Jesús%Fernández%Bes% MLG%–%3JUN2013%%jesusfbes.es/wp-content/uploads/2016/04/MLG_Spark.pdf ·...

Jesús%Fernández%Bes%MLG%–%3JUN2013%%

Índice%%

1.  Instrucciones%de%instalación%2.  Visión%general%de%Spark%3.  PySpark%

1.  Uso%interacIvo%2.  Uso%standalone)

4.  Uso)de)Spark)en)nuestro)cluster)5.  Integración)con)Amazon)EC2)

6.  Conclusiones)7.  Referencias)

INSTRUCCIONES*DE*INSTALACIÓN*

Instrucciones%de%instalación:%Prerrequisitos%

•  Java%&%Python%2.7%

•  SCALA%(Versión%2.9)%–  Descarga%Scala%2.9.2.%(Spark%necesita%Scala%para%funcionar%aunque%

programemos%en%Python%pero%ATENCIÓN%“no%es%compaIble”%con%la%úlIma%versión%de%Scala%2.10).%

h^p://www.scalaalang.org/downloads/distrib/files/scalaa2.9.2.tgz%–  Descomprime%Scala%donde%desees.%%

•  GIT%–  Por%alguna%razón,%en%Mac%OSx%necesitas%el%git.%%h^ps://code.google.com/p/gitaosxainstaller/%

Instrucciones%de%instalación:%Instalación%de%Spark%

•  Descarga%la%úlIma%versión%de%Spark%(0.7)%de%la%página%web.%Versiones%anteriores%no%se%pueden%usar%con%Python.%h^p://www.sparkaproject.org/downloadasparka0.7.0asourcesatgz%

•  Descomprime%Spark%donde%desees.%•  Ahora%es%necesario%o%bien%que%Scala%esté%en%el%path%o%mejor%

fijar%una%nueva%variable%de%estado%SCALA_HOME.%•  En%la%carpeta%conf%dentro%del%root%de%spark%y%crear%ahí%un%fichero%

sparkBenv.sh%(se%puede%uIlizar%para%ello%la%planIlla%que%hay%en%el%directorio%sparkaenv.sh.template,%cambiándole%el%nombre%y%en%el%fichero%añadir:%

spark4env.sh*

if%[%az%"$SCALA_HOME"%]%;%then%%%%%%SCALA_HOME=”<scalaapath>”%

fi%

Instrucciones%de%instalación:%Instalación%de%Spark%(2)%

Desde%el%directorio%raiz%de%Spark%ejecuta%la%siguiente%instrucción%que%compilará%Spark%y%descargará%lo%que%le%haga%falta%para%instalarlo.%%

%%Comprobaciones%(desde%el%raiz%de%Spark)%%

%cuando%se%te%abra%la%shell%interacIva%escribe%sc).%Salir%(%Ctlr%+%D%)%%

%al%igual%que%antes,%cuando%se%te%abra%la%shell%interacIva%escribe%sc.%Salir%(%Ctlr%+%D%)%%%Debería%ejecutar%el%programa%de%ejemplo%que%calcula%Pi.%Deben%salir%una%serie%de%mensajes%y%al%final%de%todo%el%valor%de%pi%(o%una%aproximación%razonable)%

%%

%%

$%sbt/sbt%package%

$%./sparkashell%

$%./pyspark%

$%./run%spark.examples.SparkPi%local%

DESCRIPCIÓN*Y*UTILIZACIÓN*DE*SPARK*

¿Qué%es%Spark?%•  Spark%es%una%infraestructura%paralela%con:%–  PrimiIvas%eficientes%para%comparIr%datos%en%memoria.%%–  APIs%en%Scala,%Java,%SQL%y%Python.*–  Aplicable%a%muchos%problemas%emergentes.%

•  MapReduce%simplificó%el%análisis%de%datos%en%clusters%grandes%y%sujetos%a%fallos.%Hoy%los%usuarios%quieren%más:%–  Aplicaciones%más%complejas,%iteraDvas%(p.e.%Machine%learning,%graph%algorithms).%

–  Consultas%adahoc%más%interacDvas.%%–  Procesado%de%flujo%en%Dempo*real.%%

Memoria%distribuida%tolerante%a%fallos%

•  Necesario%trabajar%en%memoria%de%manera%distribuida%para%ser%eficientes.%

•  Pero%entonces%¿Como%recuperarse%ante%fallos?%

%

Solución:%%* * **Resilient*Distributed*Datasets*(RDD)*

RDDs%•  Colección%parIcionada%inmutable%de%registros.%•  Se%crean%a%parIr%de%transformaciones%de%ficheros%Hadoop%u%otros%RDD.%

•  Tiene%información%sobre%lineage.)Permite%recuperarse%ante%fallos.)

