Algebraic data types for language-integrated queries

Lecture Notes in Computer Science

2021

Language-integrated query based on comprehension syntax is a powerful technique for safe database programming, and provides a basis for advanced techniques such as query shredding or query flattening that allow efficient programming with complex nested collections. However, the foundations of these techniques are lacking: although SQL, the most widely-used database query language, supports heterogeneous queries that mix set and multiset semantics, these important capabilities are not supported by known correctness results or implementations that assume homogeneous collections. In this paper we study language-integrated query for a heterogeneous query language $$\mathcal {NRC}_{\lambda }( Set,Bag )$$ NRC λ ( S e t , B a g ) that combines set and multiset constructs. We show how to normalize and translate queries to SQL, and develop a novel approach to querying heterogeneous nested collections, based on the insight that “local” query subexpressions that calculate nested subcollections can be “lifted” to the top level analogously to lambda-lifting for local function definitions.

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Query Lifting

Ricciotti

Lecture Notes in Computer Science

2021

“…To implement expression provenance in L T we would need to find a suitable (nested) relational encoding. Giorgidze et al [2013] show how to encode arbitrary nonrecursive algebraic datatypes relationally and use this in an extension to the language-integrated query library D . The datatype for expressions is recursive, but of a limited depth and thus can be unrolled.…”

Section: Other Forms Of Provenancementioning

confidence: 99%

“…Crary et al [2002] introduce λ r , a variant of λ ML i where runtime type information is explicitly represented as a datatype when necessary. Giorgidze et al [2013] show how to translate algebraic datatypes to S and translate functions to S . It seems plausible that one could put these together to translate all of L T to S without necessarily normalizing to eliminate functions, traces, and typecase.…”

Section: Related and Future Workmentioning

confidence: 99%

Language-integrated provenance

Fehrenbach

Science of Computer Programming

2018

When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given.My thanks, first and foremost, to my advisor James Cheney for his generous guidance, encouragement, and unyielding support. I have left every single one of our meetings happier and more confident than I entered it, even and especially when things had gone poorly. I would also like to thank Peter Buneman, Ian Stark, James McKinna, and Jan Stolarek for their interest and feedback. I would like to thank Torsten Grust and Sam Lindley for agreeing to be my examiners and their feedback, and Stephen Gilmore for chairing my viva.Thanks to my awesome office mates Karoliina, Fabian, Ricardo, and Rudi; to the Friday lunch crowd Brian,

“…While this model appears to generalize all of the aforementioned approaches, it appears nontrivial to implement by translation to relational queries, because it is not obvious how to represent the traces in this approach in a relational data model. (Giorgidze et al [2013] show how to support nonrecursive algebraic data types in queries, but the trace datatype is recursive.) This would be a challenging area for future work.…”

Section: Related Workmentioning

confidence: 99%

Language-integrated provenance

Fehrenbach

Proceedings of the 18th International Symposium on Principles and Practice of Declarative Programming

2016

Provenance, or information about the origin or derivation of data, is important for assessing the trustworthiness of data and identifying and correcting mistakes. Most prior implementations of data provenance have involved heavyweight modifications to database systems and little attention has been paid to how the provenance data can be used outside such a system. We present extensions to the Links programming language that build on its support for language-integrated query to support provenance queries by rewriting and normalizing monadic comprehensions and extending the type system to distinguish provenance metadata from normal data. The main contribution of this article is to show that the two most common forms of provenance can be implemented efficiently and used safely as a programming language feature with no changes to the database system.