Records with rank polymorphism
Pages 80 - 92
Abstract
In a rank-polymorphic programming language, all functions automatically lift to operate on arbitrarily high-dimensional aggregate data. By adding records to such a language, we can support computation on data frames, a tabular data structure containing heterogeneous data but in which individual columns are homogeneous. In such a setting, a data frame is a vector of records, subject to both ordinary array operations (, filtering, reducing, sorting) and lifted record operations—projecting a field lifts to projecting a column. Data frames have become a popular tool for exploratory data analysis, but fluidity of interacting with data frames via lifted record operations depends on how the language’s records are designed.
We investigate three languages with different notions of record data: Racket, Standard ML, and Python. For each, we examine several common tasks for working with data frames and how the language’s records make these tasks easy or hard. Based on their advantages and disadvantages, we synthesize their ideas to produce a design for record types which is flexible for both scalar and lifted computation.
Supplementary Material
Presentation Video
- Download
- 44.75 MB
References
[1]
J. Nathan Foster, Michael B. Greenwald, Jonathan T. Moore, Benjamin C. Pierce, and Alan Schmitt. 2007. Combinators for Bidirectional Tree Transformations: A Linguistic Approach to the View-update Problem. ACM Trans. Program. Lang. Syst. 29, 3, Article 17 (May 2007).
[2]
Jeremy Gibbons. 2016. APLicative Programming with Naperian Functors (Extended Abstract). In Proceedings of the 1st International Workshop on Type-Driven Development (TyDe 2016) . ACM, New York, NY, USA, 13–14.
[3]
Troels Henriksen. 2017. Design and Implementation of the Futhark Programming Language . Ph.D. Dissertation. University of Copenhagen, Universitetsparken 5, 2100 København.
[4]
Troels Henriksen. 2017. Dot Notation for Records. https:// futhark-lang.org/blog/2017-11-11-dot-notation-for-records.html
[5]
Kenneth E. Iverson. 1962. A programming language. John Wiley & Sons, Inc., New York, NY, USA.
[6]
Wes McKinney et al. 2010. Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference, Vol. 445. Austin, TX, 51–56.
[7]
Robin Milner, Mads Tofte, Robert Harper, and David MacQueen. 1997. The definition of standard ML: revised . MIT press.
[8]
R Core Team. 2013. R: A Language and Environment for Statistical Computing . R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
[9]
Justin Slepak, Olin Shivers, and Panagiotis Manolios. 2014. An arrayoriented language with static rank polymorphism. In European Symposium on Programming Languages and Systems . Springer, 27–46.
[10]
Satish Thatte. 1991. A type system for implicit scaling. Sci. Comput. Program. 17, 1-3 (Dec. 1991), 217–245.
[11]
Mitchell Wand. 1991. Type inference for record concatenation and multiple inheritance. Information and Computation 93, 1 (1991), 1–15.
Recommendations
Rank polymorphism viewed as a constraint problem
ARRAY 2018: Proceedings of the 5th ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array ProgrammingRank polymorphism serves as a type of control flow used in array-oriented languages, where functions are automatically lifted to operate on high-dimensional arguments. The iteration space is derived directly from the shape of the data, presenting a ...
Records and Record Types in Semantic Theory
This paper explores possibilities for formulating linguistic semantics in terms of records and record types of the kind used in recent developments of Martin-Löf type theory. We will suggest that this gives us tools to develop a single theory which ...
Introducing records by refactoring
ERLANG '07: Proceedings of the 2007 SIGPLAN workshop on ERLANG WorkshopThis paper focuses on introducing a new transformation to our existing model for refactoring Erlang programs. The goal of the transformation is to introduce a new abstraction level in data representation by substituting a group ofrelated data with a ...
Comments
Information & Contributors
Information
Published In
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 08 June 2019
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
PLDI '19
Sponsor:
PLDI '19: 40th ACM SIGPLAN Conference on Programming Language Design and Implementation
June 22, 2019
AZ, Phoenix, USA
Acceptance Rates
Overall Acceptance Rate 17 of 25 submissions, 68%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 128Total Downloads
- Downloads (Last 12 months)7
- Downloads (Last 6 weeks)0
Reflects downloads up to 11 Aug 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in