How can you identify duplicate records in a dataset and determine which records are duplicated many times?

Duplicate detection in Spark can be performed by grouping on the columns that define uniqueness and counting the occurrences. For example, if you have a DataFrame df with columns id, name, and email, you can find duplicates as follows:

from pyspark.sql import functions as F

dup_counts = (df.groupBy("id", "name", "email")
               .agg(F.count("id").alias("cnt"))
               .filter("cnt > 1"))

dup_counts now contains each unique combination that appears more than once, along with the number of times it appears (cnt). To see which specific rows are duplicated, you can join this result back to the original DataFrame:

duplicates = df.join(dup_counts, on=["id", "name", "email"], how="inner")

This gives you all rows that are part of a duplicate group. If you only need the most frequent duplicate, you can order by cnt descending and take the first row. Using window functions is another option: partition by the key columns, compute a row number, and filter where the row number is greater than 1.

Complexity is O(n) for the groupBy and O(n log n) for the join, which is efficient for large Spark datasets.