spark dataframe map example

There are some situations where you are required to Filter the Spark DataFrame based on the keys which are already available in Scala collection. Let’s see how we can achieve this in Spark. You need to use spark UDF for this – Step -1: Create a DataFrame using parallelize method by taking sample data. scala> val df = sc.parallelize(Seq((2,"a"),(3,"b"),(5,"c"))).toDF("id","name") df: org.apache.spark.sql.DataFrame = [id: int, name: string] Step -2: Create a UDF which concatenates columns inside dataframe. Below UDF accepts a collection of columns and returns concatenated column separated by the given delimiter. scala> val concatKey = udf( (xs: Seq[Any], sep:String) => xs.filter(_ != null).mkString(sep)) concatKey: org.apache.spark.sql.UserDefinedFunction = UserDefinedFu...