When receiving a VARBINARY field out of Presto, it shows up as type
`bytes` out of the pyhive driver. Then the pre 3.15 version of
simplejson attempts to convert it to utf8 by default and it craps out.
I bumped to simplejson>=3.25.0 and set `encoding=None` as documented
here
https://simplejson.readthedocs.io/en/latest/#basic-usage so that we can
handle bytes on our own.
As Superset extends flask SecurityManager with its own implementation, it's not obvious how to connect Superset with OAuth2 authorization servers that are not covered under flask.
* [webpack] setup lazy loading for all visualizations
* [lazy-load] push renderVis function to <Chart /> state
* no mapbox token
* [lazy loading] use native webpack import func to fix chunk names, add babel-plugin-syntax-dynamic-import, fix rebase bug.
* fix geojson import, undefined t, and fix async css bug
* [lazy load] actually add babel-plugin-syntax-dynamic-import
* [webpack] working dev version of webpack v4
* [webpack 4] fix url issues, use mini-css-extract-plugin and webpack-assets-manifest plugins
* [webpack 4] use splitchunks for all files, update templates to multi-file entrypoints
* [webpack 4] multiple theme entry files for markup vis css, don't uglify mapbox
* [webpack 4] lint python manifest changes, update yarn lock.
* [webpack 4] fix tests with babel-plugin-dynamic-import-node
* [webpack 4] only use 'dynamic-import-node' plugin in tests, update <Chart /> vis promise when vis type changes
* [webpack 4] clean up package.json and yarn.lock after rebase
* [webpack 4] lint?
* [webpack 4] lint for real
* [webpack 4][istanbul] ignore visualizations/index.js
* [explore] fix autocomplete on verbose names
Currently when searching for metrics or groupbys, the autocomplete
search functionality only matches based on the metric_name, though in
some cases the verbose_name is displayed and disregarded for search
purposes.
Also another issue is that not all pre-defined metrics show up in the
drop down which is confusing. Users may have simple metrics for which
they setup a nice verbose name and/or description and expect to see
those in the dropdown.
This PR addresses it for metric and column-related dropdowns.
* Add unit test
* [webpack] setup lazy loading for all visualizations
* [lazy-load] push renderVis function to <Chart /> state
* no mapbox token
* [lazy loading] use native webpack import func to fix chunk names, add babel-plugin-syntax-dynamic-import, fix rebase bug.
* fix geojson import, undefined t, and fix async css bug
* [lazy load] actually add babel-plugin-syntax-dynamic-import
* [webpack] working dev version of webpack v4
* [webpack 4] fix url issues, use mini-css-extract-plugin and webpack-assets-manifest plugins
* [webpack 4] use splitchunks for all files, update templates to multi-file entrypoints
* [webpack 4] multiple theme entry files for markup vis css, don't uglify mapbox
* [webpack 4] lint python manifest changes, update yarn lock.
* [webpack 4] fix tests with babel-plugin-dynamic-import-node
* [webpack 4] only use 'dynamic-import-node' plugin in tests, update <Chart /> vis promise when vis type changes
* [webpack 4] clean up package.json and yarn.lock after rebase
* [webpack 4] lint?
* [webpack 4] lint for real
* fetch datasources from broker endpoint when refresh new datasources
* remove get_base_coordinator_url as out of use
* add broker_endpoint in get_test_cluster_obj
This commit will try to dockerize superset in local development
environment.
The basic design is:
- Enable superset, redis and postgres service instead of using sqlite,
just want to simulate production environment settings
- Use environment variables to config various app settings. It's easy to
run and config superset to any environment if we use environment than
traditional config files
- For local development environment, we just expose postgres and redis
to local host machine thus you can connect local port via `psql` or
`redis-cli`
- Wrap start up command in a standard `docker-entrypoint.sh`, and use
`tail -f /dev/null` combined with manually `superset runserver -d` to
make sure that code error didn't cause the container to fail.
- Use volumes to share code between host and container, thus you can use
your favourite tools to modify code and your code will run in
containerized environment
- Use volumes to persistent postgres and redis data, and also
`node_modules` data.
- If we don't cache `node_modules` in docker volume, then every time
run docker build, the `node_modules` directory, will is about 500 MB
large, will be sent to docker daemon, and make the build quite slow.
- Wrap initialization commands to a single script `docker-init.sh`
After this dockerize setup, any developers who want to contribute to
superset, just follow three easy steps:
```
git clone https://github.com/apache/incubator-superset/
cd incubator-superset
cp contrib/docker/{docker-build.sh,docker-compose.yml,docker-entrypoint.sh,docker-init.sh,Dockerfile} .
cp contrib/docker/superset_config.py superset/
bash -x docker-build.sh
docker-compose up -d
docker-compose exec superset bash
bash docker-init.sh
```
Maxime Beauchemin
Gabe Lyons
timifasubaa
Chris Williams
michellethomas
Grace Guo
Jeffrey Wang
John Bodley
Louis-Etienne
Ricardo Peironcely
Riccardo Magliocchetti
Ville Brofeldt
Timi Fasubaa
Yuance.Li
Tresdon Jones
Xiao Hanyu