Download - Scalable SPARQL Querying of Large RDF Graphs

Graph Data Management Lab, School of Computer Science

GDM@FUDAGDM@FUDANN

http://gdm.fudan.edu.cnhttp://gdm.fudan.edu.cn

Scalable SPARQL Querying of Large RDF Graphs

Xu Bo

2012.06.11

In PVLDB, 4(21), 2011


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OutlineOutline• About Presenter

• Semantic Web

• Previous Work

• New Problem

• SYSTEM ARCHITECTURE

• EXPERIMENTS

• CONCLUSIONS AND FUTURE WORK

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About Presenter

• Daniel Abadi• Associate Professor of Computer Science in Yale University• Research

– Column-Oriented Database Systems– Petascale Parallel Database Systems (HadoopDB) – Semantic Web Data Management

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Semantic WebSemantic Web

• The vision of Semantic Web is to build a "web of data" that enables machines to understand the semantics of information on the Web

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Google Knowledge Graph

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Key Technology

• HTML

• XML

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The Disadvantage of XML David Billington is a lecturer of Discrete Mathematics.

• there is no standard way of assigning meaning to tag nesting

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The Disadvantage of Xpath

• Suppose we want to collect all academic staff members. A path expression in Xpath might be //academicStaffMember

• XML is semantically unsatisfactory

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RDF

• Resource Description Framework• 用 Web标识符（称作统一资源标识符， Uniform

Resource Identifiers 或 URIs）来标识事物，用简单的属性（ property）及属性值来描述资源

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RDF as Triples and a Graph

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SPARQL

• RDF query language

• A basic graph pattern

• Answering SPARQL can be seen as finding subgraphs in the RDF data that match the graph pattern

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Example for Star Pattern

• Find the names of the strikers that play for FC Barcelona.

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Another Example• Find football players playing for clubs in apopulous region where they were born.

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Previous Work• RDF In RDBMSs

• Property Tables

• Vertically Partitioned Approach

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RDF In RDBMSs• Get the title of the book(s) Joe Fox wrote

in 2001

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Property Tables

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Vertically Partitioned Approach

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New Problem• Single node RDF management systems are

abundant– Sesame– Jena– RDF3X– 3store

• Research in clustered RDF management is less significantly explored: The focus of the talk

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SYSTEM ARCHITECTURE

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Graph Partitioning• Hash vs. Graph partitioning

– Hash: Only efficient for star patterns– Graph: Taking advantage of graph model

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Graph Partitioning• Edge vs. Vertex partitioning

– Edge: Natural but inefficient for query execution

– Vertex: Superior for common graph patterns

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Vertex Partitioning• Preprocess

– remove triples whose predicate is rdf:type

• METIS partitioner

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Triple Placement• Minimizing data shuffling/exchange

– Allowing data overlap

• N-hop guarantee– The extent of data overlap– If a vertex is assigned to a machine, any

vertex that is within n-hop of this vertex is also stored in this machine

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DIRECTED N-HOP GUARANTEE

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A potential problem• triples (s, p, o) and (o, p’, o’)

– 2-hop guarantee

• triples (s, p, o) and (s’, p’, o)– not guaranteed

• “object-connected” is not unusual

• undirected n-hop guarantee

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Triple Placement Algorithm

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Query Processing• Queries are executed in RDF-stores and/or

Hadoop• Query execution is more efficient in RDF-

stores than in Hadoop– Pushing as much of the processing as possible

into RDF-stores– Minimizing the number of Hadoop jobs– The larger the hop guarantee, the more work is

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To Communicate, or not to Communicate

• Given a query and n-hop guarantee, is communication (Hadoop job) between nodes needed?– Choose the “center” of the query graph– Calculate the distance from the “center” to the

furthest edge– If distance > n, communication is needed; not

needed otherwise

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Determining whether a Query is PWOC

PWOC Query– parallelizable without communication

• DoFE– distance of farthest edge– the vertex in a graph with the smallest DoFE will be

the most central in a graph

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The algorithm

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the issue of duplicate results• naive approach

– remove duplicates after the query has completed

• owner-computes model– add triples (v, ‘<isOwned>’, ‘Yes’) to a

partition– For each query issued to the RDF-stores, add

an additional pattern (core, ‘<isOwned>’, ‘Yes’)

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A query is not PWOC• decompose the query into PWOC

subqueries

• use Hadoop jobs to join the results of the PWOC subqueries

• The number of Hadoop jobs required to complete the query increases as the number of subqueries increases

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minimal number of subqueries• reduces to the problem of finding minimal

edge partitioning of a graph into subgraphs of bounded diameter

• brute-force

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Examlple

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DoFEs for manager, footballClub, Barcelona and club are 2, 2, 2 and 1

the DoFEs for footballer, pop, region, player and club are 3, 3, 2, 2 and 2,


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Decompose Example

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EXPERIMENTS• 20-machine cluster

• Leigh University Benchmark (LUBM): 270 million triples

• Competitors:– Single-node RDF-3X– SHARD: triple-store system in Hadoop– Graph partitioning (the proposed system)– Hash partitioning on subjects

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Data Load Time

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Performance Comparison

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Varying Number of Machines

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Summary

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Download - Scalable SPARQL Querying of Large RDF Graphs

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