J
John Cieslewicz
Researcher at Columbia University
Publications - 20
Citations - 1431
John Cieslewicz is an academic researcher from Columbia University. The author has contributed to research in topics: Database tuning & Multithreading. The author has an hindex of 14, co-authored 20 publications receiving 1340 citations. Previous affiliations of John Cieslewicz include Aster & Google.
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Book ChapterDOI
STREAM: The Stanford Data Stream Management System
Arvind Arasu,Brian Babcock,Shivnath Babu,John Cieslewicz,Mayur Datar,Keith Ito,Rajeev Motwani,Utkarsh Srivastava,Jennifer Widom +8 more
TL;DR: A general-purpose prototype Data Stream Management System (DSMS), also called STREAM, is built that supports a large class of declarative continuous queries over continuous streams and traditional stored data sets.
Journal ArticleDOI
F1: a distributed SQL database that scales
Jeff Shute,Radek Vingralek,Bart Samwel,Ben Handy,Chad Whipkey,Eric Rollins,Mircea Oancea,Kyle Littlefield,David Menestrina,Stephan Ellner,John Cieslewicz,Ian Rae,Traian Stancescu,Himani Apte +13 more
TL;DR: F1 is a distributed relational database system built at Google to support the AdWords business that combines high availability, the scalability of NoSQL systems like Bigtable and the consistency and usability of traditional SQL databases.
Journal ArticleDOI
SQL/MapReduce: a practical approach to self-describing, polymorphic, and parallelizable user-defined functions
TL;DR: This paper presents a new approach to implementing a UDF, which it is called SQL/MapReduce (SQL/MR), that overcomes many of these limitations of present UDFs and facilitates highly scalable computation within the database.
Proceedings Article
Adaptive aggregation on chip multiprocessors
John Cieslewicz,Kenneth A. Ross +1 more
TL;DR: This paper examines aggregation in a multi-core environment, the Sun UltraSPARC T1, a chip multiprocessor with eight cores and a shared L2 cache, and introduces an adaptive aggregation operator that performs lightweight sampling of the input to choose the correct aggregation strategy with high accuracy.
Proceedings ArticleDOI
Improving database performance on simultaneous multithreading processors
TL;DR: This work investigates three thread-based techniques to exploit SMT architectures on memory-resident data and describes a novel implementation strategy in which individual operators are implemented in a multi-threaded fashion, and introduces a new data-structure called a work-ahead set.