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The performance of a query crucially depends on the ability of the query optimizer to choose a good execution plan from a large space of alternatives. With the discovery of algebraic transformation rules and the emergence of new application-specific contexts, extensibility has become a key requirement for query optimizers. This monograph describes extensible query optimizers in detail, focusing on the Volcano/Cascades framework used by several database systems including Microsoft SQL Server. We explain the need for extensible query optimizer architectures and how the optimizer navigates the search space efficiently. We then discuss several important transformations that are commonly used in practice. We describe cost estimation, an essential component that the optimizer relies upon to quantitatively compare alternative plans in the search space. We discuss how database systems manage plans over their lifetime as data and workloads change. We conclude with a few open challenges. [ABSTRACT FROM AUTHOR]

Using data statistics, we convert predicates on a table into data-induced predicates (diPs) that apply on the joining tables. Doing so substantially speeds up multi-relation queries because the benefits of predicate pushdown can now apply beyond just the tables that have predicates. We use diPs to skip data exclusively during query optimization; i.e., diPs lead to better plans and have no overhead during query execution. We study how to apply diPs for complex query expressions and how the usefulness of diPs varies with the data statistics used to construct diPs and the data distributions. Our results show that building diPs using zone-maps which are already maintained in today's clusters leads to sizable data skipping gains. Using a new (slightly larger) statistic, 50% of the queries in the TPC-H, TPC-DS and JoinOrder benchmarks can skip at least 33% of the query input. Consequently, the median query in a production big-data cluster finishes roughly 2 × faster. [ABSTRACT FROM AUTHOR]

Enterprises are moving their business critical workloads to public clouds at an accelerating pace. Cloud data services for Online Transaction Processing (OLTP), Data Analytics and NoSQL are essential building blocks for enterprise applications. Multi-tenancy is a crucial tenet for cloud data service providers that allows sharing of data center resources across tenants, thereby reducing cost. In this article we review architectures of today's cloud data services and identify trends and challenges that arise in multi-tenant cloud data services. We survey techniques that have been developed for enabling elasticity, providing SLAs, ensuring performance isolation and reducing cost. We review the emerging paradigm of serverless databases and point out opportunities and challenges. We identify open research problems in the fast-changing landscape of cloud data services. [ABSTRACT FROM AUTHOR]