Parallel streams can be processed on multiple threads simultaneously, leading to faster processing of large data sets. This makes it easy to take advantage of multi-core processors and improve the overall performance of your application.
This has some major advantages and applications:
1. Readable, Short and Efficient code: Every code starts with a source, followed by intermediate operations and terminal operation. Each intermediate operation is lazily executed and returns a stream as a result, hence various intermediate operations can be pipelined such as filter(), sort(), distinct() etc. Terminal operations mark the end of the stream and return the result such as sum(), average() etc. Thus it is readable, clean, and efficient. 2. Works great with Lambda Expressions and Method References: Stream functions mainly takes action as an argument. These functions can take lambda expressions multiple times. Therefore during a pass-through, a lambda expression can pass both functions and method references. Together these provide a concise way of writing code. 3. Streams can be parallelized: The streams provide options like parallel() to convert the sequential stream to a parallel stream.
Useful for Big Data Processing: Streams can be particularly useful for processing large amounts of data in real-time, making them a valuable tool for big data applications. Since streams process data lazily and only as needed, they can handle large data sets without requiring a lot of memory or causing performance issues.
Improves Code Quality and Maintainability: Since streams enable you to write clean, efficient, and readable code, they can help to improve the overall quality and maintainability of your codebase. By using streams, you can simplify complex operations, reduce code duplication, and make your code easier to understand and maintain over time.
Enables Better Error Handling: Streams support a variety of error-handling mechanisms, including try-catch blocks, checked and unchecked exceptions, and handling of errors at the terminal operation level. This makes it easier to write code that is robust and can handle errors effectively.
In summary, streams provide a powerful tool for developers to process large amounts of data efficiently and effectively. With their support for lambda expressions, method references, parallelization, and error handling, streams enable developers to write clean, concise, and maintainable code. As a result, streams have become an important part of modern software development and are a valuable tool for any developer working with big data applications.