Salting

Imagine we have two DataFrames, df1 and df2, that we want to join on a column named 'id'. Assume that the 'id' column is highly skewed.

Firstly, without any handling of skewness, the join might look something like this:

result = df1.join(df2, on='id', how='inner')

Now, let's implement salting to handle the skewness:

import pyspark.sql.functions as F
# Define the number of keys you'll use for salting 
num_salting_keys = 100
# Add a new column to df1 for salting 
df1 = df1.withColumn('salted_key', (F.rand()*num_salting_keys).cast('int'))
# Explode df2 into multiple rows by creating new rows with salted keys 
df2_exploded = df2.crossJoin(F.spark.range(num_salting_keys).withColumnRenamed('id', 'salted_key'))
# Now perform the join using both 'id' and 'salted_key' 
result = df1.join(df2_exploded, on=['id', 'salted_key'], how='inner')
# If you wish, you can drop the 'salted_key' column after the join
result = result.drop('salted_key')

In this code, we've added a "salt" to the 'id' column in df1 and created new rows in df2 for each salt value. We then perform the join operation on both 'id' and the salted key. This helps to distribute the computation for the skewed keys more evenly across the cluster.