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William Sun

dblp:385/1361 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
0009-0005-3956-6938ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
2 papers
Storage systems · 61% Performance modeling and evaluation · 39%
Databases, data mining, and information retrieval
1 paper
Query processing and optimization · 100%
Computer networks
1 paper
Network measurement and analytics · 100%

Topics — the 2 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Storage systems
storage engine
0.812024
Mach: Firefighting Time-Critical Issues in Complex Systems Using High-Frequency Telemetry · Proc. VLDB Endow. 2024
Performance modeling and evaluation
workload characterization
0.312025
Loom: Efficient Capture and Querying of High-Frequency Telemetry · SOSP 2025

Methods — techniques the papers use, named apart from their topics

telemetry capture · 2.6querying · 2.6temporal skip log · 0.8log-based data structure · 0.8
YearPublicationVenuePosition
2025 Loom: Efficient Capture and Querying of High-Frequency Telemetry
Franco Solleza, William Sun, Richard Tang, Malte Schwarzkopf, Andrew Crotty, Nesime Tatbul, Stanley B. Zdonik
SOSP3
2024 Mach: Firefighting Time-Critical Issues in Complex Systems Using High-Frequency Telemetry
abstract
To understand the complex interactions in modern software, engineers often rely on high-frequency telemetry (HFT) data generated via tools like eBPF. However, today's database systems are too slow for HFT's rate and volume and cannot process HFT within the limited resources available on individual host machines. Mach is a new storage engine for collecting and querying HFT. Key to Mach is the Temporal Skip Log (TSL)---a lightweight, write-optimized, log-based data structure specialized for HFT. Mach supports high ingest rates and makes data immediately queryable while operating within a limited on-host resource envelope. Our demo shows how Mach helps engineers collect and query HFT in near real-time when diagnosing performance problems. In contrast, current systems and data reduction techniques fail to keep up. While a widely used time series database (InfluxDB) drops much of the HFT, the audience will see how Mach loses no data and allows them to interactively explore HFT from application and kernel events as they arrive.
Franco Solleza, William Sun, Richard Tang, Malte Schwarzkopf, Nesime Tatbul, Andrew Crotty, Stanley B. Zdonik
Proc. VLDB Endow.3