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Abstract Window queries are important analytical tools for ordered data and have been researched both in streaming and stored data environments. By incorporating ideas for window queries from existing streaming and stored data systems, we propose a new window syntax that makes a wide range of window queries easier to write and optimize. We have implemented this new window syntax in SQL++, an SQL extension that supports querying semistructured data, on top of AsterixDB, a Big Data Management System, thus allowing us to process window queries over large datasets in a parallel and efficient manner. 

Database systems with hybrid data management support, referred to as HTAP or HOAP architectures, are gaining popularity. These first appeared in the relational world, and the CH-benCHmark (CH) was proposed in 2011 to evaluate such relational systems. Today, one finds NoSQL database systems gaining adoption for new applications. In this paper we present CH2, a new benchmark – created with CH as its starting point – aimed at evaluating hybrid data platforms in the document data management world. Like CH, CH2 borrows from and extends both TPC-C and TPC-H. Differences from CH include a document-oriented schema, a data generation scheme that creates a TPC-H-like history, and a “do over” of the CH queries that is more in line with TPC-H. This paper details shortcomings that we uncovered in CH, the design of CH2, and preliminary results from running CH2 against Couchbase Server 7.0 (whose Query and Analytics services provide HOAP support for NoSQL data). The results provide insight into the performance isolation and horizontal scalability properties of Couchbase Server 7.0 as well as demonstrating the efficacy of CH2 for evaluating such platforms. 

Today, data is being actively generated by a variety of devices, services, and applications. Such data is important not only for the information that it contains, but also for its relationships to other data and to interested users. Most existing Big Data systems focus on passively answering queries from users, rather than actively collecting data, processing it, and serving it to users. To satisfy both passive and active requests at scale, application developers need either to heavily customize an existing passive Big Data system or to glue one together with systems like Streaming Engines and Pub-sub services. Either choice requires significant effort and incurs additional overhead. In this paper, we present the BAD (Big Active Data) system as an end-to-end, out-of-the-box solution for this challenge. It is designed to preserve the merits of passive Big Data systems and introduces new features for actively serving Big Data to users at scale. We show the design and implementation of the BAD system, demonstrate how BAD facilitates providing both passive and active data services, investigate the BAD system’s performance at scale, and illustrate the complexities that would result from instead providing BAD-like services with a “glued” system.