Query Processing and Optimizing on SSDs Flash Group Qingling Cao [email protected]

Query Processing and Optimizing on SSDs

Flash GroupQingling Cao [email protected]

Introduction

Page Layout on SSD

Scan Approaches

Conclusion

Join Algorithms

Outline

Introduction

Page Layout on SSD

Scan Approaches

Conclusion

Join Algorithms

Outline

• 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

Introduction

Page Layout on SSD

Scan Approaches

Conclusion

Join Algorithms

Outline

Page Layout on SSD

Row Layout

Column Layout -Attributes of one column stored in continuous pages

slot

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

Page Layout on SSD

PAX Layout

• 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



Page Layout on SSD



Page Layout on SSD



Page Layout on SSD



Page Layout on SSD



Page Layout on SSD



Page Layout on SSD



Page Layout on SSD



Page Layout on SSD

Introduction

Page Layout on SSD

Scan Approaches

Conclusion

Join Algorithms

Outline

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

Scan Approaches

• 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

Scan Approaches

Table: 70m tuples, 11columns, 10GBSystem: Intel Core 2 Duo at 2.33GHz, 4GB of RAMMtron 32GB SSD

Introduction

Page Layout on SSD

Scan Approaches

Conclusion

Join Algorithms

Outline

• Block Nested Loops Join• Sort-Merge Join• Grace Hash Join• Hybrid Hash Join

Join Algorithms – past lessons

☆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, 5GBHDD: 5400RPM, 320GB SSD: OCZ Core series 60GB SATA II

Join Algorithms – RARE-join

J1

J2

Select Name, Team from Player, Game where Player.Team=Game.Geam

Player Game

Blue, P:4Green, P:3Red, P:2 → Red, P:5Orange, P:1 → Orange, P:6

Blue, G:4Red, G:1Orange, G:2 → Orange, G:3

<G:4 , P:4> <G:1 , P:2> <G:1 , P:5> <G:2 , P:1>

<G:2 , P:6> <G:3 , P:1> <G:3 , P:6>

Join Algorithms – RARE-join

Join Index ：

Total I/O cost: |J1|+ σ1|V1|+|J2|+ σ2|V2|

<Sarah , Blue> <Julie , Red> <Alex , Red> <Ben , Orange><Lena , Orange> <Ben , Orange><Lena , Orange>

Join Result ：

Join Algorithms – FlashJoin

Read(A) Read(D)

hashA, id1 hashD, id2

hashG, id1,id2hashK, id3

id1,id2,id3

id1,id2

<G:4 , P:4> <G:1 , P:2> <G:1 , P:5> <G:2 , P:1>

<G:2 , P:6> <G:3 , P:1> <G:3 , P:6>

Join Algorithms – Fetch Kernel

Join Index ：

<G:1 , P:2> <G:1 , P:5> <G:2 , P:1> <G:2 , P:6>

<G:3 , P:1> <G:3 , P:6> <G:4 , P:4>

Join Index ：

Each page is read no more than once.

Join Algorithms – Fetch Kernel

Join Index ：

<Red, G:1, P:2> <Red, G:1, P:5><Orange, G:2, P:1><Orange, G:2, P:6>

<Orange, G:3, P:1> <Orange, G:3, P:6> <Blue, G:4, P:4>

Join Index ：

<Orange, G:2, P:1><Orange, G:3, P:1> <Red, G:1, P:2> <Blue, G:4, P:4>

<Red, G:1, P:5><Orange, G:2, P:6><Orange, G:3, P:6>

Join Algorithms – FlashJoin

R: 70m tuples, 10GB S: 7m tuples, 1GBSystem: Intel Core 2 Duo at 2.33GHz, 4GB of RAMMtron 32GB SSD

• Row-based• {JI, idx, idy}• Minimize the IO to fetch the join result

Join Algorithms – DigestJoin

• 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)

• Fetching instruction table• Join candidate table Join Index: (x1,A:1,C:1) (x2,B:1,D:1)

(x3,A:2,C:2) (x4,B:2,D:2)

ft1={A:1, B:1, A:2, B:2} ft2={C:1, D:1, C:2, D: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}

• Join Graph G=(V1 V∪ 2, E) E V1 V2

• Segment e.g. {1, a, b, c}, {a, 1, 2}



• Required storage size(RSS)• Required cache size(RCS)• <join_atrr,tid1,tid2>

Introduction

Page Layout on SSD

Scan Approaches

Conclusion

Join Algorithms

Outline

• 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:

Row:• DigestJoin.• IO is much more than other join algorithms.

Column: • None• Storage is more flexible.• Utilize the technology of tuple reconstruction.

Conclusion

Documents

Query Processing and Optimizing on SSDs Flash Group Qingling Cao [email protected]