MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Pig latin: a not-so-foreign language for data processing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
SCOPE: easy and efficient parallel processing of massive data sets
Proceedings of the VLDB Endowment
Communications of the ACM
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Dremel: interactive analysis of web-scale datasets
Proceedings of the VLDB Endowment
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With the rise of cloud-computing and cloud-scale data management the importance of shipping the code of an application to its data has increased tremendously. Especially when offering data analytics on top of traditional relational databases as a service in the cloud, new data-centric programming paradigms become necessary. Traditionally, relational databases offer two approaches to ship code close to the data: declarative SQL statements and imperative stored procedures. While SQL statements can be efficiently optimized and parallelized, stored procedures allow more complex logic that can be efficiently decomposed. In this paper, we propose a novel functional language which extends SQL called FunSQL. FunSQL combines the best of both worlds: (1) it allows applications developers to implement more complex application logic as in SQL only, (2) the application logic can be decomposed efficiently and (3) it can be efficiently optimized and parallelized.