1 Chengkai Li Kevin-Chen-Chuan Chang Ihab Ilyas Sumin Song Presented by: Mariam John CSE /20/2006 RankSQL: Query Algebra and Optimization for Relational Top- k Queries

2 Contents  Introduction  RankSQL  Ranking Query Model  Rank-Relational Algebra  Ranking Query Plans:Execution Model  Conclusion

3 Introduction  Top-k queries provides only the top k query results according to a user-specified ranking function.  Most of the available solutions are in the middleware, or focus on specific operators and queries.  Top-k queries are not treated as first class query type in RDBMS. Relational algebra has no notion for ranking.

4 RankSQL  Provides seamless support and integration of top-k queries with the existing SQL query facility in RDBMS.  Supports ranking as a first-class database construct.  Extends relational algebra and query optimization.

5 Example of a Top-k Query  SELECT * FROM Hotel h, Restaurant r, Museum m WHERE c1 AND c2 AND c3 ORDER BY p1+p2+p3 LIMIT k c1: r.cuisine=Italian p1: cheap(h.price) c2: h.price+r.price<100 p2: close(h.addr,r.addr) c3: r.area=m.area p3: related(m.collection, “dinosaur ”)

6 Rank Query Model  Rank relational query has 4 types of predicates: Filtering – Boolean-selection predicates Boolean-join predicates Ranking – rank-selection predicates rank-join predicates  Goal is to support rank relational queries efficiently. Filtering Ranking

7 Rank-Relational Query  Such queries add a ranking dimension to query processing and optimization.  Filtering restricts tuple “membership” by applying a Boolean function of Boolean selection or join predicates.  Ranking restricts “order” by applying a monotonic scoring function of ranking predicates.

8 Ranking as First-Class Construct  Support for ranking as a first class construct in RDBMS is lacking.  Relational algebra models Boolean filtering as a first class construct in query processing.   c1 is a selection over R, and c2 is a join condition over R * S

9 Filtering as a First-Class Construct  Algebra framework supports the following for Boolean filtering: - splitting - interleaving  Enable query optimization to transform from canonical form to efficient query plans.

10 Ranking as First-Class Construct  Algebraic support for optimization is lacking for ranking.  The sorting operator is ‘monolithic’.  It may be beneficial to evaluate ranking predicates one by one and interleave them with Boolean filtering.

11 Challenges  First, we must extend relational algebra to do the following:  Handle ranking  Define algebraic laws to handle equivalence transformation  Second, we need to generalize query optimization techniques to integrate the parallel dimensions of Boolean filtering and ranking.

12 Rank-Relational Algebra  Rank-Relation is a relation with its tuples scored and ordered accordingly  How do we rank a relation, given

13 Ranking principle  Maximum possible score of a tuple t, denoted by, is defined as: = if = 1otherwise

14 Examples of Rank-Relations

15 Operators  Need to extend relational-algebra operators for manipulating rank- relations.  For supporting ranking as a first-class construct, define a new operator ‘μ’.  This new ‘rank’ operator should satisfy the two requirements: splitting and interleaving.

16 New Operator, μ  Extend relational algebra by adding a new rank operator, μ. What does mean?  Extend the original semantics of existing operators with rank-awareness, enabling interaction with the new rank operator.  Extend relational algebra such that it gives several equivalences relevant to ranking.

17 Results of Operators

18 Ranking Query Plans: Execution Model  Extend the common execution model to handle rank query.  Operators incrementally output rank relations.  Query has an explicitly requested result size.  Key capability of a rank-aware operator is to decide if enough information has been obtained from its input tuples in order to incrementally produce the next ranked output tuple.

19 Example

20 Conclusion  RankSQL is a system that provides a systematic framework to support efficient evaluation of top-k queries in RDBMS.  Extend relational algebra to make ranking a first-class construct.  Query execution model is extended to handle ranking query.  Rank-aware operators are selective and context-sensitive.