Parameters tuning of multi-model database based on deep reinforcement learning

Abstract

As we all know, the performance of database management system is directly linked to a vast array of knobs, which control various aspects of system operation, ranging from memory and thread counts settings to I/O optimization. Improper settings of configuration parameters are shown to have detrimental effects on performance, reliability and availability of the overall database management system. This is also true for multi-model databases, which use a single platform to support multiple data models. Existing approaches for automatic DBMS knobs tuning are not directly applicable to multi-model databases due to the diversity of multi-model database instances and workloads. Firstly, in cloud environment, they have difficulty adapting to changing environments and diverse workloads. Secondly, they rely on large-scale high-quality training samples that are difficult to obtain. Finally, they focus primarily on throughput metrics, ignoring tuning requirements for resource utilization. Therefore, in this paper, we propose a multi-model database configuration parameters tuning solution named MMDTune. It selects influential parameters, recommends the optimal configurations in a high-dimensional continuous space. For different workloads, the TD3 algorithm is improved to generate reasonable parameter adjustment plans according to the internal state of the multi-model databases. We conduct extensive experiments under 5 different workloads on real cloud databases to evaluate MMDTune. Experimental results show that MMDTune adapts well to a new hardware environment or workloads, and significantly outperforms the representative tuning tools, such as OtterTune, CDBTune.

Um Estudo sobre Modelagem Poliglota de Dados

“…Ao aplicar o mesmo critério para o texto completo, o número final obtido foi 3. [Holubová et al 2021, Vavrek et al 2019], migrac ¸ão de dados poliglotas [Niska 2024], inferência de esquemas , desenvolvimento de linguagens e interfaces de consulta [Bakhtin 2023, Koupil et al 2021] e benchmarks para avaliac ¸ão de performance em BDs multimodelo [Ye et al 2023].…”

Workload‐Aware Performance Tuning for Multimodel Databases Based on Deep Reinforcement Learning

“…Confgure multimodel database with C t (13) Perform workload q and observe new state s t+1 ⟵ cost(C t , q) and r t ⟵ reward(s t+1 ) (14) Push (s t , s t+1 , a t , r t ) into R (15) Sample a random mini-batch (s i , s i+1 , a i , r i ) from R (16) target…”

“…In our previous work [15], we proposed MMDTune, a parameter-tuning method for multimodel databases based on deep reinforcement learning. MMDTune has the problems of long training time and low computational efciency, which cannot be dynamically adjusted in the course of practical application.…”