issue_comments: 1043609198
This data as json
| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | issue | performed_via_github_app |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/simonw/datasette/issues/327#issuecomment-1043609198 | https://api.github.com/repos/simonw/datasette/issues/327 | 1043609198 | IC_kwDOBm6k_c4-NDZu | 208018 | 2022-02-17T23:21:36Z | 2022-02-17T23:33:01Z | NONE | On fly.io. This particular database goes from 1.4GB to 200M. Slower, part of that might be having no ``` $ datasette publish fly ... --generate-dir /tmp/deploy-this ... $ mksquashfs large.db large.squashfs $ rm large.db # don't accidentally put it in the image $ cat Dockerfile FROM python:3.8 COPY . /app WORKDIR /app ENV DATASETTE_SECRET 'xyzzy' RUN pip install -U datasette RUN datasette inspect large.db --inspect-file inspect-data.jsonENV PORT 8080 EXPOSE 8080 CMD mount -o loop -t squashfs large.squashfs /mnt; datasette serve --host 0.0.0.0 -i /mnt/large.db --cors --port $PORT ``` It would also be possible to copy the file onto the ~6GB available on the ephemeral container filesystem on startup. A little against the spirit of the thing? On this example the whole docker image is 2.42 GB and the squashfs version is 1.14 GB. |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
335200136 |