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Uploaded byLuiz Henrique Zambom Santana
A Workload-Aware Middleware for Storing Massive RDF Graphs into NoSQL Databases
The document presents a proposed schedule for developing and evaluating Rendezvous, a middleware for storing massive RDF graphs in NoSQL databases. Key aspects of Rendezvous include a workload-aware partitioning approach, mapping RDF data to different NoSQL data models, and a caching structure to accelerate query response. The evaluation compares Rendezvous to existing approaches using LUBM, a standard RDF benchmark, and finds that Rendezvous outperforms alternatives with graph-aware partitioning and near caching. Future work is planned to improve Rendezvous with compression, updates, additional NoSQL support, and more complex workloads.
A Workload-Aware Middleware for Storing Massive RDF Graphs into NoSQL Databases
- 1. A Workload-Aware Middlewarefor Storing Massive RDF Graphs into NoSQL Databases Exame de Qualificação de Doutorado Luiz Henrique Zambom Santana Prof. Dr. Ronaldo dos Santos Mello orientador UFSC/CTC/INE/PPGCC
- 2. Agenda ● Introduction: Motivation,objectives, and contributions ● Background ○ RDF ○ NoSQL ● State of the Art ○ Open Issues ● Rendezvous ○ Storing: Fragmentation, Indexing, Partitioning, and Mapping ○ Querying: Query decomposition and Caching ● Evaluation ● Schedule
- 3. Introduction: Motivation ● RDFis currently widespread: ○ Best buy: ■ http://www.nytimes.com/external/readwriteweb/2010/07/01/01readwriteweb-how-best-bu y-is-using-the-semantic-web-23031.html ○ Globo.com: ■ https://www.slideshare.net/icaromedeiros/apresantacao-ufrj-icaro2013 ○ US data.gov: ■ https://www.data.gov/developers/semantic-web
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- 5. Introduction: Objectives This PhDThesis proposal presents Rendezvous, a middleware for storing massing RDF graphs. This middleware includes a novel data partitioning approach, a fragmentation strategy that maps pieces of this RDF graph into NoSQL databases with different data models, and a caching structure that accelerate the querying response.
- 6. Introduction: Contributions ● (i)a mapping of RDF data to the columnar, document, and key/value NoSQL data models (SADALAGE; FOWLER, 2012) ● (ii) a workload-aware partitioner based on the current graph structure and, mainly, on the typical application workload ● (iii) a caching schema based on key/value databases for speeding up the query response time ● (iv) an experimental evaluation that compares the current version of our approach against two baselines (Rainbow (GU; HU; HUANG, 2015) and ScalaRDF (HU et al., )) by considering Redis, Apache Cassandra and MongoDB, the most popular key/value, columnar and document NoSQL databases, respectively
- 7. Agenda ● Introduction: Motivation,objectives, and contributions ● Background ○ RDF ○ NoSQL ● State of the Art ○ Open Issues ● Rendezvous ○ Storing: Fragmentation, Indexing, Partitioning, and Mapping ○ Querying: Query decomposition and Caching ● Evaluation ● Schedule
- 8. Background: RDF andSPARQL
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- 10. Agenda ● Introduction: Motivation,objectives, and contributions ● Background ○ RDF ○ NoSQL ● State of the Art ○ Open Issues ● Rendezvous ○ Storing: Fragmentation, Indexing, Partitioning, and Mapping ○ Querying: Query decomposition and Caching ● Evaluation ● Schedule
- 11. State of theArt - No NoSQL Triplestores WARP (h-hop replication), YARS, Hexastore (multiple indexes), 4store, SPIDER, RDF-3X, SHARD, SW-Store (vertical partition), SOLID, SPOVC (horizontal partition), and S2X
- 12. State of theArt - NoSQL Triplestores
- 13. State of theArt - NoSQL Triplestores RDFJoin, RDFKB, Jena+HBase, Hive+HBase, CumulusRDF, Rya, Stratustore, MAPSIN, H2RDF, AMADA, Trinity.RDF, H2RDF+, MonetDBRDF, xR2RML, W3C RDF/JSON, Rainbow, Sempala, PrestoRDF, RDFChain, Tomaszuk, Bouhali, and Laurent, Papailiou et al., and, ScalaRDF.
- 14. State of theArt - Categories ● RDF/NoSQL Converters ● Polystores/Multimodel ● In-memory Rainbow (GU; HU; HUANG, 2015) Amada (Aranda-Andújar, 2012)
- 15. State of theArt ● BUGIOTTI, F. et al. Invisible glue: scalable self-tuning multi-stores. In: Conference on Innovative Data Systems Research (CIDR). [S.l.: s.n.], 2015.