•  Permite)cierto)control)sobre)el)parGcionado)y)la)persistencia.)

)

Resilient Distributed Datasets: A Fault-Tolerant Abstraction forIn-Memory Cluster Computing

Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma,Murphy McCauley, Michael J. Franklin, Scott Shenker, Ion Stoica

University of California, Berkeley

AbstractWe present Resilient Distributed Datasets (RDDs), a dis-tributed memory abstraction that lets programmers per-form in-memory computations on large clusters in afault-tolerant manner. RDDs are motivated by two typesof applications that current computing frameworks han-dle inefficiently: iterative algorithms and interactive datamining tools. In both cases, keeping data in memorycan improve performance by an order of magnitude.To achieve fault tolerance efficiently, RDDs provide arestricted form of shared memory, based on coarse-grained transformations rather than fine-grained updatesto shared state. However, we show that RDDs are expres-sive enough to capture a wide class of computations, in-cluding recent specialized programming models for iter-ative jobs, such as Pregel, and new applications that thesemodels do not capture. We have implemented RDDs in asystem called Spark, which we evaluate through a varietyof user applications and benchmarks.

1 IntroductionCluster computing frameworks like MapReduce [10] andDryad [19] have been widely adopted for large-scale dataanalytics. These systems let users write parallel compu-tations using a set of high-level operators, without havingto worry about work distribution and fault tolerance.

Although current frameworks provide numerous ab-stractions for accessing a cluster’s computational re-sources, they lack abstractions for leveraging distributedmemory. This makes them inefficient for an importantclass of emerging applications: those that reuse interme-diate results across multiple computations. Data reuse iscommon in many iterative machine learning and graphalgorithms, including PageRank, K-means clustering,and logistic regression. Another compelling use case isinteractive data mining, where a user runs multiple ad-hoc queries on the same subset of the data. Unfortu-nately, in most current frameworks, the only way to reusedata between computations (e.g., between two MapRe-duce jobs) is to write it to an external stable storage sys-tem, e.g., a distributed file system. This incurs substantialoverheads due to data replication, disk I/O, and serializa-

tion, which can dominate application execution times.Recognizing this problem, researchers have developed

specialized frameworks for some applications that re-quire data reuse. For example, Pregel [22] is a system foriterative graph computations that keeps intermediate datain memory, while HaLoop [7] offers an iterative MapRe-duce interface. However, these frameworks only supportspecific computation patterns (e.g., looping a series ofMapReduce steps), and perform data sharing implicitlyfor these patterns. They do not provide abstractions formore general reuse, e.g., to let a user load several datasetsinto memory and run ad-hoc queries across them.

In this paper, we propose a new abstraction called re-silient distributed datasets (RDDs) that enables efficientdata reuse in a broad range of applications. RDDs arefault-tolerant, parallel data structures that let users ex-plicitly persist intermediate results in memory, controltheir partitioning to optimize data placement, and ma-nipulate them using a rich set of operators.

The main challenge in designing RDDs is defining aprogramming interface that can provide fault toleranceefficiently. Existing abstractions for in-memory storageon clusters, such as distributed shared memory [24], key-value stores [25], databases, and Piccolo [27], offer aninterface based on fine-grained updates to mutable state(e.g., cells in a table). With this interface, the only waysto provide fault tolerance are to replicate the data acrossmachines or to log updates across machines. Both ap-proaches are expensive for data-intensive workloads, asthey require copying large amounts of data over the clus-ter network, whose bandwidth is far lower than that ofRAM, and they incur substantial storage overhead.

In contrast to these systems, RDDs provide an inter-face based on coarse-grained transformations (e.g., map,filter and join) that apply the same operation to manydata items. This allows them to efficiently provide faulttolerance by logging the transformations used to build adataset (its lineage) rather than the actual data.1 If a parti-tion of an RDD is lost, the RDD has enough informationabout how it was derived from other RDDs to recompute

1Checkpointing the data in some RDDs may be useful when a lin-eage chain grows large, however, and we discuss how to do it in §5.4.

