SkewTune: mitigating skew in mapreduce applications
By YongChul Kwon, M. Balazinska, Bill Howe, J. Rolia. SIGMOD Conference, 2012
464 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/e08c74c314053733d8870718c226ad010f07f96b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ion Stoica
- Matei A. Zaharia
- M. Franklin
- Scott Shenker
- Sylvia Ratnasamy
- Byung-Gon Chun
- Sanjay Krishnan
- Eugene Wu
In Computer Networks and Communications
- Ion Stoica
- Scott Shenker
- Sylvia Ratnasamy
- Matei A. Zaharia
- M. Franklin
- Kay Ousterhout
- Byung-Gon Chun
- Reynold Xin
In Information Systems
- Ion Stoica
- Matei A. Zaharia
- Scott Shenker
- Kay Ousterhout
- M. Franklin
- Reynold Xin
- Mosharaf Chowdhury
- Jimmy J. Lin
Citations in the corpus
- Shark: SQL and rich analytics at scale (2012, result): “In this way, Spark stands in contrast to Hadoop/Hive, where us-ing the wrong number of tasks was sometimes 10 × slower than an optimal plan, and there has been considerable work to automatically choose the number of reduce tasks [21, 15].”
- Making Sense of Performance in Data Analytics Frameworks (2015, background): “Researchers have been able to identify and target a small number of underlying causes such as data skew [11] [26] [29] and popularity skew [7].”