How to flatten a nested recursive JSON struct to a list of struct

[Wayne]

This is from Spark Event log on Event SparkListenerSQLExecutionStart.
How to flatten the sparkPlanInfo struct into an array of the same struct, then later explode it. Note that the element children is an array containing the parent struct, and the level of nesting could be 0 to any random number.

[Kaniz_Fatma · Accepted solution]

Hi @Wayne, To flatten the sparkPlanInfo struct into an array of the same struct and then explode it, you can follow these steps:

Flatten the Struct:

• Use the select function to extract the fields from the sparkPlanInfo struct.
• Create a new column for each field in the struct.
• For example, if your sparkPlanInfo struct has fields like field1, field2, and field3, you can create new columns with those names.

Create an Array of the Flattened Struct:

• Use the array function to create an array containing the flattened struct columns.
• For example:from pyspark.sql.functions import col, struct, array flattened_struct = struct(col("field1"), col("field2"), col("field3")) df_with_array = df.withColumn("flattened_array", array(flattened_struct))

Explode the Array:

• Use the explode function to create a separate record for each element of the array.
• This will repeat the value(s) of the other column(s) for each element in the array.
• For example:exploded_df = df_with_array.select("id", explode("flattened_array").alias("exploded_struct"))

Now you have an exploded DataFrame where each row corresponds to an element of the original sparkPlanInfo struct. You can access the fields of the struct using dot notation, such as exploded_df.exploded_struct.field1.

Remember to adjust the column names and struct fields according to your actual data. The level of nesting can vary, but this approach should work for any random number of nested structs. 🚀

[Kaniz_Fatma · Accepted solution]

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