Stream Processing Languages in the Big Data Era

Hirzel, Martin; Baudart, Guillaume; Bonifati, Angela; Valle, Emanuele Della; Sakr, Sherif; Vlachou, Akrivi

doi:10.1145/3299887.3299892

Cited by 29 publications

(11 citation statements)

References 82 publications

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“…In those areas, the problem focus is on the speed at which streaming data is processed. Indeed, as has also been pointed out more recently by Hirzel et al (2018), many stream processing tools and languages have been developed with differing emphases on for example performance (e.g. the use of windows and parallelisa on), generality (e.g.…”

Section: Introduc Onmentioning

confidence: 92%

Robust Stream Reasoning Under Uncertainty

Leng

2019

Linköping Studies in Science and Technology. Dissertations

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Section: Introduc Onmentioning

confidence: 92%

Robust Stream Reasoning Under Uncertainty

Leng

2019

Linköping Studies in Science and Technology. Dissertations

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“…Declarative Stream Processing Languages have been around for two decades. Most of the existing solutions, including those associated with the Big Data initiative [18], present an SQL-like syntax [26] and build upon the Continuous Query Language model (CQL) [7].…”

Section: Related Workmentioning

confidence: 99%

“…The growing popularity of sensors and smart-devices is pushing the boundaries of existing data systems. While real-time analytics is becoming central in data science projects, new languages are emerging to allow data scientists to express complex information needs and perform the analyses in real-time [18]. For instance, several streaming SQL extensions allow writing queries that compute every minute the average number of people crossing the road in the last 5 minutes.…”

Section: Introductionmentioning

confidence: 99%

Semantic Foundations of Seraph Continuous Graph Query Language

Falzone¹,

Tommasini²,

Valle³

et al. 2021

Preprint

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The scientific community has been studying graph data models for decades. Their high expressiveness and elasticity led the scientific community to design a variety of graph data models and graph query languages, and the practitioners to use them to model real-world cases and extract useful information. Recently, property graphs and, in particular, Cypher 9 (the first open version of the well-known Neo4j Inc.'s language) are gaining popularity. Practitioners find Cypher useful and applicable in many scenarios. However, we are living in a streaming world where data continuously flows. A growing number of Cypher's users show interest in continuously querying graph data to act in a timely fashion. Indeed, Cypher lacks the features for dealing with streams of (graph) data and continuous query evaluation. In this work, we propose Seraph, an extension of Cypher, as a first attempt to introduce streaming features in the context of property graph query languages. Specifically, we define Seraph semantics, we propose a first version of Seraph syntax, and we discuss the potential impacts from a user perspective.

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“…Several higher-level languages for DSP [18] have been proposed. The main objective is to introduce mechanisms or concepts previously not supported, or optimize the application generation.…”

Section: B High-level Models and Generationmentioning

confidence: 99%

NAMB: A Quick and Flexible Stream Processing Application Prototype Generator

Pagliari¹,

Huet²,

Urvoy-Keller³

2020

2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)

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The importance of Big Data is nowadays established, both in industry and research fields, especially stream processing for its capability to analyze continuous data streams and provide statistics in real-time. Several data stream processing (DSP) platforms exist like the Storm, Flink, Spark Streaming and Heron Apache projects, or industrial products such as Google MillWheel. Usually, each platform is tested and analyzed using either specifically crafted benchmarks or realistic applications. Unfortunately, these applications are only briefly described and their source code is generally not available. Hence, making quick evaluations often involves rewriting complete applications on different platforms. The lack of a generic prototype application also makes it difficult for a developer to quickly evaluate the impact of some design choices. To address these issues, we present NAMB (Not only A Micro-Benchmark), a generic application prototype generator for DSP platforms. Given a high-level description of a stream processing application and its workload, NAMB automatically generates the code for different platforms. It features a flexible architecture which makes it easy to support new platforms. We demonstrate the benefits of our proposal to quickly generate application prototypes as well as benchmarks used in published papers. Overall, our approach provides easily replicable, comparable and customizable prototypes for data stream platforms. Moreover, NAMB provides similar performance in terms of latency and throughput to existing benchmarks, while only requiring a simple high-level description.

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Stream Processing Languages in the Big Data Era

Cited by 29 publications

References 82 publications

Robust Stream Reasoning Under Uncertainty

Robust Stream Reasoning Under Uncertainty

Semantic Foundations of Seraph Continuous Graph Query Language

NAMB: A Quick and Flexible Stream Processing Application Prototype Generator

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