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.

SQL Azure's concurrency control relies on multi-versioning to prevent readers and writers from blocking each other and on in-memory row locks to prevent multiple writers modifying the same row. If the number of in-memory locks exceeds a threshold, then to reduce memory used for locking, table-level lock escalation occurs which severely reduces concurrency. This paper presents a technique called transaction-id locking that drastically reduces the number of in-memory locks and eliminates lock escalation. It also describes another technique called lock after qualification where rows are qualified without locking thereby letting concurrent transactions interested in mutually exclusive sets of rows execute without blocking each other. Optimized locking combines these two techniques with the prior scheme of in-memory row locks. This combination to improve common isolation levels (like Read Committed Snapshot Isolation) while retaining support for Serializable isolation level in a developer-friendly manner distinguishes this work from prior art. The paper presents in detail this new scheme which required changes in both the storage engine and the query processing engine. It also presents the results of deploying optimized locking to more than eleven million SQL databases in Azure.