There has been a growing demand for making modern cloud-based data analytics systems cost-effective and easy to use. AI-powered intelligent resource scaling is one such effort, aiming at automating scaling decisions for serverless offerings like Amazon Redshift Serverless. The foundation of intelligent resource scaling lies in the ability to forecast query workloads and their resource consumption accurately. Although the forecasting problem has been extensively studied across various domains, there is a lack of thorough analysis of existing forecasting algorithms for large-scale, real-world cloud query workloads. This paper fills this gap by providing an in-depth analysis of forecasting algorithms for real-world cloud workloads, covering the fundamental data characteristics that distinguish query workload forecasting from prior problems and evaluating the strengths and limitations of existing algorithms in this new domain. We anticipate that our findings will provide valuable insights in informing the design of an efficient and effective solution for production use, as well as in steering the forecasting community toward more effective algorithms of high real-world impact.

Database research and development is heavily influenced by benchmarks, such as the industry-standard TPC-H and TPC-DS for analytical systems. However, these twenty-year-old benchmarks neither capture how databases are deployed nor what workloads modern cloud data warehouse systems face these days. In this paper, we summarize well-known, confirm suspected, and unearth novel discrepancies between TPC-H/DS and actual workloads using empirical data. We base our analysis on telemetrics from Amazon Redshift - one of the largest cloud data warehouse deployments. Among others, we show how write-heavy data pipelines are prominent, workloads vary over time (in both load and type), queries are repetitive, and how most properties of queries or workloads experience very long tailed distributions. We conclude that data warehouse benchmarks, just like database systems, need to become more holistic and stop focusing solely on query engine performance. Finally, we publish a dataset containing query statistics of 200 randomly selected Redshift serverless and provisioned instances (each) over a three-month period, as a basis for building more realistic benchmarks.