unit 3 DBMS.docx.pdf geometric transformer in query processing

Geometric transformer

1. GeometricTransformationinQueryProcessingandOptimization
Geometrictransformationsareessentialinvariousareasofcomputerscience,particularlyincomputer
graphics,computervision,andspatialdatabases.Inqueryprocessingandoptimization,geometric
transformationsareusedtoefficientlymanageandqueryspatialdata.Belowaresomekeypointsonthis
topic:
1.UnderstandingGeometricTransformations
●TypesofTransformations:
oTranslation:Shiftingallpointsofanobjectacertaindistanceinaspecifieddirection.
oRotation:Rotatinganobjectaroundapivotpoint.
oScaling:Resizinganobjectbyascalefactor.
oReflection:Flippinganobjectoveraspecifiedaxis.
oShearing:Distortinganobjectsuchthattheshapeisaltered.
●MatrixRepresentation:Transformationscanberepresentedusingmatrices,allowingforeasy
combinationandapplicationtospatialobjects.Forinstance,atransformationmatrixcanbe
appliedtoapointoranobjectinspacetoperformthetransformation.
2.SpatialDatabasesandQueries
●SpatialDataTypes:Datatypeslikepoints,lines,polygons,andpolyhedraareusedtorepresent
spatialdata.
●SpatialIndexing:EfficientindexingmechanismssuchasR-trees,Quad-trees,andKD-treesare
usedtooptimizethestorageandretrievalofspatialdata.
●QueryTypes:
oRangeQueries:Findingallobjectswithinacertaindistancefromapointorwithina
specificarea.
oNearestNeighborQueries:Findingtheclosestobjectstoagivenpoint.
oSpatialJoins:Combiningtwodatasetsbasedontheirspatialrelationship(e.g.,
intersection,containment).
3.QueryOptimizationTechniques
●TransformationTechniques:Optimizingqueriesbytransformingthemintoamoreefficient
formwithoutchangingtheirsemantics.Forexample,applyinggeometrictransformationsto
spatialqueriestominimizethesearchspace.
●Cost-BasedOptimization:Evaluatingdifferentqueryexecutionplansbasedontheirestimated
costandselectingthemostefficientone.
●Heuristics:Usingrulesofthumbtoreducethesearchspaceforqueryoptimization.Thismight
includereorderingofoperations,simplifyingexpressions,orusingapproximationtechniques.
4.ApplicationinSpatialQueryProcessing
●ClippingAlgorithms:Usedinspatialdatabasestooptimizetheintersectionofspatialobjectsby
reducingthesearchspace.
●GeometricPrimitives:Simplifyingcomplexgeometricobjectsintosimplerforms(e.g.,
boundingboxes)forfasterprocessing.
●SpatialJoinAlgorithms:Optimizingspatialjoinsbyusingtechniquesliketheplane-sweep
algorithm,whichefficientlyprocessesspatialdatabysweepingalineacrosstheplane.

2. 5.ChallengesandConsiderations
●Complexity:Geometrictransformationscanbecomputationallyintensive,especiallywithlarge
datasets.
●Precision:Ensuringaccuracyintransformationsandqueryresultsiscritical.
●DataRepresentation:Efficientdatarepresentationandstoragetechniquesareessentialto
minimizespaceandenhanceretrievalperformance.
6.ToolsandLibraries
●PostGIS:AnextensionofPostgreSQLthatsupportsgeographicobjectsandspatialqueries.
●GEOS(GeometryEngine-OpenSource):AC++libraryprovidingspatialoperationsand
geometricfunctions.
●Shapely:APythonpackageformanipulationandanalysisofplanargeometricobjects.
QueryProcessingandOptimization
1.QueryProcessing
Queryprocessinginvolvesthestepstakenbyadatabasemanagementsystem(DBMS)totranslatea
high-levelquery,typicallywritteninSQL,intoaseriesoflow-leveloperationsthatcanbeexecuted
efficientlytoretrievethedesiredresults.
