How do I join two rdds based on a common field? - scala

Question

I am very new to Scala and learning to work with RDDs. I have two csv files which have the following headers and data:
csv1.txt
id,"location", "zipcode"
1, "a", "12345"
2, "b", "67890"
3, "c" "54321"
csv2.txt
"location_x", "location_y", trip_hrs
"a", "b", 1
"a", "c", 3
"b", "c", 2
"a", "b", 1
"c", "b", 2
Basically, csv1 data is a distinct set of locations and zip codes, whereas csv2 data has the trip duration between location_x and location_y.
The common piece of information in these two data sets is location in csv1 and location_x in csv2 even though they have different header names.
I would like to create two RDDs with one containing the data from csv1 and the other from csv2.
Then I would like to join these two RDDs and return the location, zipcode, and sum of all trip times from that location as shown below:
("a", "zipcode", 5)
("b", "zipcode", 2)
("c", "zipcode", 2)
I was wondering if one of you can assist me with this problem. Thanks.

Answer 1

I will give you the code (a complete app in IntelliJ) with some explanations. I hope it can be helpful.
Please read the Spark documentation for the explicit details.
working-with-key-value-pairs
This problem can be done with Spark Dataframes, you can try for yourself.
import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.SparkSession
object Joining {
val spark = SparkSession
.builder()
.appName("Joining")
.master("local[*]")
.config("spark.sql.shuffle.partitions", "4") //Change to a more reasonable default number of partitions for our data
.config("spark.app.id", "Joining") // To silence Metrics warning
.getOrCreate()
val sc = spark.sparkContext
val path = "/home/cloudera/files/tests/"
def main(args: Array[String]): Unit = {
Logger.getRootLogger.setLevel(Level.ERROR)
try {
// read the files
val file1 = sc.textFile(s"${path}join1.csv")
val header1 = file1.first // extract the header of the file
val file2 = sc.textFile(s"${path}join2.csv")
val header2 = file2.first // extract the header of the file
val rdd1 = file1
.filter(line => line != header1) // to leave out the header
.map(line => line.split(",")) // split the lines => Array[String]
.map(arr => (arr(1).trim,arr(2).trim)) // to make up a pairRDD with arr(1) as key and zipcode
val rdd2 = file2
.filter(line => line != header2)
.map(line => line.split(",")) // split the lines => Array[String]
.map(arr => (arr(0).trim, arr(2).trim.toInt)) // to make up a pairRDD with arr(0) as key and trip_hrs
val joined = rdd1 // join the pairRDD by its keys
.join(rdd2)
.cache() // cache joined in memory
joined.foreach(println) // checking data
println("**************")
// ("c",("54321",2))
// ("b",("67890",2))
// ("a",("12345",1))
// ("a",("12345",3))
// ("a",("12345",1))
val result = joined.reduceByKey({ case((zip, time), (zip1, time1) ) => (zip, time + time1) })
result.map({case( (id,(zip,time)) ) => (id, zip, time)}).foreach(println) // checking output
// ("b","67890",2)
// ("c","54321",2)
// ("a","12345",5)
// To have the opportunity to view the web console of Spark: http://localhost:4041/
println("Type whatever to the console to exit......")
scala.io.StdIn.readLine()
} finally {
sc.stop()
println("SparkContext stopped")
spark.stop()
println("SparkSession stopped")
}
}
}

Answer 2

If you can read CSV into RDD already, Trips can be summarized, and then joined with Locations:
val tripsSummarized = trips
.map({ case (location, _, hours) => (location, hours) })
.reduceByKey((hoursTotal, hoursIncrement) => hoursTotal + hoursIncrement)
val result = locations
.map({ case (_, location, zipCode) => (location, zipCode) })
.join(tripsSummarized)
.map({case (location, (zipCode, hoursTotal)) => (location, zipCode, hoursTotal) })
If locations without trips are required, "leftOuterJoin" can be used.