spark-scala中的库存利润计算

Question

A table of 5-tuples (PRODUCT_ID, TRANSACTION_TYPE, QUANTITY, PRICE, DATE) . Transaction_Type could be one of "Buy" or "Sell". Quantity is the number of instances of the product is bought or sold, for the Price indicated on the Date .

A product that is sold is offset against the inventory already in hand, and that too the earliest instance of that inventory.

Net Profit is calculated by offsetting the Sold inventory against the earliest Bought inventory, and if that doesn't fully address it, then use the next Bought inventory, and so on.

For instance, consider the following table values:

1, Buy, 10, 100.0, Jan 1

2, Buy, 20, 200.0, Jan 2

1, Buy, 15, 150.0, Jan 3

1, Sell, 5, 120.0, Jan 5

1, Sell, 10, 125.0, Jan 6 

There are hundreds of files stored already on HDFS having the schema shown above.

Then the profit calculation should work as follows:

• When Product 1 is sold on Jan 5, those 5 units should offset against the Jan 1 Buy transaction first (resulting in a profit of 5*(120.0-100.0)).
• Then when Product 1 is further sold on Jan 6, since the units sold are more than what remains from Jan1 Buy lot, Jan 3's Buy lot can be considered for the remainder.
• That is, the profit from selling Product 1 on Jan 6 is 5*(125.0-100.0)+5*(125.00-150.0).
• So, the profit value for Jan 6 transaction is = 5 * (25) + 5 * (-25 ) = 125 - 125 = 0. and the net profit until Jan 6 is 100 (from Jan 5 transaction) + 0 (from Jan 6 transaction) = 100.
• Calculate the final profit as of the last date present in that data.

Below is the code snippet. But It does not work getting NullPointer Exception. Any better suggestion ?

import org.apache.spark.SparkContext._
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.sql._
import org.apache.spark.rdd._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
import org.apache.spark.sql.Row


case class Inventory(PRODUCT_ID: Int, TRANSACTION_TYPE: String, QUANTITY: Long, PRICE: Double, DATE: String)

object MatchingInventory{
    def main(args:Array[String])= {

        val conf = new SparkConf().setAppName("XYZ")
        val sc = new SparkContext(conf)


        val sqlcontext = new SQLContext(sc)
        // Create a schema RDD of Inventory objects from the data that has any number of text file.
        import sqlcontext.implicits._
        val dfInvent= sc.textFile("Invent.txt")
        .map(_.split(","))
        .map(p => Inventory(p(0).trim.toInt, p(1).trim, p(2).trim.toLong, p(3).trim.toDouble, p(4).trim))
        .toDF().cache()
        dfInvent.show()

        val idDF =  dfInvent.map{row => row.getInt(0)}.distinct 
        //idDF.show()
        val netProfit = sc.accumulator(0.0)
        idDF.foreach{id =>
        val sellDF = dfInvent.filter((dfInvent("PRODUCT_ID").contains(id)) && (dfInvent("TRANSACTION_TYPE").contains("Sell")))
        val buyDF = dfInvent.filter((dfInvent("PRODUCT_ID").contains(id)) && (dfInvent("TRANSACTION_TYPE").contains("Buy")))    
         var soldQ:Long = sellDF.map{row => row.getLong(2)}.reduce(_+_) 
         var sellPrice:Double = sellDF.map{row => row.getLong(2)*row.getDouble(3)}.reduce(_+_) //reduce sends the result back to driver
         var profit:Double = 0.0
         // profit for each bought item
         buyDF.foreach{row => 
                           if((soldQ > 0) && (soldQ < row.getLong(2))){profit += sellPrice -(soldQ*row.getDouble(3));soldQ = 0}
                           else if((soldQ > 0) && (soldQ > row.getLong(2))){profit += sellPrice - (row.getLong(2)*row.getDouble(3));soldQ = soldQ - row.getLong(2)}
                                else{}} 
        netProfit += profit}
        println("Inventory net Profit" + netProfit)
    }

}

Answer 1

I tried something like this. This is a workable code, only issue is I am using collect at the later stage to sync between buy and sell which will lead to memory issues for large data.

