Please arrange the shuffle lines or code in the correct order. Do not add, remove, rewrite, or modify any code; only reorder the lines.

When arranging code lines for a shuffle‑based data pipeline, the logical order is: 1) Initialize the environment and set up any required context (e.g., SparkSession). 2) Load or ingest raw data into a DataFrame. 3) Apply any necessary transformations or filtering. 4) Partition the data if needed to prepare for shuffle. 5) Perform the shuffle operation (e.g., repartition, groupByKey, or join). 6) Apply reduce or aggregation logic on the shuffled data. 7) Write the final result to the target storage. 8) Clean up resources. This sequence ensures that data is available before transformations, that shuffling occurs after partitioning, and that aggregation only runs on the correctly partitioned data. If you see code lines that perform aggregation before shuffling, move them after the shuffle step. Likewise, any cleanup or resource release should come last. By following this order, you avoid runtime errors such as “cannot find column” or “task not found” and maintain a clear data flow.