Iain Sproat
1c5964fc41
* Adds hadolint as pre-commit step * Addresses all hadolint comments * Use noninteractive apt-get frontend and clean after install * dockerfile RUN statements are consolidated to prevent additional layers * installed packages have pinned versions * build(circleci): use speckle pre-commit runner with built-in hadolint * Integrate pre-commit with husky bash script for git pre-commit hooks * catches errors in bash * if pre-commit is installed, it is run * if optional additional binaries are installed, further pre-commit steps are run * Update README with revised developer instructions * Adds a pre-commit yarn script
PreviewService
This component generates object previews for Speckle Objects.
It reads preview tasks from the DB and uses Puppeteer and an internal Viewer to generate previews, which are currently stored in the DB.
This is an overview of this service:
Run locally
With an updated viewer installed in the current directory, you should first build the frontend-part of the preview service: The simple webpage with the viewer that will be accessed with Puppeteer to generate the preview:
yarn build
This should be rerun whenever you make changes to the viewer (if you make local viewer changes, don't forget to build the viewer module before running this)
After the viewer web page is up to date, run the preview service with:
yarn dev
This will use the default dev DB connection of postgres://speckle:speckle@localhost/speckle. You can pass the environment variable PG_CONNECTION_STRING to change this to a different DB.
Deployment notes
When deploying the PreviewService, it's important to pay attention to the memory requirements: Speckle streams can be arbitrarily large and the preview service must load the entire stream in order to generate the preview image.
You must limit the PreviewService container memory to a value that you want to allocate for preview generation. If a stream requires more memory then the limit, the preview for that stream will fail, but the entire system remains stable.
To limit the container memory when running with docker run, you can use the -m flag.
With docker-compose, you must use a docker-compose file at version 2 (not 3) and use the mem_limit option.