1 | initial version |
Grouping in Spark Scala is the process of partitioning data based on a common attribute or key. To create a nested DataFrame, we can follow these steps:
To nest data, we can use the struct
function to create a new column of nested data types. We can then use the groupBy
function to group the data by one or more columns and apply aggregation functions such as sum
, avg
, or count
to the grouped data. Finally, we can use the agg
function to apply the aggregation functions to the grouped data and create a new DataFrame with nested data.
Example code:
import org.apache.spark.sql.functions._
// Load data into a DataFrame
val df = spark.read.format("csv")
.option("header", "true")
.load("path/to/data")
// Group data by two columns and nest the data
val nestedDF = df.groupBy("column1", "column2")
.agg(struct($"column3", $"column4", $"column5").alias("nested_data"))
// Display the nested DataFrame
nestedDF.show()