Experimental results imply that our pipeline outperforms benchmarks on predictive accuracy, contributing to more precise resource prediction for large-scale workloads, yet also reduces per-batch memory footprint by 13.5x and per-epoch training time by 3.45x. We evaluated our pipeline over 19K Presto OLAP queries from Grab, on a data lake of more than 20PB of data. In turn, we developed Prestroid, a tree convolution based data science pipeline that accurately predicts resource consumption patterns of query traces, but at a much lower cost.
Space inefficiencies of encoding techniques over large numbers of queries and excessive padding used to enforce shape consistency across diverse query plans implies 1) longer model training time and 2) the need for expensive, scaled up infrastructure to support batched training. While these models have demonstrated promising accuracy, training them over large scale industry workloads are expensive. With many companies using cloud platforms to power their data lakes for large scale analytic demands, these models form a critical part of the pipeline in managing cloud resource provisioning. The use of deep learning models for forecasting the resource consumption patterns of SQL queries have recently been a popular area of study.
Experimental results demonstrate that Neo, even when bootstrapped from a simple optimizer like PostgreSQL, can learn a model that offers similar performance to state-of-the-art commercial optimizers, and in some cases even surpass them. Furthermore, Neo naturally adapts to underlying data patterns and is robust to estimation errors. Neo bootstraps its query optimization model from existing optimizers and continues to learn from incoming queries, building upon its successes and learning from its failures.
Motivated by this shortcoming and inspired by recent advances in applying machine learning to data management challenges, we introduce Neo (Neural Optimizer), a novel learning-based query optimizer that relies on deep neural networks to generate query executions plans. Despite the progress made over the past decades, query optimizers remain extremely complex components that require a great deal of hand-tuning for specific workloads and datasets. When jurisprudence "penalizes" mergers posted on In this legal column, Philippe Gianviti, associate attorney at NMW, reviews a recent unfavorable jurisprudential development.Query optimization is one of the most challenging problems in database systems.MIFID II guidance on sustainability preferences posted on Asking investors about their sustainability preferences will become mandatory starting next summer, following the publication of the.Iris Knobloch takes a shot at Tibi funds posted on The co-founder of SPAC I2PO, which is merging with the Deezer platform, was "very disappointed" by the reception of French investors.The yield on the 10-year OAT reaches 2% posted on Eurozone government bonds are incorporating the ECB's firmness, which is starting a cycle of increases in.Private equity: Amundi's CIO backtracks on his comments posted on Vincent Mortier, head of investment strategy at the management company, had compared some parts of private equity to a.