Chaining multiple withColumn() statements in Spark can create numerous intermediate DataFrames, significantly increasing memory usage and slowing down job execution.
Each call to withColumn triggers a new transformation, which can lead to redundant re-evaluation and ultimately impact the performance of the Spark job.
A complex DAG created by multiple withColumn() calls can complicate execution, as Spark needs to optimize and execute more steps, consuming additional resources.
To improve efficiency, consolidating withColumn() statements into a single call reduces the creation of intermediate states and helps streamline the Spark job.
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