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Analyzing extended property graphs with Apache Flink
Proceedings of the 1st ACM SIGMOD Workshop on Network Data Analytics, 2016Graphs are an intuitive way to model complex relationships between real-world data objects. Thus, graph analytics plays an important role in research and industry. As graphs often reflect heterogeneous domain data, their representation requires an expressive data model including the abstraction of graph collections, for example, to analyze communities ...
Martin Junghanns +4 more
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Apache Flink: Stream Analytics at Scale
2016 IEEE International Conference on Cloud Engineering Workshop (IC2EW), 2016Apache Flink is an open source system for expressive, declarative, fast, and efficient data analysis on both historical (batch) and real-time (streaming) data. Flink combines the scalability and programming flexibility of distributed MapReduce-like platforms with the efficiency, out-of-core execution, and query optimization capabilities found in ...
Asterios Katsifodimos +1 more
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Code Generation in Serializers and Comparators of Apache Flink
Proceedings of the 12th Workshop on Implementation, Compilation, Optimization of Object-Oriented Languages, Programs and Systems, 2017There is a shift in the Big Data world. Applications used to be I/O bound. InfiniBand, SSDs reduced the I/O overhead and more sophisticated algorithms were developed. CPU became a bottleneck for some applications. Using state of the art CPUs, reduced CPU usage can lead to reduced electricity costs even when an application is I/O bound.Apache Flink is ...
Gábor Horváth 0005 +2 more
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An Efficient Topology Refining Scheme for Apache Flink
2018 International Conference on Information and Communication Technology Convergence (ICTC), 2018In the past decade, there has been a boom in the volume of data and in the popularity of cloud applications with industry and academia keenly interested in big data analytics, streaming application, and social networking applications. This led to the emergence of real-time distributed stream processing systems such as Flink, Storm, Dataflow, and Samza.
Muhammad Hanif 0003, Choonhwa Lee
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Flink-ML: machine learning in Apache Flink
Brazilian Journal of TechnologyThe emergence of Big Data has spurred the development of various frameworks designed for efficient data storage and processing. Key frameworks include Hadoop, Spark, Flink, Storm, Pig, and Zookeeper. Among these, Apache Flink stands out as a prominent open-source platform known for its powerful stream and batch processing capabilities.
Messaoud Mezati, Ines Aouria
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On the usability of Hadoop MapReduce, Apache Spark & Apache flink for data science
2017 IEEE International Conference on Big Data (Big Data), 2017Distributed data processing platforms for cloud computing are important tools for large-scale data analytics. Apache Hadoop MapReduce has become the de facto standard in this space, though its programming interface is relatively low-level, requiring many implementation steps even for simple analysis tasks.
Bilal Akil, Ying Zhou, Uwe Röhm
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Disaggregated State Management in Apache Flink® 2.0 [PDF]
Proceedings of the VLDB EndowmentWe present Apache Flink 2.0, an evolution of the popular stream processing system's architecture that decouples computation from state management. Flink 2.0 relies on a remote distributed file system (DFS) for primary state storage and uses local disks as a secondary cache, with state updates streamed continuously and directly to the DFS.
Yuan Mei +9 more
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State management in Apache Flink®
Proceedings of the VLDB Endowment, 2017Stream processors are emerging in industry as an apparatus that drives analytical but also mission critical services handling the core of persistent application logic. Thus, apart from scalability and low-latency, a rising system need is first-class support for application state together with strong consistency guarantees, and adaptivity to cluster ...
Paris Carbone +5 more
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Video2Flink: real-time video partitioning in Apache Flink and the cloud
Machine Vision and Applications, 2023Euripides G M Petrakis
exaly