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Proactive Re- Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)
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Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Dec 22, 2015

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Page 1: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Proactive Re-Optimization

Shivnath Babu, Pedo Bizarro, David DeWitt

SIGMOD 2005

(presented by Steve Blundy & Oleg Rekutin)

Page 2: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Overview

What’s wrong with reactive? Proactive via 3 core techniques Experiments

Page 3: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Reactive Re-optimization

0

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R S

select from R, S where R.a=S.a and R.b>K1 and R.c>K2σ

buffer

σ(R) actual

σ(R) estimated

A:

B:

!

!

Page 4: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Single-Point Limitation

A:

B:

Page 5: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Limited Information for Re-opt

select from R, S, T where R.a=S.a and S.b=T.b and R.c>K1 and R.d=K2

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R S T

σ(R) act

σ(R) est!

!!

Page 6: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Choosing a plan

1. Compute bounding boxes

2. Use them to generate robust plans and switchable plans

3. Use randomization to collect statistics

Page 7: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Bounding Boxes

“Representing Uncertainty in Statistics” Are the upper and lower bounds for each

estimated statistic

Page 8: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Bounding Boxes

Page 9: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Optimal Plan

1 Plan is optimal for all 3 points

Choice is easy

Optimal Plan

0 50 100 150 200 250 300 350

Page 10: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Robust Plan

1 plan is, or close to, optimal for all 3 points

1 plan can be safely chosen

Robust Plans

0 50 100 150 200 250 300 350

Page 11: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Switchable Plan

There is a plan with close to optimal cost plan at each point

Additional Requirements The decision can be

deferred Actual statistics lie must

within bounding box It is possible to switch

between the plans

Switchable

0 50 100 150 200 250 300 350

Page 12: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

What is a “Switchable” Plan

“Any two members of a switchable plan are said to be switchable with each other.”

Page 13: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Collecting statistics

1. Each operator collects some % in buffer2. The eos(f) is emitted & statistics are calculated3. Plan is chosen from switch plan members or

re-optimization is run4. Query processing proceeds

Page 14: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Questions

Prevalence of switchable plans vs. case 4 How good is Rho at preventing re-

optimizations How is Rho affected by large # estimates

Page 15: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Experiments

Traditional Optimizer (TRAD) Validity-Ranges Optimizer (VRO)

Page 16: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

2-Way Join Queries: Robust

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A C

σ(A) est

Page 17: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

2-Way Join Queries: Switchable

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A C

σ(A) est

σ(A) b. box

Page 18: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

3-Way Join Example

Shows the use of a Switchable Plan Some re-optimization still necessary

Page 19: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Pt |σ1(A)| TRAD VRO Rio Opt

A 6 MB P17a Inside range, P17a Outside box, re-optimize, P17a P17a

B 80 MB P17a Inside range, P17a Inside box, P17a P17a

C 160 MB P17a Outside range, re-optimize, P17d

Inside box, P17d P17b

D 310 MB P17a Outside range, re-optimize, P17d

Outside box, re-optimize, P17b P17b

Page 20: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)
Page 21: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Correlation-Based Mistakes

Page 22: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Query Complexity

Page 23: Proactive Re-Optimization Shivnath Babu, Pedo Bizarro, David DeWitt SIGMOD 2005 (presented by Steve Blundy & Oleg Rekutin)

Conclusion

Rho refines statistics and uses switchable plans to forestall re-optimizations and prevent partial data loss

Questions?