Blog by Rahul Mittal

Introduction One of the most impressive capabilities introduced in Java 8 was the ability to process collections in parallel with almost no changes to application code. Changing this: employees.stream() to this: employees.parallelStream() or employees.stream().parallel() appears almost magical. Suddenly, multiple CPU cores begin processing data simultaneously. But what actually happens? How does Java decide which thread processes which elements? How are…

Introduction One of the most impressive capabilities introduced in Java 8 was the ability to process collections in parallel with almost no changes to application code. Changing this: employees.stream() to this: employees.parallelStream() or employees.stream().parallel() appears almost magical. Suddenly, multiple CPU cores begin processing data simultaneously. But what actually happens? How does Java decide which thread processes which elements? How are…

Introduction Throughout this Stream API series, we've used many built-in collectors: toList() toSet() groupingBy() partitioningBy() mapping() joining() counting() summingInt() averagingDouble() maxBy() minBy() These collectors solve most day-to-day programming problems. But have you ever wondered: How does Collectors.toList() work? How does Java know how to accumulate Stream elements? How does groupingBy() create maps? How are partial results merged when using Parallel…

Introduction In the previous articles, we explored Collectors.groupingBy() and discovered how it can transform large collections into meaningful reports, dashboards, and analytical summaries. groupingBy() is ideal when data needs to be classified into multiple categories. Examples include: Employees by department Customers by city Transactions by branch Orders by status But what happens when there are only two possible outcomes? Business…

Introduction In the previous article, we explored Collectors.groupingBy() and learned how to group Stream elements into logical categories such as departments, cities, and transaction dates. While grouping data is useful, enterprise applications rarely stop there. Business stakeholders typically ask questions such as: How many employees work in each department? What is the total salary paid by each branch? Which city…

Introduction In the previous article, we learned that Collectors provide the aggregation engine for the Stream API. We explored how they accumulate Stream elements into collections, maps, and other complex result structures through mutable reduction. Now it's time to study the most powerful and widely used collector in enterprise applications: Collectors.groupingBy() If you've ever written SQL queries using: GROUP BY…

Introduction So far in this series, we've learned how to: Build Stream pipelines. Filter data. Transform objects. Flatten nested collections. Sort results. Execute pipelines using terminal operations. These capabilities are enough for many everyday tasks. However, enterprise applications rarely stop at simply filtering or mapping data. Business users ask questions like: How many customers belong to each region? What is…

Introduction In the previous article, we learned that a Stream is not a data structure but a processing pipeline that transforms data through a series of operations. We explored concepts such as lazy evaluation, internal iteration, and the difference between intermediate and terminal operations. Now it's time to examine the operations that form the heart of every Stream pipeline. Whether…