Spark Data Frames: 200 Bytes of Dependency Magic

In a recent development, a system processes records from a Mainframe system, where each record is 200 bytes long. These records are split into data frames using Spark’s explode and split functions. Each data frame represents a single record, maintaining the critical order of A, B, and C records. The dependency between these records is crucial: A records can have multiple or no B records, and B records can similarly relate to C records. The challenge lies in establishing these relationships without using inefficient for loops, which are considered anti-patterns in Spark programming. The goal is to efficiently insert these records into a database, setting up primary keys to reflect their hierarchical relationship. The current method involves iterating through data frames to identify parent-child relationships, but a more optimized Spark-based solution is sought to handle this dependency mapping in Java.

Source: stackoverflow.com