Dremel: Adaptive Configuration Tuning of RocksDB KV-Store

LSM-tree-based key-value stores like RocksDB are widely used to support many applications. However, configuring a RocksDB instance is challenging for the following reasons: 1) RocksDB has a massive parameter space to configure; 2) there are inherent trade-offs and dependencies between parameters; 3) right configurations are dependent on workload and hardware; and 4) evaluating configurations is time-consuming. Prior works struggle with handling the curse of dimensionality, capturing relationships between parameters, adapting configurations to workload and hardware, and evaluating quickly. In this work, we present a system, Dremel, to adaptively and quickly configure RocksDB with strategies based on the Multi-Armed Bandit model. To handle the massive parameter space, we propose using fused features, which encode domain-specific knowledge, to work as a compact and powerful representation for configurations. To adapt to the workload and hardware, we build an online bandit model to identify the best configuration. To evaluate quickly, we enable multi-fidelity evaluation and upper-confidence-bound sampling to speed up identifying the best configuration. Dremel not only achieves up to x2.61 higher IOPS and 57% less latency than default configurations but also achieves up to 63% improvements over prior works on 18 different settings with the same or less time budget.