How to Flatten Nested Structs in a Spark Dataframe?

Flattening a Nested Struct in Spark Dataframe

One may encounter situations where a dataframe contains complex nested structures, and flattening them becomes necessary. Consider a dataframe with the following structure:

|-- data: struct (nullable = true)
|    |-- id: long (nullable = true)
|    |-- keyNote: struct (nullable = true)
|    |    |-- key: string (nullable = true)
|    |    |-- note: string (nullable = true)
|    |-- details: map (nullable = true)
|    |    |-- key: string
|    |    |-- value: string (valueContainsNull = true)

The goal is to flatten this structure and create a new dataframe with the following simplified structure:

|-- id: long (nullable = true)
|-- keyNote: struct (nullable = true)
|    |-- key: string (nullable = true)
|    |-- note: string (nullable = true)
|-- details: map (nullable = true)
|    |-- key: string
|    |-- value: string (valueContainsNull = true)

While Spark does not explicitly provide an "explode" function for structs, the following method can be employed in Spark 1.6 or later to achieve the desired result:

df.select(df.col("data.*"))

Alternatively, if only specific fields of the "data" struct are needed, the following syntax can be used:

df.select(df.col("data.id"), df.col("data.keyNote"), df.col("data.details"))

By utilizing these techniques, it is possible to flatten complex nested structs in Spark dataframes, enabling further analysis and manipulation of the data.

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