Efficient Parallel Data Cube Generation on Shared Disk

1. Generating the Data Cube(Shared Disk) Andrew Rau-ChaplinFaculty of Computer ScienceDalhousie University Joint Work withF. DehneT. EavisS. Hambrusch

2. A B C Data Cube Generation ABC • Proposed by Gray et al in 1995 • Can be generated from a relational DB but… AC BC AB 34 12 21 18 21 B A C 83 38 50 The cuboid ABC (or CAB) ALL

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4. The Challenge • Input data set, R • |R| typically in the millions, usually will not fit into memory. • Number of dimensions, d, 10-30 • 2d cuboids in Data Cube How to solve this highly data and computational intensive problem in parallel?

5. Existing Parallel Results • Goil & Choudhary • MOLAP • Approach • Parallelize the generation of each cuboid • Challenge • > 2d comm. rounds

6. Overview • Data cubes • Review sequential cubing algorithms • Our Top-down parallel algorithm • Conclusions and open problems

7. ABCD ABC ABD ACD BCD AC AB AD BC BD CD A A B B C C D D All Optimizations based on computing multiple cuboids • Smallest-parent - computing a cuboid from the smallest previously computed cuboid. • Cache-results - cache in memory the results of a cuboid from which other cuboid are computed to reduce disk I/O. • Amortize-scans - amortizing disk read by computing as many cuboid as possible. • Share-sorts - sharing sorting cost.

8. Many Algorithms • Pipesort – [AADGNRS’96] • PipeHash – [SAG’96] • Overlap – [DANR’96] • ArrayCube – [ZDN’97] • Bottom-up-cube – [BR’99] • Partition Cube – [RS’97] • Memory Cube - [RS’97]

9. Approaches • Top Down • Pipesort – [AADGNRS’96] • PipeHash – [SAG’96] • Overlap – [DANR’96] • Bottom up • Bottom-up-cube – [BR’99] • Partition Cube – [RS’97] • Memory Cube - [RS’97] • Array Based • ArrayCube – [ZDN’97]

10. Our results • A framework for parallelization of existing sequential data cube algorithms • Top-down • Bottom-up • Array based • Architecture independent • Communication efficient • Avoids irregular communication patterns • Few large messages • Overlap computation and communication • Today’s Focus • Top down approach

11. ABCD ABC ABD ACD BCD AC AB AD BC BD CD A A B B C C D D All Top Down Algorithms • Find a “least cost” spanning tree • Use estimators of cuboid size • Exploit • Data shrinking • Pipelining • Cuts vs. Sorts

12. Cut vs. Sort • Ordering ABCD • Cutting • ABCD -> ABC • Linear time • Sorting • ABCD ->ABD • Sort time • Size • ABC may be much smaller than ABCD

13. Pipesort [AADGNRS’96] • Minimize sorting while seeking to compute cuboid from smallest parent • Pipeline sorts with common prefixes

14. Level-by-level Optimization • Minimum cost matching in a bipartite graph • Scan edges solid, Sort edges dashed • Establish dimension ordering working up the lattice

15. Overview • Data cubes • Review sequential cubing algorithms • Our Top-down parallel algorithm • Conclusions and open problems

16. Top-down parallel: The Idea • Cut the process tree into p “equal weight” subtrees • Each Proc. generates cuboids from a subtree independently • Load balance/stripe the output CBAD CBA BAD ACD BCD AC BA AD CB DB CD A A B B C C D D All

17. The Basic Algorithm (1) Construct a lattice housing all 2d views. (2) Estimate the size of each of the views in the lattice. (3) To determine the cost of using a given view to directly compute its children, use its estimated size to calculate (a) the cost of scanning the view and (b) the cost of sorting it. (4) Using the bipartite matching technique presented in the original IBM paper, reduce the lattice to a spanning tree that identifies the appropriate set of prefix-ordered sort paths. (5) Add the I/O estimates to the spanning tree. (6) Partition the tree into p sub-trees. (7) Distribute the sub-tree lists to each of the p compute nodes. (8) On each node, use the sequential Pipesort algorithm to build the set of local views.

18. Tree Partitioning • What does “Equal Weight” mean? • Want to minimize the max weight partition! • O(Rk(k + log d)+n) time - Becker, Perl and Schach ‘82 • O(n) time, Frederickson 1990 time

19. Tree Partitioning • Min-max tree k-partitioning. • Given a tree T with n vertices and a positive weight assigned to each vertex, delete k edges in the tree to obtain k connected components T1, T2, … Tk+1 such that the largest total weight of a resulting sub-tree is minimized. • O(Rk(k + log d)+n) time - Becker, Perl and Schach ‘82 • O(n) time, Frederickson 1990

20. Dynamic min-max Raw data 125 125 ABC 15 47 15 AB BC 125 8 A

21. p subtrees s * p subtrees p subsets Over-sampling

22. Implementation Issues • 1) Sort Optimization • 2) Minimizing Data Movement • 3) Efficient Aggregation Operations • 4) Disk Optimizations

23. 1) Sort Optimization • qSort is SLOW • May be O(n2) when there are duplicates • When cardinality is small range of keys is small  Radix sort • Dynamically select between well optimized Radix and Quick Sorts

24. 2) Minimizing Data Movement Never reorder the columns Sort pointers to the records!

25. 3) Efficient Aggregation Operations • One pass for each pipeline • Do lazy aggregation ABCD ABC AB A all

26. 4) Disk Optimizations • Avoid OS buffering • Implemented I/O Manager • Manages buffers to avoid thrashing • Does I/O in separate process to overlap with computation

27. Speedup - Cluster

28. Efficiency - Cluster

29. Speedup - SunFire

30. Efficiency - SunFire

31. Increasing Data Size

32. Varying Over Sampling Factor

33. Varying Skew

34. Conclusions • New communication efficient parallel cubing framework for • Top-down • Bottom up • Array based • Easy to implement (sort of), architecture independent

35. Thank you! • Questions?