1. Query Processing and Optimizing on SSDs Flash Group Qingling Cao qingling1220@sina.com

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 300-400KB. 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 32-128KB. Page Layout on SSD

9. Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be 300-400KB. 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 32-128KB. Page Layout on SSD

10. Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be 300-400KB. 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 32-128KB. Page Layout on SSD

11. Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be 300-400KB. 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 32-128KB. Page Layout on SSD

12. Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be 300-400KB. 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 32-128KB. Page Layout on SSD

13. Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be 300-400KB. 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 32-128KB. Page Layout on SSD

14. Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be 300-400KB. 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 32-128KB. Page Layout on SSD

15. Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be 300-400KB. 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 32-128KB. Page Layout on SSD

16. Disk, the sequential read speed is 100MB/s. A skip takes 3-4ms. So a mini-page should be 300-400KB. 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 32-128KB. 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