Abstract
Combining data warehousing and stream processing technologies has great potential in offering low-latency data-intensive analytics. Unfortunately, such convergence has not been properly addressed so far. The current generation of stream processing systems isin general built separately from the data warehouse and query engine, which can causesignificant overhead in data access and data movement, and is unable to take advantage of the functionalities already offered by the existing data warehouse systems.
In this work we tackle some hard problems not properly addressed previously in integrating stream analytics capability into the existing query engine. We define an extended SQL query model that unifies queries over both static relations and dynamic streaming data, and develop techniques to extend query engines to support the unified model. We propose the cut-and-rewind query execution model to allow a query with full SQL expressive power to be applied to stream data by converting the latter into a sequence of “chunks”, and executing the query over each chunk sequentially, but without shutting the query instance down between chunks for continuously maintaining the application context across the execution cycles as required by sliding-window operators. We also propose the cycle-based transaction model to support Continuous Querying with Continuous Persisting (CQCP) with cycle-based isolation and visibility.
We have prototyped our approach by extending the PostgreSQL. This work has resulted in a new kind of tightly integrated, highly efficient system with the advanced stream processing capability as well as the full DBMS functionality. We demonstrate it with the popular Linear Road benchmark, and report the performance. By leveraging the matured code base of a query engine to the maximal extent, we can significantly reduce the engineering investment needed for developing the streaming technology. Providing this capability on proprietary parallel analytics engine is work in progress.
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References
Abadi, D., Carney, D., Cetintemel, U., Cherniack, M., Convey, C., Lee, S., Stonebraker, M., Tatbul, N., Zdonik, S.: A New Model and Architecture for Data Stream Management. VLDB J. 2(12), 120–139 (2003)
Abadi, D.J., et al.: The Design of the Borealis Stream Processing Engine. In: CIDR (2005)
Arasu, A., Babu, S., Widom, J.: The CQL Continuous Query Language: Semantic Foundations and Query Execution. VLDB Journal 2(15) (June 2006)
Bryant, R.E.: Data-Intensive Supercomputing: The case for DISC, CMU-CS-07-128 (2007)
Chandrasekaran, S., et al.: TelegraphCQ: Continuous Dataflow Processing for an Uncertain World. In: CIDR 2003 (2003)
Chaiken, R., Jenkins, B., Larson, P.-Å., Ramsey, B., Shakib, D., Weaver, S., Zhou, J.: SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets. In: VLDB 2008 (2008)
Chen, J., et al.: NiagaraCQ: A Scalable Continuous Query System for Internet Databases. In: SIGMOD (2000)
Chen, Q., Hsu, M.: Cooperating SQL Dataflow Processes for In-DB Analytics. In: Proc. CoopIS 2009 (2009)
Chen, Q., Hsu, M., Liu, R.: Extend UDF Technology for Integrated Analytics. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DAWAK 2009. LNCS, vol. 5691, pp. 256–270. Springer, Heidelberg (2009)
Cooper, B.F., et al.: PNUTS: Yahoo!’s Hosted Data Serving Platform. In: VLDB 2008 (2008)
Cranor, C.D., et al.: Gigascope: A Stream Database for Network Applications. In: SIGMOD 2003 (2003)
Gedik, B., Andrade, H., Wu, K.-L., Yu, P.S., Doo, M.C.: SPADE: The System S Declarative Stream Processing Engine. In: ACM SIGMOD 2008 (2008)
Franklin, M.J., et al.: Continuous Analytics: Rethinking Query Processing in a NetworkEffect World. In: CIDR 2009 (2009)
Jain, N., et al.: Design, Implementation, and Evaluation of the Linear Road Benchmark on the Stream Processing Core. In: SIGMOD (2006)
Liarou, E., et al.: Exploiting the Power of Relational Databases for Efficient Stream Processing. In: EDBT 2009 (2009)
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Chen, Q., Hsu, M. (2010). Experience in Extending Query Engine for Continuous Analytics. In: Bach Pedersen, T., Mohania, M.K., Tjoa, A.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2010. Lecture Notes in Computer Science, vol 6263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15105-7_15
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DOI: https://doi.org/10.1007/978-3-642-15105-7_15
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