Past Details

Self-Tuning Hadoop for Big Data Analytics

Date / Time:
24/02/2014 Download ICS File
Carnegie Mellon Qatar, room 1202
Lectures and Activities
QCRI-Logo-new-2016.jpg Dr. Ashraf Aboulnaga is a Principal Scientist in the Qatar Computing Research Institute, where he heads the Distributed Systems group. His current research interests are in the area of data intensive cloud computing, with a current focus on platforms for Big Data, distributed storage systems, and data integration on the web. Dr. Aboulnaga obtained M.S. and Ph.D. degrees from the University of Wisconsin - Madison, and B.S. and M.S. degrees from Alexandria University, Egypt. He holds an Associate Professor position at the University of Waterloo in Canada, where he has worked since 2004.

Hadoop has become one of the most popular platforms for Big Data analytics, enabling flexible and powerful analysis of massive data sets on commodity computing clusters. However, tuning and administering Hadoop clusters is still not a well-studied area. In this talk, I will present two projects that aim to make Hadoop more self-tuning. The first is ReStore, a system for reusing MapReduce results. ReStore focuses on queries that are expressed in high-level query languages such as Pig Latin or Hive. The compilers of these languages translate queries into workflows of MapReduce jobs. Each job in these workflows reads its input from the Hadoop file system (HDFS) and produces output that is stored in HDFS.  The current practice is to delete all intermediate results from HDFS at the end of executing the workflow. ReStore keeps these intermediate results and reuses them for future queries, resulting in significant performance improvement. I will also present PStorM, a system for tuning Hadoop configuration parameters based on the history of previously observed MapReduce jobs. The two projects presented in the talk share the goal of automatically improving the performance of Hadoop based on workload characteristics.

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