spark性能优化要注意哪几点,很多新手对此不是很清楚,为了帮助大家解决这个难题,下面小编将为大家详细讲解,有这方面需求的人可以来学习下,希望你能有所收获。
默认用的是java序列化,但是会很慢,第二种很快,但是不一定能实现所有序列化 第二种,有些自定义类你需要在代码中注册(Kryo)
def main(args: Array[String]) {
val sparkConf = new SparkConf()
val sc = new SparkContext(sparkConf)
val names = Array[String]("G304","G305","G306")
val genders = Array[String]("male","female")
val addresses = Array[String]("beijing","shenzhen","wenzhou","hangzhou")
val infos = new ArrayBuffer[Info]()
for (i<-1 to 1000000){
val name = names(Random.nextInt(3))
val gender = genders(Random.nextInt(2))
val address = addresses((Random.nextInt(4)))
infos += Info(name, gender, address)
}
val rdd = sc.parallelize(infos)
rdd.persist(StorageLevel.MEMORY_ONLY_SER)
rdd.count()
// rdd.persist(StorageLevel.MEMORY_ONLY)
sc.stop()
}
case class Info(name:String, gender:String, address:String)
}
def main(args: Array[String]) {
val sparkConf = new SparkConf()
sparkConf.registerKryoClasses(Array(classOf[Info]))
val sc = new SparkContext(sparkConf)
val names = Array[String]("G304","G305","G306")
val genders = Array[String]("male","female")
val addresses = Array[String]("beijing","shenzhen","wenzhou","hangzhou")
val infos = new ArrayBuffer[Info]()
for (i<-1 to 1000000){
val name = names(Random.nextInt(3))
val gender = genders(Random.nextInt(2))
val address = addresses((Random.nextInt(4)))
infos += Info(name, gender, address)
}
val rdd = sc.parallelize(infos)
rdd.persist(StorageLevel.MEMORY_ONLY_SER)
rdd.count()
// rdd.persist(StorageLevel.MEMORY_ONLY_SER)
sc.stop()
sparkConf.registerKryoClasses(Array(classOf[Info]))
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原文链接:http://blog.itpub.net/69941978/viewspace-2651785/