我使用 Spark 进行员工记录累积,为此我使用 Spark 的累加器。我使用 Map[empId, emp] 作为 accumulableCollection 以便我可以通过他们的 id 搜索员工。我已经尝试了一切,但它不起作用。有人可以指出我使用 accumulableCollection 或 Map 的方式是否存在任何逻辑问题。以下是我的代码
package demo
import org.apache.spark.{SparkContext, SparkConf, Logging}
import org.apache.spark.SparkContext._
import scala.collection.mutable
object MapAccuApp extends App with Logging {
case class Employee(id:String, name:String, dept:String)
val conf = new SparkConf().setAppName("Employees") setMaster ("local[4]")
val sc = new SparkContext(conf)
implicit def empMapToSet(empIdToEmp: mutable.Map[String, Employee]): mutable.MutableList[Employee] = {
empIdToEmp.foldLeft(mutable.MutableList[Employee]()) { (l, e) => l += e._2}
}
val empAccu = sc.accumulableCollection[mutable.Map[String, Employee], Employee](mutable.Map[String,Employee]())
val employees = List(
Employee("10001", "Tom", "Eng"),
Employee("10002", "Roger", "Sales"),
Employee("10003", "Rafael", "Sales"),
Employee("10004", "David", "Sales"),
Employee("10005", "Moore", "Sales"),
Employee("10006", "Dawn", "Sales"),
Employee("10007", "Stud", "Marketing"),
Employee("10008", "Brown", "QA")
)
System.out.println("employee count " + employees.size)
sc.parallelize(employees).foreach(e => {
empAccu += e
})
System.out.println("empAccumulator size " + empAccu.value.size)
}
最佳答案
对于您的问题,使用 accumulableCollection
似乎有点矫枉过正,如下所示:
import org.apache.spark.{AccumulableParam, Accumulable, SparkContext, SparkConf}
import scala.collection.mutable
case class Employee(id:String, name:String, dept:String)
val conf = new SparkConf().setAppName("Employees") setMaster ("local[4]")
val sc = new SparkContext(conf)
implicit def mapAccum =
new AccumulableParam[mutable.Map[String,Employee], Employee]
{
def addInPlace(t1: mutable.Map[String,Employee],
t2: mutable.Map[String,Employee])
: mutable.Map[String,Employee] = {
t1 ++= t2
t1
}
def addAccumulator(t1: mutable.Map[String,Employee], e: Employee)
: mutable.Map[String,Employee] = {
t1 += (e.id -> e)
t1
}
def zero(t: mutable.Map[String,Employee])
: mutable.Map[String,Employee] = {
mutable.Map[String,Employee]()
}
}
val empAccu = sc.accumulable(mutable.Map[String,Employee]())
val employees = List(
Employee("10001", "Tom", "Eng"),
Employee("10002", "Roger", "Sales"),
Employee("10003", "Rafael", "Sales"),
Employee("10004", "David", "Sales"),
Employee("10005", "Moore", "Sales"),
Employee("10006", "Dawn", "Sales"),
Employee("10007", "Stud", "Marketing"),
Employee("10008", "Brown", "QA")
)
System.out.println("employee count " + employees.size)
sc.parallelize(employees).foreach(e => {
empAccu += e
})
println("empAccumulator size " + empAccu.value.size)
empAccu.value.foreach(entry =>
println("emp id = " + entry._1 + " name = " + entry._2.name))
虽然目前这方面的记录很少,但 Spark 代码库中的 relevant test 非常有启发性。
编辑: 事实证明,使用
accumulableCollection
确实有值(value):您不需要定义 AccumulableParam
并且以下工作。如果它们对人们有用,我将留下这两种解决方案。case class Employee(id:String, name:String, dept:String)
val conf = new SparkConf().setAppName("Employees") setMaster ("local[4]")
val sc = new SparkContext(conf)
val empAccu = sc.accumulableCollection(mutable.HashMap[String,Employee]())
val employees = List(
Employee("10001", "Tom", "Eng"),
Employee("10002", "Roger", "Sales"),
Employee("10003", "Rafael", "Sales"),
Employee("10004", "David", "Sales"),
Employee("10005", "Moore", "Sales"),
Employee("10006", "Dawn", "Sales"),
Employee("10007", "Stud", "Marketing"),
Employee("10008", "Brown", "QA")
)
System.out.println("employee count " + employees.size)
sc.parallelize(employees).foreach(e => {
// notice this is different from the previous solution
empAccu += e.id -> e
})
println("empAccumulator size " + empAccu.value.size)
empAccu.value.foreach(entry =>
println("emp id = " + entry._1 + " name = " + entry._2.name))
两种解决方案均使用 Spark 1.0.2 进行测试。
关于scala - Spark accumulableCollection 不适用于 mutable.Map,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/25917476/