Flatten Nested Spark Dataframe

Question

Is there a way to flatten an arbitrarily nested Spark Dataframe? Most of the work I\'m seeing is written for specific schema, and I\'d like to be able to generically flatten

Answer 1

This issue might be a bit old, but for anyone out there still looking for a solution you can flatten complex data types inline using select *:

first let's create the nested dataframe:

from pyspark.sql import HiveContext
hc = HiveContext(sc)
nested_df = hc.read.json(sc.parallelize(["""
{
  "field1": 1, 
  "field2": 2, 
  "nested_array":{
     "nested_field1": 3,
     "nested_field2": 4
  }
}
"""]))

now to flatten it:

flat_df = nested_df.select("field1", "field2", "nested_array.*")

You'll find useful examples here: https://docs.databricks.com/delta/data-transformation/complex-types.html

If you have too many nested arrays, you can use:

flat_cols = [c[0] for c in nested_df.dtypes if c[1][:6] != 'struct']
nested_cols = [c[0] for c in nested_df.dtypes if c[1][:6] == 'struct']
flat_df = nested_df.select(*flat_cols, *[c + ".*" for c in nested_cols])