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Shannon Bales

dblp:385/5229 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

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

Databases, 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.

Databases, data mining, and information retrieval
1 paper
Data models and query languages · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data models and query languages
query language design
0.812024
SQL has problems. We can fix them: Pipe syntax in SQL · Proc. VLDB Endow. 2024
Data models and query languages
SQL
0.812024
SQL has problems. We can fix them: Pipe syntax in SQL · Proc. VLDB Endow. 2024
Data models and query languages › SQL
SQL extension
0.812024
SQL has problems. We can fix them: Pipe syntax in SQL · Proc. VLDB Endow. 2024
YearPublicationVenuePosition
2024 SQL has problems. We can fix them: Pipe syntax in SQL
abstract
SQL has been extremely successful as the de facto standard language for working with data. Virtually all mainstream database-like systems use SQL as their primary query language. But SQL is an old language with significant design problems, making it difficult to learn, difficult to use, and difficult to extend. Many have observed these challenges with SQL, and proposed solutions involving new languages. New language adoption is a significant obstacle for users, and none of the potential replacements have been successful enough to displace SQL. In GoogleSQL, we've taken a different approach - solving SQL's problems by extending SQL. Inspired by a pattern that works well in other modern data languages, we added piped data flow syntax to SQL. The results are transformative - SQL becomes a flexible language that's easier to learn, use and extend, while still leveraging the existing SQL ecosystem and existing userbase. Improving SQL from within allows incrementally adopting new features, without migrations and without learning a new language, making this a more productive approach to improve on standard SQL.
Jeff Shute, Shannon Bales, Matthew Brown 0006, Jean-Daniel Browne, Brandon Dolphin, Romit Kudtarkar, Andrey Litvinov, Jingchi Ma, John D. Morcos, Michael Shen, David Wilhite, Lulan Yu
Proc. VLDB Endow.2