Streams a la carte: Extensible Pipelines with Object Algebras

Biboudis, Aggelos; Palladinos, Nick; Fourtounis, George; Smaragdakis, Yannis

doi:10.4230/lipics.ecoop.2015.591

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…In particular, we report the most used features of the Stream API, an information that could be considered when prioritizing future extensions and optimizations to the API. Complementary to prior work proposing alternatives or extensions to the Stream API to improve data processing in Java, [70][71][72][73] we suggest improving the Stream API by leveraging the knowledge that it has about both the pipeline structure and the characteristics of the data source upon stream execution.…”

Section: Discussionmentioning

confidence: 98%

Large‐scale characterization of Java streams

et al. 2023

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Java streams are receiving the attention of developers targeting the Java virtual machine (JVM) as they ease the development of data‐processing logic, while also favoring code extensibility and maintainability through a concise and declarative style based on functional programming. Recent studies aim to shedding light on how Java developers use streams. However, they consider only small sets of applications and mainly apply manual code inspection and static analysis techniques. As a result, the large‐scale dynamic analysis of stream processing remains an open research question. In this article, we present the first large‐scale empirical study on the use of streams in Java code exercised via unit tests. We present stream‐analyzer, a novel dynamic program analysis (DPA) that collects runtime information and key metrics, which enable a fine‐grained characterization of sequential and parallel stream processing. We use a fully automatic approach to massively apply our DPA for the analysis of open‐source software projects hosted on GitHub. Our findings advance the understanding of the use of Java streams. Both the scale of our analysis and the profiling of dynamic information enable us to confirm with more confidence the outcome highlighted at a smaller scale by related work. Moreover, our study reports the popularity of many features of the Stream API and highlights multiple findings about runtime characteristics unique to streams, while also revealing inefficient stream processing and stream misuses. Finally, we present implications of our findings for developers of the Stream API, tool builders and researchers, and educators.

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Section: Discussionmentioning

confidence: 98%

Large‐scale characterization of Java streams

et al. 2023

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“…To be able to study optimization opportunities in a more controlled environment and to present manageable examples in the following sections, we have created a simple implementation of a push-style stream library with an API that is similar to that of Java's streams and also to stream implementations studied in related work [Biboudis et al 2015]. This library suffers from the same performance issues as Java's standard stream library (see Section 8).…”

Section: Background: Pull-and Push-style Stream Apismentioning

confidence: 99%

“…To isolate the performance impact of our optimization we evaluate the approach on a suite of 11 micro-benchmarks that consist only of stream pipelines. This suite builds upon micro-benchmarks from previous work [Biboudis et al 2015] and includes a new benchmark that uses the allMatch terminal operation. This operation terminates the execution of the pipeline as soon as an element that does not satisfy the supplied predicate is found (it is a short-circuiting operation), and therefore follows an alternative code path in the Java stream library.…”

Section: Rq1: Performance Evaluationmentioning

confidence: 99%

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Eliminating abstraction overhead of Java stream pipelines using ahead-of-time program optimization

Møller

2020

Proc. ACM Program. Lang.

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Java 8 introduced streams that allow developers to work with collections of data using functional-style operations. Streams are often used in pipelines of operations for processing the data elements, which leads to concise and elegant program code. However, the declarative data processing style comes at a cost. Compared to processing the data with traditional imperative language mechanisms, constructing stream pipelines requires extra heap objects and virtual method calls, which often results in significant run-time overheads. In this work we investigate how to mitigate these overheads to enable processing data in the declarative style without sacrificing performance. We argue that ahead-of-time bytecode-to-bytecode transformation is a suitable approach to optimization of stream pipelines, and we present a static analysis that is designed to guide such transformations. Experimental results show a significant performance gain, and that the technique works for realistic stream pipelines. For 10 of 11 micro-benchmarks, the optimizer is able to produce bytecode that is as effective as hand-written imperative-style code. Additionally, 77% of 6879 stream pipelines found in real-world Java programs are optimized successfully.

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An Empirical Study on the Use and Misuse of Java 8 Streams

Khatchadourian

Tang

Bagherzadeh

et al. 2020

Lecture Notes in Computer Science

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Streaming APIs allow for big data processing of native data structures by providing MapReduce-like operations over these structures. However, unlike traditional big data systems, these data structures typically reside in shared memory accessed by multiple cores. Although popular, this emerging hybrid paradigm opens the door to possibly detrimental behavior, such as thread contention and bugs related to non-execution and non-determinism. This study explores the use and misuse of a popular streaming API, namely, Java 8 Streams. The focus is on how developers decide whether or not to run these operations sequentially or in parallel and bugs both specific and tangential to this paradigm. Our study involved analyzing 34 Java projects and 5.53 million lines of code, along with 719 manually examined code patches. Various automated, including interprocedural static analysis, and manual methodologies were employed. The results indicate that streams are pervasive, parallelization is not widely used, and performance is a crosscutting concern that accounted for the majority of fixes. We also present coincidences that both confirm and contradict the results of related studies. The study advances our understanding of streams, as well as benefits practitioners, programming language and API designers, tool developers, and educators alike.

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Streams a la carte: Extensible Pipelines with Object Algebras

Cited by 6 publications

References 18 publications

Large‐scale characterization of Java streams

Large‐scale characterization of Java streams

Eliminating abstraction overhead of Java stream pipelines using ahead-of-time program optimization

An Empirical Study on the Use and Misuse of Java 8 Streams

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