Extending LargeRDFBench for Multi-Source Data at Scale for SPARQL Endpoint Federation
•
0 likes•25 views
"Extending LargeRDFBench for Multi-Source Data at Scale for SPARQL Endpoint Federation" as presented in SSWS 2018 co-located with the 17th International Semantic Web Conference ISWC, 8th - 12th of October 2018, held in Monterey, California, USA This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
Recommended
More Related Content
Similar to Extending LargeRDFBench for Multi-Source Data at Scale for SPARQL Endpoint Federation
Similar to Extending LargeRDFBench for Multi-Source Data at Scale for SPARQL Endpoint Federation (20)
More from Holistic Benchmarking of Big Linked Data
More from Holistic Benchmarking of Big Linked Data (20)
Recently uploaded
Recently uploaded (20)
Extending LargeRDFBench for Multi-Source Data at Scale for SPARQL Endpoint Federation
- 1. Extending LargeRDFBench for Multi-Source Data at Scale for SPARQL Endpoint Federation Ali Hasnain, Muhammad Saleem, Axel-Cyrille Ngonga Ngomo, Dietrich Rebholz-Schuhmann
- 2. Agenda • SPARQL Endpoints Query Federation • Federated Queries Benchmark – Datasets – Input queries – Important query features – Benchmark generation • LargeRDFBench and extended LargeRDFBench • Evaluation and results • Conclusion 2
- 3. SPARQL Endpoints Query federation 3 Endpoint 1 Endpoint 2 Endpoint 3 Endpoint 4 RDF RDF RDF RDF Parsing Source Selection Federator Optimzer Integrator Rewrite query and get Individual Triple Patterns Identify capable source against Individual Triple Patterns Generate optimized sub- query Exe. Plan Integrate sub- queries results Execute sub- queries Federation Engine
- 4. Federated Benchmark Components • Datasets • Queries • Performance metrics • Execution rules 4
- 5. Important Datasets Features RDF Datasets used in the federation benchmark should vary: – Number of triples – Number of classes – Number of resources – Number of properties – Number of objects – Average properties per class – Average instances per class – Average in-degree and out-degree – Structuredness or coherence 5
- 6. Important Queries Features SPARQL queries used in the federation benchmark should vary: – Number of triple patterns – Number of join vertices – Mean join vertex degree, – Number of sources span – Query result set sizes – Mean triple pattern selectivity – BGP-restricted triple pattern selectivity – Join-restricted triple pattern selectivity – Join vertex types (`star', `path', `hybrid', `sink') – SPARQL clauses used (e.g., LIMIT, UNION, OPTIONAL, FILTER etc.) 6
- 7. SPARQL Queries as Directed Hypergraphs 7
- 8. Important performance metrics – Result set completeness and correctness – Number of sources selected – Number of SPARQL ASK requests used during source selection – Source selection time – Number of endpoint requests – Number of intermediate results – Overall query execution time 8
- 9. LargeRDFBench • SPARQL query federation benchmark • 13 interconnect real datasets – 4 Life sciences – 6 Cross domain – 3 Large data • 32 queries of varying complexities – 14 simple – 10 complex – 8 large data • Multiple performance metrics • Contains both SPARQL 1.0 and SPARQL 1.1 versions of the same queries 9
- 11. Why Extended LargeRDFBench? • 24 queries only span over 2 datasets, i.e. 2 SPARQL SERVICES used in the SPARQL 1.1 version of the queries • Federation engines that optimize the ordering of SPARQL SERVICES cannot be fully tested • Two SERVICES means only Two possible ordering • Goal: add more queries which span over more than two datasets 11 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 S1 S3 S5 S7 S9 S11 S13 C1 C3 C5 C7 C9 L1 L3 L5 L7#RelevantSources
- 12. Extended LargeRDFBench • SPARQL query federation benchmark • 13 interconnect real datasets – 4 Life sciences – 6 Cross domain – 3 Large data • 32 queries of varying complexities – 14 simple – 10 complex – 8 large data – 8 Complex+High sources (CH1-CH8) • Multiple performance metrics • Contains both SPARQL 1.0 and SPARQL 1.1 versions of the same queries 12
- 13. Complex+High Data Sources Queries • CH1: – 4 LargeRDFBench data sources involved – Total triple patterns = 16, result size = 384 – High mean join vertex degree – High number of incoming and outgoing edges of a join node – High number of triple patterns in a single SERVICE – Runtime for this query ranges from 1 second (costFed) to over 1 hour (SemaGrow) 13
- 14. Complex+High Data Sources Queries • CH2: – 4 LargeRDFBench data sources involved – Total triple patterns = 10, result size = 840 – Low mean triple patterns selectivity (i.e., 0.00005) and high BGP- restricted (i.e., 0.2595) and Join-restricted (i.e., 0.58115) triple pattern selectivities – Triple patterns of this query are selective, i.e., they can have smaller result sizes – Less selective when involved in joins with other triple patterns. – Choosing the right join order which quick converges to smaller result size is crucial – The FILTER combined with REGEX made this query particularly very selective 14
