A Performance Comparison of Java Streams and Imperative Loops

Abstract: The Stream API was added in Java 8. With the help of lambda expressions (anonymous functions), streams enable functional-style operations on sequences of elements. In this project, we evaluate how streams perform in comparison to imperative loops in terms of execution time, from the perspective of how streams are commonly used in public GitHub repositories. Additionally, two algorithms are implemented with and without streams, to assess the impact of stream usage on algorithmic performance. Parallel streams are only examined briefly due to their infrequent usage. We find that sequential streams in general are slower than imperative loops. However, stream performance heavily relies on how many elements are being processed, which is referred to as input size. For input sizes smaller than 100, most stream pipelines are several times slower than imperative loops. Meanwhile, for input sizes between 10 000 and 1 000 000, streams are on average only 39% to 74% slower than loops, and in some cases, they even slightly outperform them. Additionally, we observe that using streams when implementing algorithms in some cases leads to much slower execution times, while in other cases, it barely affects the execution time at all. We conclude that stream performance primarily depends on input size, presumably because of the high overhead abstraction cost of creating streams, but their performance also depends on other factors, such as operation type and pipeline length.