ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints

ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints
ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints
Bibliographical Metadata
Authors:	Maribel Acosta, Maria-Esther Vidal, Tomas Lampo, Julio Castillo
Content Metadata
Problem:	SPARQL Query FederationQuery ExecutionSource Selection
Approach:	Querying Distributed RDF Data Sources

Abstract

Following the design rules of Linked Data, the number of available SPARQL endpoints that support remote query processing is quickly growing; read more.,

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Approach

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Implementations

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Evaluation

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Abstract

Following the design rules of Linked Data, the number of available SPARQL endpoints that support remote query processing is quickly growing; however, because of the lack of adaptivity, query executions may frequently be unsuccessful. First, fixed plans identified following the traditional optimize-then execute paradigm may time out as a consequence of endpoint availability. Second, because blocking operators are usually implemented, endpoint query engines are not able to incrementally produce results and may become blocked if data sources stop sending data. We present ANAPSID, an adaptive query engine for SPARQL endpoints that adapts query execution schedules to data availability and run-time conditions. ANAPSID provides physical SPARQL operators that detect when a source becomes blocked or data traÆc is bursty, and opportunistically, the operators produce results as quickly as data arrives from the sources. Additionally, ANAPSID operators implement main memory replacement policies to move previously computed matches to secondary memory avoiding duplicates. We compared ANAPSID performance with respect to RDF stores and endpoints and observed that ANAPSID speeds up execution time, in some cases, in more than one order of magnitude.

Technology

Suports SPARQL 1.1

ANAPSID Query Processing Engine

The ANAPSID query engine provides a set of operators able to gather data from different endpoints. Opportunistically, these operators produce results by joining tuples previously received even when endpoints become blocked. Additionally, the physical operators implement main memory replacement policies to move previously computed matches to secondary memory, ensuring no duplicate generation. Each join operator maintains a data structure called Resource Join Tuple (RJT), that records for each instantiation of the joint variable(s), the tuples that have already matched. Suppose that or the instantiation of the variable ?X with the resource r, the tuples {T1, ..., Tn} have matched, then the RJT will be the pair (r, {T1, ..., Tn}), where the first argument, head of the RJT, corresponds to the resource and the second, tail of the RJT, is the list of tuples.

Conclusion

We have defined ANAPSID, an adaptive query processing engine for RDF Linked Data accessible through SPARQL endpoints. ANAPSID provides a set of physical operators and an execution engine able to adapt the query execution to the availability of the endpoints and to hide delays from users. Reported experimental results suggest that our proposed techniques reduce execution times and are able to produce answers when other engines fail. Also, depending on the selectivity of the join operator and the data transfer delays, ANAPSID operators may overcome state-of-the-art Symmetric Hash Join operators. In the future, we plan to extend ANAPSID with more powerful and lightweight operators like Eddy and MJoin, which are able to route received responses through different operators and adapt the execution to unpredictable delays by changing the order in which each data item is routed.