Abstract Multi-key membership testing plays a crucial role in computing systems and networking applications, encompassing web search, mail systems, distributed databases, firewalls, and network routing. Traditional approaches, such as the Bloom filter, encounter limitations within this specific context. Addressing these challenges, we propose the Multi-key Learned Bloom Filter (MLBF), a hybrid method that combines machine learning techniques with the Bloom filter. The MLBF introduces a value-interaction-based multi-key classifier and a multi-key Bloom filter. Furthermore, we introduce an Interval-based MLBF approach, which categorizes keys into specific intervals based on data distribution to minimize the False Positive Rate (FPR). Additionally, MLBF incorporates an out-of-distribution (OOD) detection component to identify data shifts. Through extensive experimental evaluations on three authentic datasets, we demonstrate the superiority of the proposed MLBF in terms of FPR and query efficiency.

Query cost estimation, which is to estimate the query plan cost and query execution cost, is of utmost importance to query optimizers. Query plan cost estimation heavily relies on accurate cardinality estimation, and query execution cost estimation gives good hints on query latency, both of which are challenging in database management systems. Despite decades of research, existing studies either over-simplify the models only using histograms and polynomial calculation that leads to inaccurate estimates, or over-complicate them by using cumbersome neural networks with the requirements for large amounts of training data hence poor computational efficiency. Besides, most of the studies ignore the diversity of query plan structures. In this work, we propose a plan-based query cost estimation framework, called Saturn, which can eStimate cardinality and latency accurately and efficiently, for any query plan structures. Saturn first encodes each query plan tree into a compressed vector by using a traversal-based query plan autoencoder to cope with diverse plan structures. The compressed vectors can be leveraged to distinguish different query types, which is highly useful for downstream tasks. Then a pseudo label generator is designed to acquire all cardinality and latency labels with the execution part of the query plans in the training workload, which can significantly reduce the overhead of collecting the real cardinality and latency labels. Finally, a chain-wise transfer learning module is proposed to estimate the cardinality and latency of the query plan in a pipeline paradigm, which further enhances the efficiency. An extensive empirical study on benchmark data offers evidence that Saturn outperforms the state-of-the-art proposals in terms of accuracy, efficiency, and generalizability for query cost estimation.

Join order selection plays an important role in DBMS query optimizers. The problem aims to find the optimal join order with the minimum cost, and usually becomes an NP-hard problem due to the exponentially increasing search space. Recent advanced studies attempt to use deep reinforcement learning (DRL) to generate better join plans than the ones provided by conventional query optimizers. However, DRL-based methods require time-consuming training, which is not suitable for online applications that need frequent periodic re-training. In this paper, we propose a novel framework, namely efficient Join Order selection learninG with Graph-basEd Representation (JOGGER). We firstly construct a schema graph based on the primary-foreign key relationships, from which table representations are well learned to capture the correlations between tables. The second component is the state representation, where a graph convolutional network is utilized to encode the query graph and a tailored-tree-based attention module is designed to encode the join plan. To speed up the convergence of DRL training process, we exploit the idea of curriculum learning, in which queries are incrementally added into the training set according to the level of difficulties. We conduct extensive experiments on JOB and TPC-H datasets, which demonstrate the effectiveness and efficiency of the proposed solutions.