How to find similar rows by matching column values spark? - scala

Question

So i have a data set like
{"customer":"customer-1","attributes":{"att-a":"att-a-7","att-b":"att-b-3","att-c":"att-c-10","att-d":"att-d-10","att-e":"att-e-15","att-f":"att-f-11","att-g":"att-g-2","att-h":"att-h-7","att-i":"att-i-5","att-j":"att-j-14"}}
{"customer":"customer-2","attributes":{"att-a":"att-a-9","att-b":"att-b-7","att-c":"att-c-12","att-d":"att-d-4","att-e":"att-e-10","att-f":"att-f-4","att-g":"att-g-13","att-h":"att-h-4","att-i":"att-i-1","att-j":"att-j-13"}}
{"customer":"customer-3","attributes":{"att-a":"att-a-10","att-b":"att-b-6","att-c":"att-c-1","att-d":"att-d-1","att-e":"att-e-13","att-f":"att-f-12","att-g":"att-g-9","att-h":"att-h-6","att-i":"att-i-7","att-j":"att-j-4"}}
{"customer":"customer-4","attributes":{"att-a":"att-a-9","att-b":"att-b-14","att-c":"att-c-7","att-d":"att-d-4","att-e":"att-e-8","att-f":"att-f-7","att-g":"att-g-14","att-h":"att-h-9","att-i":"att-i-13","att-j":"att-j-3"}}
I have flattened the data in the DF like this
+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+-----------+
| att-a| att-b| att-c| att-d| att-e| att-f| att-g| att-h| att-i| att-j| customer|
+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+-----------+
| att-a-7| att-b-3|att-c-10|att-d-10|att-e-15|att-f-11| att-g-2| att-h-7| att-i-5|att-j-14| customer-1|
| att-a-9| att-b-7|att-c-12| att-d-4|att-e-10| att-f-4|att-g-13| att-h-4| att-i-1|att-j-13| customer-2|
I want to complete the comapreColumns function.
which compares the columns of the two dataframes(userDF and flattenedDF) and returns a new DF as sample output.
how to do that? Like, compare each row's and column in flattenedDF with userDF and count++ if they match? e.g att-a with att-a att-b with att-b.
def getCustomer(customerID: String)(dataFrame: DataFrame): DataFrame = {
dataFrame.filter($"customer" === customerID).toDF()
}
def compareColumns(customerID: String)(dataFrame: DataFrame): DataFrame = {
val userDF = dataFrame.transform(getCustomer(customerID))
userDF.printSchema()
userDF
}
Sample Output:
+--------------------+-----------+
| customer | similarity_score |
+--------------------+-----------+
|customer-1 | -1 | its the same as the reference customer so to ignore '-1'
|customer-12 | 2 |
|customer-3 | 2 |
|customer-44 | 5 |
|customer-5 | 1 |
|customer-6 | 10 |
Thanks