Darrell Ulm | Computer Science | Notes and Blog

The code shown below computes an approximation algorithm, greedy heuristic, for the 0-1 knapsack problem in Apache Spark. Having worked with parallel dynamic programming algorithms a good amount, wanted to see what this would look like in Spark. The Github code repo. for the Knapsack approximation algorithms is here , and it includes a Scala solution. The work on a Java version is in progress at time of this writing. Below we have the code that computes the solution that fits within the knapsack W for a set of items each with it's own weight and profit value. We look to maximize the final sum of selected items profits while not exceeding the total possible weight, W. First we import some spark libraries into Python. # Knapsack 0-1 function weights, values and size-capacity. from pyspark.sql import SparkSession from pyspark.sql.functions import lit from pyspark.sql.functions import col from pyspark.sql.functions import sum Now define the function, which will take a Spark

Made post at Databricks forum, thinking about how to take two DataFrames of the same number of rows and combine, merge, all columns into one DataFrame. This is straightforward, as we can use the  monotonically_increasing_id() function to assign unique IDs to each of the rows, the same for each Dataframe. It would be ideal to add extra rows which are null to the Dataframe with fewer rows so they match, although the code below does not do this. Once the IDs are added, a DataFrame join will merge all the columns into one Dataframe. # For two Dataframes that have the same number of rows, merge all columns, row by row. # Get the function monotonically_increasing_id so we can assign ids to each row, when the # Dataframes have the same number of rows. from pyspark.sql.functions import monotonically_increasing_id #Create some test data with 3 and 4 columns. df1 = sqlContext.createDataFrame([("foo", "bar","too","aaa"), ("bar&qu