Marcus Pinnecke

FeatureIDE: Empowering Third-Party Developers

FeatureIDE is a popular open-source tool for modeling, implementing, configuring, and analyzing software product lines. However, FeatureIDEs initial design was lacking mechanisms that facilitate extension and reuse of core implementations. In current releases, we improve these traits by providing a modular concept for core data structures and functionalities. As a result, we are facilitating the usage of external implementations for feature models and file formats within FeatureIDE. Additionally, we provide a Java library containing FeatureIDEs core functionalities, including feature modeling and configuration. This allows developers to use these functionalities in their own tools without relying on external dependencies, such as the Eclipse framework.

Query Optimization in Heterogenous Event Processing Federations

Continuous processing of event streams evolved to an important class of data management over the last years and will become even more important due to novel applications such as the Internet of Things. Because systems for data stream and event processing have been developed independent of each other, often in competition and without the existence of any standards, the Stream Processing System (SPS) landscape is extremely heterogeneous today. To overcome the problems caused by this heterogeneity, a novel event processing middleware, the Java Event Processing Connectivity (JEPC), has been presented recently. However, despite the fact that SPSs can be accessed uniformly using JEPC, their different performance profiles caused by different algorithms and implementations remain. This gives the opportunity to query optimization, because individual system strengths can be exploited. In this paper, we present a novel query optimizer that exploits the technical heterogeneity in a federation of different unified SPSs. Taking into account different performance profiles of SPSs, we address query plan partitioning, candidate selection, and reducing inter-system communication in order to improve the overall query performance. We suggest a heuristic that finds a good initial mapping of sub-plans to a set of heterogenous SPSs. An experimental evaluation clearly shows that heterogeneous federations outperform homogeneous federations, in general, and that our heuristic performs well in practice.

Composing annotations without regret? Practical experiences using FeatureC

Software product lines enable developers to derive similar products from a common code base. Existing implementation techniques can be categorized as composition‐based and annotation‐based approaches, with both approaches promising complementary benefits. However, annotation‐based approaches are commonly used in practice despite composition allowing physical separation of features and, thus, improving traceability and maintenance. A main hindrance to migrate annotated systems toward a composition‐based product line is the challenging and time‐consuming transformation task. For a company, it is difficult to predict the corresponding costs, and a successful outcome is uncertain. To overcome such problems, a solution proposed by the previous work is to use a hybrid approach, utilizing composition and annotation simultaneously. Based on this idea, we introduce a stepwise migration process from annotation‐based toward composition‐based approaches to lower the adoption barrier of composition. This process itself is independent of used implementation techniques and enables developers to incrementally migrate toward composition. We support our approach with detailed examples by partially migrating a real‐world system. In detail, we describe the following: (1) our migration process, (2) its application on a real‐world system, and (3) discuss practical challenges we face. We implemented the proposed approach and show that appropriate tool support helps to migrate toward composition‐based product lines. Based on the case study, we show that the hybrid product lines work correctly and can compete with the performance of the original annotated system. However, the results also illustrate open issues that have to be solved to apply such migrations in practice.

Are Databases Fit for Hybrid Workloads on GPUs? A Storage Engine's Perspective

Employing special-purpose processors (e.g., GPUs) in database systems has been studied throughout the last decade. Research on heterogeneous database systems that use both general-and special-purpose processors has addressed either transaction-or analytic processing, but not the combination of them. Support for hybrid transaction-and analytic processing (HTAP) has been studied exclusively for CPU-only systems. In this paper we ask the question whether current systems are ready for HTAP workload management with cooperating generaland special-purpose processors. For this, we take the perspective of the backbone of database systems: the storage engine. We propose a unified terminology and a comprehensive taxonomy to compare state-of-the-art engines from both domains. We show similarities and differences, and determine a necessary set of features for engines supporting HTAP workload on CPUs and GPUs. Answering our research question, our findings yield a resolute: not yet.

GridFormation: Towards Self-Driven Online Data Partitioning using Reinforcement Learning

In this paper we define a research agenda to develop a general framework supporting online autonomous tuning of data partitioning and layouts with a reinforcement learning formulation. We establish the core elements of our approach: agent, environment, action space and supporting components. Externally predicted workloads and the current physical design serve as input to our agent. The environment guides the search process by generating immediate rewards based on fresh cost estimates, for either the entirety or a sample of queries from the workload, and by deciding the possible actions given a state. This set of actions is configurable, enabling the representation of different partitioning problems. For use in an online setting the agent learns a fixed-length sequence of n actions that maximize the temporal reward for the predicted workload. Through an initial implementation we assert the feasibility of our approach. To conclude, we list open challenges for this work.

SIMD Vectorized Hashing for Grouped Aggregation

Grouped aggregation is a commonly used analytical function. The common implementation of the function using hashing techniques suffers lower throughput rate due to the collision of the insert keys in the hashing techniques. During collision, the underlying technique searches for an alternative location to insert keys. Searching an alternative location increases the processing time for an individual key thereby degrading the overall throughput. In this work, we use Single Instruction Multiple Data (SIMD) vectorization to search multiple slots at an instant followed by direct aggregation of results. We provide our experimental results of our vectorized grouped aggregation with various open-addressing hashing techniques using several dataset distributions and our inferences on them. Among our findings, we observe different impacts of vectorization on these techniques. Namely, linear probing and two-choice hashing improve their performance with vectorization, whereas cuckoo and hopscotch hashing show a negative impact. Overall, we provide in this work a basic structure of a dedicated SIMD accelerated grouped aggregation framework that can be adapted with different hashing techniques.

Memory Management Strategies in CPU/GPU Database Systems: A Survey.

GPU-accelerated in-memory database systems have gained a lot of popularity over the last several years. However, GPUs have limited memory capacity, and the data to process might not fit into the GPU memory entirely and cause a memory overflow. Fortunately, this problem has many possible solutions, like splitting the data and processing each portion separately, or storing the data in the main memory and transferring it to the GPU on demand. This paper provides a survey of four main techniques for managing GPU memory and their applications for query processing in cross-device powered database systems.