question: public static Schema schemaForStructType ( StructType structType ) { List < String > fieldNames = Lists . newArrayList ( ) ; List < String > fieldTypes = Lists . newArrayList ( ) ; for ( StructField field : structType . fields ( ) ) { fieldNames . add ( field . name ( ) ) ; DataType fieldType = field . dataType ( ) ; if ( fieldType . equals ( DataTypes . StringType ) ) { fieldTypes . add ( "string" ) ; } else if ( fieldType . equals ( DataTypes . FloatType ) ) { fieldTypes . add ( "float" ) ; } else if ( fieldType . equals ( DataTypes . DoubleType ) ) { fieldTypes . add ( "double" ) ; } else if ( fieldType . equals ( DataTypes . IntegerType ) ) { fieldTypes . add ( "int" ) ; } else if ( fieldType . equals ( DataTypes . LongType ) ) { fieldTypes . add ( "long" ) ; } else if ( fieldType . equals ( DataTypes . BooleanType ) ) { fieldTypes . add ( "boolean" ) ; } else { throw new RuntimeException ( "Unsupported Spark SQL field type: " + fieldType ) ; } } return RecordUtils . schemaFor ( fieldNames , fieldTypes ) ; }
target:  The equivalent Avro schema for the given Spark SQL schema.