{"id":158743,"date":"2009-12-01T00:00:00","date_gmt":"2009-12-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/comet-batched-stream-processing-in-data-intensive-distributed-computing\/"},"modified":"2018-10-16T20:00:07","modified_gmt":"2018-10-17T03:00:07","slug":"comet-batched-stream-processing-in-data-intensive-distributed-computing","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/comet-batched-stream-processing-in-data-intensive-distributed-computing\/","title":{"rendered":"Comet: Batched Stream Processing in Data Intensive Distributed Computing"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Performance and resource optimization is an important research problem in data intensive distributed computing. We present a new batched stream processing model that captures query correlations to expose I\/O and computation redundancies for optimizations. The model is inspired by our empirical study on a trace from a production large-scale data processing cluster, which reveals significant redundancies caused by strong temporal and spatial correlations among queries.<\/p>\n<p>We have developed Comet, a query processing system that embraces the batched stream processing model for optimizations. We have integrated Comet with DryadLINQ. With its roots in query optimizations for database systems, Comet enables a set of new heuristics and opportunities tailored for distributed computing in DryadLINQ. Optimizations in Comet are effective. The evaluation of a micro-benchmark on a 40-machine cluster shows a 42% reduction in total machine time and over 40% reduction in total I\/O. Our simulation on a real trace covering over 19 million machine hours shows an estimated I\/O saving of over 50%.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Performance and resource optimization is an important research problem in data intensive distributed computing. We present a new batched stream processing model that captures query correlations to expose I\/O and computation redundancies for optimizations. The model is inspired by our empirical study on a trace from a production large-scale data processing cluster, which reveals significant [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193718],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-158743","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"Microsoft 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