How to Join And Group Multiple Datasets in Spark

The COGROUP operation in Spark is used to group data from multiple datasets based on a common key. It is similar to the GROUP BY operation in SQL, but it can be applied to more than two datasets.

The COGROUP operation requires at least two datasets to operate on. The resulting dataset will have one row for each distinct key in the datasets. Each row will contain the key and a tuple of all the values from the corresponding rows in each dataset.

COGROUP is a powerful operation that can be used to join datasets, perform complex aggregations, and more. It can be particularly useful when working with large datasets that cannot be easily handled by a single machine.

Example of Using COGROUP

Here is an example of how to use the COGROUP operation in Scala Spark:

Group and Join Multiple Dataset Using CoGroup

val dataset1 = Seq((1, "John"), (2, "Mary"), (3, "Steve")).toDF("id", "name") 
val dataset2 = Seq((1, "Sales"), (2, "Marketing"), (3, "IT")).toDF("id", "department") 
val dataset3 = Seq((1, 100), (2, 200), (3, 300)).toDF("id", "salary") 

val cogroupedData = dataset1.cogroup(dataset2, dataset3)("id")
    .agg(collect_list("name"), collect_list("department"), collect_list("salary")) 
    
cogroupedData.show()

In this example, we have three datasets: dataset1, dataset2, and dataset3. Each dataset has a column id which is the common key.

We use the cogroup function to group the data by the id column, and then use the agg function to perform aggregations on the other columns. In this case, we use the collect_list function to collect all the values from the name, department, and salary columns into lists.

The resulting dataset, cogroupedData, contains one row for each unique id, with the corresponding lists of values from each dataset.

Conclusion

COGROUP is a powerful operation that can be used to perform a wide variety of operations on datasets in Spark. With its ability to group data from multiple datasets based on a common key, it is a key tool for anyone working with large, complex datasets in Spark.