1. By: Peter J. Haas and Joseph M. Hellerstein published in June 1999 : Presented By: Sthuti Kripanidhi 9/28/20101 CSE 6339 - Data Exploration

2. Overview What the paper is all about Traditional Algorithms Online Aggregation Ripple Joins: Introduction How different is Ripple join Ripple Join variants Aspect ratios Future Work 9/28/20102CSE 6339 - Data Exploration

3. What the paper is about.. The paper talks about a class of join algorithms called Ripple joins for the online processing of multi-table aggregation queries. This paper tells how to join a bunch of tables and get the SUM, COUNT, or AVG in GROUP BY clauses showing approximate results immediately and the confidence interval of the results from the first few tuples retrieved. 9/28/20103CSE 6339 - Data Exploration

4. Traditional Algorithms Traditional algorithms take a lot of time since they have to process the entire tables or relations The users have to wait for a long time before the results are returned. An better method is Online Aggregation. 9/28/20104CSE 6339 - Data Exploration

5. Online Aggregation A running estimate of the final aggregates are continuously displayed to the user. Quick results rather than minimize time for completion. The proximity of the running estimate to the final result is also displayed to the user.(confidence interval). 9/28/2010CSE 6339 - Data Exploration5

6. GUI 9/28/2010CSE 6339 - Data Exploration6

7. Ripple Joins: Introduction Generalize the traditional block nested loops and hash joins. Non blocking Square ripple join – samples are drawn at the same rate Rectangular ripple join – samples out one relation at a higher rate than another. 9/28/2010CSE 6339 - Data Exploration7

8. Ripple Join: Introduction Typical query forms SELECT op(expression) FROM R 1, R 2, …, R K WHERE predicate GROUP BY columns; 9/28/2010CSE 6339 - Data Exploration8

9. How different is Ripple join? Traditional hash join blocks until the entire query output is finished. Ripple join reports approximate results after each sampling step, and allows user intervention. In the inner loop, an entire table is scanned. Ripple join expands the sample set incrementally. Ripple joins avoid complete scan of the relations. 9/28/20109CSE 6339 - Data Exploration

10. How Ripple Join works.. Assume ripple join of relations R and S  Select a random tuple r from R.  Join with previously selected S tuples.  Select a random tuple s from S.  Join with previously selected R tuples.  Join r and s. 9/28/201010CSE 6339 - Data Exploration

11. Ripple Join: Square two table join 9/28/2010CSE 6339 - Data Exploration11 R S X N = 1

12. 9/28/2010CSE 6339 - Data Exploration12 R S X X X N = 2

13. 9/28/2010CSE 6339 - Data Exploration13 R S X X X X X X N = 3

14. Ripple Join Algorithm For(max=1 to infinity) { for(i=1 to max-1) if(predicate(R[i],s[max])) output(R[i],S[max]); for(i=1 to max) if(predicate(R[max],s[i])) output(R[max],S[i]); } 9/28/201014CSE 6339 - Data Exploration

15. Ripple Join Iterator An iterator based DBMS invokes an iterator’s next() method each time an output tuple is needed. The iterator needs to store the next position to be fetched from each of its inputs R and S. 9/28/2010CSE 6339 - Data Exploration15

16. Pipelining Can easily be pipelined for multiple binary joins Cannot do three-table joins as two binary ripple joins. 9/28/201016CSE 6339 - Data Exploration

17. Ripple Join Variants Block Ripple Join Hash Ripple Join Index Ripple Join 9/28/2010CSE 6339 - Data Exploration17

18. 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. Has I/O saving since each block is taken out at a time. 9/28/2010CSE 6339 - Data Exploration18

19. Index and Hash Ripple Joins Index Ripple Join Identical to indexed-enhanced nested loop join Hash Ripple Join Used only for Equijoin queries. 9/28/2010CSE 6339 - Data Exploration19

20. Statistical Considerations Goal-to provide efficient, accurate, interactive estimation. Estimator unbiased, consistent Running average is biased but consistent Capable of giving tight confidence intervals 9/28/2010CSE 6339 - Data Exploration20

21. Aspect Ratios Aspect ratio: how many tuples are retrieved from each base relation per sampling step. e.g.β 1 = 1, β 2 = 3, … Ripple join adjusts the aspect ratio according to the sizes of the base relations. 9/28/2010CSE 6339 - Data Exploration21

22. Why is it called Ripple Join? 9/28/2010CSE 6339 - Data Exploration22 1. The algorithm seems to ripple out from a corner of the join. 2. Acronym: "Rectangles of Increasing Perimeter Length"

23. Performance 9/28/2010CSE 6339 - Data Exploration23

24. Conclusions and Future Work Complete implementation of online aggregation must be able to handle multi-table queries. This paper introduces ripple joins, a family of join algorithms designed to meet the performance needs of online aggregation system. 9/28/2010CSE 6339 - Data Exploration24

25. Though ripple joins are symmetric, it is still not clear how a query optimizer should choose among the ripple join variants, nor how it should order a sequence of ripple joins. 9/28/2010CSE 6339 - Data Exploration25

26. References Haas & Hellerstein, “Ripple Joins for Online Aggregation” (SIGMOD ’99) Haas & Hellerstein, “Online Query Processing: A Tutorial” P. J Haas, J.M Hellerstein and H.J Wang Online aggregation. In Proc. 1997 ACM SIGMOD Intl Conf. Management of data pages. 9/28/2010CSE 6339 - Data Exploration26