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Diversified Stress Testing of RDF Data Management Systems
"... Abstract. The Resource Description Framework (RDF) is a standard for conceptually describing data on the Web, and SPARQL is the query language for RDF. As RDF data continue to be published across heterogeneous domains and integrated at Web-scale such as in the Linked Open Data (LOD) cloud, RDF data ..."
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Abstract. The Resource Description Framework (RDF) is a standard for conceptually describing data on the Web, and SPARQL is the query language for RDF. As RDF data continue to be published across heterogeneous domains and integrated at Web-scale such as in the Linked Open Data (LOD) cloud, RDF data management systems are being exposed to queries that are far more diverse and workloads that are far more varied. The first contribution of our work is an indepth experimental analysis that shows existing SPARQL benchmarks are not suitable for testing systems for diverse queries and varied workloads. To address these shortcomings, our second contribution is the Waterloo SPARQL Diversity Test Suite (WatDiv) that provides stress testing tools for RDF data management systems. Using WatDiv, we have been able to reveal issues with existing systems that went unnoticed in evaluations using earlier benchmarks. Specifically, our experiments with five popular RDF data management systems show that they cannot deliver good performance uniformly across workloads. For some queries, there can be as much as five orders of magnitude difference between the query execution time of the fastest and the slowest system while the fastest system on one query may unexpectedly time out on another query. By performing a detailed analysis, we pinpoint these problems to specific types of queries and workloads.
Evaluating SPARQL queries on massive RDF datasets
- Proc. VLDB Endowment
, 2015
"... ABSTRACT Distributed RDF systems partition data across multiple computer nodes. Partitioning is typically based on heuristics that minimize inter-node communication and it is performed in an initial, data pre-processing phase. Therefore, the resulting partitions are static and do not adapt to chang ..."
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ABSTRACT Distributed RDF systems partition data across multiple computer nodes. Partitioning is typically based on heuristics that minimize inter-node communication and it is performed in an initial, data pre-processing phase. Therefore, the resulting partitions are static and do not adapt to changes in the query workload; as a result, existing systems are unable to consistently avoid communication for queries that are not favored by the initial data partitioning. Furthermore, for very large RDF knowledge bases, the partitioning phase becomes prohibitively expensive, leading to high startup costs. In this paper, we propose AdHash, a distributed RDF system which addresses the shortcomings of previous work. First, AdHash initially applies lightweight hash partitioning, which drastically minimizes the startup cost, while favoring the parallel processing of join patterns on subjects, without any data communication. Using a locality-aware planner, queries that cannot be processed in parallel are evaluated with minimal communication. Second, AdHash monitors the data access patterns and adapts dynamically to the query load by incrementally redistributing and replicating frequently accessed data. As a result, the communication cost for future queries is drastically reduced or even eliminated. Our experiments with synthetic and real data verify that AdHash (i) starts faster than all existing systems, (ii) processes thousands of queries before other systems become online, and (iii) gracefully adapts to the query load, being able to evaluate queries on billion-scale RDF data in subseconds. In this demonstration, audience can use a graphical interface of AdHash to verify its performance superiority compared to state-of-the-art distributed RDF systems.
Executing Queries over Schemaless RDF Databases
"... the Semantic Web have led to a rapid increase in both the quantity as well as the variety of Web applications that rely on the SPARQL interface to query RDF data. Thus, RDF data management systems are increasingly exposed to workloads that are far more diverse and dynamic than what these systems wer ..."
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the Semantic Web have led to a rapid increase in both the quantity as well as the variety of Web applications that rely on the SPARQL interface to query RDF data. Thus, RDF data management systems are increasingly exposed to workloads that are far more diverse and dynamic than what these systems were designed to handle. The problem is that existing systems rely on a workload-oblivious physical representation that has a fixed schema, which is not suitable for diverse and dynamic workloads. To address these issues, we propose a physical representation that is schemaless. The resulting flexibility enables an RDF dataset to be clustered based purely on the workload, which is key to achieving good performance through optimized I/O and cache utilization. Consequently, given a workload, we develop techniques to compute a good clustering of the database. We also design a new query evaluation model, namely, schemaless-evaluation that leverages this workload-aware clustering of the database whereby, with high probability, each tuple in the result set of a query is expected to be contained in at most one cluster. Our query evaluation model exploits this property to achieve better performance while ensuring fast generation of query plans without being hindered by the lack of a fixed physical schema. I.
Scheduling for SPARQL Endpoints
"... Abstract. When providing public access to data on the Semantic Web, publishers have various options that include downloadable dumps, Web APIs, and SPARQL endpoints. Each of these methods is most suitable for particular scenarios. SPARQL provides the richest access capabili-ties and is the most suita ..."
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Abstract. When providing public access to data on the Semantic Web, publishers have various options that include downloadable dumps, Web APIs, and SPARQL endpoints. Each of these methods is most suitable for particular scenarios. SPARQL provides the richest access capabili-ties and is the most suitable option when granular access to the data is needed. However, SPARQL expressivity comes at the expense of high evaluation cost. The potentially large variance in the cost of different SPARQL queries makes guaranteeing consistently good quality of ser-vice a very difficult task. Current practices to enhance the reliability of SPARQL endpoints, such as query timeouts and limiting the number of results returned, are far from ideal. They can result in under utilisation of resources by rejecting some queries even when the available resources are sitting idle and they do not isolate “well-behaved ” users from “ill-behaved ” ones and do not ensure fair sharing among different users. In similar scenarios, where unpredictable contention for resources exists, scheduling algorithms have proven to be effective and to significantly enhance the allocation of resources. To the best of our knowledge, using scheduling algorithms to organise query execution at SPARQL endpoints has not been studied. In this paper, we study, and evaluate through simu-lation, the applicability of a few algorithms to scheduling queries received at a SPARQL endpoint. 1
