Post on 19-Aug-2015
BIG DATA - FAST DATAUSING MAPREDUCE IN HAZELCAST
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Christoph Engelbert (@noctarius2k)8+ years of Java WeirdonessPerformance, GC, traffic topicsApache CommitterGaming, Travel Management, ...CastMapR MapReduce for Hazelcast 3
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TOPICSHazelcastDistributed ComputingMap & ReduceDemonstrationQuestions
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HAZELCASTPICKIN' DIAMONDS
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WHAT IS HAZELCAST?In-Memory Data-GridData Partioning (Sharding)Java Collections ImplementationDistributed Computing Platform
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WHY HAZELCAST?Automatic PartitioningFault ToleranceSync / Async BackupsFully DistributedIn-Memory for Highest Speed
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WHY HAZELCAST?
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WHY DISTRIBUTED COMPUTING?
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WHY IN-MEMORYCOMPUTING?
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DISTRIBUTEDCOMPUTING
OR
MULTICORE CPU ON STEROIDS
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THE IDEA OF DISTRIBUTED COMPUTING
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THE BEGINNING
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SUPER COMPUTER
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CLOUD COMPUTING
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MAP & REDUCETHE BLACK MAGIC FROM PLANET GOOGLE
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USE CASESLog AnalysisData QueryingAggregationDistributed SortETL (Extract Transform Load)and more...
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BASIC STEPSReadMap / TransformReduce
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FULL STEPSReadMap / TransformCombineGroup / ShuffleReduceCollate
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MAPREDUCE WORKFLOW
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Data are mapped / transformed in a set of key-value pairs
SOME PSEUDO CODE (1/3)
MAPPING
map( key:String, document:String ):Void -> for each w:Word in document: emit( w, 1 )
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Multiple values are combined to an intermediate result to preserve traffic
SOME PSEUDO CODE (2/3)
COMBINING
combine( word:Word, counts:List[Int] ):Void -> emit( word, sum( counts ) )
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Values are reduced / aggregated to the requested result
SOME PSEUDO CODE (3/3)
REDUCING
reduce( word:String, counts:List[Int] ):Int -> return sum( counts )
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FOR MATHEMATICIANSProcess: (K x V)* → (L x W)* ⇒ [(l1, w1), …, (lm, wm)]
Mapping: (K x V) → (L x W)* ⇒ (k, v) → [(l1, w1), …, (ln, wn)]
Reducing: L x W* → X* ⇒ (l, [w1, …, wn]) → [x1, …,xn]
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MAPREDUCE PROGRAMS INGOOGLE SOURCE TREE
Source:
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DEMONSTRATION
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@noctarius2k@hazelcast
http://www.sourceprojects.comhttp://github.com/noctarius
THANK YOU!ANY QUESTIONS?
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