- 16. State of theArt - Open Issues ● To avoid indexing all the triple component permutations ● To consider workload and the usage of statistics for data partitioning ● To exploit in-memory possibilities ● To combine RDF storage with multiple NoSQL models
- 17. Agenda ● Introduction: Motivation,objectives, and contributions ● Background ○ RDF ○ NoSQL ● State of the Art ○ Open Issues ● Rendezvous ○ Storing: Fragmentation, Indexing, Partitioning, and Mapping ○ Querying: Query decomposition and Caching ● Evaluation ● Schedule
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- 19. Rendezvous: Storing ● Fragmentation ●Indexing ● Partitioning ● Mapping
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- 26. Storing: Indexing -Simple queries and fragmentation
- 27. Storing: Partitioning ● Thepartition is used when the dataset is bigger than each server capabilities
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- 30. Rendezvous: Querying ● Querydecomposition ● Caching
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- 32. Querying: Decomposition Q1: SELECT?x WHERE {x? p5 y? . y? p2 z? .} Q2: SELECT ?x WHERE {x? p9 y? . M p10 y? .} D1: db.partition1.find({p5:{$exists:true}, p2:{$exists:true}}}) D2: db.partition1.find({p9:{$exists:true}, subject:M}})
- 33. Querying: Decomposition Q3: SELECT?x WHERE { x? p1 y? . y? p2 z? . z? p3 w? . } C1: SELECT S1,O1 FROM p1 SELECT S2,O2 FROM p2 WHERE O=S1 SELECT S3,O3 FROM p3 WHERE O=S2 AND S=D (C1) Q4: SELECT ?x WHERE {x? p2 y? .y? p3 z? .x? p5 w? .w? p9 k? .L p11 k?.} SQ5: SELECT ?x WHERE {x? p2 y?. y? p3 z?. x? p5 w?.} SQ6: SELECT ?x WHERE {x? p5 w?. w? p9 k?. L p11 k?.}
- 34. Querying: Decomposition Q5: SELECT?x WHERE { x? p1 y? . y? p2 z? . z? p3 w? . z? p5 ?k . k? p6 G . k? p7 I . k? p8 H } P1: {k? p6 G . k? p7 I . k? p8 H } P2: {x? p1 y? . y? p2 z? . z? p3 w?} P3: { z? p5 ?k}
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- 36. Querying: Caching (chainqueries)
- 37. Agenda ● Introduction: Motivation,objectives, and contributions ● Background ○ RDF ○ NoSQL ● State of the Art ○ Open Issues ● Rendezvous ○ Storing: Fragmentation, Indexing, Partitioning, and Mapping ○ Querying: Query decomposition and Caching ● Evaluation ● Schedule
- 38. Evaluation ● LUBM: ontologyfor the University domain, synthetic RDF data scalable to any size, and 14 extensional queries representing a variety of properties ● Generated dataset with 4000 universities (around 100 GB and contains around 500 million triples) ● 12 queries with joins, all of them have at least one subject-subject join, and six of them also have at least one subject-object join ● Apache Jena version 3.2.0 with Java 1.8, and we use Redis 3.2, MongoDB 3.4.3, and Apache Cassandra 3.10 ● Amazon m3.xlarge spot with 7.5 GB of memory and 1 x 32 SSD capacity
- 39. Evaluation: Rendezvous vs.Rainbow
- 40. Evaluation: Rendezvous vs.ScalaRDF
- 41. Evaluation: Conclusions ● Fragmentsare scalable ● Bigger boundaries are not necessarily related to bigger storage size ● Graph-aware partitions are better than NoSQL partitions ● Near cache is fast but it makes more difficult to keep data consistency
- 42. Evaluation: Future Work ●Compression of triples during the storage ● Update and delete operations ● Other NoSQL types (e.g., graph) ● Better datasets
- 43. Agenda ● Introduction: Motivation,objectives, and contributions ● Background ○ RDF ○ NoSQL ● State of the Art ○ Open Issues ● Rendezvous ○ Storing: Fragmentation, Indexing, Partitioning, and Mapping ○ Querying: Query decomposition and Caching ● Evaluation ● Schedule
- 44. Schedule ● Middleware development(continuously until 2018) ○ Compression ○ Graph database ○ More complex and abstract workload awareness ● Submission of papers (continuously until 2018) ○ Special Interest Group On Management of Data (SIGMOD) ○ Very Large Databases (VLDB) ○ IEEE Transactions on Knowledge and Data Engineering (TKDE) ● Defense of the PhD thesis (2019)
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