Using PySpark select() transformations one can select the nested struct columns from DataFrame. While working with semi-structured files like JSON or structured files like Avro, Parquet, ORC we often have…
In Spark SQL, select() function is used to select one or multiple columns, nested columns, column by index, all columns, from the list, by regular expression from a DataFrame. select()…
In PySpark, select() function is used to select single, multiple, column by index, all columns from the list and the nested columns from a DataFrame, PySpark select() is a transformation…
PySpark StructType & StructField classes are used to programmatically specify the schema to the DataFrame and create complex columns like nested struct, array, and map columns. StructType is a collection…
Use PySpark withColumnRenamed() to rename a DataFrame column, we often need to rename one column or multiple (or all) columns on PySpark DataFrame, you can do this in several ways.…
By default Spark SQL infer schema while reading JSON file, but, we can ignore this and read a JSON with schema (user-defined) using spark.read.schema("schema") method. What is Spark Schema Spark…
Spark SQL provides Encoders to convert case class to the spark schema (struct StructType object), If you are using older versions of Spark, you can create spark schema from case…
Spark Schema defines the structure of the DataFrame which you can get by calling printSchema() method on the DataFrame object. Spark SQL provides StructType & StructField classes to programmatically specify…
Problem: How to create a Spark DataFrame with Array of struct column using Spark and Scala? Using StructType and ArrayType classes we can create a DataFrame with Array of Struct…
Problem: How to explode Array of StructType DataFrame columns to rows using Spark. Solution: Spark explode function can be used to explode an Array of Struct ArrayType(StructType) columns to rows…