Abstract 1 Domain-specific generators will increasingly rely on graphical languages for declarative specifications of target applications. Such languages will provide front-ends to generators and related tools to produce customized code on demand. Critical to the success of this approach will be domain-specific design wizards, tools that guide users in their selection of components for constructing particular applications. In this paper, we present the P3 ContainerStore graphical language, its generator, and design wizard. 1

We present an overview and initial performance assessment of a rule-based query optimizer written in VenusDB. VenusDB is an active-database rule language embedded in C++. Following the developments in extensible database query optimizers, first in rule-based form followed by optimizers written as object-oriented programs, the VenusDB optimizer avails the advantages of both. To date, development of rule-based query optimizers have included the definition and implementation of custom rule languages. Thus, extensibility required detailed understanding and often further development of the underlying search mechanism of the rule system. Objectoriented query optimizers appear to have achieved their goals with respect to a clear organization and encapsulation of an optimizer's elements. They do not, however, provide for the concise, declarative expression of domain specific heuristics. Our experience demonstrates that a rule-based query optimizer developed in VenusDB can be well structured, ...

The complexi ~ of queries in relational DBMSs is increas-ing particularly in the decision support area and interactive client server environments. This calls for a more power-ful and flexible optimization of complex queries. In [19] we introduced query rewrite as a distinct query optimiza-tion phase mainly targeted to responding to this require-ment. This approach has enabled us to extensively enrich the optimization rides in our system. Further, it has made it easier to incrementally enrich and adapt the system as need arises. Examples of such query optimizations are predicate pushdown, subquery and magic sets transformations, and decorrelating subqueries. In this paper we describe the de-sign and implementation of a rule engine for query rewrite optimization. Each transformation is implemented as a rule which consists of a pair of rule condition and action [19]. Rules can be grouped into rule classes for higher efficiency, better understandability and more extensibility. The rule engine has a number of novelties in that it supports a full spectrum of control--from totally data-driven to totally procedural. Furthermore, it incorporates a budget control scheme for controlling the resources taken for query opti-mization as well as guaranteeing the termination of rule ex-ecution. The rule engine and a suite of query rewrite rules have been implemented in Starburst relational DBMS pro-totype and a significant portion of this technology has been integrated into IBM DB2 Common Server relational DBMS. 1

Declarative programming, in the form of forward-chaining rule languages, offers advantages complementary to procedurally based object-oriented programming languages. The Venus rule language addresses certain deficiencies with rule-based programming by adding object-oriented features to the rule paradigm. This differs from most other efforts at hybridizing the two paradigms. Most other efforts seek advantage by starting with an object-oriented language and adding rule facilities. Object-oriented features of the Venus language includes encapsulation of parameterized rule modules such that the behavior of a rule group may be defined using inheritance and improved embeddability via resolving polymorphism. An implementation of a large subset of Venus has been exploited for the implementation of benchmark programs and applications. Experience with the application of Venus as the basis of rule-based database query optimizers and a financial expert system demonstrates the reusability of Venus-...

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Many database applications and environments, such as mediation over heterogeneous database sources and data warehousing for decision support, lead to complex queries. Queries are often nested, defined over previously defined views, and may involve unions. There are good reasons why one might want to "remove" pieces (sub-queries or sub-views) from such queries: some sub-views of a query may be effectively cached from previous queries, or may be materialized views; some may be known to evaluate empty, by reasoning over the integrity constraints; and some may match protected queries, which for security cannot be evaluated for all users. In this paper, we present a new evaluation strategy with respect to queries defined over views, which we call tuple-tagging, that allows for an efficient "removal" of sub-views from the query. Other approaches to this are to rewrite the query so the sub-views to be removed are effectively gone, then to evaluate the rewritten query. With the tuple...

Machine Interface.............................................................................................. 26 3.2.3 Concurrency Control....................................................................................................... 27 3.3 VENUSDB LANGUAGE SEMANTICS: AN EVALUATION ............................................................ 28 3.3.1 Related Work.................................................................................................................... 30 3.3.2 The Mortgage Pool Allocation Problem .......................................................................... 33 3.3.3 Quantitative Results ......................................................................................................... 37 3.3.4 Discussion and Conclusions ............................................................................................ 43 CHAPTER 4 APPLICATION SEMANTICS FOR ACTIVE LOG MONITORING APPLICATIONS ............................................................................................45 4.1 MOTIVATION ........................................................................................................................... 46 4.1.1 Coupling Modes............................................................................................................... 47 4.1.2 Example 1 ........................................................................................................................ 48 4.2 BACKGROUND.......................................................................................................................... 50 4.2.1 LMAs, Datalog, and Confluence ..................................................................................... 50 4.2.2 Previous Work....................................................

We present an overview and initial performance assessment of a rule-based query optimizer written in VenusDB. VenusDB is an active-database rule language embedded in C++. Following the developments in extensible database query optimizers, first in rule-based form followed by optimizers written as object-oriented programs, the VenusDB optimizer avails the advantages of both. To date, development of rule-based query optimizers have included the definition and implementation of custom rule languages. Thus, extensibility required detailed understanding and often further development of the underlying search mechanism of the rule system. Objectoriented query optimizers appear to have achieved their goals with respect to a clear organization and encapsulation of an optimizer’s elements. They do not, however, provide for the concise, declarative expression of domain specific heuristics. Our experience demonstrates that a rule-based query optimizer developed in VenusDB can be well structured, flexible, and demonstrate good performance. We compare a relational optimizer developed with Volcano and a functionally identical optimizer developed with VenusDB. The results demonstrate comparable performance on small queries with few joins, while the VenusDB optimizer scales better and outperforms Volcano on larger joinarity queries. Since we did not have to develop a specialized rule language or consider application specific programming constructs, the source code for the optimizer is small and straightforward, about one-third the size. Similar code comparisons with an object-based optimizer, the VenusDB optimizer reveals similar benefit.

Query optimizers are complex subsystems of database management systems. Modifying query optimizers to admit new algorithms or storage structures is quite difficult, but partly alleviated by extensible approaches to optimizer construction. Rule-based optimizers are a step in that direction, but from our experience, the rule sets of such optimizers are rather monolithic and brittle. Conceptually minor changes often require wholesale modifications to a rule set. Consequently, much can be done to improve the extensibility of rule-based optimizers. As a remedy, we present a tool called Prairie that is based on an algebra of layered optimizers. This algebra naturally leads to a building-blocksapproach to rule-set construction. Defining customized rule sets and evolving previously defined rule sets is accomplished by composing building-blocks. We explain an implementation of Prairie and present experimental results that show how classical relational optimizers can be synthesized from building-blocks, and that the efficiency of query optimization is not sacrificed. 1