1 files changed, 7 insertions, 18 deletions
diff --git a/co-purchase-analysis/src/main/scala/Main.scala b/co-purchase-analysis/src/main/scala/Main.scala
index a711136..fbca3ac 100644
--- a/co-purchase-analysis/src/main/scala/Main.scala
+++ b/co-purchase-analysis/src/main/scala/Main.scala
@@ -20,15 +20,6 @@ import org.apache.spark.HashPartitioner
   */
 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
@@ -81,17 +72,17 @@ object CoPurchaseAnalysis {
     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.
+  /** Parses a single line from the input file into a tuple. 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
+    *   (Int, Int) tuple containing the parsed data
     */
-  def parseLine(line: String): OrderProduct = {
+  def parseLine(line: String): (Int, Int) = {
     val parts = line.split(",")
-    OrderProduct(parts(0).toInt, parts(1).toInt)
+    (parts(0).toInt, parts(1).toInt)
   }
 
   /** Processes the order data to generate co-purchase statistics.
@@ -101,18 +92,17 @@ object CoPurchaseAnalysis {
     * product pair
     *
     * @param data
-    *   RDD containing OrderProduct instances
+    *   RDD containing (Int, Int)
     * @param partitionsNumber
     *   Number of partitions used by HashPartitioner
     * @return
     *   RDD containing CoPurchase instances with purchase frequency counts
     */
   def processData(
-      data: RDD[OrderProduct],
+      data: RDD[(Int, Int)],
       partitionsNumber: Int
   ): RDD[String] = {
     val pairs = data
-      .map(order => (order.orderId, order.productId))
       .partitionBy(new HashPartitioner(partitionsNumber))
       .groupByKey()
       .flatMap { case (_, productIds) =>
@@ -122,7 +112,6 @@ object CoPurchaseAnalysis {
           y <- products if x < y
         } yield (ProductPair(x, y), 1)
       }
-      .partitionBy(new HashPartitioner(partitionsNumber))
 
     val coProducts = pairs.reduceByKey(_ + _)