G-SPARQL: A Hybrid Engine for Querying Large Attributed

Graphs

Sherif Sakr Sameh Elnikety

Yuxiong He

NICTA & UNSWSydney, Australia

Microsoft Research

Redmond, WA

CIKM 2012

Microsoft Research

Redmond, WA

Example 1: Social Network

Bob

Hillary Alice

Chris David

FranceEd George

Bob

Hillary Alice

Chris David

FranceEd George

Photo1

Photo2

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Photo4Photo5 Photo6

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2

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Example 2: Bibliographical Network

Alice JohnSmith

age: 28office: 518

Age:42location: Sydney

age:45

Paper 1 Paper 2

UNSW Microsoft

VLDB¶12

Keyword: graph Keyword: XMLtype: Demo

location: Istanbul

country: Australiaestablished: 1949

country: USAestablished: 1975

citedBy

title: Professor

title: Senior Researcher

order: 1order: 2 order: 1 order: 2

Month: 1Month: 3

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Contributions1. G-SPARQL language

– Pattern matching– Reachability

2. Hybrid execution engine– Graph topology in main memory– Graph data in relational database

3. Algebraic transformation– Operators– Optimizations

4. Experimental evaluation

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1. G-SPARQL Query Language•Extends a subset of SPARQL

– Based on triple pattern: (subject, predicate, object)

•Sub-graph matching patterns on– Graph structure– Node attribute– Edge attribute

•Reachability patterns on– Path– Shortest path

subject object

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G-SPARQL Syntax

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G-SPARQL Pattern Matching•Node attribute

– ?Person @officeNumber “518”

•Edge attribute– ?E @Role “Programmer”

•Structural– ?Person worksAt Microsoft– ?Person ?E(worksAt) Microsoft

Alice Microsoft

officeNu mber=518

Role = Programmer

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G-SPARQL Reachability•Path

– Subject ??PathVar Object

•Shortest path– Subject ?*PathVar Object

•Path filters– Path length– All edges– All nodes

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Example: G-SPARQL QuerySELECT ?L1 ?L2WHERE {

?X ??P ?Y.

?X @Label ?L1. ?Y @Label ?L2.?X @Age ?Age1. ?Y @Age ?Age2.?X Affiliated UNSW. ?Y ?E(Affiliated) Microsoft.?X LivesIn Sydney. ?E @Title "Researcher".

FILTER(?Age1 >= 40). FILTER(?Age2 >= 40).FILTERPATH( Length( ??P, <= 3) ).

}

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Outline1. G-SPARQL language

– Pattern matching– Reachability

2. Hybrid execution engine– Graph topology in main memory– Graph data in relational database

3. Algebraic transformation– Operators– Optimizations

4. Experimental evaluation

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2. Hybrid Execution Engine•Reachability queries

– Main memory algorithms– Example: BFS and Dijkstra’s algorithm

•Pattern matching queries– Relational database– Indexing

» Example: B-tree– Query optimizations,

» Example: selectivity estimation, and join ordering– Recursive queries

» Not efficient: large intermediate results and multiple joins

Bob

Hillary Alice

Chris David

FranceEd George

Photo1

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Photo4Photo5 Photo6

Photo8

Photo7

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Graph RepresentationID Value1 John2 Paper 23 Alice4 Microsoft5 VLDB’126 Paper 17 UNSW8 Smith

ID Value1 453 428 28

ID Value8 518

ID Value3 Sydney5 Istanbul

ID Value2 XML6 graph

ID Value2 Demo

ID Value4 USA7 Australia

ID Value4 19757 1949

eID sID dID1 1 25 3 26 3 611 8 6

Node Label age office location keyword type established

country

authorOf

eID sID dID

3 1 4

8 3 7

12 8 7

affiliated

eID sID dID

4 2 5

10 6 5

published

eID sID dID

9 6 2

citedBy

eID sID dID

7 3 8

supervise

eID sID dID

2 1 3

know ID Value

3 Senior Researcher

8 Professor

title

ID Value

1 2

5 1

6 2

11 1

order

ID Value

4 3

10 1

month

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Hybrid Execution Engine: interfaces

Bob

Hillary Alice

Chris David

FranceEd George

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G-SPARQL query

SQL commands

Traversal

operations

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3. Intermediate Language & Compilation

Physical execution

planSQL commands

Traversal

operations

G-SPARQL query

Algebraic query plan

Front-end compilation

Step 2

Back-end compilation

Step 1

Bob

Hillary Alice

Chris David

FranceEd George

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Intermediate Language•Objective

– Generate query plan and chop it» Reachability part -> main-memory algorithms on topology» Pattern matching part -> relational database

– Optimizations•Features

– Independent of execution engine and graph representation– Algebraic query plan

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G-SPARQL Algebra•Variant of “Tuple Algebra”•Algebra details

– Data: tuples» Sets of nodes, edges, paths.

– Operators» Relational: select, project, join» Graph specific: node and edge attributes, adjacency» Path operators

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Relational

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Relational

NOT Relational

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Front-end Compilation (Step 1)• Input

– G-SPARQL query•Output

– Algebraic query plan•Technique

– Map» from triple patterns» To G-SPARQL operators

– Use inference rules

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Front-end Compilation: Inference Rules

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Front-end Compilation: Optimizations•Objective

– Delay execution of traversal operations•Technique

– Order triple patterns, based on restrictiveness•Heuristics

– Triple pattern P1 is more restrictive than P21. P1 has fewer path variables than P22. P1 has fewer variables than P23. P1’s variables have more filter statements than P2’s variables

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Back-end Compilation (Step 2)• Input

– G-SPARQL algebraic plan•Output

– SQL commands– Traversal operations

•Technique– Substitute G-SPARLQ relational operators with SPJ– Traverse

» Bottom up» Stop when reaching root or reaching non-relational operator» Transform relational algebra to SQL commands

– Send non-relational commands to main memory algorithms

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Back-end Compilation: Optimizations•Optimize a fragment of query plan

– Before generating SQL command•All operators are Select/Project/Join•Apply standard techniques

– For example pushing selection

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Example: G-SPARQL QuerySELECT ?L1 ?L2WHERE {

?X ??P ?Y.

?X @label ?L1. ?Y @label ?L2.?X @age ?Age1. ?Y @age ?Age2.?X affiliated UNSW. ?Y ?E(affiliated) Microsoft.?X livesIn Sydney. ?E @title "Researcher"

FILTER(?Age1 >= 40). FILTER(?Age2 >= 40).}

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Example: Query Plan

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4. Experimental Evaluation•Objective

– This is a good idea– Good performance from DBMS and main memory topology

•Data sets– Real ACM bibliographic network

– Synthetic graphs» See technical report

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Experimental Environment•Workload

– Created Q1 … Q12•Process

– Compare to Neo4J (non-optimized, optimized)•Environment

– Implementation» Main memory algorithms in C++» IBM DB2

– PC Server

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Results on Real Dataset

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Response time on ACM Bibliographic Network

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Conclusions•G-SPARQL Language

– Expresses pattern matching and reachability queries on attributed graphs

•Hybrid engine– Graph topology in main memory– Graph data in database

•Compilation into algebraic plan– Operators and optimizations

•Evaluation– Real and synthetic datasets– Good performance

» Leveraging database engine and main memory topology