【Spark Java API】Action(1)—reduce、aggregate

reduce
公式ドキュメントの説明:

Reduces the elements of this RDD using the specified commutative and associative binary operator.

関数のプロトタイプ:

def reduce(f: JFunction2[T, T, T]): T

マッピング関数fに従ってRDD中の要素を二元計算(交換則と結合則を満たす)し、計算結果を返す.
ソース分析:

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"))
}

ソースコードから分かるように、reduce関数はRDDの要素に対してreduceLeft関数操作を行うことに相当し、reduceLeft関数はリストの左側から右側にreduce関数を適用する.その後、結果はdriver側でマージ処理されるため、パーティション数が多すぎるか、カスタム関数が複雑すぎるとdriver側の負荷が重くなる.
例:

JavaSparkContext javaSparkContext = new JavaSparkContext(sparkConf);

List data = Arrays.asList(5, 1, 1, 4, 4, 2, 2);

JavaRDD javaRDD = javaSparkContext.parallelize(data,3);

Integer reduceRDD = javaRDD.reduce(new Function2() {    
  @Override    
  public Integer call(Integer v1, Integer v2) throws Exception {        
      return v1 + v2;    
  }
});
System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~" + reduceRDD);

aggregate
公式ドキュメントの説明:

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.

関数のプロトタイプ:

def aggregate[U](zeroValue: U)(seqOp: JFunction2[U, T, U],  combOp: JFunction2[U, U, U]): U

Aggregateは、各区分の各要素をマージし、パーティション結果をmerge処理します.この関数が最終的に返すタイプはRDDの要素タイプと一致する必要はありません.
ソース分析:

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
}

ソースコードからaggregate関数は、各パーティションに対してscala集合を用いてaggregateを操作し、comb()を用いて前の各パーティション結果を集約することがわかる.
例:

JavaSparkContext javaSparkContext = new JavaSparkContext(sparkConf);
List data = Arrays.asList(5, 1, 1, 4, 4, 2, 2);
JavaRDD javaRDD = javaSparkContext.parallelize(data,3);
Integer aggregateRDD = javaRDD.aggregate(2, new Function2() {    
    @Override    
    public Integer call(Integer v1, Integer v2) throws Exception {        
        return v1 + v2;    
    }
}, new Function2() {    
    @Override    
    public Integer call(Integer v1, Integer v2) throws Exception {          
        return v1 + v2;    
    }
});
System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~" + aggregateRDD);