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Auflistung nach Autor:in "Baumstark, Alexander"

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  • Konferenzbeitrag
    Accelerating Large Table Scan using Processing-In-Memory Technology
    (BTW 2023, 2023) Baumstark, Alexander; Jibril, Muhammad Attahir; Sattler, Kai-Uwe
    Today’s systems are capable of storing large amounts of data in main memory. In-memoryDBMSs can benefit particularly from this development. However, the processing of the data fromthe main memory necessarily has to run via the CPU. This creates a bottleneck, which affects thepossible performance of the DBMS. The Processing-In-Memory (PIM) technology is a paradigm toovercome this problem, which was not available in commercial systems for a long time. However, withthe availability of UPMEM, a commercial system is finally available that provides PIM technologyin hardware. In this work, the main focus was on the optimization of the table scan, a fundamental,and memory-bound operation. Here a possible approach is shown, which can be used to optimizethis operation by using PIM. This method was then tested for parallelism and execution time inbenchmarks with different table sizes and compared to the usual table scan. The result is a table scanthat outperforms the scan on the usual CPU significantly.
  • Konferenzbeitrag
    GTPC: Towards a Hybrid OLTP-OLAP Graph Benchmark
    (BTW 2023, 2023) Jibril, Muhammad Attahir; Baumstark, Alexander; Sattler, Kai-Uwe
    Graph databases are gaining increasing relevance not only for pure analytics but alsofor full transactional support. Business requirements are evolving to demand analytical insights onfresh transactional data, thereby triggering the emergence of graph systems for hybrid transactional-analytical graph processing (HTAP). In this paper, we present our ongoing work on GTPC, a hybridgraph benchmark targeting such systems, based on the TPC-C and TPC-H benchmarks.
  • Textdokument
    Lock-free Data Structures for Data Stream Processing
    (BTW 2019 – Workshopband, 2019) Baumstark, Alexander
    The ever-growing amounts of data in the digital world require more and more computing power to meet the requirements. Especially in the area of social media, sensor data processing or Internet of Things, the data need to be handled on the fly during its creation. A common way to handle these data, in form of endless data streams, is the data stream processing technology. The key requirements for data stream processing are high throughput and low latency. These requirements can be accomplished with the parallelization of operators and multithreading. However, in order to realize a higher degree of parallelism, the efficient synchronization of threads is a necessity. This work examines the design principles of lock-free data structures and how this synchronization method can improve the performance of algorithms in data stream processing. For this purpose, lock-free data structures are implemented for the data stream processing engine Pipefabric and compared with current implementations. The result is an improvement for the tuple exchanging between threads and a significant improvement for the symmetric hash join algorithm based on lock-free hash maps.
  • Zeitschriftenartikel
    Lock-free Data Structures for Data Stream Processing
    (Datenbank-Spektrum: Vol. 19, No. 3, 2019) Baumstark, Alexander; Pohl, Constantin
    Processing data in real-time instead of storing and reading from tables has led to a specialization of DBMS into the so-called data stream processing paradigm. While high throughput and low latency are key requirements to keep up with varying stream behavior and to allow fast reaction to incoming events, there are many possibilities how to achieve them. In combination with modern hardware, like server CPUs with tens of cores, the parallelization of stream queries for multithreading and vectorization is a common schema. High degrees of parallelism, however, need efficient synchronization mechanisms to allow good scaling with threads for shared memory access.In this work, we identify the most time-consuming operations for stream processing exemplarily for our own stream processing engine PipeFabric. In addition, we present different design principles of lock-free data structures which are suited to overcome those bottlenecks. We will finally demonstrate how lock-freedom greatly improves performance for join processing and tuple exchange between operators under different workloads. Nevertheless, the efficient usage of lock-free data structures comes with additional efforts and pitfalls, which we also discuss in this paper.