MapReduce and parallel DBMSs: friends or foes?

By M. Stonebraker, Daniel J. Abadi, David J. DeWitt, Samuel Madden, Erik Paulson, Andrew Pavlo, A. Rasin. CACM, 2010

530 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/a2c259d7a968cab1b120474f567024598cb61c01

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Sergey Melnik
2. Jimmy J. Lin
3. Chris Dyer
4. Lian Duan
5. Lida Xu
6. Fei Tao
7. Shancang Li
8. Partha Pratim Ray

In Information Systems

1. Lian Duan
2. Sergey Melnik
3. Jimmy J. Lin
4. Chris Dyer
5. Lida Xu
6. Rodrigo Nogueira
7. Zeynep Akkalyoncu Yilmaz
8. Wei Yang

Citations in the corpus

• Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer (2010, background): “With colleagues, they ran a series of benchmarks that demonstrated the supposed superiority of column-oriented parallel databases over Hadoop [120, 144].”
• Big data for cyber physical systems in industry 4.0: a survey (2019, background): “Each round of MapReduce incurs a lot of I/O operations as the reduced result will be saved in hard-drives.”
• Big Data Processing in Cloud Computing Environments (2012, unknown): “As the debate continues, the final result shows that neither is good at the other does well, and the two technologies are complementary[17].”
• Dremel (2010, background): “Recent articles [13, 22] contrast MR and parallel DBMSs.”
• The Hadoop distributed filesystem: Balancing portability and performance (2010, unknown): “In one study, Hadoop applications performed poorly in experiments when compared to similar programs using parallel databases [18], [22].”