scala spark how to get latest day's record

Question

data=
"""
user date      item1 item2
1    2015-12-01 14  5.6
1    2015-12-01 10  0.6
1    2015-12-02 8   9.4
1    2015-12-02 90  1.3
2    2015-12-01 30  0.3
2    2015-12-01 89  1.2
2    2015-12-30 70  1.9
2    2015-12-31 20  2.5
3    2015-12-01 19  9.3
3    2015-12-01 40  2.3
3    2015-12-02 13  1.4
3    2015-12-02 50  1.0
3    2015-12-02 19  7.8
"""

if I have some data like above, how can I get each user's latest day's record? I tried to use the groupByKey, but have no idea.

val user = data.map{
case(user,date,item1,item2)=>((user,date),Array(item1,item2))
}.groupByKey()

and then I don't know how to deal with it. Can anyone give me some suggestions? Thanks a lot:)

update:

I changed my data,and now user has several records in the latest day, and I want to get all of them. Thx:)

second update:

I want to get the result is:

user1 (2015-12-02,Array(8,9.4),Array(90,1.3))
user2 (2015-12-31,Array(20,2.5))
user3 (2015-12-02,Array(13,1.4),Array(50,1.0),Array(19,7,8))

and now I write some code:

val data2=data.trim.split("\\n").map(_split("\\s+")).map{
f=>{(f(0),ArrayBuffer(
                    f(1),
                    f(2).toInt,
                    f(3).toDouble)
    )}
}
val data3 = sc.parallelize(data2)
data3.reduceByKey((x,y)=>
             if(x(0).toString.compareTo(y(0).toString)>=0) x++=y
                  else y).foreach(println)

result is:

(2,ArrayBuffer(2015-12-31, 20, 2.5))
(1,ArrayBuffer(2015-12-02, 8, 9.4, 2015-12-02, 90, 1.3))
(3,ArrayBuffer(2015-12-02, 13, 1.4, 2015-12-02, 50, 1.0, 2015-12-02, 19, 7.8))

Is there anything can do to improve it? :)

Answer 1

I think your best bet is to map your input data to an RDD of tuples of (user, (date, item1, item2)) so the rdd will be userRdd: RDD[(Int, (Date, Int, Double))]

From here you can create a reducer that will take two tuples and produce another of the same format which is the tuple with the greater date value:

reduceMaxDate(a: (Date, Int, Double), b: (Date, Int, Double)) : (Date, Int, Double) = {
     if(a._1 > b._1) a else b
} 

From here you can find the max value for each user by calling:

userRdd.reduceByKey(reduceMaxDate).

This will yield the tuple with the max timestamp for each user.

Answer 2

Here are the scripts

For scala

val data = sc.textFile("file:///home/cloudera/data.txt")
val dataMap = data.map(x => (x.split(" +")(0), x))
val dataReduce = dataMap.reduceByKey((x, y) =>
  if(x.split(" +")(1) >= y.split(" +")(1)) x 
  else y)

val dataUserAndDateKey = data.map(rec => ((rec.split(" +")(0), rec.split(" +")(1)), rec))

val dataReduceUserAndDateKey = dataReduce.map(rec => ((rec._2.split(" +")(0), rec._2.split(" +")(1)), rec(1)))

val joinData = dataUserAndDateKey.join(dataReduceUserAndDateKey)

joinData.map(rec => rec._2._1).foreach(println)

For pyspark

import re

data = sc.textFile("file:///home/cloudera/data.txt")
dataMap = data.map(lambda rec: (re.split('\s+', rec)[0], rec))
dataReduce = dataMap.reduceByKey(lambda x, y: x if(re.split('\s+', x)[1] >= re.split('\s+', y)[1]) else y)

dataUserAndDateKey = data.map(lambda rec: ((re.split('\s+', rec)[0], re.split('\s+', rec)[1]), rec))

dataReduceUserAndDateKey = dataReduce.map(lambda rec: ((re.split('\s+', rec[1])[0], re.split('\s+', rec[1])[1]), rec[1]))

joinData = dataUserAndDateKey.join(dataReduceUserAndDateKey)
for i in joinData.collect(): print(i[1][0])

Here is the output:

3    2015-12-02 13  1.4
3    2015-12-02 50  1.0
3    2015-12-02 19  7.8
2    2015-12-31 20  2.5
1    2015-12-02 8   9.4
1    2015-12-02 90  1.3

You can also use SQL in HiveContext of SparkContext using data frames.