Azure SQL DB offers disaggregated storage architecture called Hyperscale. While this architecture provides storage scale out, it comes at the cost of performance of I/O from remote storage. In-memory caches on the Compute nodes are small and lost on process restarts. This paper introduces Resilient Buffer Pool Extension (RBPEX) which is a persistent cache present on both compute and storage nodes. These caches significantly improve performance of I/O from Compute while at the same time maintaining correctness. This paper presents the architecture of RBPEX and how it stores the most relevant pages in an efficient and correct way. It uses innovative techniques like statistics, selective caching and reduction of small writes to improve performance in Hyperscale. It also presents details about handling multiple versions of pages from storage to maintain correctness.

Azure Data Lake Store (ADLS) is a fully-managed, elastic, scalable, and secure file system that supports Hadoop distributed file system (HDFS) and Cosmos semantics. It is specifically designed and optimized for a broad spectrum of Big Data analytics that depend on a very high degree of parallel reads and writes, as well as collocation of compute and data for high bandwidth and low-latency access. It brings together key components and features of Microsoft?s Cosmos file system-long used by internal customers at Microsoft and HDFS, and is a unified file storage solution for analytics on Azure. Internal and external workloads run on this unified platform. Distinguishing aspects of ADLS include its design for handling multiple storage tiers, exabyte scale, and comprehensive security and data sharing features. We present an overview of ADLS architecture, design points, and performance.