arXiv subjects

Wangda Zhang

Publications and source records attributed to Wangda Zhang.

6 recordsLinked to original sources

arXiv2026

Reverse Migration of Cloud Applications to On-premises

Cloud has become ubiquitous to modern applications due to its agility and scalability. However, regulated industries still prefer to deploy on-premises due to security and compliance reasons. This creates a paradox for vendors who need to develop in the cloud but deploy on-premises, leading to long release cycles and complex maintenance. In this paper, we present Diel, the Tursio On-premises Migrator, a tool that automates reverse migration of cloud applications to on-premises environments. Diel applies a combination of simulate, replicate, and delegate strategies to transform cloud services into on-premises counterparts. We describe the design and implementation of Diel, along with lessons learned from using it in practice. With Diel, we have been able to keep Tursio AI's cloud and on-premises versions in sync, releasing new stable versions every three weeks.

cs.DB↗

arXiv2026

Guided Table Retrieval for Structured Data Search

Answering natural language questions over structured databases requires identifying the relevant tables and determining how to join them---a task that demands both schema knowledge and semantic understanding of the user's intent. We present guided table retrieval, a four-phase pipeline that combines deterministic grounding via hash-based predictors, structural exploration of join-graph reachability, LLM-powered disambiguation of sources and targets, and algorithmic merging into minimal, topologically ordered join trees. By decomposing the problem into phases with distinct responsibilities--- determinism, coverage, semantic reasoning, and coherence--- the pipeline avoids the brittleness of end-to-end LLM approaches while leveraging LLMs where their contextual judgment is most needed. We evaluate on BIRD-DEV and the enterprise-scale BEAVER benchmark, achieving 94% and 70% precision respectively, with 92% and 53% F1---substantially outperforming existing baselines on precision and F1 while producing exact join trees that can be directly consumed by downstream query compilers.

cs.DB↗

arXiv2023

Parallel Prefix Sum with SIMD

The prefix sum operation is a useful primitive with a broad range of applications. For database systems, it is a building block of many important operators including join, sort and filter queries. In this paper, we study different methods of computing prefix sums with SIMD instructions and multiple threads. For SIMD, we implement and compare horizontal and vertical computations, as well as a theoretically work-efficient balanced tree version using gather/scatter instructions. With multithreading, the memory bandwidth can become the bottleneck of prefix sum computations. We propose a new method that partitions data into cache-sized smaller partitions to achieve better data locality and reduce bandwidth demands from RAM. We also investigate four different ways of organizing the computation sub-procedures, which have different performance and usability characteristics. In the experiments we find that the most efficient prefix sum computation using our partitioning technique is up to 3x faster than two standard library implementations that already use SIMD and multithreading.

cs.DC↗

arXiv2023

Conditionally Risk-Averse Contextual Bandits

Contextual bandits with average-case statistical guarantees are inadequate in risk-averse situations because they might trade off degraded worst-case behaviour for better average performance. Designing a risk-averse contextual bandit is challenging because exploration is necessary but risk-aversion is sensitive to the entire distribution of rewards; nonetheless we exhibit the first risk-averse contextual bandit algorithm with an online regret guarantee. We conduct experiments from diverse scenarios where worst-case outcomes should be avoided, from dynamic pricing, inventory management, and self-tuning software; including a production exascale data processing system.

stat.ML↗

arXiv2022

Deploying a Steered Query Optimizer in Production at Microsoft

Modern analytical workloads are highly heterogeneous and massively complex, making generic query optimizers untenable for many customers and scenarios. As a result, it is important to specialize these optimizers to instances of the workloads. In this paper, we continue a recent line of work in steering a query optimizer towards better plans for a given workload, and make major strides in pushing previous research ideas to production deployment. Along the way we solve several operational challenges including, making steering actions more manageable, keeping the costs of steering within budget, and avoiding unexpected performance regressions in production. Our resulting system, QQ-advisor, essentially externalizes the query planner to a massive offline pipeline for better exploration and specialization. We discuss various aspects of our design and show detailed results over production SCOPE workloads at Microsoft, where the system is currently enabled by default.

cs.DB↗

arXiv2019

Exploiting Data Skew for Improved Query Performance

Analytic queries enable sophisticated large-scale data analysis within many commercial, scientific and medical domains today. Data skew is a ubiquitous feature of these real-world domains. In a retail database, some products are typically much more popular than others. In a text database, word frequencies follow a Zipf distribution with a small number of very common words, and a long tail of infrequent words. In a geographic database, some regions have much higher populations (and data measurements) than others. Current systems do not make the most of caches for exploiting skew. In particular, a whole cache line may remain cache resident even though only a small part of the cache line corresponds to a popular data item. In this paper, we propose a novel index structure for repositioning data items to concentrate popular items into the same cache lines. The net result is better spatial locality, and better utilization of limited cache resources. We develop a theoretical model for analyzing the cache behavior, and implement database operators that are efficient in the presence of skew. Our experiments on real and synthetic data show that exploiting skew can significantly improve in-memory query performance. In some cases, our techniques can speed up queries by over an order of magnitude.

cs.DB↗