![]() ![]() Calculating Ultrastrong and Extended Solutions for Nine Men's Morris, Morabaraba, and Lasker Morris. ![]() Kryo: a fast and efficient Object Graph Serialization Framework for Java. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering 36, 4 (2015), 28-38. Apache Flink™: Stream and batch processing in a single engine. Paris Carbone, Asterios Katsifodimos, Stephan Ewen, Volker Markl, Seif Haridi, and Kostas Tzoumas.The Stratosphere platform for big data analytics. Sax, Sebastian Schelter, Mareike Höger, Kostas Tzoumas, and Daniel Warneke. Alexander Alexandrov, Rico Bergmann, Stephan Ewen, Johann-Christoph Freytag, Fabian Hueske, Arvid Heise, Odej Kao, Marcus Leich, Ulf Leser, Volker Markl, Felix Naumann, Mathias Peters, Astrid Rheinländer, Matthias J.We achieved more than 6X performance improvement in the serialization which was a 20% overall improvement. Using this information it is possible to generate specialized code with great performance. Flink has a delicate type system which provides us with lots of information about the types that need to be serialized. ![]() As a Google Summer of Code project in 2016, we implemented code generation for serializers and comparators for POJOs to improve the performance of Apache Flink. Moreover, the structure of the code is harder to optimize for the JIT compiler. Flink has a custom serialization method that enables operators to work on the serialized formats.Ĭurrently, Apache Flink uses reflection to serialize Plain Old Java Objects (POJOs). This approach can improve the performance significantly. Not only for sending data over the network, saving it to the hard disk, or for fault tolerance, but also some of the operators can work on the serialized representation of the data instead of Java objects. It is using serialization for wide variety of purposes. Using state of the art CPUs, reduced CPU usage can lead to reduced electricity costs even when an application is I/O bound.Īpache Flink is an open source framework for processing streams of data and batch jobs. CPU became a bottleneck for some applications. InfiniBand, SSDs reduced the I/O overhead and more sophisticated algorithms were developed. ![]()
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