reduce
官方文档描述：
Reduces the elements of this RDD using the specified commutative and associative binary operator.
函数原型：
def reduce(f: JFunction2[T, T, T]): T
根据映射函数f，对RDD中的元素进行二元计算（满足交换律和结合律），返回计算结果。
源码分析：
def reduce(f: (T, T) => T): T = withScope {
val cleanF = sc.clean(f)
val reducePartition: Iterator[T] => Option[T] = iter => {
if (iter.hasNext) {
Some(iter.reduceLeft(cleanF))
} else {
None
}
}
var jobResult: Option[T] = None
val mergeResult = (index: Int, taskResult: Option[T]) => {
if (taskResult.isDefined) {
jobResult = jobResult match {
case Some(value) => Some(f(value, taskResult.get))
case None => taskResult
}
}
}
sc.runJob(this, reducePartition, mergeResult)
// Get the final result out of our Option, or throw an exception if the RDD was empty
jobResult.getOrElse(throw new UnsupportedOperationException("empty collection"))
}
从源码中可以看出，reduce函数相当于对RDD中的元素进行reduceLeft函数操作，reduceLeft函数是从列表的左边往右边应用reduce函数；之后，在driver端对结果进行合并处理，因此，如果分区数量过多或者自定义函数过于复杂，对driver端的负载比较重。
实例：
JavaSparkContext javaSparkContext = new JavaSparkContext(sparkConf);
List<Integer> data = Arrays.asList(5, 1, 1, 4, 4, 2, 2);
JavaRDD<Integer> javaRDD = javaSparkContext.parallelize(data,3);
Integer reduceRDD = javaRDD.reduce(new Function2<Integer, Integer, Integer>() {
@Override
public Integer call(Integer v1, Integer v2) throws Exception {
return v1 + v2;
}
});
System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~" + reduceRDD);
aggregate
官方文档描述：
Aggregate the elements of each partition, and then the results for all the partitions,
using given combine functions and a neutral "zero value". This function can return a different result type, U,
than the type of this RDD, T. Thus, we need one operation for merging a T into an U and one operation for merging two U's,
as in scala.TraversableOnce. Both of these functions are allowed to modify and return their first argument
instead of creating a new U to avoid memory allocation.
函数原型：
def aggregate[U](zeroValue: U)(seqOp: JFunction2[U, T, U], combOp: JFunction2[U, U, U]): U
aggregate合并每个区分的每个元素，然后在对分区结果进行merge处理，这个函数最终返回的类型不需要和RDD中元素类型一致。
源码分析：
def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = withScope {
// Clone the zero value since we will also be serializing it as part of tasks
var jobResult = Utils.clone(zeroValue, sc.env.serializer.newInstance())
val cleanSeqOp = sc.clean(seqOp)
val cleanCombOp = sc.clean(combOp)
val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp)
val mergeResult = (index: Int, taskResult: U) => jobResult = combOp(jobResult, taskResult)
sc.runJob(this, aggregatePartition, mergeResult)
jobResult
}
从源码中可以看出，aggregate函数针对每个分区利用scala集合操作aggregate，再使用comb()将之前每个分区结果聚合。
实例：
JavaSparkContext javaSparkContext = new JavaSparkContext(sparkConf);
List<Integer> data = Arrays.asList(5, 1, 1, 4, 4, 2, 2);
JavaRDD<Integer> javaRDD = javaSparkContext.parallelize(data,3);
Integer aggregateRDD = javaRDD.aggregate(2, new Function2<Integer, Integer, Integer>() {
@Override
public Integer call(Integer v1, Integer v2) throws Exception {
return v1 + v2;
}
}, new Function2<Integer, Integer, Integer>() {
@Override
public Integer call(Integer v1, Integer v2) throws Exception {
return v1 + v2;
}
});
System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~" + aggregateRDD);
