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A compiler’s optimizer operates over abstract syntax trees (ASTs), continuously applying rewrite rules to replace sub- trees of the AST with more efficient ones. Especially on large source repositories, even simply finding opportunities for a rewrite can be expensive, as optimizer traverses the AST naively. In this paper, we leverage the need to repeatedly find rewrites, and explore options for making the search faster through indexing and incremental view maintenance (IVM). Concretely, we consider bolt-on approaches that make use of embedded IVM systems like DBToaster, as well as two new approaches: Label-indexing and TreeToaster, an AST- specialized form of IVM. We integrate these approaches into an existing just-in-time data structure compiler and show experimentally that TreeToaster can significantly improve performance with minimal memory overheads. 

Functional (aka immutable) data structures are used extensively in data management systems. From distributed systems to data persistence, immutability makes complex programs significantly easier to reason about and implement. However, immutability also makes many runtime optimizations like tree rebalancing, or adaptive organizations, unreasonably expensive. In this paper, we propose Fluid data structures, an approach to data structure design that allows limited physical changes that preserve logical equivalence. As we will show, this approach retains many of the desirable properties of functional data structures, while also allowing runtime adaptation. To illustrate Fluid data structures, we work through the design of a lazy-loading map that we call a Fluid Cog. A Fluid Cog is a lock-free data structure that incrementally organizes itself in the background by applying equivalence-preserving structural transformations. Our experimental analysis shows that the resulting map structure is flexible enough to adapt to a variety of performance goals, while remaining competitive with existing structures like the C++ standard template library map.