This paper presents Polybase, a feature of SQL Server PDW V2 that allows users to manage and query data stored in a Hadoop cluster using the standard SQL query language. Unlike other database systems that provide only a relational view over HDFSresident data through the use of an external table mechanism, Polybase employs a split query processing paradigm in which SQL operators on HDFS-resident data are translated into MapReduce jobs by the PDW query optimizer and then executed on the Hadoop cluster. The paper describes the design and implementation of Polybase along with a thorough performance evaluation that explores the benefits of employing a split query processing paradigm for executing queries that involve both structured data in a relational DBMS and unstructured data in Hadoop. Our results demonstrate that while the use of a splitbased query execution paradigm can improve the performance of some queries by as much as 10X, one must employ a cost-based query optimizer that considers a broad set of factors when deciding whether or not it is advantageous to push a SQL operator to Hadoop. These factors include the selectivity factor of the predicate, the relative sizes of the two clusters, and whether or not their nodes are co-located. In addition, differences in the semantics of the Java and SQL languages must be carefully considered in order to avoid altering the expected results of a query.

As traditional and mission-critical relational database workloads migrate to the cloud in the form of Databaseas-a-Service (DaaS), there is an increasing motivation to provide performance goals in Service Level Objectives (SLOs). Providing such performance goals is challenging for DaaS providers as they must balance the performance that they can deliver to tenants and the data center's operating costs. In general, aggressively aggregating tenants on each server reduces the operating costs but degrades performance for the tenants, and vice versa. In this paper, we present a framework that takes as input the tenant workloads, their performance SLOs, and the server hardware that is available to the DaaS provider, and outputs a costeffective recipe that specifies how much hardware to provision and how to schedule the tenants on each hardware resource. We evaluate our method and show that it produces effective solutions that can reduce the costs for the DaaS provider while meeting performance goals.

There has been an information explosion in fields of science such as high energy physics, astronomy, environmental sciences and biology. There is a critical need for automated systems to manage scientific applications and data. Database technology is well-suited to handle several aspects of workflow management. Contemporary workflow systems are built from multiple, separately developed components and do not exploit the full power of DBMSs in handling data of large magnitudes. We advocate a holistic view of a WFMS that includes not only workflow modeling but planning, scheduling, data management and cluster management. Thus, it is worthwhile to explore the ways in which databases can be augmented to manage workflows in addition to data. We present a language for modeling workflows that is tightly integrated with SQL. Each scientific program in a workflow is associated with an active table or view. The definition of data products is in relational format, and invocation of programs and querying is done in SQL. The tight coupling between workflow management and data-manipulation is an advantage for data-intensive scientific programs.

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In recent years, Massively Parallel Processors have increasingly been used to manage and query vast amounts of data. Dramatic performance improvements are achieved through distributed execution of queries across many nodes. Query optimization for such system is a challenging and important problem.In this paper we describe the Query Optimizer inside the SQL Server Parallel Data Warehouse product (PDW QO). We leverage existing QO technology in Microsoft SQL Server to implement a cost-based optimizer for distributed query execution. By properly abstracting metadata we can readily reuse existing logic for query simplification, space exploration and cardinality estimation. Unlike earlier approaches that simply parallelize the best serial plan, our optimizer considers a rich space of execution alternatives, and picks one based on a cost-model for the distributed execution environment. The result is a high-quality, effective query optimizer for distributed query processing in an MPP.