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Jingren Zhou

Microsoft Corp.
One Microsoft Way
Redmond, WA 98052
Phone: (425) 703-7047
Fax: (425) 936-7329

I am a Partner Development Manager, Big Data Product Unit (previous Bing Search Infrastructure Team) at Microsoft. I manage a team to develop a cloud-scale distributed computation system, called SCOPE, targeted for massive data analysis over tens of thousands of machines at Microsoft.


SCOPE combines benefits from both traditional parallel databases and MapReduce execution engines to allow easy programmability and deliver massive scalability and high performance through advanced optimization. Similar to parallel databases, the system has a SQL-like declarative scripting language with no explicit parallelism, while being amenable to efficient parallel execution on large clusters. An optimizer is responsible for converting scripts into efficient execution plans for the distributed computation engine. A physical execution plan consists of a directed acyclic graph (DAG) of vertices. Execution of the plan is orchestrated by a job manager that schedules execution on available machines and provides fault tolerance and recovery, much like MapReduce systems. SCOPE is being used daily for a variety of data analysis and data mining applications over tens of thousands of machines at Microsoft, powering Bing and other online services.


I used to be a researcher in the Database Group at Microsoft Research. My research is in the area of database, in particular query processing, query optimization, large scale distributed computing, and architecture-conscious database systems. Before joining Microsoft, I obtained my Ph.D. in Computer Science at Columbia University and B.S. at University of Science and Technology of China.



Recent Publications ([All Publications])

  • “Apollo: Scalable and Coordinated Scheduling for Cloud-Scale Computing” Eric Boutin, Jaliya Ekanayake, Wei Lin, Bing Shi, Jingren Zhou, Zhengping Qian, Ming Wu, and Lidong Zhou, in Proc. of the 2014 OSDI Conference (OSDI'14). pdf
  • “Continuous Cloud-Scale Query Optimization and Processing” Nico Bruno, Sapna Jain, and Jingren Zhou, in Proc. of the 2013 VLDB Conference (VLDB'13). pdf
  • “SCOPE: Parallel Databases Meet MapReduce” Jingren Zhou, Nicolas Bruno, Ming-chuan Wu, Paul Larson, Ronnie Chaiken, Darren Shakib, The VLDB Journal, 2012. pdf
  • “Spotting Code Optimizations in Data-Parallel Pipelines through PeriSCOPE” Zhenyu Guo, Xuepeng Fan, Rishan Chen, Jiaxing Zhang, Hucheng Zhou, Sean McDirmid, Chang Liu, Wei Lin, Jingren Zhou, and Lidong Zhou, in Proc. of the 2012 OSDI Conference (OSDI'12). pdf
  • “Advanced Partitioning Techniques for Massively Distributed Computation” Jingren Zhou, Nicolas Bruno, and Wei Lin, in Proc. of the 2012 SIGMOD Conference (SIGMOD'12). pdf
  • “Recurring Job Optimization in Scope” Nicolas Bruno, Sameer Agarwal, Srikanth Kandula, Bing Shi, Ming-Chuan Wu, and Jingren Zhou, in Proc. of the 2012 SIGMOD Conference (SIGMOD'12). pdf
  • “Exploiting Common Subexpressions for Cloud Query Processing” Yasin Silva, Paul Larson, and Jingren Zhou, in Proc. of the 2012 ICDE Conference (ICDE'12). pdf
  • “Incorporating Partitioning and Parallel Plans into the SCOPE Optimizer” Jingren Zhou, Per-Åke Larson, and  Ronnie Chaiken, in Proc. of the 2010 ICDE Conference (ICDE’10). pdf
  • “SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets” Ronnie Chaiken, Bob Jenkins, Per-Åke Larson, Bill Ramsey, Darren Shakib, Simon Weaver, and Jingren Zhou, in Proc. of the 2008 VLDB Conference (VLDB'08). pdf
  Professional Activities


Last updated 07/06/2012 by Jingren Zhou