Indexing on Modern Hardware: Hekaton and Beyond
Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2014 |
Published by ACM
Recent OLTP support exploits new techniques, running on modern hardware, to achieve unprecedented performance compared with prior approaches. In SQL Server, the Hekaton main-memory database engine embodies this new OLTP support. Hekaton uses the Bw-tree to achieve its great indexing performance. The Bw-Tree is a latch-free B-tree index that also exploits log-structured storage when used “beyond” Hekaton as a separate key value store. It is designed from the ground up to address two hardware trends: (1) Multi-core and main memory hierarchy: the Bw-tree is completely latch-free, using an atomic compare-and-swap instruction to install state changes on a “page address” mapping table; it performs updates as “deltas” to avoid update-in-place. These improve performance by eliminating thread blocking while improving cache hit ratios. (2) flash storage: the Bw-tree organizes secondary storage in a log-structured manner, using large sequential writes to avoid the adverse performance impact of random writes. We demontrate the architectural versatility and performance of the Bw-tree in two scenarios: (a) running live within Hekaton and (2) running as a standalone key value store compared to both BerkeleyDB and a state-of-the-art in-memory range index (latch-free skiplists). Using workloads from real-world applications (Microsoft XBox Live Primetime and enterprise deduplication), we show the Bw-tree is 19x faster than BerkeleyDB and 3x faster than skiplists.