Rhyme Native: Efficient Code Generation for Structured and Semi-Structured Workloads
TL;DR
This work builds on Rhyme, a declarative language whose object-notation syntax mirrors the structure of query results, and refine Rhyme's semantics for generator binding and missing values, allowing co-iteration, inner/outer joins, and nested-loop traversals to be expressed under different uses of generator symbols.
Abstract
Modern data processing spans two worlds: flat relational tables, served by decades of database research producing highly optimized query engines, and nested semi-structured data such as JSON, for which expressive query languages exist but compilation and optimization techniques have been applied far less comprehensively. We ask whether a single query language can express both regimes naturally while compiling to efficient native code. We build on Rhyme, a declarative language whose object-notation syntax mirrors the structure of query results, and contribute on three fronts. We refine Rhyme's semantics for generator binding and missing values, allowing co-iteration, inner/outer joins, and nested-loop traversals to be expressed under different uses of generator symbols. We show that Rhyme's prior dependency-driven loop scheduler can generate incorrect code on hierarchical queries, and present a new scheduler based on finer-grained per-statement constraints that ensures correctness. We introduce a gradual type system and a C code generation backend that emits tag-less, statically typed code and specializes data loading and internal data structures for idiomatic SQL patterns. Our system matches state-of-the-art compiled engines on SQL workloads such as TPC-H and outperforms modern JSON-capable databases and DSLs on JSONBench and other hierarchical queries.