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.

We present an overview of SAP HANA's Native Store Extension (NSE). This extension substantially increases database capacity, allowing to scale far beyond available system memory. NSE is based on a hybrid in-memory and paged column store architecture composed from data access primitives. These primitives enable the processing of hybrid columns using the same algorithms optimized for traditional HANA's in-memory columns. Using only three key primitives, we fabricated byte-compatible counterparts for complex memory resident data structures (e.g. dictionary and hash-index), compressed schemes (e.g. sparse and run-length encoding), and exotic data types (e.g. geo-spatial). We developed a new buffer cache which optimizes the management of paged resources by smart strategies sensitive to page type and access patterns. The buffer cache integrates with HANA's new execution engine that issues pipelined prefetch requests to improve disk access patterns. A novel load unit configuration, along with a unified persistence format, allows the hybrid column store to dynamically switch between in-memory and paged data access to balance performance and storage economy according to application demands while reducing Total Cost of Ownership (TCO). A new partitioning scheme supports load unit specification at table, partition, and column level. Finally, a new advisor recommends optimal load unit configurations. Our experiments illustrate the performance and memory footprint improvements on typical customer scenarios.