Question

我一直在尝试从包含双打的1列Spark数据帧中提取信息，并将其放入Breeze SparseVector中。为此，我遍历我的1列DataFrame的每个元素，强制它为Double，然后将其添加到VectorBuilder。我的VectorBuilder在foreach循环中正确地改变了它的状态，然后在循环结束后清除所有更改。为什么会这样？有解决方法吗？

编辑1：

我在本地运行1核心;它不在群集上

代码：

val accum = sc.accumulator(0, "Counter") 

def correlate() : Unit = {

  val cols = df.columns
  val id = cols(0)       
  val id2 = cols(1)

  //id1 and id2 are there for 
  val df1 = sqlContext.sql(s"SELECT ${id} FROM dataset WHERE (${id} IS NOT NULL AND ${id2} IS NOT NULL)")

  /* df1 is a dataframe that has 1 column*/   
  df1.show();
  accum.value_=(0);

  /******************** Problem starts here **********************/
  val builder = new VectorBuilder[Double](5)
  df1.foreach{ x =>
    x(0) match{             
      case d : Double => 
        builder.add(accum.value, d); 
        //This print statement prints out correct values
        println(s"index: ${accum.value} value: ${builder(accum.value)}")      
        accum.value += 1;
        println(s"builder's active size in loop: ${builder.activeSize}")  
      case _ => throw new ClassCastException("Pattern-Matching for Double failed");
    } 
  }
  //temp becomes empty at this point
  println(s"builder's active size out of loop: ${builder.activeSize}")

  val sparse = builder.toSparseVector     
  sparse.foreachPair{(i,v) => println(s"index: ${i} and value: ${v}")}
}
this.correlate()

输出：

+-------+
|   RowX|
+-------+
|  145.0|
|   -1.0|
|-212.21|
|   23.3|
|   21.4|
+-------+

index: 0 value: 145.0
builder's active size in loop: 1
index: 1 value: -1.0
builder's active size in loop: 2
index: 2 value: -212.21
builder's active size in loop: 3
index: 3 value: 23.3
builder's active size in loop: 4
index: 4 value: 21.4
builder's active size in loop: 5

//the loop ends here and builder's state disappears

builder's active size out of loop: 0
index: 0 and value: 0.0
index: 1 and value: 0.0
index: 2 and value: 0.0
index: 3 and value: 0.0
index: 4 and value: 0.0

Answer 1

它为每个工人添加了构建器的本地副本。要获取本地对象：

SparseVector(df1.rdd.map(_.getDouble(0)).collect)

在DataFrame中使用值构建SparseVector

1 个答案: