The shift to multi-core and multi-socket hardware brings new challenges to database systems, as the software parallelism determines performance. Even though database systems traditionally accommodate simultaneous requests, a multitude of synchronization barriers serialize execution. Write-ahead logging is a fundamental, omnipresent component in ARIES-style concurrency and recovery, and one of the most important yet-to-be addressed potential bottlenecks, especially in OLTP workloads making frequent small changes to data. In this paper, we identify four loggingrelated impediments to database system scalability. Each issue challenges different level in the software architecture: (a) the high volume of small-sized I/O requests may saturate the disk, (b) transactions hold locks while waiting for the log ?ush, (c) extensive context switching overwhelms the OS scheduler with threads executing log I/Os, and (d) contention appears as transactions serialize accesses to in-memory log data structures. We demonstrate these problems and address them with techniques that, when combined, comprise a holistic, scalable approach to logging. Our solution achieves a 20–69% speedup over a modern database system when running log-intensive workloads, such as the TPC-B and TATP benchmarks, in a single-socket multiprocessor server. Moreover, it achieves log insert throughput over 2.2 GB/s for small log records on the single-socket server, roughly 20 times higher than the traditional way of accessing the log using a single mutex. Furthermore, we investigate techniques on scaling the performance of logging to multi-socket servers. We present a set of optimizations which partly ameliorate the latency penalty that comes with multi-socket hardware, and then we investigate the feasibility of applying a distributed log buffer design at the socket level.