Online bit flip detection for in-memory B-trees live!

Abstract

Hardware vendors constantly decrease the feature sizes of integrated circuits to obtain higher performance and energy efficiency. As a side-effect, integrated circuits - like CPUs and main memory - become more and more vulnerable to external influences and thus unreliable, which results in increasing numbers of (multi-) bit flips. From a database perspective bit flip errors in main memory will become a major challenge for modern in-memory database systems, which keep all their enterprise data in volatile, unreliable main memory. Existing hardware error control techniques like ECC-DRAM are able to detect and correct memory errors, but their detection and correction capabilities are limited and come along with several downsides. To underline this we heat up RAM live on-site to show possible error rates of future hardware. We previously presented various techniques for the B-Tree - as a wide-spread index structure - for online error detection and thus increase its overall reliability [Kea14b]. We also show live performance comparisons in terms of throughput and error detection rates between several bit flip detecting B-Tree variants. By that, we demonstrate the tradeoff between detection accuracy and index throughput. Furthermore, we show annotated structural information about the trees like corrupted nodes and inaccessible sub-trees.