Logo des Repositoriums

Auflistung nach Schlagwort "In-Memory Database Systems"

1 - 2 von 2
  • Konferenzbeitrag
    Using OPEN.xtrace and Architecture-Level Models to Predict Workload Performance on In-Memory Database Systems
    (Softwaretechnik-Trends Band 39, Heft 4, 2019) Barnert, Maximilian; Streitz, Adrian; Rank, Johannes; Kienegger, Harald; Krcmar, Helmut
    In-Memory Database Systems (IMDB) come into operation on highly dynamic on-premise and cloud environments. Existing approaches use classical modeling notations such as queuing network models (QN) to reflect performance on IMDB. Changes to workload or hardware come along with a recreation of entire models. At the same time, new paradigms for IMDB increase parallelism within database workload, which intensifies the effort to create and parameterize models. To simplify and reduce the effort for researchers and practitioners to model workload performance on IMDB, we propose the use of architecture level performance models and present a model creation process, which transforms database traces of SAP HANA to the Palladio Component Model (PCM). We evaluate our approach based on experiments using analytical workload. We receive prediction errors for response time and throughput below 4 %.
  • Konferenzbeitrag
    Workload-Driven Data Placement for Tierless In-Memory Database Systems
    (BTW 2023, 2023) Hurdelhey, Ben; Weisgut, Marcel; Boissier, Martin
    High main memory consumption is a significant cost factor for in-memory database systems. Tiering, i.e., placing parts of the data on memory or storage devices other than DRAM, reduces the main memory footprint. A controlled data placement can assign rarely accessed data to slow devices while frequently used data remains on fast devices, such as main memory, to maintain acceptable query latencies. We present an automatic data placement decision system for the in-memory database Hyrise. The system organizes the memory and storage devices in a tierless pool, with no fixed device class categorization or performance order. The system supports data placement use cases, such as minimizing end-to-end query latencies and making cost-optimal purchase recommendations in cloud environments. In this paper, we introduce an efficient calibration process to derive cost models for various storage devices. To determine data placements, we introduce a linear programming-based approach, which yields optimal configurations, and an efficient heuristic. With a set of main memory and SSD devices, we can reduce the main memory consumption for base table data of the TPC-DS benchmark by 74 percent when accepting a workload latency increase of 52 percent. In a comparison of data placement algorithms and cost models, we find that simplistic algorithms (e.g., greedy algorithms) can present viable alternatives to optimal linear programming algorithms, especially under cost prediction inaccuracies.