloud as short running stateless functions. Using smaller functions to decompose processing of larger tasks or workflows introduces the question of how to instrument application control flow to orchestrate an overall task or workflow. In this paper, we examine implications of using different methods to orchestrate the control flow of a serverless data processing pipeline composed as a set of independent FaaS functions. We performed experiments on the AWS Lambda FaaS platform and compared how four different patterns of control flow impact the cost and performance of the pipeline. We investigate control flow using client orchestration, microservice controllers, event-based triggers, and state-machines. Overall, we found that asynchronous methods led to lower orchestration costs, and that event-based orchestration incurred a performance penalty.
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Hyperspecialized Compilation for Serverless Data Analytics
Serverless functions can be spun up in milliseconds and scaled out quickly, forming an ideal platform for quick, interactive parallel queries over large data sets. Modern databases use code generation to produce efficient physical plans, but compiling such a plan on each serverless function is costly: every millisecond spent executing on serverless functions multiplies in cost by the number of functions running. Existing serverless data science frameworks therefore generate and compile code on the client, which precludes specializing this code to patterns that may exist in the input data of individual serverless functions. This paper argues for exploring a trade-off space between one-off code generation on the client, and hyperspecialized compilation that generates bespoke code on each serverless function. Our preliminary experiments show that hyperspecialization outperforms client-based compilation on typical heterogeneous datasets in both cost and performance by 2–4×.
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- Award ID(s):
- 2039354
- PAR ID:
- 10486258
- Publisher / Repository:
- CEUR-WS
- Date Published:
- Journal Name:
- Joint Proceedings of Workshops at the 49th International Conference on Very Large Data Bases (VLDB 2023)
- Format(s):
- Medium: X
- Location:
- Vancouver, Canada
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
