Arrow type system as base for the type system

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#4,472 2 comentarios 2 reacciones 0 asignados Ver en GitHub

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
25/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Estancado
Stack tecnológico
rust
Área
data

Línea de trabajo

Start by reading the Arrow schema specification linked in the issue and compare it with the RFC in #1964, as requested. Then review the type-system work tracked in #1965 to understand the project context. The issue does not identify files or tests; done means documenting similarities and differences to support further discussion.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Motivation

Arrow is increasingly becoming a Lingua Franca interchange format in the data world. Its aims are to have efficient in memory representations of data structures that allow zero-copy reuse of data between different systems (e.g. between Python and R). The creators have thought a lot about making the format memory and cache efficient and what hurdles they encountered in their previous work (e.g. Pandas, dplyr, ...). As such it benefits from a wealth of experience and is geared towards modern analytics workloads. It is therefore no surprise that we are seeing it increasingly being adopted as the basis for new data tools such as DataFusion, Polars, InfluxDB v3, Lance v2, ... to name a few that I have personally encountered.

I therefore think that it would be a good starting point for the type system for PRQL as tracked in #1965 .

Resources

Further work

Compare it with the RFC in #1964 and highlight similarities and differences for further discussion.

Lenguaje dominante
Rust
Estrellas
10.9k
Forks
261
Merge medio
3 d 21 h
PR fusionados (30 d)
84

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