Write PySpark code to filter rows where status is 'success' and aggregate the total amount per customer using groupBy and sum.

The provided code demonstrates how to use PySpark’s DataFrame API to filter rows and aggregate data. First, filter(col('status') == 'success') keeps only rows where the status column equals 'success'. Then groupBy('customer_id') groups the remaining rows by customer ID. Finally, agg(sum('amount').alias('total_amount')) calculates the sum of the amount column for each group and names the resulting column total_amount. The resulting DataFrame contains one row per customer with the total amount of successful transactions. The operation is performed in a distributed manner; each partition computes partial sums, which are then combined. The time complexity is O(n) where n is the number of rows, and the space complexity is O(k) for k distinct customers. This pattern is common for summarizing transactional data in Spark.