Aggregating in RDDs
Now that you have conducted analytics with DataFrames in PySpark, let's briefly do a similar task with an RDD. Using the provided code, get the sum of the values of an RDD in PySpark.
A Spark session called spark has already been made for you.
This exercise is part of the course
Introduction to PySpark
Exercise instructions
- Create an RDD from the provided DataFrame.
- Apply the provided Lambda Function to the keys of the RDD.
- Collect the results of the aggregation.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# DataFrame Creation
data = [("HR", "3000"), ("IT", "4000"), ("Finance", "3500")]
columns = ["Department", "Salary"]
df = spark.createDataFrame(data, schema=columns)
# Map the DataFrame to an RDD
rdd = df.rdd.____(lambda row: (row["Department"], row["Salary"]))
# Apply a lambda function to get the sum of the DataFrame
rdd_aggregated = rdd.____(lambda x, y: x + y)
# Show the collected Results
print(rdd_aggregated.____())