Non-Volatile RAM (NVRAM) is a novel class of hardware technology which is an interesting blend of two storage paradigms: byte-addressable DRAM and block-addressable storage (e.g. HDD/SSD). Most of the existing enterprise relational data management systems such as SAP HANA have their internal architecture based on the inherent assumption that memory is volatile and base their persistence on explicit handling of block-oriented storage devices. In this paper, we present the early adoption of Non-Volatile Memory within the SAP HANA Database, from the architectural and technical angles. We discuss our architectural choices, dive deeper into a few challenges of the NVRAM integration and their solutions, and share our experimental results. As we present our solutions for the NVRAM integration, we also give, as a basis, a detailed description of the relevant HANA internals.
Database replication is widely known and used for high availability or load balancing in many practical database systems. In this paper, we show how a replication engine can be used for three important practical cases that have not previously been studied very well. The three practical use cases include: 1) scaling out OLTP/OLAP-mixed workloads with partitioned replicas, 2) efficiently maintaining a distributed secondary index for a partitioned table, and 3) efficiently implementing an online re-partitioning operation. All three use cases are crucial for enabling a high-performance shared-nothing distributed database system. To support the three use cases more efficiently, we propose the concept of
asymmetric-partition replication
, so that replicas of a table can be independently partitioned regardless of whether or how its primary copy is partitioned. In addition, we propose the
optimistic synchronous commit
protocol which avoids the expensive two-phase commit without sacrificing transactional consistency. The proposed asymmetric-partition replication and its optimized commit protocol are incorporated in the production versions of the SAP HANA in-memory database system. Through extensive experiments, we demonstrate the significant benefits that the proposed replication engine brings to the three use cases.
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 inmemory 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.
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