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Efficient multi-join query processing is crucial but remains a com- plex, ongoing challenge for high-performance data management systems (DBMSs). This paper studies the impact of different memory distribution techniques among join operators on different classes of multi-join query plans under different assumptions regarding memory availability and storage devices such as HDD and SSD on Amazon Web Services (AWS). We re-evaluate the results of one of the early impactful studies from the 1990s that was originally done using a simulator for the Gamma database system. The main goal of our study is to scientifically re-evaluate and build upon previous studies whose results have become the basis for the design of past and modern database systems, and to provide a solid foundation for understanding basic “join physics", which is essential for eventually designing a resource-based scheduler for concurrent complex workloads. 

Secondary indexes in relational database systems are traditionally built under the assumption that one data record maps to one indexed value. Nowadays, particularly in NoSQL systems, single data records can hold collections of values that users want to access efficiently in an ad-hoc manner. Multi-valued indexes aim to give users the best of both worlds: (i) to keep a more natural data model of records with collections of values, and (ii) to reap the benefits of a secondary index. In this paper, we detail the steps taken to realize multi-valued indexes in AsterixDB, a Big Data management system with a structured query language operating over a collection of docu- ments. This includes (a) creating the specification language for such indexes, (b) illustrating data flows for bulk-loading and maintaining an index, and (c) discussing query plans to take advantage of multi-valued indexes for use in predicates with existential and universal quantification. We conclude with ex- periments that compare AsterixDB multi-valued indexes against similar indexes in MongoDB and Couchbase Query. 

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. 

Abstract The Vera C. Rubin Observatory is expected to start the Legacy Survey of Space and Time (LSST) in early to mid-2025. This multiband wide-field synoptic survey will transform our view of the solar system, with the discovery and monitoring of over five million small bodies. The final survey strategy chosen for LSST has direct implications on the discoverability and characterization of solar system minor planets and passing interstellar objects. Creating an inventory of the solar system is one of the four main LSST science drivers. The LSST observing cadence is a complex optimization problem that must balance the priorities and needs of all the key LSST science areas. To design the best LSST survey strategy, a series of operation simulations using the Rubin Observatory scheduler have been generated to explore the various options for tuning observing parameters and prioritizations. We explore the impact of the various simulated LSST observing strategies on studying the solar system’s small body reservoirs. We examine what are the best observing scenarios and review what are the important considerations for maximizing LSST solar system science. In general, most of the LSST cadence simulations produce ±5% or less variations in our chosen key metrics, but a subset of the simulations significantly hinder science returns with much larger losses in the discovery and light-curve metrics.