Stream API was introduced in Java 8, providing various methods to write clear and expressive code when working with a collection of data. “GroupingBy” and “PartitioningBy” are two of the many methods offered by the Stream API that are particularly useful for effectively classifying data.
In this blog, we’ll explore how to use these methods when we need to organize the data according to a certain criterion, accompanied by practical examples.
Grouping and Partitioning in Data Collections:
To understand the concepts of GroupingBy and PartitioningBy methods, let’s imagine a daily scenario involving the organization of a product collection within an e-commerce store. To facilitate this, let’s create a Product class equipped with attributes such as name, brand, and price.
Suppose we have a collection of products in our e-commerce store with various brands and prices. In such scenarios, we can leverage the Stream API to efficiently categorize them by grouping or partitioning based on a certain criteria.
Using GroupingBy() method:
The groupingBy collector in the Java Stream API can be used to group a collection of Products into different categories based on a certain property. In this case, the products are categorized based on their respective brands.
Using PartitioningBy() method:
The partitioningBy collector in the Java Stream API can be used to segregate a collection of Products into two categories based on a specified condition. In this case, the condition is whether the product’s price exceeds 1000 dollars, categorizing them as Expensive and Affordable.
Conclusion
In conclusion, Java Stream API provides countless methods for organizing and categorizing data. In this blog, we discussed two of those methods, which can be used to group and partition a collection of data. These methods improve the readability of the code and provide flexibility in organizing diverse datasets.