from pyspark.sql import  SQLContext
from pyspark import SparkConf
from pyspark import SparkContext
import sys
from pyspark.sql.functions import *

if __name__ == "__main__":

    sc = SparkContext()

    sqlContext = SQLContext(sc)
    df = sqlContext.read.format('com.databricks.spark.csv').options(header='false', inferschema='true').load('test.csv')

    df = df.withColumn("C1", ltrim(df.C1))

    df.registerTempTable("tempTable")
    df = sqlContext.sql("select * from tempTable order by C0")

    dt = df.map(lambda s: (str(s[0])+'-'+ s[1], str(s[2]) + ',' +str(s[3])))
    dt = dt.reduceByKey(lambda a, b : a + '-' + b)

    ds = dt.collect()

    dicTran = {}
    for x in ds:
        key = (x[0].split('-'))[0]
        tratype = (x[0].split('-'))[1]


        val = {}
        if key in dicTran:
            val = dicTran[key]

        val[tratype] = x[1]
        dicTran[key] = val

    profit = 0

    for key, value in dicTran.iteritems():
        if 'Sell' in value:
            buy = value['Buy']
            sell = value['Sell']

            ls = sell.split('-')
            sellAmount = 0
            sellquant = 0
            for x in ls:
                y = x.split(',')
                sellAmount= sellAmount + float(y[0]) * float(y[1])
                sellquant = sellquant + float(y[0])

            lb = buy.split('-')
            for x in lb:
                y = x.split(',')

                if float(y[0]) >= sellquant:
                    profit += sellAmount - sellquant * float(y[1])
                else:
                    sellAmount -= float(y[0]) * float(y[1])
                    sellquant -= float(y[0])

    print 'profit', profit    



    #

Here is the logic I thought

1) For all same ids and transaction type I concat the quantity and price via a separator 2) Then I collect and split them to calculate the profit

I know this will crash on large data sets as collect is used but could not thing of anything better. I will try out your solution as well.

Answer 2

So here I come up with a solution

import org.apache.spark.SparkContext._
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.sql._
import org.apache.spark.rdd._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
import org.apache.spark.sql.Row
import java.text.SimpleDateFormat
import java.sql.Date
import scala.math.Ordering


//Defining Schema
case class Inventory(PRODUCT_ID: Int, TRANSACTION_TYPE: String, QUANTITY: Long, PRICE: Double, pDate:java.sql.Date)


object MatchingInventory{
    def main(args:Array[String])= {

        val conf = new SparkConf().setAppName("XYZ")
        val sc = new SparkContext(conf)


        val sqlcontext = new SQLContext(sc)

        import sqlcontext.implicits._

        val format = new SimpleDateFormat("MMM d")
        //Read data from directory which has multple files
        val dfInvent= sc.textFile("data/*.txt")
        .map(_.split(","))
        .map(p => Inventory(p(0).trim.toInt, p(1).trim, p(2).trim.toLong, p(3).trim.toDouble, new Date(format.parse(p(4)).getTime)))
        .cache()

        def calculateProfit(data:Iterable[Inventory]):Double  = {
            var soldQ:Long = 0
            var sellPrice:Double = 0
            var profit:Double = 0
            val v = data

            for(i <- v ){
                if(i.TRANSACTION_TYPE == "Sell")
                {
                  soldQ = soldQ + i.QUANTITY
                  profit = profit+ i.PRICE*i.QUANTITY

                }
            }

            for(i <- v){
                if(i.TRANSACTION_TYPE == "Buy")
                {
                    if((soldQ > 0) && (soldQ < i.QUANTITY || soldQ == i.QUANTITY)){profit = profit -(soldQ*i.PRICE);soldQ = 0}
                    else if((soldQ > 0) && (soldQ > i.QUANTITY)){profit = profit - (i.QUANTITY*i.PRICE);soldQ = soldQ - i.QUANTITY}
                    else{}
                }
            }
           profit
        }

        val key: RDD[((Int), Iterable[Inventory])] = dfInvent.keyBy(r => (r.PRODUCT_ID)).groupByKey
        val values: RDD[((Int), List[Inventory])] = key.mapValues(v => v.toList.sortBy(_.pDate.getTime))


        val pro = values.map{ case(k,v) => (k, calculateProfit(v))}
        val netProfit = pro.map{ case(k,v) => v}.reduce(_+_)
        println("Inventory NetProfit" + netProfit)

    }