Task skew occurs when a small subset of keys holds a disproportionate amount of data, causing certain tasks to process significantly more records than others. Databricks documentation recommends salting as an effective mitigation technique.
Salting introduces a random or calculated prefix to skewed keys, distributing records across multiple partitions and balancing the workload during the shuffle stage. After aggregation, a second pass re-aggregates results by removing the prefix to restore key integrity.
Increasing memory (B) does not resolve distribution imbalance; reduceByKey (C) still triggers shuffles; and filtering (D) would remove valid business data. Hence, salting is the correct and officially recommended approach to address skew in Spark aggregations.