data parallelism课件.ppt

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query optimization degrees of freedom objective functions observations approaches 精品文档 degrees of freedom conventional query planning stuff: access methods, join order, join algorithms, selection/projection placement, … parallel join strategy (repartition, f-and-r) partition choices (“coloring”) degree of parallelism scheduling of operators onto nodes pipeline vs. materialize between nodes 精品文档 objective functions want: low response time for jobs low overhead (high system throughput) these are at odds e.g., pipelining two operators may decrease response time, but incurs more overhead 精品文档 proposed objective functions Hong: linear combination of response time and overhead Ganguly: minimize response time, with limit on extra overhead minimize response time, as long as cost-benefit ratio is low 精品文档 observations response time metric violates “principle of optimality” every subplan of an optimal plan is optimal dynamic programming relies on this property hence, so does System-R (w/“interesting orders” patch) example: A index B index A A join B 20 10 10 10 精品文档 approaches two-phase [Hong]: find optimal sequential plan find optimal parallelization of above (“coloring”) optimal for shared-memory w/intra-operator parallelism only one-phase (still open research): model sources deterrents of parallelism in cost formulae can’t use DP, but can still prune search space using partial orders (i.e., some subplans dominate others) [Ganguly] 精品文档 data parallelism Chris Olston Yahoo! Research 精品文档 set-oriented computation data management operations tend to be “set-oriented”, e.g.: apply f() to each member of a set compute intersection of two sets easy to parallelize parallel data management is parallel computing’s biggest success story 精品文档 history relational datatabase systems (declarative set-oriented primitives) parallel relational database systems renaissance: map-reduce etc. 1970’s 1980’s now 精品文档 architectures shared-memory shared-disk shared-nothing (clusters) message o

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