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Abstract. We introduce JCC-H, a drop-in replacement for the data and query generator of TPC-H, that introduces Join-Crossing-Correlations (JCC) and skew into its dataset and query workload. These correlations are carefully designed such that the filter predicates on table columns in the existing TPC-H queries now suddenly can have effects on the value-, frequency-and join-fan-out-distributions, experienced by operators in the query plan. The query generator of JCC-H is able to generate parameter bindings for the 22 query templates in two different equivalence classes: query templates that receive "normal" parameters do not experience skew and behave very similar to default TPC-H queries. Query templates expanded with the "skewed" parameters, though, experience strong join-crossing-correlations and skew in filter, aggregation and join operations. In this paper we discuss the goals of JCC-H, its detailed design, as well as show initial experiments on both a single-server and MPP database system, that confirm that our design goals were largely met. In all, JCC-H provides a convenient way for any system that is already testing with TPC-H to examine how the system can handle skew and correlations, so we hope the community can use it to make progress on issues like skew mitigation and detection and exploitation of join-crossing-correlations in query optimizers and data storage.

Cloud data warehouse systems lower the barrier to access data analytics. These applications often lack a database administrator and integrate data from various sources, potentially leading to data not satisfying strict constraints. Automatic schema optimization in self-managing databases is difficult in these environments without prior data cleaning steps. In this paper, we focus on constraint discovery as a subtask of schema optimization. Perfect constraints might not exist in these unclean datasets due to a small set of values violating the constraints. Therefore, we introduce the concept of a generic PatchIndex structure, which handles exceptions to given constraints and enables database systems to define these approximate constraints. We apply the concept to the environment of distributed databases, providing parallel index creation approaches and optimization techniques for parallel queries using PatchIndexes. Furthermore, we describe heuristics for automatic discovery of PatchIndex candidate columns and prove the performance benefit of using PatchIndexes in our evaluation.

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