KeyStepsinQueryProcessing:
1.Parsing:
oThequeryisparsedtocheckitssyntaxandsemantics.Theoutputisaparsetreeorsyntax
tree,representingthestructureofthequery.
oAnysyntacticalerrorsinthequeryarecaughtduringthisstage.
2.Translation:
oTheparsetreeisconvertedintoarelationalalgebraexpressionoranintermediateform
thatrepresentsthelogicalstepstoexecutethequery.
oThetranslationalsoincludestheidentificationofrelations(tables),attributes(columns),
andoperations(likejoins,selections,projections).
3.Optimization:
oTheintermediatequeryrepresentationisoptimizedtogenerateamoreefficientexecution
plan.Thisinvolvesselectingthebestpossibleexecutionstrategybasedoncostestimates.
oOptimizationcanbedoneattwolevels:
▪LogicalOptimization:Transformingthequeryintoanequivalentbutpotentially
moreefficientform.Examplesincludereorderingjoins,pushingdownselections,
andcombiningoperations.
▪PhysicalOptimization:Decidingonthespecificalgorithmsanddatastructures
touseforexecutingeachoperation(e.g.,whichjoinalgorithmtouse,howto
accessdata).
4.Execution:
oTheDBMSexecutestheoptimizedqueryplanbyinteractingwiththestoragesystemto
retrieveorupdatedata.
oTheresultofthequeryisreturnedtotheuser.
2.QueryOptimizationProcess

3. Queryoptimizationaimstofindthemostefficientexecutionplanforaquery,minimizingresourceusage
(likeCPUtime,memory,andI/Ooperations)whileensuringcorrectresults.
StepsinQueryOptimization:
1.ExpressionTransformation:
oTransformationsareappliedtotherelationalalgebraexpressiontocreateequivalent
expressionsthatmaybemoreefficient.
oExamplesinclude:
▪JoinReordering:Changingtheorderofjoinstominimizethesizeof
intermediateresults.
▪SelectionPushdown:Movingselectionoperationsasclosetothebasetablesas
possibletoreducetheamountofdataprocessedinsubsequentsteps.
2.PlanGeneration:
oTheDBMSgeneratesvariouspotentialexecutionplansforthequery,eachrepresentinga
differentwaytoperformtheoperations.
oTheseplansarebasedondifferentphysicaloperationslikeusinganindexscanversusa
fulltablescan,usingnestedloopjoinsversushashjoins,etc.
3.CostEstimation:
oEachexecutionplanisassignedacostbasedonestimatesofresourceusage,including
I/O,CPU,andmemory.
oThecostestimationinvolvesfactorssuchasthesizeofthetables,theselectivityof
predicates,andavailableindexes.
oTheDBMSusesstatisticsaboutthedata(likehistograms,rowcounts)tomakethese
estimates.
4.PlanSelection:
oTheplanwiththelowestestimatedcostischosenastheoptimalplanforexecution.
oInsomesystems,acost-basedoptimizerisused,whereallpossibleplansareevaluated,
andtheonewiththeleastcostisselected.
oInothersystems,heuristic-basedoptimizationisemployed,whererulesofthumbare
usedtoquicklychooseagood(butnotnecessarilythebest)plan.
5.ExecutionofthePlan:
oThechosenplanisexecuted,interactingwiththestoragesystemtofetchormodifydata.
oTheexecutionisoftendoneinapipelinefashion,wheretheoutputofoneoperation
becomestheinputtothenextwithoutneedingtobestoredinintermediatetables.
3.TechniquesUsedinQueryOptimization
●Indexing:Usingindexestospeedupdataretrieval.
●MaterializedViews:Pre-computingandstoringtheresultsoffrequentorcomplexqueries.
●Partitioning:Dividingatableintosmallerpiecesforfasteraccess.
●ParallelQueryExecution:Distributingthequeryexecutionacrossmultipleprocessorsor
machines.
