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import org.apache.spark.sql.SparkSession
import org.apache.spark.rdd.RDD
import org.apache.spark.HashPartitioner
/** A Spark application that analyzes co-purchased products.
*
* This object processes a CSV input file containing order-product pairs and
* generates a report of products that are frequently purchased together. The
* analysis helps identify product relationships and potential recommendations
* for a shopping system.
*
* Input format: CSV file with each line containing "orderId,productId" Output
* format: CSV file with each line containing "product1,product2,count"
*
* @example
* {{{
* // Run the application with input and output paths
* spark-submit co-purchase-analysis.jar input.csv output/
* }}}
*/
object CoPurchaseAnalysis {
/** Represents an order-product relationship.
*
* @param orderId
* The unique identifier for the order
* @param productId
* The unique identifier for the product
*/
case class OrderProduct(orderId: Int, productId: Int)
/** Represents a pair of products that were purchased together.
*
* @param product1
* The identifier of the first product
* @param product2
* The identifier of the second product
*/
case class ProductPair(product1: Int, product2: Int)
/** Validates command line arguments and checks file existence.
*
* @param args
* Command line arguments array containing input file path and output
* directory path
* @return
* Some(errorMessage) if validation fails, None if validation succeeds
*/
def checkArguments(args: Array[String]): Option[String] = {
if (args.length != 2) {
Some("You must define input file and output folder.")
} else {
None
}
}
/** Creates and configures a SparkSession.
*
* @param appName
* The name of the Spark application
* @param master
* The Spark master URL (e.g., "local", "yarn")
* @return
* Configured SparkSession instance
*/
def createSparkSession(appName: String, master: String): SparkSession = {
var session = SparkSession.builder
.appName(appName)
.config("spark.master", master)
val creds = System.getenv("GOOGLE_APPLICATION_CREDENTIALS")
if (creds != null) {
session
.config("spark.hadoop.google.cloud.auth.service.account.enable", "true")
.config(
"spark.hadoop.google.cloud.auth.service.account.json.keyfile",
creds
)
}
session.getOrCreate()
}
/** Parses a single line from the input file into an OrderProduct instance.
* Expects the line to be in CSV format with orderId and productId.
*
* @param line
* Input line in format "orderId,productId"
* @return
* OrderProduct instance containing the parsed data
*/
def parseLine(line: String): OrderProduct = {
val parts = line.split(",")
OrderProduct(parts(0).toInt, parts(1).toInt)
}
/** Processes the order data to generate co-purchase statistics.
*
* The processing pipeline includes: (1) Grouping orders by orderId, (2)
* Generating product pairs for each order, (3) Counting occurrences of each
* product pair
*
* @param data
* RDD containing OrderProduct instances
* @return
* RDD containing CoPurchase instances with purchase frequency counts
*/
def processData(data: RDD[OrderProduct]): RDD[String] = {
val pairs = data
.map(order => (order.orderId, order.productId))
.groupByKey()
.flatMap { case (_, productIds) =>
val products = productIds.toSeq
for {
x <- products
y <- products if x < y
} yield (ProductPair(x, y), 1)
}
.partitionBy(new HashPartitioner(50))
val coProducts = pairs.reduceByKey(_ + _)
coProducts.map { case (ProductPair(product1, product2), count) =>
s"${product1},${product2},${count}"
}
}
/** Main entry point for the application.
*
* @param args
* Command line arguments array
*/
def main(args: Array[String]): Unit = {
val argsError = checkArguments(args)
if (!argsError.isEmpty) {
println(argsError.get)
return
}
// Configuration values should be passed as parameters
val config = Map(
"appName" -> "Co-Purchase Analysis",
"master" -> "local[*]",
"inputPath" -> args(0),
"outputPath" -> args(1)
)
// Program execution composed of pure functions
val spark = createSparkSession(config("appName"), config("master"))
try {
spark.sparkContext.setLogLevel("ERROR")
val inputRDD = spark.sparkContext
.textFile(config("inputPath"))
.map(parseLine)
val result = processData(inputRDD)
.saveAsTextFile(config("outputPath"))
} finally {
spark.stop()
}
}
}
|
