Alberto Lerner | Searcharxiv

arXiv2026

Window Function Optimization: Co-Evaluation and Other Techniques

Window functions are among the most expressive features of modern SQL. Surprisingly, relatively little has been written about their optimization. Some techniques exist, such as pushing predicates through a window under ideal conditions, but known optimizations no longer apply when those conditions are even slightly unmet. We show that these limitations are not fundamental, but persist because a reasoning framework for window function optimization has been missing. We provide such a framework, introducing techniques we call Frame Analysis, Partition Analysis, and a new execution strategy called Co-Evaluation. These clarify when and how optimizations can be applied. Co-Evaluation, in particular, allows early evaluation of predicates even when they depend on the window function's result. We present each technique and organize the results as a table of algebraic equivalences for window functions. We test these optimizations in an open-source engine, where they never hurt performance and make certain common queries up to 40.7 times faster, with larger tables yielding larger gains.

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arXiv2024

CXL and the Return of Scale-Up Database Engines

The trend toward specialized processing devices such as TPUs, DPUs, GPUs, and FPGAs has exposed the weaknesses of PCIe in interconnecting these devices and their hosts. Several attempts have been proposed to improve, augment, or downright replace PCIe, and more recently, these efforts have converged into a standard called Compute Express Link (CXL). CXL is already on version 2.0 in terms of commercial availability, but its potential to radically change the conventional server architecture has only just started to surface. For example, CXL can increase the bandwidth and quantity of memory available to any single machine beyond what that machine can originally provide, most importantly, in a manner that is fully transparent to software applications. We argue, however, that CXL can have a broader impact beyond memory expansion and deeply affect the architecture of data-intensive systems. In a nutshell, while the cloud favored scale-out approaches that grew in capacity by adding full servers to a rack, CXL brings back scale-up architectures that can grow by fine-tuning individual resources, all while transforming the rack into a large shared-memory machine. In this paper, we describe why such architectural transformations are now possible, how they benefit emerging heterogeneous hardware platforms for data-intensive systems, and the associated research challenges.

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