Minimizing intermediate results is critical for efficient multi-join query processing. Although the seminal Yannakakis algorithm offers strong guarantees for acyclic queries, cyclic queries remain an open challenge. In this paper, we propose SplitJoin, a framework that introduces split as a first-class query operator. By partitioning input tables into heavy and light parts, SplitJoin allows different data partitions to use distinct query plans, with the goal of reducing intermediate sizes using existing binary join engines. We systematically explore the design space for split-based optimizations, including threshold selection, split strategies, and join ordering after splits. Implemented as a front-end to DuckDB and Umbra, SplitJoin achieves substantial improvements: on DuckDB, SplitJoin completes 43 social network queries (vs. 29 natively), achieving 2.1x faster runtime and 7.9x smaller intermediates on average (up to 13.6x and 74x, respectively); on Umbra, it completes 45 queries (vs. 35), achieving 1.3x speedups and 1.2x smaller intermediates on average (up to 6.1x and 2.1x, respectively).

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