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To achieve high performance, recent research has shown that it is important to automatically tune the configuration knobs present in database systems. However, as database systems usually have 100s of knobs, auto-tuning frameworks spend a significant amount of time exploring the large configuration space and need to repeat this as workloads change. Given this challenge, we ask a more fundamental question of how many knobs do we need to tune in order to achieve good performance. Surprisingly, we find that with YCSB workload-A on Cassandra, tuning just five knobs can achieve 99% of the performance achieved by the best configuration that is obtained by tuning many knobs. We also show that our results hold across workloads and applies to other systems like PostgreSQL, motivating the need for tools that can automatically filter out the knobs that need to be tuned. Based on our results, we propose an initial design for accelerating auto-tuners and detail some future research directions. 

We introduce consistency-aware durability or CAD, a new approach to durability in distributed storage that enables strong consistency while delivering high performance. We demonstrate the efficacy of this approach by designing cross-client monotonic reads, a novel and strong consistency property that provides monotonic reads across failures and sessions in leader-based systems. We build ORCA, a modified version of ZooKeeper that implements CAD and cross-client mono- tonic reads. We experimentally show that ORCA provides strong consistency while closely matching the performance of weakly consistent ZooKeeper. Compared to strongly consistent ZooKeeper, ORCA provides significantly higher through- put (1.8 – 3.3x), and notably reduces latency, sometimes by an order of magnitude in geo-distributed settings.