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Meta-issue: Deployment

Abierto
  • ¿Algo no está bien?
#359 0 comentarios 0 reacciones 0 asignados Ver en GitHub

Los mantenedores suelen responder en 2 días

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
20/100
Tipo de issue
Nueva funcionalidad
Claridad
Necesita aclaración
Estado de actividad
Tranquilo
Stack tecnológico
sql
Área
backend, cloud, security

Línea de trabajo

No se han identificado archivos de implementación ni pruebas. Empieza revisando las preguntas de despliegue para Connect desde Positron y Workbench, incluidos los informes de Quarto, las aplicaciones Shiny, el versionado, los modos de conexión al backend y los modos de credenciales. Se considerará completado cuando el proyecto tenga un diseño de despliegue acordado y un plan de implementación delimitado.

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

Descripción

integration

This issue exist to track discussions around deployment. For the initial phase we will focus on Connect deployment from Positron and Workbench as this is the stack we control - others may think about deployment in other context if they wish to do so in the mean time.

Key questions:

What does ggsql deployment look like?

Right now deployment would be in the form of a quarto report or some sort. However, the R bindings also support deploying shiny applications with ggsql code inside.

I do envision a mode where you deploy a single .ggsql file and you get an embeddable visualisation. Depending on how ggsql's own multiplot support evolves this may end up being a viable dashboard solution, though I'm not sure SQL provides the best syntax for layout work. If not, do we need a new format for dashboard-y deployments or does quarto serve that need?

For deployment, we also need to think about ggsql versioning. We are in a blissful state of just having to declare a single version and not black pit of 100s of dependencies, but declare it we must somehow. Is this a Connect responsibility or should ggsql itself do something about this?

What kind of backend connection is kept - with what kind of credentials?

Our happy path is that an analyst sits on their computer, developing an analysis using ggsql either in Positron or Workbench. They have their credentials set up so they can connect to e.g. Snowflake and visualise that data directly. They arrive at an end product and hit deploy. The deployment target is send to Connect, along with the credentials and everything just magically works. We must strive for this to be the case!

However, I can see multiple modes of connection with a backend depending on the needs, and also multiple modes of credentials:

Connection modes:

  • Keep a connection to the original backend, updates to the database is reflected in the visualisation whenever it is presented
  • Cache the main data sources in a local DuckDB instance and query against that in the backend - this may be updated periodically but not real-time
  • Embed the data in a "static" deployment (i.e. connection is severed)

Credential mode:

  • Use the authors credentials/credentials provided specifically for this deployment. All visits will use the same credentials and it is up to the analyst to not expose confidential information in the visualization
  • Visitor brings their own credentials and these are used when querying the database and creating the plot. The visitor only sees data for which they have been granted access. This mode explicitly requires the first connection mode described above
Lenguaje dominante
Rust
Estrellas
562
Forks
31
Merge medio
2 d 15 h
PR fusionados (30 d)
15

Preparar el entorno

Este proyecto no incluye contenedor de desarrollo, Dockerfile ni guía de contribución, así que la configuración corre por tu cuenta: empieza por su README y consulta nuestra guía para la primera contribución para los pasos generales.

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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