id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,pull_request,body,repo,type,active_lock_reason,performed_via_github_app,reactions,draft,state_reason
377155320,MDU6SXNzdWUzNzcxNTUzMjA=,370,Integration with JupyterLab,82988,open,0,,,4,2018-11-04T13:57:13Z,2022-09-29T08:17:47Z,,CONTRIBUTOR,,"I just watched a demo video for the [JupyterLab Chart Editor](https://www.crowdcast.io/e/introducing-JupyterLab-Chart-Editor/) which wraps the plotly chart editor app in a JupyterLab panel and lets you open a plotly chart JSON file in that editor.  Essentially, it pops an HTML app into a panel in JupyterLab, and I think registers the app as a file viewer for a particular file type. (I'm not completely taken by it, tbh, because it means you can do irreproducible things to the chart definition file, but that's another issue).

JupyterLab extensions can also open files from a dialogue as the iframe/html previewer shows: https://github.com/timkpaine/jupyterlab_iframe.

This made me wonder about what `datasette` integration with JupyterLab might do.

For example, by right-clicking on a CSV file (for which there is already a CSV table view) in the file browser, offer a *View / Run as datasette* file viewer option that will:

- run the CSV file through `csvs-to-sqlite`;
- launch the `datasette` server and display the `datasette` view in a JupyterLab panel.

(? Create a new SQLite db for each CSV file and launch each datasette view on a new port? Or have a JupyterLab (session?) SQLite db that stores all `datasette` viewed CSVs and runs on a single port?) 

As a freebie, the `datasette` API would allow you to run efficient SQL queries against the file eg using using `pandas.read_sql()` queries in a notebook in the same space.

Related:

- [JupyterLab extensions docs](https://jupyterlab.readthedocs.io/en/stable/user/extensions.html)
-  a [cookiecutter for wrting JupyterLab extensions using Javascript](https://github.com/jupyterlab/extension-cookiecutter-js)
- a [cookiecutter for writing JupyterLab extensions using Typescript](https://github.com/jupyterlab/extension-cookiecutter-ts)
- tutorial: [Let’s Make an xkcd JupyterLab Extension](https://jupyterlab.readthedocs.io/en/stable/developer/xkcd_extension_tutorial.html)",107914493,issue,,,"{""url"": ""https://api.github.com/repos/simonw/datasette/issues/370/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,
377156339,MDU6SXNzdWUzNzcxNTYzMzk=,371,datasette publish digitalocean plugin,82988,closed,0,,,3,2018-11-04T14:07:41Z,2021-01-04T20:14:28Z,2021-01-04T20:14:28Z,CONTRIBUTOR,,"Provide support for launching `datasette` on Digital Ocean.

Example: [Deploy Docker containers into Digital Ocean](https://blog.machinebox.io/deploy-machine-box-in-digital-ocean-385265fbeafd).

Digital Ocean also has a preconfigured VM running Docker that can be launched from the command line via the Digital Ocean API: [Docker One-Click Application](https://www.digitalocean.com/docs/one-clicks/docker/).

Related:
- Launching containers in Digital Ocean servers running docker: [How To Provision and Manage Remote Docker Hosts with Docker Machine on Ubuntu 16.04](https://www.digitalocean.com/community/tutorials/how-to-provision-and-manage-remote-docker-hosts-with-docker-machine-on-ubuntu-16-04)
- [How To Use Doctl, the Official DigitalOcean Command-Line Client](https://www.digitalocean.com/community/tutorials/how-to-use-doctl-the-official-digitalocean-command-line-client)",107914493,issue,,,"{""url"": ""https://api.github.com/repos/simonw/datasette/issues/371/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed
377166793,MDU6SXNzdWUzNzcxNjY3OTM=,372,Docker build tools,82988,open,0,,,0,2018-11-04T16:02:35Z,2018-11-04T16:02:35Z,,CONTRIBUTOR,,"In terms of small pieces lightly joined, I note that there are several tools starting to appear for building generating Dockerfiles and building Docker containers from simpler components such as `requirements.txt` files.

If plugin/extensions builders want to include additional packages, then things like incremental builds of composable builds that add additional items into a base `datasette` container may be required.

Examples of Dockerfile generators / container builders:

- [openshift/source-to-image (s2i)](https://github.com/openshift/source-to-image)
- [jupyter/repo2docker](https://github.com/jupyter/repo2docker)
- [stencila/dockter](https://github.com/stencila/dockter)

Discussions / threads  (via Binderhub gitter) on:
- [why `repo2docker` not `s2i`](http://words.yuvi.in/post/why-not-s2i/)
- [why `dockter` not `repo2docker`](https://twitter.com/choldgraf/status/1058499607309647872)
- [composability in `s2i`](https://trello.com/c/AexIVZNf/1008-8-composable-builds-builds-evg)

Relates to things like:

- https://github.com/simonw/datasette/pull/280",107914493,issue,,,"{""url"": ""https://api.github.com/repos/simonw/datasette/issues/372/reactions"", ""total_count"": 2, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 2, ""rocket"": 0, ""eyes"": 0}",,