Logo des Repositoriums

Auflistung nach Schlagwort "query optimization"

1 - 2 von 2
  • Konferenzbeitrag
    Learn What Really Matters: A Learning-to-Rank Approach for ML-based Query Optimization
    (BTW 2023, 2023) Behr, Henriette; Markl, Volker; Kaoudi, Zoi
    Query optimization is crucial for any data management system to achieve good performance. Recent advancements in Machine Learning (ML) have led to several efforts in the database research community that aim at improving query optimization with the help of ML. In particular, many works propose replacing the cost model used during plan enumeration with an ML model. The goal of these works is to learn a regression model from previously executed query plans that estimates the runtime of a given plan. Interestingly, it is well-known that what really matters in query optimization is the order of the query plans and not their actual cost or runtime. We thus take a learning-to-rank approach and propose a novel neural network model architecture that considers a plan in comparison with other equivalent plans that belong to the same query. We use our model architecture together with a loss function that incorporates ranking metrics into the learning process to highlight the learning-to-rank objective.To enable training, we first extract features from query plans by adapting a state-of-the-art deep learning approach so that all features are independent of the input dataset schema. Second, we devise two score functions that map the runtime of plans to scores which are then used as labels. We integrate the trained model into an adapted bottom-up plan enumeration algorithm that finds the best possible execution plan for a given query. We evaluate our approach against two state-of-the-art ML models and the highly tuned cost model of a commercial database and measure the runtime of the plans chosen in each case when executed in the database. We show that our approach achieves up to an order of magnitude better query performance than the comparison models and is able to either match (for short and medium-running queries) or outperform the commercial database (up to 5x for long-running queries).
  • Textdokument
    Optimized Theta-Join Processing
    (BTW 2021, 2021) Weise, Julian; Schmidl, Sebastian; Papenbrock, Thorsten
    The Theta-Join is a powerful operation to connect tuples of different relational tables based on arbitrary conditions. The operation is a fundamental requirement for many data-driven use cases, such as data cleaning, consistency checking, and hypothesis testing. However, processing theta-joins without equality predicates is an expensive operation, because basically all database management systems (DBMSs) translate theta-joins into a Cartesian product with a post-filter for non-matching tuple pairs. This seems to be necessary, because most join optimization techniques, such as indexing, hashing, bloom-filters, or sorting, do not work for theta-joins with combinations of inequality predicates based on <, ?, ?, ?, >. In this paper, we therefore study and evaluate optimization approaches for the efficient execution of theta-joins. More specifically, we propose a theta-join algorithm that exploits the high selectivity of theta-joins to prune most join candidates early; the algorithm also parallelizes and distributes the processing (over CPU cores and compute nodes, respectively) for scalable query processing. The algorithm is baked into our distributed in-memory database system prototype A2DB. Our evaluation on various real-world and synthetic datasets shows that A2DB significantly outperforms existing single-machine DBMSs including PostgreSQL and distributed data processing systems, such as Apache SparkSQL, in processing highly selective theta-join queries.