J. Blakeley, W. McKenna, and G. Graefe. Experiences building the Open OODB query optimizer. In Proc. of the ACM SIGMOD Conf. on Management of Data, pages 287--295, 1993.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....and selectivity estimation formulas, and performance studies have been reported in [SJS01b] The latter paper did not cover the enhancements to Volcano, which are the foci of this paper. While to our knowledge, nobody has enhanced existing optimizers with support for sets, multisets, and lists, [BKG93] reports on experiences from building the query optimizer for Texas Instruments Open OODB system using Volcano. That paper concludes that when building an optimizer, it is essential to have a full optimization framework that includes not only an algebra and its transformations, but also the ....

J. A. Blakeley, W. J. McKenna, and G. Graefe. Experiences Building the Open OODB Query Optimizer. In Proceedings of ACM SIGMOD, Washington, DC, pp. 287--296 (1993).

....In case we are assembling two trees of operators having partitions of the same original relation, the IDs of the partitions produced by the first tree would be used to probe the sparse B Tree indexes of the partitions in the second. This operator is similar to the materialize operator proposed in [4] for evaluating pointer joins in object oriented database systems. Consider the simple join query considered in the previous section. First consider how the search space of plans is explored for the NSM model. For simplicity we assume that the database has no indexes to use for this query. The ....

J.Blakeley, W.J.McKenna, G.Graefe. Experiences building the open OODB Query Optimizer. Proceedings of ACM SIGMOD 1993.

....tablecan and indexescan are usually parallelised based on the partitioning of the data being read. Thus, even if an attribute sensitive predicate is specified in any of these scan operations, no exchange operator is required to do data repartitioning. The operator materialise, first proposed in [2], is mainly responsible for materialising path expressions by bringing into scope objects of collections referred to in a path expression. The operator reduce is considered as attribute insensitive, as its role is simply to structure the results of a query. However, an exchange operator may be ....

J. A. Blakeley, W. J. McKenna, and G. Graefe. Experiences building the Open OODB query optimizer. In Proceedings of the ACM SIGMOD, pages 287-296, Washington, DC, USA, 1993.

....A n ] for an extent belonging to object type E. It returns tuples consisting of the object identifier e and projects on the (possibly set valued) attributes A 1 ; A n . The algebraic counterpart of the dot operator in OQL is the expand operator c [KM90] also called, e.g. materialize [BMG93]. It may be used both to retrieve attributes and to invoke member functions of a referenced object. In this paper, we only need the attribute access variant (The operator ffi denotes tuple concatenation and g is a newly introduced attribute) c g : e:a (E) fe ffi [g : e:a] j e 2 Eg To flatten ....

J. A. Blakeley, W. J. McKenna, and G. Graefe. Experiences building the Open OODB Query Optimizer. In Proc. of the ACM SIGMOD Conf. on Management of Data, pages 287--295, Washington, DC, USA, May 1993.

....a lot of work on algebras in the context of object oriented databases. Our work has been influenced mostly by [FM95a] and [FM95b] Monoid homomorphisms are a strong candidate for an XML calculus that we plan to devise in our future work (see Section 9) The object oriented algebra presented in [BMG93] uses a combination of materialize and unnest to access set valued attributes which is close to what our Follow operator does (Section 5.2) Unnest extracts references from sets and materialize resolves these references to in memory objects. Our approach di#ers in two aspects: 1. It centers on ....

Jose A. Blakeley, William J. McKenna, and Goetz Graefe. Experiences building the open OODB query optimizer. In Peter Buneman and Sushil Jajodia, editors, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., May 26-28, 1993, pages 287--296. ACM Press, 1993.

....TPC H queries in Section 5. Section 6 concludes the paper. 2. Preliminaries In this section we review the concept of a compact representation of the plan space in form of the MEMO structure. This concept was developed by Graefe and DeWitt in the context of transformation based query optimization [4, 5, 1]. Independent of this development, a similar structure has been developed for bottom up enumeration of join trees in Starburst [8] Our technique is based on the MEMO but could be transferred easily to the Starburst enumerator. We will briefly introduce the essential aspects of the MEMO and refer ....

J. A. Blakeley, W. J. McKenna, and G. Graefe. Experiences Building the Open OODB Query Optimizer. In Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Washington, DC, USA, May 1993.

.... operators operators Get Table scan Index scan Join Nested loop Merge join Materialize Materialize Nest Nest Groupby Unnest Unnest Sort Reduce Reduce Union Union Map Map The physical algebra outlined in table 1 is similar to the one described in [6] However, the Materialize operator [1] has been added to this algebra, providing the execution engine with an operator capable of bringing into scope objects from another extent and performing a join between those objects and the input objects, by following the relationships between the input objects and the objects of the hidden ....

