Compile-Time Java Stream Fusion via mapMulti

TL;DR

This paper presents an open-source Java optimizer that merges consecutive stream operations into a single mapMulti() call, reducing overhead and improving performance.

Contribution

It introduces a novel compile-time optimization for Java streams that overcomes limitations of previous tools by merging operations into mapMulti(), enhancing efficiency.

Findings

Achieved better performance in two benchmarks

Maintained comparable performance in most benchmarks

Successfully optimized Apache Kafka bytecode with all tests passing

Abstract

The Java Stream API, introduced in Java 8, makes data processing more expressive and concise compared to imperative loops. However, this abstraction can come with significant performance overhead, often due to the creation of multiple intermediate objects during pipeline execution. In functional languages such as Haskell, this problem is addressed through stream fusion, a compile-time optimization that eliminates unnecessary intermediate structures. Inspired by this idea, Streamliner was the first tool to perform ahead-of-time, bytecode-to-bytecode stream optimization for Java by unrolling stream pipelines into imperative loops. In this paper, we introduce an open-source optimizer that takes a different approach. Instead of unrolling streams into loops, it merges consecutive map() and filter() operations into a single mapMulti() call, available since Java 16. Our method avoids several limitations of Streamliner, including its sensitivity to escaping objects in lambda expressions and its restrictions on assigning or passing streams as variables. We evaluated our optimizer on nine benchmarks and observed superior performance in two cases and comparable results in most others. We also applied it to the bytecode of Apache Kafka, successfully executing all 31,799 unit tests without failures.