1. Ripple Joins for Online Aggregation
Peter J. Haas
Joseph M. Hellerstein
Joseph, Z.M. – CSE, UTA
2/16/2006

2. Ripple Joins: Introduction
Follow up to Online Aggregation
Extends Online Aggregation to a family of join algorithms
Allows online aggregation to be used on multiple table queries

3. Ripple Joins: Introduction
Targets queries of the form:
SELECT op(expression) FROM R1, R2, … , RK
WHERE predicate
GROUP BY columns;
Running estimates can be calculated based statistical properties of data already seen.
User can control frequency of updates to this estimate.

4. Ripple Join vs. Online Nested Loop
Problems with online nested loop
If one table is large → Long time between updates
Confidence Interval may not narrow down enough
Ripple Join avoids complete relation scan.

5. Ripple Join: Operation
Assume ripple join of relations R and S
Select random tuple r from R
Join with previously selected S tuples
Do random select s from S
Join with previous R tuples

6. Ripple Join: Square Two-Table Join
S X
N = 1

7. Ripple Join: Square Two-Table Join
S X X
X X
N = 2

8. Ripple Join: Square Two-Table Join
S X X X
X X X
N = 3

9. Ripple Join: Square Two-Table Join
S X X X X
X X X X
N = 4

10. Ripple Join: Operation
Thus is like nested loop join, but alternates between sampling and scanning from either relation.
Can have various aspect ratios (non unitary)
Select more samples from one table
Leads to rectangular ripple
Configurable by user

11. Enhanced Ripple Join Iterator: Rectangular
Requires special
handling by iterator
to ensure that ripple
grows correctly.

12. Pipelined Ripple Join
Can easily be pipelined for multiple binary joins
Cannot do three-table joins as two binary ripple joins.
Authors recommend additional steps to handle building of such K-dimensional hyper rectangles.

13. Block Ripple Join Takes disk blocks of R and S in turn (not tuples)
Read a disk block of R and scan against old S
Evict from memory
Read Block of S and compare with older R tuples.
Exact same growth as normal, except thicker.
Has I/O saving since each block is taken out at a time.

14. Further Variations of Ripple Joins
Index Ripple Join
Identical to indexed-enhanced nested loop join
Hash Ripple Join
Used only for Equijoin

15. Statistics
As with online aggregation, ripple joins allow continuously updating running estimates
Estimator unbiased, consistent
Running average is biased but consistent
Capable of giving tight confidence intervals
Variance can also be calculated

16. Optimization and Design
Can choose aspect ratios
Animation Speed – Sweep out of rectangles
Aim is to maximize the rate of updates
Make confidence interval get as narrow as fast as possible

17. Conclusion
Gives users visible progress of query as it zones in on average
Useful UI enhancement
Achieves reasonable answer in up to two orders of magnitude faster than normal offline techniques.
Sublinear confidence interval guarantee
Prototypes in Informix, IBM DB2

18. References
Haas & Hollerstein, “Ripple Joins for Online Aggregation” (SIGMOD ’99)
Haas & Hollerstein, “Online Query Processing: A Tutorial”
Elmasri & Navathe, “Fundamentals of Database Systems”