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Getting back into parallel computing with Apache Spark

Getting back into parallel computing with Apache Spark has been great, and it has been interesting to see the McColl and Valiant BSP (Bulk Synchronous Parallel) model finally start becoming mainstream beyond GPUs.

While Spark can be some effort to setup on actual clusters and does have an overhead, thinking that these will be optimized over time and Spark will become more and more efficient. 

I have started a GitHub repo for Spark snippets if any are of interest as Apache Spark moves forward 'in parallel' to the HDFS (Hadoop Distributed File System).


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A way to Merge Columns of DataFrames in Spark with no Common Column Key

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.

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# For two Dataframes that have the same number of rows, merge all columns, row by row.
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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.

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# 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
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