Rhino: Efficient Management of Very Large Distributed State for Stream Processing Engines
Author:
Affiliation:
1. Technische Universität Berlin & DFKI GmbH, Berlin, Germany
2. Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
Funder
German Ministry for Education and Research
German Federal Ministry for Economic Affairs and Energy
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3318464.3389723
Reference49 articles.
1. Tyler Akidau Robert Bradshaw Craig Chambers Slava Chernyak Rafael J Fernández-Moctezuma Reuven Lax Sam McVeety Daniel Mills Frances Perry Eric Schmidt etal 2015. The dataflow model: a practical approach to balancing correctness latency and cost in massive-scale unbounded out-of-order data processing. PVLDB (2015). Tyler Akidau Robert Bradshaw Craig Chambers Slava Chernyak Rafael J Fernández-Moctezuma Reuven Lax Sam McVeety Daniel Mills Frances Perry Eric Schmidt et al. 2015. The dataflow model: a practical approach to balancing correctness latency and cost in massive-scale unbounded out-of-order data processing. PVLDB (2015).
2. Paris Carbone Stephan Ewen Gyula Fóra Seif Haridi Stefan Richter and Kostas Tzoumas. 2017. State Management in Apache Flink: Consistent Stateful Distributed Stream Processing. PVLDB Endow. (2017). Paris Carbone Stephan Ewen Gyula Fóra Seif Haridi Stefan Richter and Kostas Tzoumas. 2017. State Management in Apache Flink: Consistent Stateful Distributed Stream Processing. PVLDB Endow. (2017).
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