●In-memoryProcessing:KeepingfrequentlyaccesseddatainmemorytoreducediskI/O.
4.ChallengesinQueryOptimization

4. ●Complexity:Asqueriesbecomemorecomplex,thenumberofpossibleexecutionplansgrows
exponentially.
●InaccurateCostEstimation:Theoptimizerreliesondatastatistics,whichmaynotalwaysbe
accurate,leadingtosuboptimalplanchoices.
●DynamicData:Changingdatadistribution,growth,orworkloadcanmakepreviouslyoptimal
plansinefficientovertime.
MeasuresofQueryCostEstimationinQueryOptimization
Querycostestimationisacrucialcomponentofqueryoptimization,asithelpsthedatabasemanagement
system(DBMS)decideonthemostefficientexecutionplan.Thecostofaqueryistypicallyestimated
basedonseveralfactors,eachofwhichcontributestotheoverallresourceusageofthequeryexecution.
KeyMeasuresofQueryCostEstimation
1.I/OCost(DiskAccesses)
oDescription:Referstothecostofreadingandwritingdatatoandfromdisk.Sincedisk
I/Oissignificantlyslowerthanin-memoryoperations,itisoftenthemostsubstantial
componentofquerycost.
oFactorsConsidered:
▪Numberofdiskpagesthatneedtobeaccessed.
▪Sequentialvs.randomaccesspatterns.
▪Useofindexes,whichcanreducethenumberofpagesread.
oExample:AfulltablescantypicallyincurshigherI/Ocostscomparedtoanindexed
lookup.
2.CPUCost
oDescription:Involvesthecostofprocessingdatainmemory,includingtaskslike
filteringrows,performingjoins,aggregatingresults,andsortingdata.
oFactorsConsidered:
▪Numberoftuples(rows)tobeprocessed.
▪Complexityofoperations(e.g.,computationalcomplexityofjoinalgorithms).
▪Numberofcomparisonsorarithmeticoperationsneeded.
oExample:NestedloopjoinsgenerallyhavehigherCPUcostscomparedtohashjoinsfor
largedatasets.
3.MemoryUsage
oDescription:Theamountofmemoryrequiredtoexecutethequery,includingbuffer
spaceforintermediateresults,hashtablesforjoins,andsortbuffers.
oFactorsConsidered:
▪Sizeofthedatasetsbeingprocessed.
▪Requirementsforsorting,grouping,orjoininglargetables.
▪Availabilityofmemoryforcachingdata.
oExample:Queriesthatcanbeexecutedentirelyinmemoryarefasterthanthosethat
requirespillingdatatodiskduetoinsufficientmemory.
4.NetworkCost
oDescription:Relevantindistributeddatabasesystems,wheredatamightneedtobe
transferredacrossdifferentnodesormachines.
oFactorsConsidered:
▪Volumeofdatatransferredoverthenetwork.
▪Numberofmessagesexchangedbetweennodes.
▪Networklatencyandbandwidth.

5. oExample:Adistributedjoinoperationmayincurhighnetworkcostsiflargeamountsof
dataneedtobeshuffledbetweennodes.
5.CardinalityEstimation
oDescription:Estimatingthenumberoftuples(rows)producedateachstepofthequery
executionplan,whichdirectlyaffectsothercostmeasureslikeI/OandCPU.
oFactorsConsidered:
▪Selectivityofpredicates(howmanyrowssatisfythequeryconditions).
▪Joinselectivity(expectedsizeofresultafterjoiningtables).
▪Availabilityofdatadistributionstatistics.
oExample:Ahighlyselectivefilterconditionthatreducesthenumberofrows
significantlycanlowerthecostofsubsequentoperations.
6.LatencyandResponseTime
oDescription:Thetimetakenforthequerytoreturnthefirstresultandcomplete
execution.Latencyiscriticalforinteractivequerieswherequickresponsetimesare
expected.
oFactorsConsidered:
▪Pipelineabilityofthequeryexecutionplan(howwelloperationscanbe
overlapped).
