Comprehensive and Detailed Explanation From Exact Extract:
To ensure that data written out to disk is sorted, it is important to consider how Spark writes data when saving to Parquet tables. The methods.sort()or.orderBy()apply a global sort but do not guarantee that the sorting will persist in the final output files unless certain conditions are met (e.g. a single partition via.coalesce(1)— which is not scalable).
Instead, the proper method in distributed Spark processing to ensure rows are sorted within their respective partitions when written out is:
sortWithinPartitions("column_name")
According to Apache Spark documentation:
"sortWithinPartitions()ensures each partition is sorted by the specified columns. This is useful for downstream systems that require sorted files."
This method works efficiently in distributed settings, avoids the performance bottleneck of global sorting (as in.orderBy()or.sort()), and guarantees each output partition has sorted records — which meets the requirement of consistently sorted data.
Thus:
Option A and B do not guarantee the persisted file contents are sorted.
Option C introduces a bottleneck via.coalesce(1)(single partition).
Option D correctly applies sorting within partitions and is scalable.
[Reference: Databricks & Apache Spark 3.5 Documentation → DataFrame API → sortWithinPartitions()]
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