executing sparql queries over mapped document stores with sparqlmap-m

Executing SPARQL queries over Mapped DocumentStores with SparqlMap-M

J. Unbehauen M. Martin

IIS // AKSW // BIS // IfILeipzig University

SEMANTiCS 2016

J. Unbehauen, M. Martin (Leipzig Univ.) SPARQL over Document Stores: SparqlMap-M SEMANTiCS 2016 1 / 25

Outline

1 Motivation and Scope

2 Approach

3 Evaluation

4 Conclusions and Future Work


Scoping

[1] S. Auer, J. Lehmann, A. Ngonga Ngomo. Introduction to Linked Data and ItsLifecycle on the Web, Reasoning Web. Semantic Technologies for the Web of

Data, LNCS 6848, 2011


Motivation

NoSQL DBMS and document stores are thriving

Document stores used in Rapid Application Development Frameworks

Visit our Adding Semantics to Model-Driven Software DevelopmentPoster

Use cases in both research and industry

Current solutions support R2RML and relational databases


Outline


2 Approach

3 Evaluation



SparqlMap Architecture

BindingTranslat.

SparqlMap

QueryAnalysis

QueryParsing

MappingBinding

QuerySELECT DISTINCT ?name {

?person foaf:name ?name. #(tp1)

?person :inDepartment ?dep. #(tp2)

?dep rdfs:label ’Research’ #(tp3) }

Result?name

------------

’Mary R.’

’James T.’

Translat.Exec.

[2] J. Unbehauen, C. Stadler, and S. Auer. Accessing relational data on the webwith sparqlmap. In JIST. 2012.[3] J. Unbehauen, C. Stadler, and S. Auer. Optimizing sparql-to-sql rewriting. InIIWAS, 2013.


SparqlMap-M Architecture

BindingTranslat.

SparqlMap-MSparqlMap

QueryAnalysis

QueryParsing

MappingBinding

SelectiveMaterialization




?dep rdfs:label ’Research’ #(tp3)

}Mapping

DeduplicationUnion Decom-

position

MaterializedExecution?name

------------

’Mary R.’

’James T.’

Translat.Exec.

Result

1 Data Models and Mapping

2 Query Structure

3 Querying Capabilities

4 Data Model Specific Optimization


Data Models and Mapping

BindingTranslat.


QueryAnalysis

QueryParsing

MappingBinding






}Mapping

Deduplication

Union De-composition


------------

’Mary R.’

’James T.’

Translat.Exec.

Result



Key-Value pairs

Nested documents

Schema less



A relational view on documents by:

Goal: reuse existing (R2RML) concepts

Unnesting documents by joining them with parent → Flat structure

Naming attributes to reflect hierarchy → Key-Value treated as tuples

Schema imposed by mapping

#Department{ i d : 2 , name : ” Resea rch ” ,emp : [{ i d : 1 , name : ”Mary R. ”} ,

{ i d : 2 , name : ”James T. ” } ] } ,

i d | name | emp . i d | emp . name−−+−−−−−−−−−+−−−−−−−+−−−−−−−−2 | Resea rch |1 |Mary R .2 | Resea rch |2 | James T.


Query Structure

BindingTranslat.


QueryAnalysis

QueryParsing

MappingBinding






}Mapping

Deduplication



------------

’Mary R.’

’James T.’

Translat.Exec.

Result


Query Structure

SparqlMap

Recursive translation yields nested unions

Index hits require careful query design

Complex expressions for joins

SparqlMap-M / MongoDB

No direct equivalents for joins

No complex equivalence expression


Query Structure: Union Decomposition

Nested Unions:

./?dep=?dep

σname=Research

trm3

./?person=?person⋃trm1 trm4

⋃trm2 trm5

Pushed Union: ⋃./?dep=?dep

trm3 ./?person=?person

trm1 trm2

./?dep=?dep

trm3 ./?person=?person

trm4 trm5


Selective Materialization

BindingTranslat.


QueryAnalysis

QueryParsing

MappingBinding






}Mapping

Deduplication



------------

’Mary R.’

’James T.’

Translat.Exec.

Result


Selective Materialization

Delegate to abstraction layer (Apache MetaModel)

Execute unpushable SPARQL operators in memory

Πname

./id=depid

σname=”Research”

department employee

MaterializedExecution



De-Duplication

BindingTranslat.


QueryAnalysis

QueryParsing

MappingBinding






}Mapping

Deduplication



------------

’Mary R.’

’James T.’

Translat.Exec.

Result


De-Duplication

Documents are nested for fastretrieval and filtering

Naive mapping introduces overhead

Declaratively labelR2RML-TriplesMaps as duplicated

Only use denormalized data in joins


Outline


2 Approach

3 Evaluation



Benchmark Setup

BSBM for availability of both SQL and RDF representation

SQL representation translated into MongoDB documents

Additionally performed denormalization


Benchmark Results

BSBM 10 million triples

PostgreSQL Fastest

MongoDB-Naive/-Dup Dup required for performance

SparqlMap-M-Naive/ -Dup/ -DupAwareOverhead by rewriting/materialization


Benchmark Results

BSBM Q4

Medium selectivity

Naive modes touch a lot of data

Performance gain by duplicatedata (MongoDB, SparqlMap-M)


Benchmark Results

BSBM Q5

Low selectivity join

SparqlMap-M: expensive selfjoin in memory, dominates cost

MongoDB: Self-join inaggregate pipeline, slower thanPostgreSQL

BSBM Q9

High selectivity join

SparqlMap-M-Dup(Aware):duplicates increase overhead.Unpushable join dominates cost


Outline


2 Approach

3 Evaluation



Future Work

Enable Updates

Integrate Caching

Evaluate Join capable query language

MongoDB left outer join ($lookup)Multimodel databases: ArangoDB, OrientDBDB virtualizations: JBoss Teiid, Apache HAWQ


Conclusion

Architecture for a SPARQL execution layer over document stores

Harness duplicates for increasing performance

Evaluated with BSBM on MongoDB


executing sparql queries over mapped document stores with sparqlmap-m

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