We present insert-optimized implementations of three fundamental data sketching algorithms: Count Sketch (CS), SpaceSaving (SS), and Karnin-Lang-Liberty (KLL).While these sketches are widely used for approximate query processing and stream analytics, their practical insert performance often falls short of their full potential.Through careful engineering and novel implementation strategies, we achieve substantial throughput improvements over both naïve and existing implementations.Our approach demonstrates speedups of up to 12.30x, 2.00x, and 1.52x for CS, SS, and KLL respectively when compared to the industry-standard Apache DataSketches library.When measured against naïve implementations, we achieve even more dramatic improvements: 8.63x for CS, 7.03x for SS, and 446.00x for KLL.We also measure against available open-source implementations used as baselines in other works and outperform them by a large margin.We detail the technical optimizations enabling these improvements, including fast hash range reduction and hash sharing for Count Sketch, SIMD vectorization for SpaceSaving, and preallocation and branching improvements for Karnin-Lang-Liberty.Our implementations maintain the theoretical guarantees of the original algorithms while providing substantially better practical insert performance, making them particularly valuable for high-throughput streaming applications where update speed is critical.

Cloud service providers commonly use standard benchmarks like TPC-H and TPC-DS to evaluate and optimize cloud data analytics systems. However, these benchmarks rely on fixed query patterns and fail to capture real execution statistics of production cloud workloads. Although some cloud database vendors have recently released real workload traces, these traces alone do not qualify as benchmarks, as they typically lack essential components (i.e., queries and databases). To overcome this limitation, this paper studies a new problem of workload synthesis with real statistics , which generates synthetic workloads that closely approximate real execution statistics, including key performance metrics and operator distributions. To address this problem, we propose PBench, a novel workload synthesizer that constructs synthetic workloads by (1) selecting and combining workload components from existing benchmarks and (2) augmenting new workload components. This paper studies the key challenges in PBench. First, we address the challenge of balancing performance metrics and operator distributions by introducing a multi-objective optimization-based component selection method. Second, to capture the temporal dynamics of real workloads, we design a timestamp assignment method that progressively reines workload timestamps. Third, to handle the disparity between the original workload and the candidate workload, we propose a component augmentation approach that leverages large language models (LLMs) to generate additional workload components while maintaining statistical idelity. Experimental results show that PBench reduces approximation error by up to 6X compared to state-of-the-art methods.

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.

Hashing is a fundamental operation in database management, playing a key role in the implementation of numerous core database data structures and algorithms. Traditional hash functions aim to mimic a function that maps a key to a random value, which can result in collisions, where multiple keys are mapped to the same value. There are many well-known schemes like chaining, probing, and cuckoo hashing to handle collisions. In this work, we aim to study if using learned models instead of traditional hash functions can reduce collisions and whether such a reduction translates to improved performance, particularly for indexing and joins. We show that learned models reduce collisions in some cases, which depend on how the data is distributed. To evaluate the effectiveness of learned models as hash function, we test them with bucket chaining, linear probing, and cuckoo hash tables. We find that learned models can (1) yield a 1.4x lower probe latency, and (2) reduce the non-partitioned hash join runtime with 28% over the next best baseline for certain datasets. On the other hand, if the data distribution is not suitable, we either do not see gains or see worse performance. In summary, we find that learned models can indeed outperform hash functions, but only for certain data distributions.