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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. 

Creating or modifying a primary index is a time-consuming process, as the index typically needs to be rebuilt from scratch. In this paper, we explore a more graceful "just-in-time" approach to index reorganization, where small changes are dynamically applied in the background. To enable this type of reorganization, we formalize a composable organizational grammar, expressive enough to capture instances of not only existing index structures, but arbitrary hybrids as well. We introduce an algebra of rewrite rules for such structures, and a framework for defining and optimizing policies for just-in-time rewriting. Our experimental analysis shows that the resulting index structure is flexible enough to adapt to a variety of performance goals, while also remaining competitive with existing structures like the C++ standard template library map. 

Embedded database libraries provide developers with a com- mon and convenient data persistence layer. They have spread to many systems, including interactive devices like smart- phones, appearing in all major mobile systems. Their perfor- mance affects the response times and resource consumption of millions of phone apps and billions of phone users. It is thus critical that we better understand how they work, so they can be used more efficiently, and so developers can make faster libraries. Mobile databases differ significantly from server-class storage in terms of platform, usage, and measurement. Phones are multi-tenant, end-user devices that the database must share with other apps. Contrary to traditional database design goals, workloads on phones are single-app, bursty, and rarely saturate the CPU. We argue that mobile storage design should refocus on what matters on the mobile platform: latency and energy. As accurate per- formance measurement tools are necessary to evaluation of good database design, this uncovers another issue: Tradi- tional database benchmarking methods produce misleading results when applied to mobile devices, due to evaluating performance at saturation. Development of databases and measurements specifically designed for the mobile platform is necessary to optimize user experience of the most common database usage in the world. 

Data is becoming increasingly personal. Individuals regularly interact with a wide variety of structured data, from SQLite databases on phones, to HR spreadsheets, to personal sensors, to open government data appearing in news articles. Although these workloads are important, many of the classical challenges associated with scale and Big Data do not apply. This panel brings together experts in a variety of fields to explore the new opportunities and challenges presented by "Small Data".