We present batch-based consistency, a new approach for consistency optimization that allows programmers to specialize consistency with application-level integrity properties. We implement the approach with a two-step process: we statically infer optimal consistency requirements for executions of bounded sets of operations, and then, use the inferred requirements to parameterize a new distributed protocol to relax operation reordering at run time when it is safe to do so. Our approach supports standard notions of consistency. We implement batch-based consistency in Peepco , demonstrate its expressiveness for partial data replication, and examine Peepco’s run-time performance impact in different settings.

We present a new general-purpose synthesis technique for generating programs from input-output examples. Our method, called metric program synthesis, relaxes the observational equivalence idea (used widely in bottom-up enumerative synthesis) into a weaker notion of observational similarity, with the goal of reducing the search space that the synthesizer needs to explore. Our method clusters programs into equivalence classes based on an expert-provided distance metric and constructs a version space that compactly represents “approximately correct” programs. Then, given a “close enough” program sampled from this version space, our approach uses a distance-guided repair algorithm to find a program that exactly matches the given input-output examples. We have implemented our proposed metric program synthesis technique in a tool called SyMetric and evaluate it in three different domains considered in prior work. Our evaluation shows that SyMetric outperforms other domain-agnostic synthesizers that use observational equivalence and that it achieves results competitive with domain-specific synthesizers that are either designed for or trained on those domains.

Optimizing the physical data storage and retrieval of data are two key database management problems. In this paper, we propose a language that can express both a relational query and the layout of its data. Our language can express a wide range of physical database layouts, going well beyond the row- and column-based methods that are widely used in database management systems. We use deductive program synthesis to turn a high-level relational representation of a database query into a highly optimized low-level implementation which operates on a specialized layout of the dataset. We build an optimizing compiler for this language and conduct experiments using a popular database benchmark, which shows that the performance of our specialized queries is better than a state-of-the-art in memory compiled database system while achieving an order-of-magnitude reduction in memory use.

Missing values are common in data analysis and present a usability challenge. Users are forced to pick between removing tuples with missing values or creating a cleaned version of their data by applying a relatively expensive imputation strategy. Our system, ImputeDB, incorporates imputation into a cost-based query optimizer, performing necessary imputations on-the-fly for each query. This allows users to immediately explore their data, while the system picks the optimal placement of imputation operations. We evaluate this approach on three real-world survey-based datasets. Our experiments show that our query plans execute between 10 and 140 times faster than first imputing the base tables. Furthermore, we show that the query results from on-the-fly imputation differ from the traditional base-table imputation approach by 0--8%. Finally, we show that while dropping tuples with missing values that fail query constraints discards 6--78% of the data, on-the-fly imputation loses only 0--21%.

We present a method for example-guided synthesis of functional programs over recursive data structures. Given a set of input-output examples, our method synthesizes a program in a functional language with higher-order combinators like map and fold. The synthesized program is guaranteed to be the simplest program in the language to fit the examples. Our approach combines three technical ideas: inductive generalization, deduction, and enumerative search. First, we generalize the input-output examples into hypotheses about the structure of the target program. For each hypothesis, we use deduction to infer new input/output examples for the missing subexpressions. This leads to a new subproblem where the goal is to synthesize expressions within each hypothesis. Since not every hypothesis can be realized into a program that fits the examples, we use a combination of best-first enumeration and deduction to search for a hypothesis that meets our needs. We have implemented our method in a tool called λ2, and we evaluate this tool on a large set of synthesis problems involving lists, trees, and nested data structures. The experiments demonstrate the scalability and broad scope of λ2. A highlight is the synthesis of a program believed to be the world's earliest functional pearl.