Adaptive Recovery for SCM-Enabled Databases

Storage Class Memory (SCM) has the potential to drastically change the database system landscape – much like high core count CPUs and large DRAM capacities spurred a shift to in-memory databases a decade ago. One of the possibilities provided by SCM is to significantly improve restart performance. SCM-enabled databases can evince a single-level main-memory architecture that stores, accesses, and modifies data directly in SCM, removing the traditional recovery bottleneck for main-memory databases: reloading data from durable media to main memory. Almost instantaneous recovery is possible, but at the cost of reduced throughput as latency sensitive data structures such as indexes need to be kept on SCM whose read and write latencies are projected to be noticeably slower than those of DRAM. We can regain this throughput by fully storing secondary data structures in DRAM and rebuilding them after restart, concurrently with processing incoming requests. While these data structures are being rebuilt, request throughput will be reduced not only because of the rebuilding overhead but, more significantly, because their (temporary) absence will result in sub-optimal access plans. Hence, rebuilding DRAM-based data structures becomes the new bottleneck for SCM-enabled databases recovery. In this paper, we address this bottleneck and describe Adaptive Recovery, a novel recovery technique that significantly reduces the impact on request throughput during the recovery period over naı̈ve approaches such as rebuilding data structures when first referenced.