如何使用我的相等比较器对 Spark DataFrame 进行分组? [英] How to GroupBy Spark DataFrame with my Equality Comparators?

问题描述

我想在带有我自己的相等比较器的 DataFrame 上使用 GroupBy 运算符.

I would like to use GroupBy operator on a DataFrame with my own equality comparators.

假设我想执行以下操作:

Let's assume that I want to execute something like:

df.groupBy("Year","Month").sum("Counter")

在这个数据帧中:

Year    | Month      | Counter  
---------------------------
2012    | Jan        | 100          
12      | January    | 200       
12      | Janu       | 300       
2012    | Feb        | 400       
13      | Febr       | 500

我必须实现两个比较器:

I have to implement two comparators:

1) 对于列年份:p.e.2012"==12"

1) For column Year: p.e. "2012" == "12"

2) 对于列 Month: p.e.一月"==一月"==一月"

2) For column Month: p.e. "Jan" == "January" == "Janu"

假设我已经实现了这两个比较器.我怎样才能调用它们?正如在这个示例中一样，我已经知道我必须将我的 DataFrame 转换为 RDD 才能使用我的比较器.

Let's assume that I already implemented these two comparators. How can I invoke them? As in this example, I already know that I have to convert my DataFrame into an RDD to make possible to use my comparators.

我考虑过使用 RDD GroupBy.

请注意我确实需要使用比较器来做到这一点.我无法使用 UDF、更改数据或创建新列.未来的想法是拥有密文列，其中我有一些函数可以让我比较两个密文是否相同.我想在我的比较器中使用它们.

Note that I really need to do this using comparators. I can't use UDFs, change the data or create new columns. The future idea is to have ciphertext columns, in which I have functions that allow me to compare if two ciphertexts are the same. I want to use them in my comparators.

此刻，我试图只用一列来做到这一点，例如:

In this moment, I am trying to do this with only one column, like:

df.groupBy("Year").sum("Counter")

我有一个包装类:

class ExampleWrapperYear (val year: Any) extends Serializable {
      // override hashCode and Equals methods
}

然后，我这样做:

val rdd = df.rdd.keyBy(a => new ExampleWrapperYear(a(0))).groupByKey()

我的问题是如何进行求和"，以及如何将 keyBy 与多列一起使用以使用 ExampleWrapperYear 和 ExampleWrapperMonth.

My question here is how to do the "sum", and how to use keyBy with multiple columns to use ExampleWrapperYear and ExampleWrapperMonth.

推荐答案

这个解决方案应该有效.以下是实现 hashCode 和 equals 的 case 类(我们可以称之为比较器).

This solution should work.Here are the case classes (we can call these as comparators) which implements hashCode and equals.

您可以根据不同的密文修改/更新hashCode和equals

You can modify/update hashCode and equals based on different ciphertexts

  case class Year(var year:Int){

    override def hashCode(): Int = {
      this.year = this.year match {
        case 2012 => 2012
        case 12 => 2012
        case 13 => 2013
        case _ => this.year
      }
      this.year.hashCode()
    }

    override def equals(that: Any): Boolean ={
      val year1 = 2000 + that.asInstanceOf[Year].year % 100
      val year2 = 2000 + this.year % 100
      if (year1 == year2)
        true
      else
        false
    }
  }

  case class Month(var month:String){

    override def hashCode(): Int = {
      this.month = this.month match {
        case "January" => "Jan"
        case "Janu" => "Jan"
        case "February" => "Feb"
        case "Febr" => "Feb"
        case _ => this.month
      }
      this.month.hashCode
    }

    override def equals(that: Any): Boolean ={
      val month1 = this.month match {
        case "January" => "Jan"
        case "Janu" => "Jan"
        case "February" => "Feb"
        case "Febr" => "Feb"
        case _ => this.month
      }
      val month2 = that.asInstanceOf[Month].month match {
        case "January" => "Jan"
        case "Janu" => "Jan"
        case "February" => "Feb"
        case "Febr" => "Feb"
        case _ => that.asInstanceOf[Month].month
      }
      if (month1.equals(month2))
        true
      else
        false
    }
  }

这里是分组键的重要比较器，它简单地使用了单独的 col 比较器

Here is the important comparator for the grouped keys, which simply uses the individual col comparator

  case class Key(var year:Year, var month:Month){

    override def hashCode(): Int ={
      this.year.hashCode() + this.month.hashCode()
    }

    override def equals(that: Any): Boolean ={
      if ( this.year.equals(that.asInstanceOf[Key].year) && this.month.equals(that.asInstanceOf[Key].month))
        true
      else
        false
    }
  }

  case class Record(year:Int,month:String,counter:Int)

  val df = spark.read.format("com.databricks.spark.csv")
      .option("header", "true")
      .option("inferSchema", "true")
      .load("data.csv").as[Record]

  df.rdd.groupBy[Key](
      (record:Record)=>Key(Year(record.year), Month(record.month)))
      .map(x=> Record(x._1.year.year, x._1.month.month, x._2.toList.map(_.counter).sum))
      .toDS().show()

给出

+----+-----+-------+
|year|month|counter|
+----+-----+-------+
|2012|  Feb|    800|
|2013|  Feb|    500|
|2012|  Jan|    700|
+----+-----+-------+

for this input in data.csv

Year,Month,Counter
2012,February,400
2012,Jan,100
12,January,200
12,Janu,300
2012,Feb,400
13,Febr,500
2012,Jan,100

请注意，对于案例类 Year 和 Month，也将值更新为标准值(否则无法预测它选择哪个值).

Please note that, for the case classes Year and Month, also updated the value to standard value (otherwise it is unpredictable which value it picks).

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