PrimiIvas%Spark%

TransformaDons* AcDons*

map%flatMap%filter%

groupBy%join%(…)%

count%reduce%collect%(…)%

Dos%Ipos.%•  Transformaciones:%RDD%!%RDD’%%•  Acciones:%RDD%!%resultado%%

Spark%Streaming%Tratamiento%de%flujos%de%datos%en%pseudo%Iempoareal.%%•  Aplicaciones%|picas:%%

–  Procesar%Twi^er.%%–  EstadísIcas%%y%

monitorización%de%siIos%web.%%

•  ObjeIvos:%–  Escalable%a%grandes%clusters.)–  Latencias%de%segundos.%–  Modelo%simple%de%

programación.%–  Integración%con%procesado%

en%bloque%e%interacIvo.%–  Tolerancia%a%fallos.%

live%data%stream%

Spark*

Spark*Streaming*

batches%of%X%seconds%

processed%results%

Nueva%Abstracción:%%Dstream*

Secuencia%de%RDDs%que%representa%un%flujo%de%datos.%

Shark%

•  Extensión%de%Hive%para%hacerlo%compaIble%con%Spark.%

•  Sistema%de%almacén%de%datos%(data)warehouse).%

•  Permite%hacer%consultas%Ipo%SQL.%–  Hasta%100%%más%rápido%que%Hive.%

–  ConverIrlos%en%RDDs.%•  Usar%Spark%sobre%ellos.%

Programación%

•  API%en%Scala,%Java%y%Python:%– Originalmente%en%Scala%(Recomendado).%%– En%Java%sólo%modo%standalone.%%– Python%añadido%recientemente%(02/2013).%

•  Se%puede%uIlizar%modo%interacDvo*o*standalone.%•  No%se%puede%uIlizar%con%Spark%Stream%(Desarrollo%futuro).%•  Permite%uIlizar%cualquier%biblioteca%Python:%numpy,%scipy...%(????)%

%

PySpark:%Uso%InteracIvo%

•  Vamos%a%uIlizar%el%dataset%de%libros%que%uIlizamos%en%el%nltk.%

•  Abrir%la%consola%interacIva%en%python*% %%%%[%$./sparkBshell))(si%quisiéramos%usar%Scala)%%]%

•  Variable)sc%conIene%un%SparkContext.%– Objeto%que%“sabe%como%interactuar%con%el%cluster”%y%permite%crear%RDDs%a%parIr%de%ficheros%o%colecciones%de%python.%

%

$./pyspark%

Sintaxis%Python%lambda)

•  Python%soporta%el%definir%miniafunciones%anónimas%de%una%línea%sobre%la%marcha.%%– Originalmente%en%Lisp*

*[*lambda*variable:*operación*]*

– Muy%úIl%dentro%de%filter,%map,%reduce.%%

%

>>>)g)=)lambda)x:)x**2)))

>>>)g(3))

9)

>>>)words.filter(lambda)w:)w.startswith(‘a’)))

Ejercicio%Pyspark%interacIvo%

•  Construir%RDDs%a%parIr%de%un%fichero%(directorio)%%%

•  Ver%los%primeros%campos%del%registro%

%%

>>>)

>>>))

Ejercicio%Pyspark%interacIvo%

•  Construir%RDDs%a%parIr%de%un%fichero%(directorio)%%%


%%

>>>)bookLines)=)sc.textFile('books'))

>>>)bookLines.take(10))

Ejercicio%Pyspark%interacIvo%•  Construir%RDDs%a%parIr%de%un%fichero%(directorio)%%%


•  Un%poco%feo,%¿no?.%Vamos%a%mostrarlo%un%poco%más%limpio%%%%

•  Otras%acciones%%%



>>>))

>>>)

>>>)

Ejercicio%Pyspark%interacIvo%•  Construir%RDDs%a%parIr%de%un%fichero%(directorio)%%%


•  Un%poco%feo,%¿no?.%Vamos%a%mostrarlo%un%poco%más%limpio%%%%

•  Otras%acciones%%%



>>>)for)line)in)bookLines.take(10):)print)line)

>>>)bookLines.count())

>>>)bookLines.collect())

Ejercicio%Pyspark%interacIvo%(2)%

•  Pasar%de%lista%de%líneas%a%lista%de%palabras%para%contar%número%de%palabras.%1.  Transformar%RDD:%¿flatMap%or%map?%

%

2.  Contar%las%palabras%

>>>)

>>>)



