Traditionally, query optimizers rely on cost models to choose the best execution plan from several candidates, making precise cost estimates critical for efficient query execution. In recent years, cost models based on machine learning have been proposed to overcome the weaknesses of traditional cost models. While these models have been shown to provide better prediction accuracy, only limited efforts have been made to investigate how well Learned Cost Models (LCMs) actually perform in query optimization and how they affect overall query performance. In this paper, we address this by a systematic study evaluating LCMs on three of the core query optimization tasks: join ordering, access path selection, and physical operator selection . In our study, we compare seven state-of-the-art LCMs to a traditional cost model and, surprisingly, find that the traditional model often still outperforms LCMs in these tasks. We conclude by highlighting major takeaways and recommendations to guide future research toward making LCMs more effective for query optimization.

Learned Cost Model s (LCMs) have shown superior results over traditional database cost models as they can significantly improve the accuracy of cost predictions. However, LCMs still fail for some query plans, as prediction errors can be large in the tail. Unfortunately, recent LCMs are based on complex deep neural models, and thus, there is no easy way to understand where this accuracy drop is rooted, which critically prevents systematic troubleshooting. In this demo paper, we present the very first approach for opening the black box by bringing AI explainability approaches to LCMs. As a core contribution, we developed new explanation techniques that extend existing methods that are available for the general explainability of AI models and adapt them significantly to be usable for LCMs. In our demo, we provide an interactive tool to showcase how explainability for LCMs works. We believe this is a first step for making LCMs debuggable and thus paving the road for new approaches for systematically fixing problems in LCMs.

Learned cost models (LCMs) have recently gained traction as a promising alternative to traditional cost estimation techniques in data management, offering improved accuracy by capturing complex interactions between queries, data, and runtime behavior. While initially developed for batch systems, LCMs are now increasingly applied to stream processing as well, where real-time demands pose new challenges. This tutorial presents the first unified overview of LCMs across both batch and stream processing systems, examining their role as essential components in modern query optimizers. We explore key aspects of LCM design—including input representations and model architectures—and highlight how these models deal with query optimization tasks.

This paper introduces ZEROTuNE, a novel cost model for parallel and distributed stream processing that can be used to effectively set initial parallelism degrees of streaming queries. Unlike existing models, which rely majorly on online learning statistics that are non-transferable, context-specific, and require extensive training, ZEROTuNE proposes data-efficient zero-shot learning techniques that enable very accurate cost predictions without having observed any query deployment. To overcome these challenges, we propose ZEROTuNE, a graph neural network architecture that can learn from the structural complexity of parallel distributed stream processing systems, enabling them to adapt to unseen workloads and hardware configurations. In our experiments, we show when integrating ZEROTuNE in a distributed streaming system such as Apache Flink, we can accurately set the degree of parallelism, showing an average speed-up of around 5× in comparison to existing approaches.

In this work, we present Costream, a novel learned cost model for Distributed Stream Processing Systems that provides accurate predictions of the execution costs of a streaming query in an edge-cloud environment. The cost model can be used to find an initial placement of operators across heterogeneous hardware, which is particularly important in these environments. In our evaluation, we demonstrate that Costream can produce highly accurate cost estimates for the initial operator placement and even generalize to unseen placements, queries, and hardware. When using Costream to optimize the placements of streaming operators, a median speedup of around 21 × can be achieved compared to baselines.

In this paper, we present a novel pipelined GPU join that accelerates the performance of distributed DBMSs by leveraging GPU resources on fast networks. A key insight is that we enable pipelined join execution by overlapping the network shuffling with the build and probe phases, thereby significantly reducing the GPU idle time. To demonstrate this, we propose novel algorithms for distributed pipelined GPU joins with RDMA and GPUDirect for both arbitrarily large probe- and build-side tables. In our evaluation, we show our pipelined distributed GPU join can reduce the overall runtime of a full query by up to 6× against a state-of-the-art CPU-only join.

Complex Event Processing (CEP) is a powerful paradigm for scalable data management that is employed in many real-world scenarios such as detecting credit card fraud in banks. The so-called complex events are expressed using a specification language that is typically implemented and executed on a specific runtime system. While the tight coupling of these two components has been regarded as the key for supporting CEP at high performance, such dependencies pose several inherent challenges as follows. (1) Application development atop a CEP system requires extensive knowledge of how the runtime system operates, which is typically highly complex in nature. (2) The specification language dependence requires the need of domain experts and further restricts and steepens the learning curve for application developers.In this paper, we propose CEPless, a scalable data management system that decouples the specification from the runtime system by building on the principles of serverless computing. CEPless provides "operator as a service" and offers flexibility by enabling the development of CEP application in any specification language while abstracting away the complexity of the CEP runtime system. As part of CEPless, we designed and evaluated novel mechanisms for in-memory processing and batching that enable the stateful processing of CEP operators even under high rates of ingested events. Our evaluation demonstrates that CEPless can be easily integrated into existing CEP systems like Apache Flink while attaining similar throughput under high scale of events (up to 100K events per second) and dynamic operator update in ~238 ms.