A comparison of join algorithms for log processing in MaPreduce

By Spyros Blanas, Jignesh M. Patel, Vuk Ercegovac, Jun Rao, Eugene J. Shekita, Yuanyuan Tian. SIGMOD Conference, 2010

478 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f013b6cf77fe265262d1438ec8ef74382002ecb8

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Citations in the corpus

Big Data: A Survey (2014, background): “The initial MapReduce framework did not support multiple datasets in a task, which has been mitigated by some recent enhancements [96, 97].”
A Survey of Large Scale Data Management Approaches in Cloud Environments (2011, background): “[58] have reported about a study that followed this approach and evaluated the performance of different distributed join algorithms (e.”
IoT-Based Big Data Storage Systems in Cloud Computing: Perspectives and Challenges (2017, methodology): “MapReduce [44] and its open source implementation Hadoop is one of the most popular parallel processing methods in cloud platform.”
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, methodology): “Join methods can be roughly classified into two groups: Map-side join [270] and Reduce-side join [271].”
GraphX: Graph Processing in a Distributed Dataflow Framework (2014, background): “[3] show that broadcast joins and semi-joins compare favorably with the standard MapReduce style shuffle joins when joining a large table (e.”
BlinkDB: queries with bounded errors and bounded response times on very large data (2012, methodology): “Additionally BlinkDB can use knowledge about sample sizes to better allocate cluster resources (parallelism/memory) and leverage standard distributed query optimization techniques [10].”
Big data related technologies, challenges and future prospects (2015, background): “Nowadays, the internal connection networks of most data centers are fat-tree, two-layer or three-layer structures based on multicommodity network flows [23, 26].”
Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce (2013, methodology): “According to the equal-join condition, the program uses the Standard Repartition Algorithm [16] to execute the query.”