Database systems running on a cluster of machines, i.e. rack-scale databases, are a common architecture for many large databases and data appliances. As the data movement across machines is often a significant bottleneck, these systems typically use a low-latency, high-throughput network such as InfiniBand. To achieve the necessary performance, parallel join algorithms must take advantage of the primitives provided by the network to speed up data transfer.

Database scale-out is commonly implemented by partitioning data across several database instances. This approach, however, has several restrictions. In particular, partitioned databases are inflexible in large-scale deployments and assume a partition-friendly workload in order to scale. In this paper, we analyze an alternative architecture design for distributed relational databases that overcomes the limitations of partitioned databases. The architecture is based on two fundamental principles: We decouple query processing and transaction management from data storage, and we share data across query processing nodes. The combination of these design choices provides scalability, elasticity, and operational flexibility without making any assumptions on the workload. As a drawback, sharing data among multiple database nodes causes synchronization overhead. To address this limitation, we introduce techniques for scalable transaction processing in shared-data environments. Specifically, we describe mechanisms for efficient data access, concurrency control, and data buffering. In combination with new hardware trends, the techniques enable performance characteristics that top state-of-the-art partitioned databases.

Cloud computing promises a number of advantages for the deployment of data-intensive applications. One important promise is reduced cost with a pay-as-you-go business model. Another promise is (virtually) unlimited throughput by adding servers if the workload increases. This paper lists alternative architectures to effect cloud computing for database applications and reports on the results of a comprehensive evaluation of existing commercial cloud services that have adopted these architectures. The focus of this work is on transaction processing (i.e., read and update workloads), rather than analytics or OLAP workloads, which have recently gained a great deal of attention. The results are surprising in several ways. Most importantly, it seems that all major vendors have adopted a different architecture for their cloud services. As a result, the cost and performance of the services vary significantly depending on the workload.

This demonstration presents Cloudy, a modular cloud storage system. Cloudy provides a highly flexible architecture for distributed data storage and is designed to operate with multiple workloads. Based on a generic data model, Cloudy can be customized to meet application requirements. The goal of this demonstration is to show the ability of Cloudy to efficiently process different query languages, and to automatically adapt to varying load scenarios.