Service-Based Distributed Query Service-Based Distributed Query Processing on the GridProcessing on the Grid

M.Nedim AlpdemirDepartment of Computer

ScienceUniversity of Manchester

Service-based approaches

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A convenient cooperation model for Distributed Systems (e.g. Grid)A convenient cooperation model for Distributed Systems (e.g. Grid)

facilitate

Leads to

Context

Computational Complexity

DataComplexity

Web Services Are Not Enough Lack facilities for:

Computational resource description. Computational resource discovery. Application staging.

Grid Services combine: Web Services for service description and

invocation. Grid middleware for computational resource

description and utilisation.

Open Grid Services Architecture (OGSA) OGSA services are

described using WSDL.

OGSA service instances are:

Created dynamically by factories.

Identified through Grid Service Handles.

Self describing through Service Data Elements.

Stateful, with soft state lifetime management.

Current status: Globus 3 beta

release in June 2003: www.globus.org.

Supports service instances and access to other Globus services.

Core database services from OGSA-DAI project tracking Globus releases.

Grid Database Service (GDS)

Databases are made available on the Grid through integration with other Grid services, and provision of standard interfaces

Build upon OGSA to deliver high-level data management functionality for the Grid.

Seek to provide two classes of components: Data access components Data integration components

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GDS interactions

Distributed Query Processing DQP involves a single query referencing

data stored at multiple sites. The locations of the data may be

transparent to the author of the query.

select p.proteinId, Blast(p.sequence)from protein p, proteinTerm twhere t.termId = ‘GO:0005942’ and p.proteinId = t.proteinId

J. Smith, A. Gounaris, P. Watson, N. Paton, A. Fernandes, R. Sakellariou, Distributed Query Processing on the Grid, 3rd Int. Workshop on Grid Computing, Springer-Verlag, 279-290, 2002.

Mutual Benefit The Grid needs

DQP: Declarative, high-

level resource integration with implicit parallelism.

DQP-based solutions should in principle run faster than those manually coded.

DQP needs the Grid: Systematic access

to remote data and computational resources.

Dynamic resource discovery and allocation.

A Service-Based DQP Architecture

O G S A -D A I

O G S A

D is tribu te d Q u e ry Pro ce s s o r

A pplica t io n /Pre s e n ta t io n L a y e r

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Service-based DQP framework Service-based in two orthogonal sense:

Supports querying over data storagedata storage and analysis analysis resources made available as servicesservices

ConstructionConstruction of distributed query plans and their executionexecution over the grid are factored out as services

Uses the emerging standard for GDSs to provide consistent access to database metadata and to interact with databases on the Grid.

A query may refer to database (GDS) and computational services.

Extends OGSA & OGSA-DAI …

By adding a new port type and two new services ( and their corresponding factories) :

Grid Distributed Query (GDQ) Port Type importSchema operation

GDQSR importSchema(GDQDataSourceList GDSL)

GDSL : A document containing: the list of Data Sources. The items on this list should contain the

handles of the GDS Factories, along with an instance creation document for each factory.

And/or a set of WSDL URLs for the analysis services to be used

Continued …

Grid Distributed Query Service (GDQS) Wraps an existing query compiler/optimiser system

which compile, optimise, partition and schedule distributed query execution plans

Obtains and maintains metadata and computational resource information required for above

Grid Query Evaluator Service (GQES) Each GQES instance is an execution node and is

dynamically created by the GDQS on the node it is scheduled to run

A GQES is in charge of a partition of the query execution plan assigned to it by the GDQS and is responsible for dispatching the partial results to other GQESs.

Setting up a GDQS Set-up strategy depends on the life-time

model of GDQS and GDSs GDQS instance is created per-client But it can serve multiple-queries This model avoids complexity of multi-user

interactions while ensures that the set-up cost is not high

Setup phase involves: Importing schemas of participating data sources Importing WSDL documents of participating

analysis services Collecting computational resource metadata (implicit)

Issues in Initialisation Q: When is a GDQS

bound to a particular GDS?

A: When the schema of the GDS is imported.

Q: What is the lifespan of a GDS used by a GDQS?

A: The GDS is kept alive until the GDQS expires.

Q: Are GDSs shared by multiple GDQSs?

A: No.

Q: When is a GQES created?

A: When a query is about to be evaluated that needs it.

Q: What is the lifespan of a GQES?

A: It lasts only as long as a single query.

Q: Is a GQES shared among several queries or GDQSs?

A: No.

