Programmatically specifying the schema

There are a few cases where case classes might not work; one of these cases is where case classes cannot take more than 22 fields. Another case can be that you do not know about the schema beforehand. In this approach, data is loaded as an RDD of the Row objects. The schema is created separately using the StructType and StructField objects, which represent a table and a field, respectively. The schema is applied to the Row RDD to create a DataFrame.

How to do it...

1. Start the Spark shell or Databricks Cloud Scala notebook:

$ spark-shell  

2. Import the Spark SQL datatypes and Row objects:

        scala> import org.apache.spark.sql._
        scala> import org.apache.spark.sql.types._

3. Create the schema using the StructType and StructField objects. The StructField object takes parameters in the form of param name, param type, and nullability:

scala> val schema = StructType(
    Array(StructField("first_name",StringType,true),
StructField("last_name",StringType...