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Query-based explanations for missing answers identify which operators of a query are responsible for the failure to return a missing answer of interest. This type of explanations has proven useful, e.g., to debug complex analytical queries. Such queries are frequent in big data systems such as Apache Spark. We present a novel approach to produce query-based explanations. It is the first to support nested data and to consider operators that modify the schema and structure of the data (e.g., nesting, projections) as potential causes of missing answers. To efficiently compute explanations, we propose a heuristic algorithm that applies two novel techniques: (i) reasoning about multiple schema alternatives for a query and (ii) re-validating at each step whether an intermediate result can contribute to the missing answer. Using an implementation on Spark, we demonstrate that our approach is the first to scale to large datasets while often finding explanations that existing techniques fail to identify. 

We present Breadcrumb, a system that aids developers in debugging queries through query-based explanations for missing answers. Given as input a query and an expected, but missing, query result, Breadcrumb identifies operators in the input query that are responsible for the failure to derive the missing answer. These operators form explanations that guide developers who can then focus their debugging efforts on fixing these parts of the query. Breadcrumb is implemented on top of Apache Spark. Our approach is the first that scales to big data dimensions and is capable of finding explanations for common errors in queries over nested and de-normalized data, e.g., errors based on misinterpreting schema semantics. 

We present the first query-based approach for explaining missing answers to queries over nested relational data which is a common data format used by big data systems such as Apache Spark. Our main contributions are a novel way to define query-based why-not provenance based on repairs to queries and presenting an implementation and preliminary experiments for answering such queries in Spark.