J. A. Blakeley, W. J. McKenna, and G. Graefe. Experiences building the Open OODB query optimizer. In Proceedings of the ACM SIGMOD, pages 287--296, Washington, DC, USA, 1993.

....a set of reasonable execution plans for processing a given query, and selects the one with the minimum estimated cost. One of the most important factors that affects the cost estimation of an execution plan is the intermediate result cardinality the number of objects in an intermediate result [3, 7, 19, 21]. This is because the intermediate result becomes the operand of the next operation in the execution plan, and the cost of the next operation can be estimated mainly based on the number of objects in its operands [18, 19, 21] Hence, accurate estimation of intermediate result cardinality is a ....

....The selectivity of this nested predicate is the ratio of the number of path instances of the j D j A j D i A C i Fig. 4: Condition C i :A i [D j ] A j = val . path C i :A i [D j ] A j that satisfy A j = val to the total number of the path instances. Most of the conventional literature [2, 3, 9, 16, 17] on object oriented databases have no explicit estimation techniques for path selectivity. Kim et al. 11] use the technique proposed by Selinger et al. 19] for estimating the path selectivity as follows: sel(C i :A i [D j ] A j = val ) sel(D j :A j = val ) 1 dv(D j :A j ) 10) However, ....

J. A. Blakeley, W. J. McKenna, and G. G. Graefe. Experiences building the Open OODB query optimizer. In Proc. ACM SIGMOD Conference, pp. 287--296, Washington, DC (1993).

....Toolkit (Alpha release from texas Instruments, Dallas [WBT92] as the underlying platform for this effort. We intend to concentrate on the open architecture and the issues mentioned above and plan on using available tools where possible. Our short immediate term goal is to use the Volcano [Mck92, BMG92] optimizer generator (by modifying it where necessary) to move towards the open architecture. Initially, we will try to group rules into modules that roughly correspond to local experts. This requires modifications to relevant parts of Volcano to match our requirements. As an intermediate term ....

Jose A. Blakeley, William J. Mckenna, and Gectz Graefe. Experiences building the open oodb query optimizer. Technical report, Texas Instruments INC., 1992.

.... memory) The materialization 1 Note that this blocking phenomenon arises even if operators are ones that support intra operator parallelism such the exchange operator of Volcano [CG94] 2 This notion of a materialization operator is not related to the operator for path expressions described in [BMG93] INRIA Dynamic Query Operator Scheduling for Wide Area Remote Access 7 operator provides tuples in response to next( requests from its parent operator when the parent is eventually able to execute. As stated in the introduction, there are three key policy questions for the implementation of a ....

J. Blakeley, W. McKenna, and G. Graefe. Experiences building the open OODB query optimizer. In Proc. of the ACM SIGMOD Int. Conf., page 287, Washington, DC, May 1993.

....version of the object algebra in typical object oriented databases to process spatial predicates as well as non spatial predicates [27] The operators in SOA are classified into algebraic operators and predicate operators. The algebraic operators are similar to the logical operators in Open OODB [6], MOOD [13] Paradise [17, 31] etc. They process inputs and outputs with a collection as a unit. The algebraic operators consist of the following: 1. relational algebra operators such as SELECT, PROJECT, JOIN, UNION, and DIFFERENCE, 2. NEST and UNNEST operators for set attributes in the extended ....

J.A. Blakeley, W.J. McKenna, and G. Graefe. Experiences building the open oodb query optimizer. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 287--296, Washington, D.C., ACM Press (1993).

....analogous to our mediator blades. Experimental database designs have gone beyond these more limited blade facilities to introduce far reaching extensibility. They have opened the entire database to modification through plug in modules. The Open OODB at Texas Instruments was one such project [5]. It enabled designers to change large granularity modules in order to introduce new or optimized functionality. The operating systems community has also experimented with component based approaches. Maybe the most famous of these is the Mach operating system [1, 26] It offered a kernel, ....

J.A. Blakeley, W.J. McKenna, and G. Graefe. Experiences building the open oodb query optimizer. In Proc. of the ACM SIGMOD Conf., 1993.

....Target object language, cost model, path expression, performance measurements. Cost Controlled OFL Rewriting Rules for Multiple Collection Traversals 2 Laboratoire PRiSM 1. Introduction The evaluation of path expressions is a crucial aspect in the optimization of objectoriented queries [Blakeley93, Kim95]. In fact, path expressions are the mechanisms most often used to query object bases [Carey88, Gardarin92, Kifer92] Path expressions describe multiple collection traversals navigations in the deeply nested structures of objects. Path expressions may be single valued or set valued, and may ....