▪Parallelismanddistributionofoperations.
▪I/OandCPUcosts,astheyimpacttheoverallexecutiontime.
oExample:Aqueryplanthatallowsforearlyoutputofresultswhilecontinuingtoprocess
therestofthedatacanimproveperceivedlatency.
7.SelectivityEstimation
oDescription:Thefractionofdatathatsatisfiesaqueryconditionorpredicate.
oFactorsConsidered:
▪Datadistributionandvaluefrequencies.
▪Histograms,indexes,andotherstatisticsthathelpestimateselectivity.
▪Correlationbetweenattributes.
oExample:Foraqueryfilteringonacolumnwithuniformdistribution,selectivity
estimationisstraightforward,butforskeweddata,itmightbemorecomplex.
8.AccessPathCost
oDescription:Thecostassociatedwithdifferentaccessmethodsusedtoretrievedata,
suchasfulltablescans,indexscans,andindex-onlyscans.
oFactorsConsidered:
▪Typeofaccesspathchosen(e.g.,indexscanvs.tablescan).
▪Numberoftuplesaccessedviathechosenpath.
▪Clusteringofdataondisk.
oExample:Usingaclusteredindexmighthavealoweraccesspathcostcomparedtoa
non-clusteredindexifthedataisaccessedsequentially.
PipeliningandMaterializationinQueryProcessing
Inqueryprocessing,twoprimarytechniquesareusedforexecutingaseriesofoperations:pipeliningand
materialization.Thesetechniquesdeterminehowintermediateresultsarehandledduringquery
executionandhavesignificantimplicationsforperformanceandresourceusage.
1.Pipelining
Pipeliningisaqueryexecutiontechniquewheretheoutputofoneoperationispasseddirectlytothenext
operationwithoutbeingstoredasanintermediateresult.Thisallowsforoperationstobeexecutedina
continuousstreamor"pipeline,"reducingtheneedforintermediatestorageandpotentiallyspeedingup
queryexecution.

6. KeyConcepts:
●Tuple-at-a-TimeProcessing:Inpipelining,eachtuple(row)producedbyanoperationis
immediatelyprocessedbythenextoperationinthesequence.Thisminimizesthelatencybetween
operations.
●EarlyOutput:Resultscanstarttobereturnedtotheuserbeforetheentirequeryhasfinished
executing,whichimprovesresponsetime.
●MemoryEfficiency:Byavoidingthestorageoflargeintermediateresults,pipeliningcanbe
morememory-efficient,althoughitrequiresenoughmemorytokeepthepipelineactive.
TypesofPipelining:
●Demand-Driven(Lazy)Pipelining:Operationsareexecutedwhenthenextoperatorrequests
data.Thisisoftenusediniterator-basedqueryexecutionmodels.
●Producer-Driven(Eager)Pipelining:Assoonasanoperatorproducesaresult,itisimmediately
passedtothenextoperator,regardlessofwhetherthenextoperatorisreadyforit.
Advantages:
●ReducedI/OCosts:Byavoidingthematerializationofintermediateresultsondisk,I/O
operationsareminimized.
●LowerMemoryUsage:Requireslessmemorycomparedtomaterializingintermediateresults,
especiallybeneficialforlargedatasets.
●ImprovedLatency:Sinceresultscanbestreamedandprocessedon-the-fly,thetimetofirst
result(latency)isreduced.
Disadvantages:
●Complexity:Pipeliningcanbemorecomplextoimplement,especiallywhendealingwith
blockingoperations(e.g.,sortoperations)thatrequiretheentireinputbeforeproducingany
output.
●LimitedFlexibility:Notalloperationscanbeeffectivelypipelined,especiallywhenintermediate
resultsareneededforsubsequentsteps(e.g.,incertainjoinoraggregationoperations).
