GraphX is a new component in Spark for graphs and graph-parallel computation. At a high level, GraphX extends the Spark RDD by introducing a new Graph abstraction: a directed multigraph with properties attached to each vertex and edge. To support graph computation, GraphX exposes a set of fundamental operators (e.g., subgraph, joinVertices, and aggregateMessages) as well as an optimized variant of the Pregel API. In addition, GraphX includes a growing collection of graph algorithms and builders to simplify graph analytics tasks.
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References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Joana M. F. da Trindade, Konstantinos Karanasos, Carlo Curino, Samuel Madden, Julian Shun: Kaskade: Graph Views for Efficient Graph Analytics (2019) arXiv
- Yu, Hong; Chen, Yun; Lingras, Pawan; Wang, Guoyin: A three-way cluster ensemble approach for large-scale data (2019)
- Condie, Tyson; Das, Ariyam; Interlandi, Matteo; Shkapsky, Alexander; Yang, Mohan; Zaniolo, Carlo: Scaling-up reasoning and advanced analytics on BigData (2018)
- Kepner, Jeremy; Jananthan, Hayden: Mathematics of big data. Spreadsheets, databases, matrices, and graphs. With a foreword by Charles E. Leiserson (2018)
- Morihata, Akimasa; Emoto, Kento; Matsuzaki, Kiminori; Hu, Zhenjiang; Iwasaki, Hideya: Optimizing declarative parallel distributed graph processing by using constraint solvers (2018)
- Cui, Huanqing; Niu, Jian; Zhou, Chuanai; Shu, Minglei: A multi-threading algorithm to detect and remove cycles in vertex- and arc-weighted digraph (2017)
- Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I. Jordan, Ion Stoica: Ray: A Distributed Framework for Emerging AI Applications (2017) arXiv
- Meng, Xiangrui; Bradley, Joseph; Yavuz, Burak; Sparks, Evan; Venkataraman, Shivaram; Liu, Davies; Freeman, Jeremy; Tsai, Db; Amde, Manish; Owen, Sean; Xin, Doris; Xin, Reynold; Franklin, Michael J.; Zadeh, Reza; Zaharia, Matei; Talwalkar, Ameet: MLlib: machine learning in Apache Spark (2016)
- Christopher R. Aberger, Susan Tu, Kunle Olukotun, Christopher Ré: EmptyHeaded: A Relational Engine for Graph Processing (2015) arXiv