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Query Processing and Optimizing on SSDs Flash Group Qingling Cao

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Presentation on theme: "Query Processing and Optimizing on SSDs Flash Group Qingling Cao"— Presentation transcript:

1 Query Processing and Optimizing on SSDs Flash Group Qingling Cao

2 Introduction Page Layout on SSD Scan Approaches Conclusion Join Algorithms Outline

3 Introduction Page Layout on SSD Scan Approaches Conclusion Join Algorithms Outline

4 Page layout and data structure Leverage fast random read to speed up selection 、 projection and join operation Database query processing engines traditionally emphasize on sequential I/O Introduction

5 Page Layout on SSD Scan Approaches Conclusion Join Algorithms Outline

6 Page Layout on SSD Row Layout Column Layout - Attributes of one column stored in continuous pages slot

7 PAX Layout is efficient for SSD but not for disk. Why? Page Layout on SSD PAX Layout

8 Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be KB. Then full page size will be MB. IDE flash drive, the sequential read bandwidth is 28MB/s. Seek time is 0.25ms, so mini-page should be 7KB. Then full page size can be KB. Page Layout on SSD

9 Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be KB. Then full page size will be MB. IDE flash drive, the sequential read bandwidth is 28MB/s. Seek time is 0.25ms, so mini-page should be 7KB. Then full page size can be KB. Page Layout on SSD

10 Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be KB. Then full page size will be MB. IDE flash drive, the sequential read bandwidth is 28MB/s. Seek time is 0.25ms, so mini-page should be 7KB. Then full page size can be KB. Page Layout on SSD

11 Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be KB. Then full page size will be MB. IDE flash drive, the sequential read bandwidth is 28MB/s. Seek time is 0.25ms, so mini-page should be 7KB. Then full page size can be KB. Page Layout on SSD

12 Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be KB. Then full page size will be MB. IDE flash drive, the sequential read bandwidth is 28MB/s. Seek time is 0.25ms, so mini-page should be 7KB. Then full page size can be KB. Page Layout on SSD

13 Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be KB. Then full page size will be MB. IDE flash drive, the sequential read bandwidth is 28MB/s. Seek time is 0.25ms, so mini-page should be 7KB. Then full page size can be KB. Page Layout on SSD

14 Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be KB. Then full page size will be MB. IDE flash drive, the sequential read bandwidth is 28MB/s. Seek time is 0.25ms, so mini-page should be 7KB. Then full page size can be KB. Page Layout on SSD

15 Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be KB. Then full page size will be MB. IDE flash drive, the sequential read bandwidth is 28MB/s. Seek time is 0.25ms, so mini-page should be 7KB. Then full page size can be KB. Page Layout on SSD

16 Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be KB. Then full page size will be MB. IDE flash drive, the sequential read bandwidth is 28MB/s. Seek time is 0.25ms, so mini-page should be 7KB. Then full page size can be KB. Page Layout on SSD

17 Introduction Page Layout on SSD Scan Approaches Conclusion Join Algorithms Outline

18 NSMScan – Always read the whole relation. FlashScan – Read only the related columns. e.g. select S from R where J Scan Approaches

19 FlashScanOPT(U) – read only the mini-pages consist the tuples needed. e.g. select S from R where J FlashScanOPT(S) – Attributes are sorted, so the mini-pages are read at most once. Scan Approaches

20 Table: 70m tuples, 11columns, 10GB System: Intel Core 2 Duo at 2.33GHz, 4GB of RAM Mtron 32GB SSD

21 Introduction Page Layout on SSD Scan Approaches Conclusion Join Algorithms Outline

22 Block Nested Loops Join Sort-Merge Join Grace Hash Join Hybrid Hash Join Join Algorithms – past lessons

23 ☆ Algorithms that stress random reads, and avoid random writes as much as possible see bigger improvements on flash Join Algorithms – past lessons Customer: 450w tuples, 730MB Order: 4500w tuples, 5GB HDD: 5400RPM, 320GB SSD: OCZ Core series 60GB SATA II

24 Join Algorithms – RARE-join J1 J2 Select Name, Team from Player, Game where Player.Team=Game.Geam Player Game Blue, P:4 Green, P:3 Red, P:2 → Red, P:5 Orange, P:1 → Orange, P:6 Blue, G:4 Red, G:1 Orange, G:2 → Orange, G:3

25 Join Algorithms – RARE-join Join Index : Total I/O cost: |J1|+ σ 1 |V1|+|J2|+ σ 2 |V2| Join Result :

26 Join Algorithms – FlashJoin Read(A) Read(D) hashA, id1hashD, id2 hashG, id1,id2 hashK, id3 id1,id2,id3 id1,id2

27 Join Algorithms – Fetch Kernel Join Index : Join Index : Each page is read no more than once.

28 Join Algorithms – Fetch Kernel Join Index : Join Index :

29 Join Algorithms – FlashJoin R: 70m tuples, 10GB S: 7m tuples, 1GB System: Intel Core 2 Duo at 2.33GHz, 4GB of RAM Mtron 32GB SSD

30 Row-based {JI, id x, id y } Minimize the IO to fetch the join result Join Algorithms – DigestJoin

31 Sort-merge join Join results are clustered Memory is enough Fetch the pages of the tuples as soon as they are produced Join Algorithms – Page Fetching(1)

32 Fetching instruction table Join candidate table Join Index: (x 1,A:1,C:1) (x 2,B:1,D:1) (x 3,A:2,C:2) (x 4,B:2,D:2) ft1={A:1, B:1, A:2, B:2} ft2={C:1, D:1, C:2, D:2} Join Algorithms – Page Fetching(2) jct1={x1,x2,x3,x4} jct2={y1,y2,y3,y4} ft1={A:1, A:2, B:1, B:2} ft2={C:1, C:2, D:1, D:2}

33 Join Graph G=(V 1 ∪ V 2, E) E  V 1  V 2 Segment e.g. {1, a, b, c}, {a, 1, 2} Join Algorithms – Page Fetching(3)

34 Required storage size(RSS) Required cache size(RCS)

35 Introduction Page Layout on SSD Scan Approaches Conclusion Join Algorithms Outline

36 Scan algorithm has little room for improvement. RARE-Join 、 FlashJoin. No write. Join index will be sorted many times. The size of minipage is not fixed. Conclusion PAX:

37 Row: DigestJoin. IO is much more than other join algorithms. Column: None Storage is more flexible. Utilize the technology of tuple reconstruction. Conclusion

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