Projection Pushing Revisited

  • Benjamin J. McMahan
  • Guoqiang Pan
  • Patrick Porter
  • Moshe Y. Vardi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2992)


The join operation, which combines tuples from multiple relations, is the most fundamental and, typically, the most expensive operation in database queries. The standard approach to join-query optimization is cost based, which requires developing a cost model, assigning an estimated cost to each query-processing plan, and searching in the space of all plans for a plan of minimal cost. Two other approaches can be found in the database-theory literature. The first approach, initially proposed by Chandra and Merlin, focused on minimizing the number of joins rather then on selecting an optimal join order. Unfortunately, this approach requires a homomorphism test, which itself is NP-complete, and has not been pursued in practical query processing. The second, more recent, approach focuses on structural properties of the query in order to find a project-join order that will minimize the size of intermediate results during query evaluation. For example, it is known that for Boolean project-join queries a project-join order can be found such that the arity of intermediate results is the treewidth of the join graph plus one.

In this paper we pursue the structural-optimization approach, motivated by its success in the context of constraint satisfaction. We chose a setup in which the cost-based approach is rather ineffective; we generate project-join queries with a large number of relations over databases with small relations. We show that a standard SQL planner (we use PostgreSQL) spends an exponential amount of time on generating plans for such queries, with rather dismal results in terms of performance. We then show how structural techniques, including projection pushing and join reordering, can yield exponential improvements in query execution time. Finally, we combine early projection and join reordering in an implementation of the bucket-elimination method from constraint satisfaction to obtain another exponential improvement.


Constraint Satisfaction Straightforward Approach Tree Decomposition Conjunctive Query Graph Instance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Benjamin J. McMahan
    • 1
  • Guoqiang Pan
    • 1
  • Patrick Porter
    • 2
  • Moshe Y. Vardi
    • 1
  1. 1.Department of Computer ScienceRice UniversityHoustonU.S.A.
  2. 2.Scalable Software 

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