title: How to explode and select normal and nested columns in pyspark?
body: <p>I have a table in Bigquery, and its schema I have uploaded as Image.
  <a href="https://i.stack.imgur.com/Ulclo.png" rel="nofollow noreferrer">Schema</a></p>
  <p>I am trying to make some transformation in spark using pySpark.</p>
  <p>For my transformation,I need to select content.id( STRUCT) ,content.links ( STRUCT),
  ,content.authors  (RECORD REPEATED(i;e, array of STRUCTS))</p>
  <p>But,I am not able to select them in a single statement.
  Can anyone show me how I can achieve it using PySpark and SparkSQL?</p>
  <p>Here is a dummy table and expected output:</p>
  <p><a href="https://i.stack.imgur.com/9AwF6.png" rel="nofollow noreferrer">expected output</a></p>