JSON Schema is maturing into the de-facto schema language for JSON documents. When JSON Schema declarations evolve, the question arises how the new schema will deal with JSON documents that still adhere to the legacy schema. This is particularly crucial in the maintenance of software APIs. In this paper, we present the results of our empirical study of the first generation of tools for checking JSON Schema containment which we apply to a diverse collection of over 230 realworld schemas and their altogether 1k historic versions. We assess two such special-purpose tools w.r.t. their applicability to real-world schemas and identify weak spots. Based on this analysis, we enumerate specific open research challenges that are based on real-world problems.

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The performance of database systems is usually characterised by their average-case (i.e., throughput) behaviour in standardised or defacto standard benchmarks like TPC-X or YCSB. While tails of the latency (i.e., response time) distribution receive considerably less attention, they have been identified as a threat to the overall system performance: In large-scale systems, even a fraction of requests delayed can build up into delays perceivable by end users. To eradicate large tail latencies from database systems, the ability to faithfully record them, and likewise pinpoint them to the root causes, is imminently required. In this paper, we address the challenge of measuring tail latencies using standard benchmarks, and identify subtle perils and pitfalls. In particular, we demonstrate how Java-based benchmarking approaches can substantially distort tail latency observations, and discuss how the discovery of such problems is inhibited by the common focus on throughput performance. We make a case for purposefully re-designing database benchmarking harnesses based on these observations to arrive at faithful characterisations of database performance from multiple important angles.

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