CODEWITHSUNDEEP
scalaapache-spark
jd2050
jd2050

Reputation: 302

Spark/Scala: RDD[List<String>] to RDD[Custom Object]

I have data in an input text file. It contains the input data in format: "PriceId, DateTime, PriceForUSA, PriceForUK, PriceForAUS".

It looks like this:

0000002,11-05-08-2016,0.92,1.68,0.81

0000003,07-05-08-2016,0.80,1.05,1.49

0000008,07-05-08-2016,1.43,1.29,1.22

The list of countries is fixed (USA, UK, AUS), and the order of prices in lines is fixed too (PriceForUSA, PriceForUK, PriceForAUS).

I read this data from file using Spark Context and transform it to RDD[List[String[]. Every List in my RDD represents one line from the input text file.

For example,

first List contains Strings 

"0000002", "11-05-08-2016", "0.92", "1.68", "0.81"

second list contains Strings 

"0000003", "07-05-08-2016" , "0.80", "1.05" , "1.49"

etc.

I also have the custom class PriceInfo

case class PriceInfo(priceId: String, priceDate: String, country: String, price: Double) {

  override def toString: String = s"$priceId,$priceDate,$country,$price"
}

It is not difficult to transform every List[String] to this class' object, ( I can do it already), but in this case my task is to get several custom objects from every single List[String].

For example, List which contains 

"0000002", "11-05-08-2016", "0.92", "1.68", "0.81"

should be transformed to:

And every List[String] in my RDD[List[String]] must be "splitted" to several PriceInfo objects in the same way.

The result should be a RDD[PriceInfo].

The only solution that came to my mind is to iterate RDD[List[String]] with foreach() function, create 3 PriceInfo objects in every iteration, then add all created objects in List[PriceObjects] and use this result-List in SparkContext.parallelize(List...).

Something like this:

rawPricesList.foreach(list => {

      //...create PriceInfo1 from list
      //...create PriceInfo2 from list
      //...create PriceInfo3 from list

      //...add them all to result List<PriceInfo>

    })

    //...sc.parallelize(List<PriceInfo>...)

But such a solution has many shortcomings.

The main thing is that it will not work if we have no link to SparkContext. For example, if we will have a method getPrices() which will have only 1 parameter - RDD[List[String]].

def getPrices(rawPricesList: RDD[List[String]]): RDD[PriceInfo] = {



    rawPricesList.foreach(list => {

      //...create PriceInfo1 from list
      //...create PriceInfo2 from list
      //...create PriceInfo3 from list

      //...add them all to result List<PriceInfo>

    })

    //...but we can't sc.parallelize(List...) here, because there is no SparkContext sc in method parameters
  }

In addition, it seems to me that Scala contains a more elegant solution.

I tried to find similar samples in books "Scala for impatient" and "Learning Spark: Lightning-Fast Big Data Analysis", but unfortunately did not find anything like this case. I will be very grateful for the help and tips.

Upvotes: 2

Views: 1233

Answers (1)

Leo C
Leo C

Reputation: 22449

Here's one approach:

  1. Load the text file and split each line into an Array[String] of (id, date, price1, price2, price3)
  2. Transform each row into (id, date, Array[(country, numericPrice)]) using zip
  3. Flatten the (country, numericPrice) tuples in each row into rows of PriceInfo objects using flatMap

Example code below:

case class PriceInfo(priceId: String, priceDate: String, country: String, price: Double) {
  override def toString: String = s"$priceId,$priceDate,$country,$price"
}

val countryList = List("USA", "UK", "AUS")

val rdd = sc.textFile("/path/to/textfile").
  map( _.split(",") ).
  map{ case Array(id, date, p1, p2, p3) =>
    (id, date, countryList.zip(List(p1.toDouble, p2.toDouble, p3.toDouble)))
  }.
  flatMap{ case (id, date, countryPrices) =>
    countryPrices.map( cp => PriceInfo(id, date, cp._1, cp._2) ) 
  }
// rdd: org.apache.spark.rdd.RDD[PriceInfo] = ...

rdd.collect
// res1: Array[PriceInfo] = Array(
//    0000002,11-05-08-2016,USA,0.92,
//    0000002,11-05-08-2016,UK,1.68,
//    0000002,11-05-08-2016,AUS,0.81,
//    0000003,07-05-08-2016,USA,0.8,
//    0000003,07-05-08-2016,UK,1.05,
//    0000003,07-05-08-2016,AUS,1.49,
//    0000008,07-05-08-2016,USA,1.43,
//    0000008,07-05-08-2016,UK,1.29,
//    0000008,07-05-08-2016,AUS,1.22
// )

Upvotes: 2

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