Explore Darrell Ulm's SlideShare profile, where you can find a curated collection of his bookmarked presentations. These resources cover a range of key technology areas, offering insights into Computer Science principles, the powerful data processing capabilities of Apache Spark, web development and content management with Drupal, and various methodologies and practices within Software Development.
This is the Scala version of the approximation algorithm for the knapsack problem using Apache Spark. I ran this on a local setup, so it may require modification if you are using something like a Databricks environment. Also you will likely need to setup your Scala environment. All the code for this is at GitHub First, let's import all the libraries we need. import org.apache.spark._ import org.apache.spark.rdd.RDD import org.apache.spark.SparkConf import org.apache.spark.SparkContext._ import org.apache.spark.sql.DataFrame import org.apache.spark.sql.SparkSession import org.apache.spark.sql.functions.sum We'll define this object knapsack, although it could be more specific for what this is doing, it's good enough for this simple test. object knapsack { Again, we'll define the knapsack approximation algorithm, expecting a dataframe with the profits and weights, as well as W, a total weight. def knapsackApprox(knapsackDF: DataFrame, W: Double): Da...