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Importing Schemas

<GDQDataSourceList ><importedDataSource> <GDSFactoryHandle> http://130.88.198.203:8080/ogsa/services/ogsadai/GridDataServiceFactory</GDSFactoryHandle> <GDSCreateDocument>

<gridDataServiceFactoryCreate > <dataResourceName>

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An example of data source import list

<request name = “myRequest”> <oqlQueryStatement name=“myStat"> <dataResource=“myGenomeDB"> <expression> 

select p.proteinId, Blast(p.sequence)from proteins p, proteinTerms twhere t.termId = ‘GO:0005942 ’ and p.proteinId = t.proteinId

</expression> </oqlQueryStatement > <deliverToGDT name="delivery">    <fromLocal name=“myStat"> <toGDT streamId="otherrequestasynch/d1" mode=“full"> http://ogsadai.org.uk/GDTService/my/GDT/GSH </toGDT> </deliverToGDT>  </request>

An example of a Query Document

LogicalOptimiser

PhysicalOptimiser

Partitioner Scheduler

Evaluator

OQLParser

Single-nodeOptimiser

Multi-nodeoptimiser

Query Compilation

Plan is expressed using a logical algebra.

Heuristic-based application of equivalence laws.

Multiple equivalent plans generated. scan

(protein)scantermID=…(proteinTerm)

reduce reduce

join(proteinId)

op_call(Blast)

reduce

Logical Optimisation

Plan is expressed using a physical algebra.

Logical operators replaced with physical operators.

Cost-based ranking of plans. table_scan

(protein)index_scantermID=…(proteinTerm)

reduce reduce

hash_join(proteinId)

op_call(Blast)

reduce

Physical Optimisation

Partitioning Plan is expressed

in a parallel algebra.

Parallel algebra = physical algebra + exchange.

Exchange operators are placed where data movement may be required.

table_scan(protein)

index_scantermID=…(proteinTerm)

reduce reduce

hash_join(proteinId)

op_call(Blast)

reduce

exchange exchange

exchange

Scheduling Partitions are

allocated to Grid nodes; partitions may be merged during scheduling.

Expressed by decorating parallel algebra expression.

Heuristic algorithm considers memory use, network costs.

table_scan(protein)

table_scantermID=S92(proteinTerm)

reduce reduce

hash_join(proteinId)

op_call(Blast)

reduce

exchange exchange

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Query installation: GQESs created for partitions as

required. Partitions sent to GQESs.

Query evaluation: Partitions evaluated using iterator

model. Pipelined and partitioned parallelism. Results conveyed to client.

Query Evaluation

<Partition isRoot="0"><evaluatorURI> http://mach1.cs.man.ac.uk:8080/ogsa/services/ogsadai/GQESFactory/GQES1</evaluatorURI> ... <Operator operatorID="2" operatorType="SEQ_SCAN"> <SEQ_SCAN>

<tupleType> <type> string </type> <name> proteinTerms.GOproteinID </name> <type> string </type> <name> proteinTerms.term </name></tupleType><inputOperator> <OperatorID> </OperatorID></inputOperator><DataResourceName>proteinTermsDataResource</DataResourceName><GDSHandle> http://mach1.cs.man.ac.uk:8080/…/GridDataServiceFactoryP2R1/GDS1 </GDSHandle><predicateExpr> <predicate>

<comparativeOperator>EQ</comparativeOperator><leftOperand name="proteinTerms.term" type="tuplefield"/><rightOperand name="GO:0008372" type="string"/>

</predicate></predicateExpr>

</SEQ_SCAN> </Operator>

... </Partition>

An example of a query sub-plan passed to a GQES

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Interactions of SB-DQP components

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Summary DQP on the Grid provides:

The normal benefits of DQP. Some added benefits from a Grid

setting. The Grid specifically enables:

Runtime computational resource discovery.

Dynamic creation of remote evaluators.

Authentication/Transport services. Access to non-database services.

Features of Our GDQS Low cost of entry:

Imports source descriptions through GDSs. Imports service descriptions as WSDL.

Throw-away GDQS: Import sources on a task-specific basis. Discard GDQS when task completed.

Builds on parallel database technology: Implicit parallelism. Pipelined + partitioned parallel evaluation.

Public release in July 2003.

The SB-DQP Team Manchester:

Nedim Alpdemir Anastasios

Gounaris Alvaro Fernandes Norman Paton Rizos Sakellariou

Newcastle: Arijit Mukherjee Jim Smith Paul Watson