Let SQL drive the XQuery workhorse (XQuery join graph isolation)

Grust, Torsten; Mayr, Manuel; Rittinger, Jan

doi:10.1145/1739041.1739062

Cited by 16 publications

(18 citation statements)

References 14 publications

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“…For instance, we factor references to attributes to enable merging of projections without blow-off in expression size, pull up projections that create provenance annotations, and remove unnecessary duplicate elimination and window operators. We infer local and non-local properties [17] such as candidate keys for the algebra operators of a query. This enables us to define transformations that rely on non-local information.…”

Section: Solution Overviewmentioning

confidence: 99%

“…Approaches that compile non-relational languages (e.g., XQuery [17], [26]) or extensions of relational languages (e.g., temporal [27] and nested collection models [28]) into SQL face similar challenges as we do. Grust et al [17] optimize the compilation of XQuery into SQL. The approach heuristically applies algebraic transformations to cluster join operations with the goal to produce an SQL query that can successfully be optimized by a relational database.…”

Section: Compilation Of Non-relational Languages Into Sqlmentioning

confidence: 99%

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Heuristic and Cost-Based Optimization for Diverse Provenance Tasks

Niu

Kapoor

Glavic

et al. 2019

IEEE Trans. Knowl. Data Eng.

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A well-established technique for capturing database provenance as annotations on data is to instrument queries to propagate such annotations. However, even sophisticated query optimizers often fail to produce efficient execution plans for instrumented queries. We develop provenance-aware optimization techniques to address this problem. Specifically, we study algebraic equivalences targeted at instrumented queries and alternative ways of instrumenting queries for provenance capture. Furthermore, we present an extensible heuristic and cost-based optimization framework utilizing these optimizations. Our experiments confirm that these optimizations are highly effective, improving performance by several orders of magnitude for diverse provenance tasks. ! INTRODUCTIONDatabase provenance, information about the origin of data and the queries and/or updates that produced it, is critical for debugging queries, auditing, establishing trust in data, and many other use cases. The de facto standard for database provenance [1], [2] is to model provenance as annotations on data and define a query semantics that determines how annotations propagate. Under such a semantics, each output tuple t of a query Q is annotated with its provenance, i.e., a combination of input tuple annotations that explains how these inputs were used by Q to derive t.Database provenance systems such as Perm [3], GProM [4], DBNotes [5], LogicBlox [2], declarative Datalog debugging [6], ExSPAN [7], and many others use a relational encoding of provenance annotations. These systems typically compile queries with annotated semantics into relational queries that produce this encoding of provenance annotations following the process outlined in Fig. 23a. We refer to this reduction from annotated to standard relational semantics as provenance instrumentation or instrumentation for short. The example below introduces a relational encoding of provenance polynomials [1] and the instrumentation approach for this model implemented in Perm [3].Example 1. Consider a query over the database in Fig. 1 returning shops that sell items which cost more than $20:The query's result is shown in Fig. 1d. Using provenance The instrumentation we are using here is defined for any SPJ (Select-Project-Join) query (and beyond) based on a set of algebraic rewrite rules (see [3] for details).The present paper extends [8]. Additional details are presented in the appendix. Instrumentation PipelinesIn this work, we focus on optimizing instrumentation pipelines such as the one from Example 1. These pipelines divide the compilation of a frontend language to a target language into multiple compilation steps using one or more intermediate languages. We now introduce a subset of the pipelines supported by our approach to illustrate the breadth of applications supported by instrumentation. Our approach can be applied to any data management task that can be expressed as instrumentation. Notably, our implementation already supports additional pipelines, e.g., for summarizing provenance and managing ...

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Section: Solution Overviewmentioning

confidence: 99%

Section: Compilation Of Non-relational Languages Into Sqlmentioning

confidence: 99%

Heuristic and Cost-Based Optimization for Diverse Provenance Tasks

Niu

Kapoor

Glavic

et al. 2019

IEEE Trans. Knowl. Data Eng.

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“…While XQuery Core does not have a location step expression, the reason why our target has is that (1) evaluating path expressions is more efficient than "for"-expressions [8,19], although theoretically, it can be translated into "for"-expressions; and (2) previous work on XQuery dealt with location steps [14,10,9]. Figure 2 shows the semantics of our target XQuery using a set of inference rules.…”

Section: Formal Semanticsmentioning

confidence: 99%

Context-Preserving XQuery Fusion

Kato

Hidaka

et al. 2010

Programming Languages and Systems

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Abstract. XQuery is a DBPL for querying XML databases. The semantics of XQuery is context sensitive and requires preservation of document order. In this paper, we propose, as far as we are aware, the first XQuery fusion that can deal with both the document order and the context of XQuery expressions. More specifically, we carefully design a context representation of XQuery expressions based on the Dewey order encoding, develop a context-preserving XQuery fusion for ordered trees by static emulation of the XML store, and prove that our fusion is correct. Our XQuery fusion has been implemented, and all the examples in this paper have passed the system.

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“…Grust et al [60] suggest to outsource the task of ordering the joins in an XQuery to relational database systems. They start by noting that the performance of relational optimizers is unsatisfyingly low when handling XQueries.…”

Section: Join Ordering In Xmlmentioning

confidence: 99%

“…Efficient and robust ordering algorithms are highly needed. Moreover, we notice that the proposed techniques, except for [60], do not take into account relational joins, i.e. XPath steps and relational joins are not optimized indifferently of each other.…”

Section: Join Ordering In Xmlmentioning

confidence: 99%

ROX : Run-Time Opitization of XQueries

Kader¹

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Let SQL drive the XQuery workhorse (XQuery join graph isolation)

Cited by 16 publications

References 14 publications

Heuristic and Cost-Based Optimization for Diverse Provenance Tasks

Heuristic and Cost-Based Optimization for Diverse Provenance Tasks

Context-Preserving XQuery Fusion

ROX : Run-Time Opitization of XQueries

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