2.Materialization
Materializationisatechniquewheretheintermediateresultsofaqueryoperationarestored(or
"materialized")intemporarystorage(e.g.,diskormemory)beforebeingusedinthenextoperation.This
approachismorestraightforwardandisusedwhenpipeliningisnotfeasible.
KeyConcepts:
●BatchProcessing:Operationsareprocessedinbatches,withtheentireresultofoneoperation
beingstoredbeforemovingtothenext.
●IntermediateStorage:Resultsarewrittentotemporarystorage(e.g.,atemporarytableorafile)
andreadbackforsubsequentoperations.
●Flexibility:Materializationallowsforcomplexoperationsthatmaynotbepossiblewith
pipelining,suchasthoserequiringmultiplepassesoverdata(e.g.,sorting,group-by).

7. Advantages:
●Simplicity:Easiertoimplementandmanage,especiallyforcomplexquerieswithmultiplestages.
●Robustness:Suitableforoperationsthatrequiretheentiredatasettobeprocessed(e.g.,sorting,
aggregation).
●ExecutionIndependence:Allowseachqueryoperationtobeexecutedindependently,whichcan
beusefulwhenoperationsarecomplexorhavedifferentresourcerequirements.
Disadvantages:
●HigherI/OCosts:StoringandretrievingintermediateresultscanleadtoincreaseddiskI/O
operations,whichcanslowdownqueryexecution.
●IncreasedMemoryUsage:Requiressufficientmemoryordiskspacetostoreintermediate
results,whichcanbeabottleneckforlargedatasets.
●Latency:Allintermediateresultsmustbefullyprocessedandstoredbeforemovingontothenext
operation,leadingtoslowertime-to-first-result.
3.ChoosingBetweenPipeliningandMaterialization
Thechoicebetweenpipeliningandmaterializationdependsonvariousfactors,includingthenatureofthe
query,theoperationsinvolved,andtheavailablesystemresources.
●Pipeliningispreferred:
oWhenmemoryislimitedandthequerycanbeexecutedinastreamingmanner.
oForqueriesthatcanbenefitfromearlyoutputofresults.
oWhenoperationsarenon-blockingandcanbeprocessedsequentially.
●Materializationispreferred:
oWhendealingwithblockingoperationslikesortingorcomplexjoinsthatrequirethefull
setofinputdata.
oWhentheintermediateresultsarereusedmultipletimesindifferentpartsofthequery.
oInscenarioswheretheoverheadofmanagingapipelinewouldoutweighitsbenefits.
StructureofQueryEvaluationPlans
Aqueryevaluationplan(orexecutionplan)outlinesthesequenceofoperationsadatabasemanagement
system(DBMS)willperformtoexecuteaSQLquery.Thisplaniscreatedbythequeryoptimizerto
ensureefficientdataretrieval,anditisusuallyrepresentedasatreeoradirectedacyclicgraphwhereeach
noderepresentsanoperation.
KeyComponentsofQueryEvaluationPlans
1.Operators:
oRelationalOperators:Thesearefundamentaloperationsinrelationalalgebrasuchas
selection(σ),projection(π),join(⨝),union(∪),intersection(∩),difference(-),and
Cartesianproduct(×).
oPhysicalOperators:Thesearetheactualimplementationsofrelationaloperationsinthe
database,suchasnestedloopjoin,hashjoin,indexscan,andsort.
2.Nodes:

8. oLeafNodes:Representthebasetablesorindexesthatareaccessedbythequery.Theyare
thestartingpointsoftheexecutionplan.
oInternalNodes:Representoperationsthatcombineortransformdata,suchasjoins,
sorts,aggregations,orprojections.
3.Edges:
oDataFlow:Edgesintheplanindicatetheflowofdatabetweenoperations,typically
movingfromtheleafnodesuptowardstheroot.
4.RootNode:
oTherootnodeoftheplanrepresentsthefinaloperationwhoseoutputistheresultofthe
query.Thiscouldbeaprojection,anaggregation,orafinaljoin.
