Microsoft operates the Azure SQL Database (ASD) cloud service, one of the dominant relational cloud database services in the market today. To aid the academic community in their research on designing and efficiently operating cloud database services, Microsoft is introducing the release of production-level telemetry traces from the ASD service. This telemetry data set provides, over a wide set of important hardware resources and counters, the consumption level of each customer database replica. The first release will be a multi-month time-series data set that includes the full cluster traces from two different ASD global regions.

Cloud SQL Server is a relational database system designed to scale-out to cloud computing workloads. It uses Microsoft SQL Server as its core. To scale out, it uses a partitioned database on a shared-nothing system architecture. Transactions are constrained to execute on one partition, to avoid the need for two-phase commit. The database is replicated for high availability using a custom primary-copy replication scheme. It currently serves as the storage engine for Microsoft's Exchange Hosted Archive and SQL Azure.

Cloud SQL Server is an Internet scale relational database service which is currently used by Microsoft delivered services and also offered directly as a fully relational database service known as "SQL Azure". One of the principle design objectives in Cloud SQL Server was to provide true SQL support with full ACID transactions within controlled scale "consistency domains" and provide a relaxed degree of consistency across consistency domains that would be viable to clusters of 1,000's of nodes. In this paper, we describe the implementation of Cloud SQL Server with an emphasis on this core design principle.

This paper presents an architecture overview of the distributed, heterogeneous query processor (DHQP) in the Microsoft SQL server database system to enable queries over a large collection of diverse data sources. The paper highlights three salient aspects of the architecture. First, the system introduces well-defined abstractions such as connections, commands, and rowsets that enable sources to plug into the system. These abstractions are formalized by the OLE DB data access interfaces. The generality of OLE DB and its broad industry adoption enables our system to reach a very large collection of diverse data sources ranging from personal productivity tools, to database management systems, to file system data. Second, the DHQP is built-in to the relational optimizer and execution engine of the system. This enables DH queries and updates to benefit from the cost-based algebraic transformations and execution strategies available in the system. Finally, the architecture is inherently extensible to support new data sources as they emerge as well as serves as a key extensibility point for the relational engine to add new features such as full-text search and distributed partitioned views.