- 15. Complex+High Data Sources Queries • CH3: – 5 LargeRDFBench data sources involved – Triple patterns = 11 and result size = 48 – High number of join vertices (i.e., 7 from 11 triple patterns), moderate join vertex degree (i.e., 2.71), and low mean BGP- restricted triple pattern selectivity (i.e., 0.0196) – Mix values for the important query features that can challenge the federation engines 15
- 16. Complex+High Data Sources Queries • CH4: – 6 LargeRDFBench data sources involved – Total number of triple patterns = 12, result size = 1248 – High join vertices (i.e., 10 join vertices from 12 triple patterns) – Join order optimisation is challenging due to more joins vs. less number of triple patterns 16
- 17. Complex+High Data Sources Queries • CH5: – 7 LargeRDFBench data sources involved – Triple patterns = 18, result size = 5 using LIMIT clause – Number of bounds subjects and objects in the triple patterns – 4 triple patterns with subject bound and 2 triple patterns with object bound – One triple pattern that contains unbound predicate – Challenging to accurately estimate the triple patterns as well as the joins cardinalities 17
- 18. • CH6: – 8 LargeRDFBench data sources involved along with bound subjects and objects – Triple patterns = 24, result size = 16 – Low mean triple patterns selectivity (i.e., 0.00002) and high BGP- restricted (i.e., 0.2522) and Join-restricted (i.e., 0.3186) triple pattern selectivities 18 Complex+High Data Sources Queries
- 19. • CH7: – 9 LargeRDFBench data sources involved – Triple patterns = 21, result size = 775 using LIMIT clause – There are a total of 14 join nodes in this query with 5 Star, 3 Path, 4 Sink, and 2 Hybrid join nodes – more join nodes and join ordering could not be a trivial task 19 Complex+High Data Sources Queries
- 20. • CH8 – 10 LargeRDFBench data sources involved and contains OPTIONAL, FILTER, and LIMIT – Highest number of triple patterns = 33, result size = 1 – 19 join nodes in this query with 7 Star, 4 Path, 6 Sink, and 2 Hybrid join nodes – None of the federation engines is able to execute this query within the limit of 1 hour 20 Complex+High Data Sources Queries
- 22. Evaluation Results 22 Hard to rank the federation engines due to many RE, ZR, TO Unstability exposed
- 23. This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
- 24. Thanks 24
- 26. Key Findings • Extended LargeRDF-Bench can be extremely costly or can be executed extremely fast when proper optimised query plan is selected. • However, the number of timeout and runtime errors suggesting that choosing the optimised query plans for these queries is not a trivial task. • The results revealed FedX, CostFed, and ANAPSID can result in incomplete or zero results. 26
- 27. Why extended LargeRDFBench • In LargeRDFBench 24 out of total 32 queries which only require 2 data sources to get the complete result set of the queries. • Federation engines which optimise the ordering of the execution of SPARQL SERVICES in federated SPARQL 1.1 queries (e.g. QuWeDa) cannot be fully tested with existing LargeRDFBench queries. • This is because if there are only two SERVICES used in the query, there are only two possible orderings of the execution of these SERVICES. • The goal of this extension was to fill this gap by adding more federated queries which require more data sources to get the complete result set of the query. 28
- 28. Number of Triple patterns 29 0 5 10 15 20 25 30 35 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 L1 L2 L3 L4 L5 L6 L7 L8 CH1 CH2 CH3 CH4 CH5 CH6 CH7 CH8 #TriplePatterns FedBench FedBench-Mean LargeRDFBench LargeRDFBench-Mean STD. ± 1.33 (FedBench) vs. ± 6.15 (LargeRDBFench)
- 29. Number of Join Vertices 30 STD. ± 1.33 (FedBench) vs. ± 3.63 (LargeRDBFench) 0 2 4 6 8 10 12 14 16 18 20 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 L1 L2 L3 L4 L5 L6 L7 L8 CH1 CH2 CH3 CH4 CH5 CH6 CH7 CH8 #JoinVertices FedBench FedBench-Mean LargeRDFBench LargeRDFBench-Mean
- 30. Mean Join Vertices degree 31 STD. ± 1.33 (FedBench) vs. ± 6.15 (LargeRDBFench) 0 1 2 3 4 5 6 7 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 L1 L2 L3 L4 L5 L6 L7 L8 CH1 CH2 CH3 CH4 CH5 CH6 CH7 CH8 MeanJoinVerticesDegree FedBench FedBench-Mean LargeRDFBench LargeRDFBench-Mean
- 31. Number of Results 32 STD. ± 2397 (FedBench) vs. ± 104236 (LargeRDBFench) 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 1.E+06 S1 S3 S5 S7 S9 S11 S13 C1 C3 C5 C7 C9 L1 L3 L5 L7 CH1 CH3 CH5 CH7 #Results(logscale) FedBench FedBench-Mean LargeRDFBench LargeRDFBench-Mean
- 32. Mean triple patterns selectivity 33 STD. ± 0.11 (FedBench) vs. ± 0.13 (LargeRDBFench) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 L1 L2 L3 L4 L5 L6 L7 L8 CH1 CH2 CH3 CH4 CH5 CH6 CH7 CH8 MeanTriplePatternSelectivity FedBench LargeRDFBench
- 33. Mean BGP-Rest. TP selectivity 34 STD. ± 0.31 (FedBench) vs. ± 0.22 (LargeRDBFench) 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 L1 L2 L3 L4 L5 L6 L7 L8 CH1 CH2 CH3 CH4 CH5 CH6 CH7 CH8 MeanBGP-restrictedTriplePatternSelectivity FedBench LargeRDFBench
- 34. Mean Join-Rest. TP selectivity 35 STD. ± 0.13 (FedBench) vs. ± 0.15 (LargeRDBFench) 0.000001 0.00001 0.0001 0.001 0.01 0.1 1 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 L1 L2 L3 L4 L5 L6 L7 L8 CH1 CH2 CH3 CH4 CH5 CH6 CH7 CH8 MeanJoin-restrictedTriplePatternSelectivity(logscale) FedBench LargeRDFBench