Volcano

Volcano - an extensible and parallel query evaluation system. To investigate the interactions of extensibility and parallelism in database query processing, we have developed a new dataflow query execution system called Volcano. The Volcano effort provides a rich environment for research and education in database systems design, heuristics for query optimization, parallel query execution, and resource allocation. Volcano uses a standard interface between algebra operators, allowing easy addition of new operators and operator implementations. Operations on individual items, e.g., predicates, are imported into the query processing operators using support functions. The semantics of support functions is not prescribed; any data type including complex objects and any operation can be realized. Thus, Volcano is extensible with new operators, algorithms, data types, and type-specific methods. Volcano includes two novel meta-operators. The choose-plan meta-operator supports dynamic query evaluation plans that allow delaying selected optimization decisions until run-time, e.g., for embedded queries with free variables. The exchange meta-operator supports intra-operator parallelism on partitioned datasets and both vertical and horizontal inter-operator parallelism, translating between demand-driven dataflow within processes and data-driven dataflow between processes. All operators, with the exception of the exchange operator, have been designed and implemented in a single-process environment, and parallelized using the exchange operator. Even operators not yet designed can be parallelized using this new operator if they use and provide the interator interface. Thus, the issues of data manipulation and parallelism have become orthogonal, making Volcano the first implemented query execution engine that effectively combines extensibility and parallelism

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References in zbMATH (referenced in 22 articles )

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  1. Luigi Bellomarini, Georg Gottlob, Emanuel Sallinger: The Vadalog System: Datalog-based Reasoning for Knowledge Graphs (2020) arXiv
  2. Rompf, Tiark; Amin, Nada: A SQL to C compiler in 500 lines of code (2019)
  3. Shaikhha, Amir; Dashti, Mohammad; Koch, Christoph: Push versus pull-based loop fusion in query engines (2018)
  4. Safaei, Ali A.: Real-time processing of streaming big data (2017)
  5. Akinde, Michael; Böhlen, Michael H.; Chatziantoniou, Damianos; Gamper, Johann: (\theta)-constrained multi-dimensional aggregation (2011) ioport
  6. Boehm, Matthias; Habich, Dirk; Preissler, Steffen; Lehner, Wolfgang; Wloka, Uwe: Cost-based vectorization of instance-based integration processes (2011) ioport
  7. Green, Todd J.; Ives, Zachary G.; Tannen, Val: Reconcilable differences (2011)
  8. Gounaris, Anastasios; Smith, Jim; Paton, Norman W.; Sakellariou, Rizos; Fernandes, Alvaro A. A.; Watson, Paul: Adaptive workload allocation in query processing in autonomous heterogeneous environments (2009) ioport
  9. Neumann, Thomas; Moerkotte, Guido: Generating optimal DAG-structured query evaluation plans (2009) ioport
  10. Paton, Norman W.; Buenabad-Chavez, Jorge; Chen, Mengsong; Raman, Vijayshankar; Swart, Garret; Narang, Inderpal; Yellin, Daniel M.; Fernandes, Alvaro A. A.: Autonomic query parallelization using non-dedicated computers: an evaluation of adaptivity options (2009) ioport
  11. Cornacchia, Roberto; Héman, Sándor; Zukowski, Marcin; de Vries, Arjen P.; Boncz, Peter: Flexible and efficient IR using array databases (2008) ioport
  12. Neumann, Thomas; Michel, Sebastian: Algebraic query optimization for distributed top-(k) queries (2007) ioport
  13. Taniar, David; Khaw, Hui Yee; Tjioe, Haorianto Cokrowijoyo; Pardede, Eric: The use of hints in SQL-nested query optimization (2007) ioport
  14. Wang, Shan; Du, Xiao-Yong; Meng, Xiao-Feng; Chen, Hong: Database research: achievements and challenges (2006) ioport
  15. Zheng, Xiao-Qing; Chen, Hua-Jun; Wu, Zhao-Hui; Mao, Yu-Xin: Dynamic query optimization approach for semantic database grid (2006) ioport
  16. Ewen, Stephan; Ortega-Binderberger, Michael; Markl, Volker: A learning optimizer for a federated database management system (2005) ioport
  17. Ewen, Stephan; Ortega-Binderberger, Michael; Markl, Volker: A learning optimizer for a federated database management system (2005) ioport
  18. Ewen, Stephan; Ortega-Binderberger, Michael; Markl, Volker: A learning optimizer for a federated database management system (2005) ioport
  19. Dalvi, Nilesh N.; Sanghai, Sumit K.; Roy, Prasan; Sudarshan, S.: Pipelining in multi-query optimization. (2003)
  20. Ambite, J. L.; Knoblock, C. A.: Flexible and scalable cost-based query planning in mediators: A transformational approach (2000)

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