How can you convert a struct to an array to handle schema mismatches when loading incremental XML data using com.databricks.spark.xml in Azure Databricks?

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To convert a struct to an array in order to handle schema mismatches when loading incremental XML data using com.databricks.spark.xml in Azure Databricks, you can use the explode function.

Here is an example:

import org.apache.spark.sql.functions._

val df = spark.read
  .option("rowTag", "book")
  .xml("/path/to/xml/file.xml")

val explodedDF = df
  .select(explode(col("author")).alias("author"), col("*"))
  .drop("author")

In this example, we first read the XML file into a DataFrame using the com.databricks.spark.xml library. We then use the explode function to convert the “author” field from a struct to an array. This allows us to handle schema mismatches when new data is added to the XML file, as the array can be easily extended to accommodate additional fields.

Finally, we drop the “author” field from the DataFrame since it is no longer needed.