TypesofQueryEvaluationPlans
1.LogicalPlan:
oAbstractRepresentation:Thelogicalplanisahigh-levelrepresentationthatoutlinesthe
operationsneededtoexecutethequery,usingrelationalalgebraoperatorswithout
specifyinghowtheywillbephysicallyexecuted.
oTransformations:Theoptimizermayapplytransformationstothelogicalplan(e.g.,
reorderingjoins,pushingdownselections)toimproveefficiency.
2.PhysicalPlan:
oImplementation-Specific:Thephysicalplanisaconcreteplanthatspecifiestheexact
algorithmsanddatastructuresusedtoperformeachoperation.Thisplanincludesdetails
likeusingahashjoininsteadofanestedloopjoinoranindexscaninsteadofafulltable
scan.
oCostConsiderations:Thephysicalplanisselectedbasedoncostestimates,which
considerfactorssuchasI/O,CPUusage,andmemoryrequirements.
3.AlternativePlans:
oPlanCandidates:Theoptimizertypicallygeneratesmultiplecandidateplans,each
representingadifferentwaytoexecutethequery.Itthenevaluatesthecostofeachand
selectsthemostefficientone.
oPlanEnumeration:Variousstrategieslikedynamicprogrammingorheuristicsmaybe
usedtoexploredifferentplanpossibilities.
ComponentsofaPhysicalQueryPlan
1.AccessMethods:
oSpecifieshowdataisretrievedfromstorage.Commonaccessmethodsinclude:
▪FullTableScan:Readingallrowsfromatable.
▪IndexScan:Usinganindextofindrowsthatmatchthequerycondition.
▪Index-OnlyScan:Retrievingdatadirectlyfromanindexwithoutaccessingthe
table.
2.JoinMethods:
oSpecifieshowtablesarecombined:
▪NestedLoopJoin:Foreachrowintheoutertable,itsearchestheinnertablefor
matchingrows.
▪HashJoin:Usesahashtabletoquicklyfindmatchingrowsfromthejoined
tables.
▪MergeJoin:Sortsbothtablesonthejoinkeyandthenmergesthem.
3.SortingandGrouping:

9. oOperationsfororderingresults(usingalgorithmslikeexternalsort)andgroupingdata
(usingalgorithmslikehash-basedgrouping).
4.Aggregation:
oOperationsforcalculatingaggregatefunctions(SUM,COUNT,AVG,etc.),often
combinedwithgrouping.
5.DataManipulation:
oSelection(Filtering):Applyingpredicatestofilterrows.
oProjection:Selectingspecificcolumnstooutput.
oSetOperations:UNION,INTERSECT,andEXCEPToperationsonmultipleresultsets.
6.Pipeliningvs.Materialization:
oDescribeswhetherintermediateresultsarepasseddirectlytothenextoperation
(pipelining)orstoredtemporarily(materialization)beforebeingprocessedfurther.
ExampleofaQueryEvaluationPlan
Considerthequery:
LogicalPlan:
1.Selection:Applythefilteremp.salary>50000.
2.Join:Performanequi-joinbetweenempanddeptonemp.dept_id=dept.id.
3.Projection:Selectthecolumnsemp.nameanddept.name.
PhysicalPlan:
1.IndexScan:Useanindexonemp.salarytoretrieveonlyemployeeswithsalary>50000.
2.HashJoin:Performahashjoinbetweenthefilteredemprowsandthedepttable.
3.Projection:Returnthenamecolumnsfromempanddept.
VisualizationofaQueryEvaluationPlan
Thequeryplancanbevisualizedasatree,where:
●Leafnodesmightrepresenttheindexscansorfulltablescans.
●Internalnodesrepresentoperationslikejoinsoraggregations.
●Therootnoderepresentsthefinaloutputoperation.
SELECTemp.name,dept.name
FROMemp
JOINdeptONemp.dept_id=dept.id
WHEREemp.salary>50000;