%


>>>)bookWords1)=)bookLines.map()lambda)x:)x.split(')'))))

>>>)bookWords1.take(50))

>>>)

>>>)



%




>>>)bookWords2)=)bookLines.flatMap()lambda)x:)x.split(')'))))

>>>)bookWords2.take(50)))


>>>)

>>>)



%







>>>)bookWords)=)bookWords2.cache())


>>>)



%







>>>)bookWords)=)bookWords2.cache())


>>>)bookWords.count()*

MapReduce%en%Spark*

• Return%a%new%distributed%dataset%formed%by%passing%each%element%of%the%source%through%a%funcIon%func.%

map(%func%)%

• Similar%to%map,%but%each%input%item%can%be%mapped%to%0%or%more%output%items%(so%func%should%return%a%Seq%rather%than%a%single%item).%

flatMap(%func%)%

• Aggregate%the%elements%of%the%dataset%using%a%funcIon%func%(which%takes%two%arguments%and%returns%one).%The%funcIon%should%be%commutaIve%and%associaIve%so%that%it%can%be%computed%correctly%in%parallel.%

reduce(%func%)%

• When%called%on%a%dataset%of%(K,%V)%pairs,%returns%a%dataset%of%(K,%V)%pairs%where%the%values%for%each%key%are%aggregated%using%the%given%reduce%funcIon.%%

reduceByKey(%func%)%


•  Calcula%la%línea%de%longitud%máxima%%%•  Muestra%las%líneas%de%longitud%máxima%%

>>>)

>>>)



>>>%linesMax%=%bookLines.map(lambda%line:%len(line.split('%'))).reduce(max)%

>>>)



>>>%linesMax%=%bookLines.map(lambda%line:%len(line.split('%'))).reduce(max)%

>>>%bookLines.filter(lambda%line:%len(line.split('%'))==linesMax%).collect()%

Ejercicio%Pyspark%%interacIvo%(4)%•  Más%MapReduce:%WordCount*•  A%parIr%de%los%ficheros%extraer%pares%(palabra,conteo)%

•  En%primer%lugar%hace%falta%importar%el%operador%suma%para%usarlo%de%manera%funcional%

%%A%parIr%del%RDD%de%palabras%bookWords:%

1.   Map:%Generar%pares%(w,1)%2.   Reduce:%sumar%aquellos%pares%que%tengan%la%misma%

clave%w.%

>>>%from%operator%import%add%



clave%w.%

%%Mostrar%el%resultado%

%%

>>>)

>>>)



clave%w.%


%%

>>>)counts)=)bookWords.map(lambda)x:)(x,1))).reduceByKey(add).cache())

>>>)



clave%w.%


%%

>>>)counts)=)bookWords.map(lambda)x:)(x,1))).reduceByKey(add).cache())

>>>)counts.take(50))

>>>)for)pair)in)counts.take(50):)print)pair)

Modo%standalone)Hacer%programas%completos%que%se%ejecuten%en%bloque%en%lugar%de%en%la%consola%interacIva.%%Invocación%|pica:%

./pyspark)file.py)<master>)<inputs>)

[En%local%./pyspark)file.py)local)<inputs>)]%

%Dos%pasos%básicos:%1.  importar%SparkContext%

%from)pyspark)import)SparkContext)

2.  Crear%SparkContext%en%el%nodo%master,%nombre%del%trabajo%[y%dependencias%que%hay%que%enviar%al%nodo.]%

sc)=)SparkContext(sys.argv[1],)”job",)pyFiles=['MyFile.py',)'lib.zip',)'app.egg’]))

%

wordCount.py*

import%sys%from%operator%import%add%%from%pyspark%import%SparkContext%%%if%__name__%==%"__main__":%%%%%if%len(sys.argv)%<%3:%%%%%%%%%print%>>%sys.stderr,%\%%%%%%%%%%%%%"Usage:%PythonWordCount%<master>%<file>"%%%%%%%%%exit(a1)%%%%%%%%%sc%=%SparkContext(sys.argv[1],%"PythonWordCount")%%%%%lines%=%sc.textFile(sys.argv[2],%1)%%%%%%%%%%counts%=%lines.flatMap(lambda%x:%x.split('%'))%\%%%%%%%%%%%%%%%%%%%.map(lambda%x:%(x,%1))%\%%%%%%%%%%%%%%%%%%%.reduceByKey(add)%%%%%output%=%counts.collect()%%%%%for%(word,%count)%in%output:%%%%%%%%%print%"%s%:%%i"%%%(word,%count)%%%

