PPredictor: Workload-Aware Performance Prediction for Analytical Distributed Databases using Graph Encoding

Predicting query performance is essential for database tasks like resource allocation and scheduling. However, existing methods, designed for single-node systems, often fail in distributed analytical databases. This is because they overlook key features such as data partitioning, cross-node data transfer, and parallel query execution. To address these challenges, we propose a novel method for predicting query performance in analytical distributed databases that is grounded in graph representation models. First, we introduce a graph model that encodes both data partitions and query execution plans within distributed databases. In this model, vertices represent partitioned tables, whereas edges capture their relationships, such as partitioned tables located on the same data node and data transfers between partitioned tables within an execution plan. Second, we present a prediction model that effectively uses a graph attention mechanism network to encode graph features and uses deep learning techniques for performance prediction. Third, considering the dynamic workloads and various database environments, we introduce an incremental learning method based on feature replay. Extensive experiments conducted on real-world datasets demonstrate that our approach significantly outperforms the state-of-the-art methods.