Berlin SPARQL Benchmark (BSBM)

Presented by: Nikhil Rajguru

Christian Bizer and Andreas Schultz

Agenda

• Need for a benchmark for RDF stores• Existing benchmarks• Design of BSBM, Dataset generator and query

mixes• Evaluation results• Contributions• My work• Q&A

Motivation

• A large number of Semantic web applications represent their data as RDF

• Many RDF stores support the SPARQL query language and SPARQL protocol

• Need to compare performance of various RDF stores and also traditional Relational DB solutions (SPARQL wrappers)

Existing benchmarks• SP2Bench

• Uses a synthetic, scalable version of the DBLP bibliography dataset • Queries designed for comparison of different RDF Store layouts - Not designed towards realistic workloads, no parameterized queries and

no warmup• DBPedia Bechmark

• Uses DBPedia as the benchmark dataset - Very specific queries and dataset not scalable

• Lehigh University Benchmark (LUBM)• Compares OWL reasoning engines - Does not cover SPARQL specific features like OPTIONAL filters, UNION,

DESCRIBE, etc. - Does not employ parameterized queries, concurrent clients and warm-up

Main Goals of BSBM

• Compare different stores that expose SPARQL endpoints

• Have realistic use case motivated data sets and Query mixes

• Test query performance (integration and visualization) against large RDF datasets rather than complex reasoning

BSBM Dataset

• Built around an e-commerce use case• Dataset generator• Scales to arbitrary sizes (scale factor = # of

products)• Data generation is deterministic

• Dataset objects: Product, ProductType, ProductFeature, Producer, Vendor, Offer, Review, Reviewer and ReviewingSite.

BSBM Data set sizes

BSBM Query Mix

• Simulates how customers browse, review and select items online

• Operations include• Look for products with some generic features• Look for products without some specific features• Look for similar products• Look for reviews and offers• Pull up all information about a specific product• Find the best deal for a product

BSBM Query Mix

BSBM Queries

BSBM Queries

BSBM Query Characteristics

Experimental Setup• RDF Stores tested– Jena SDB– Virtuoso– Sesame– DR2 Server (with MySQL as underlying RDBMS)

• DELL workstation • Processor: Intel Core 2 Quad Q9450 2.66GHz• Memory: 8GB DDR2 667• Hard disks: 160GB (10,000 rpm)SATA2, 750GB (7,200 rpm)

SATA2) • OS: Ubuntu 8.04 64-bit

Load times (sec)

• Data loaded as,• D2R server: Relational representation of BSBM dataset

(MySQL dumps)• Triple Stores: N-triples representation of BSBM Dataset

3.6 hr

7.7 hr

13.6 hr

3.3 min

Overall Run Time

• 50 query mixes, 1250 queries in all• Test driver and store under test running on the

same machine• 10 query mixes executed for warm up

Average Run Time Per Query

• Gives a different perspective on query performance for the stores

• No data store performs optimally for all query types at all Data set sizes (50K – 25M triples)

• Sesame best for Queries 1 - 4 but has bad performance for queries 5 – 9

• DR2 server fastest for queries 6 – 9 but bad for all the lower ones

• Similar results for Jena SDB and Virtuoso

Average Run Time Per Query

Average Run Time Per Query

Average Run Time Per Query

Contributions

• First benchmark to compare stores that implement SPARQL query language and protocol for data access

• Dataset generator (RDF, XML and Relational representation)

• First benchmark to test RDF stores with realistic workloads of use case motivated queries

My Work

• Build a scalable RDF store for storing the Smart Grid data– Sensor readings, building information, weather

data, Time schedule for each customer• Scale to 50000 sensors (20M triples to be

loaded every 15mins)• Load Fast and slow changing data

My work

• Support a range of SPARQL queries on the store• Web Portal: (latency ~sec)– 100 customers x 100 columns = 10000 triples

• Schedule trigger: (latency ~min)– ~50,000 customers x 5 schedule events per day x 4

triples = 1,000,000 triples• Forecast training: (latency ~hrs)– 3 years x 365 days x 100 readings x 200 buildings x 2

sensor x 25 columns = 1,095,000,000 triples

Thank you

Questions ?