Ejemplos%ML%de%Pyspark%•  Junto%con%Spark%hay%una%serie%de%ejemplos%de%algoritmos%Ipo%ML:%–  Regresión%LogísIca%–  Kmeans%–  ALS%%

•  Tienen%graves%errores%de%programación%logisDc_regression.py*

for%i%in%range(1,%ITERATIONS%+%1):%%%%%%%%%print%"On%iteraIon%%i"%%%i%%%%%%%%%%gradient%=%points.map(lambda%p:%%%%%%%%%%%%%(1.0%/%(1.0%+%exp(ap.y%*%np.dot(w,%p.x))))%*%p.y%*%p.x%).reduce(add)%%%%%%%%%w%a=%gradient%%

•  Spark%provee%de%scripts%para%lanzar%trabajos%directamente%a%Amazon%ElasIc%Compute%Cloud%(EC2).%%

•  Configuran%automáIcamente)Mesos,)Spark)y)HDFS)en%el%clúster%para%I.%

•  Necesario%pagar%Computo%($0,260%por%instancia/hora%para%la%predeterminada)%+%Coste%de%almacenamiento.%

•  Muy%fácil%de%usar.%%•  Grants!!!!%h^p://aws.amazon.com/es/grants/%

•  Registrarse%en%Amazon%Web%Services%(AWS).%–  h^p://aws.amazon.com/es/%

•  En%la%pantalla%principal%de%mi%cuenta%crear%Claves%de%acceso.%–  Pestaña%de%credenciales%de%

seguridad.%

•  Antes%de%ejecutar%los%scripts%Ienes%que%fijar%las%variables%de%estado%AWS_ACCESS_KEY_ID%y%AWS_SECRET_ACCESS_KEY_ID%a%esos%valores.%

•  Generar%par%de%claves:%– Accede%a%tu%consola%AWS%y%allí%entra%en%el%servicio%EC2.%

–  Selecciona%keyPairs%y%crea%el%par%

– Descargate%el%fichero%y%en%Linux/Mac%Osx%cambiale%los%permiso%a%600%

–  chmod%600%mykey.pem%

%

•  En%el%directorio%ec2%de%spark%está%el%script%para%lanzar%un%cluster,%acceder,%pararlo,%destruirlo.%

•  Más%detalles%aquí.%h^p://sparkaproject.org/docs/latest/ec2ascripts.html%

%

>>>%./sparkaec2%ak%<keypair>%ai%<keyafile>%as%<numaslaves>%launch%<clusteraname>%

>>>%./sparkaec2%ak%<keypair>%ai%<keyafile>%login%<clusteraname>%

>>>%./sparkaec2%ak%<keypair>%ai%<keyafile>%as%<numaslaves>%launch%<clusteraname>%

>>>%./sparkaec2%destroy%<clusteraname>%

Conclusiones%•  Spark%propone%una%infraestructura%interesante%para%computación%en%clusters.%–  API%Scala,%Python%y%Java%sencillo.%Sobretodo%para%gente%que%sabe%programación%funcional.%

– Modo%interacIvo%(extracción%rápida%de%caracterísIcas%de%grandes%datasets).%

–  Funciona%bien%con%procesado%secuencial.%–  Fácil%de%usar%en%EC2.%

•  Muy%(demasiado)%nuevo.%Python%en%febrero%de%este%año.%%

•  No%existe%una%comunidad%de%Machine%Learning%como%Mahout%en%torno%a%Spark.%

Algunas%Referencias%•  Documentación%oficial%de%Spark%

h^p://sparkaproject.org/docs/latest/index.html%

•  MiniaCurso%del%AMPLAB%sobre%BigData:%Spark,%Shark%y%EC2.%h^p://ampcamp.berkeley.edu/bigadataaminiacourse/%%

•  Algunos%vídeos%con%presentaciones%de%las%desarrolladores.%–  NIPS%2011:%%

h^ps://www.youtube.com/watch?v=qLvLgasqxKc%–  Berkley%Analizing%Big%Data%with%twi^er%(lesson%16):%

%h^ps://www.youtube.com/watch?v=rpXxsp1vSEs%–  PyData%2013:%

%h^p://vimeo.com/63274414%%%

•  Libro%de%O’Reilly%en%camino%(Agosto%de%2013):%h^p://shop.oreilly.com/product/0636920028512.do%

%%

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