....explored by the classic query rewriting and query planning phases of an optimizer is uniformly described by OFL programs. Query rewriting is concerned with finding equivalent expressions to the one given by the user [Haas90] In algebraic approaches, an algebraic expression represents a user query [Shaw90, Cluet92, Blakeley93]. Within the OFL formalism, an OFL program (a functional expression) represents the user query. Thus, equivalent expressions are equivalent OFL programs. Query planning explores the search space of execution plans to find the best one [Valduriez91, Lanzellote92, Mitchell93, Finance94] Within the ....

[Article contains additional citation context not shown here]

Blakeley J., Graefe G., "Experiences Building the Open OODB Query Optimizer", Intl. ACM SIGMOD Conf. on Management of Data, 1993.

....analogous to our mediator blades. Experimental database designs have gone beyond these more limited blade facilities to introduce far reaching extensibility. They have opened the entire database to modification through plug in modules. The Open OODB at Texas Instruments was one such project [6]. It enabled designers to change large granularity modules in order to introduce new or optimized functionality. The operating systems community has also experimented with component based approaches. Maybe the most famous of these is the Mach operating system [1, 24] It offered a kernel, ....

J.A. Blakeley, W.J. McKenna, and G. Graefe. Experiences building the open oodb query optimizer. In Proc. of the ACM SIGMOD Conf., 1993.

....as others to develop more rapidly new database query optimizers for noveldata models, query algebras, and database management systems. The Volcano optimizer generator has been used to develop optimizers for computations overscientific databases [WoG93] and for Texas Instruments sOpen OODB project [BMG93, WBT92], which introduces a new materialize or scope operator that captures the semantics of path expressions in a logical algebra expression. Both of these optimizers have recently become operational. Moreover, the Volcano optimizer generator is currently being evaluated by several academic and ....

J. A. Blakeley, W.J.McKenna, and G. Graefe, Experiences Building the Open OODB Query Optimizer, Proc. ACM SIGMOD Conf.,Washington, DC, May 1993, 287.

....as a globally known repository of system, object, name, and type information. Open OODB does not implement all its modules from scratch. The Exodus storage manager [CDR86] is used as an ASM for permanent storage of objects, and the Volcano optimizer generator to generate the query optimizer [BMG93]. For our purposes, the use of multiple threads, preferably on a multiprocessor platform, for event composition and rule firing in the active DBMS is essential. Therefore, we committed early to a Solaris 2.x platform. This required the porting of Exodus to the Solaris environment. Currently, a ....

Blakeley, J. A., McKenna, W. J., Graefe, G.; Experiences Building the Open OODB Query Optimizer. Proc. ACM SIGMOD, 1993.

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J. Blakeley, W. McKenna, and G. Graefe. Experiences building the Open OODB query optimizer. In Proc. of the ACM SIGMOD Conf. on Management of Data, pages 287--295, 1993.

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J. A. Blakeley, W. J. McKenna, and G. Graefe. Experiences building the Open OODB query optimizer. In Proceedings of the ACM SIGMOD, pages 287--296, Washington, DC, USA, 1993.

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J. A. Blakeley, W. J. McKenna, and G. Graefe. Experiences building the Open OODB query optimizer. In Proceedings of the ACM SIGMOD, pages 287--296, Washington, DC, USA, 1993.

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J. A. Blakeley, W. J. McKenna, and G. Graefe. Experiences Building the Open OODB Query Optimizer. In Proceedings of ACM SIGMOD, pp. 287--296 (1993).

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J. Blakeley, W. McKenna, and G. Graefe. Experiences building the Open OODB query optimizer. In Proc. of the ACM SIGMOD Conf. on Management of Data, pages 287--295, 1993.

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J. A. Blakeley, W. J. McKenna, and G. Graefe. Experiences building the open OODB query optimizer. In P. Buneman and S. Jajodia, editors, Proceedings of the 1993.

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J. A. Blakeley, W. J. McKenna, and G. Graefe. Experiences building the open oodb query optimizer. In Proceedings of the SIGMOD Conference, pp. 287--296, 1993.

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J.A. Blakeley, W.J. McKenna, and G. Graefe. Experiences Building the Open OODB Query Optimizer. In Proc. ACM SIGMOD Conference, pp. 287--296, Washington, 1993.

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J. A. Blakeley, W. J. McKenna, and G. Graefe. Experiences building the open OODB query optimizer. In P. Buneman and S. Jajodia, editors, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 287--296. ACM Press, 1993. ftp://ftp.cs.pdx.edu/pub/ faculty/graefe/papers/OpenOODB.ps.

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