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`restart: always` will cause containers to restart when the machine is rebooted or the docker process restarts. Setting this to `unless-stopped` will mean the container will restart only if the docker process/computer was rebooted without stopping the container first. Otherwise, running `docker-compose stop` and rebooting will cause the docker containers all to start again (and goodbye laptop battery!) We might also consider `restart: on-failures` which would mean that rebooting would _not_ cause containers to start again unless requested via `docker-compose up`. |
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| .github | ||
| bin | ||
| client | ||
| migrations | ||
| old_migrations | ||
| redash | ||
| setup | ||
| tests | ||
| .codeclimate.yml | ||
| .coveragerc | ||
| .dockerignore | ||
| .editorconfig | ||
| .gitignore | ||
| CHANGELOG.md | ||
| circle.yml | ||
| CONTRIBUTING.md | ||
| docker-compose.production.yml | ||
| docker-compose.yml | ||
| Dockerfile | ||
| LICENSE | ||
| manage.py | ||
| package-lock.json | ||
| package.json | ||
| pytest.ini | ||
| README.md | ||
| requirements_all_ds.txt | ||
| requirements_dev.txt | ||
| requirements_oracle_ds.txt | ||
| requirements.txt | ||
| setup.cfg | ||
| webpack.config.js | ||
Redash is our take on freeing the data within our company in a way that will better fit our culture and usage patterns.
Prior to Redash, we tried to use traditional BI suites and discovered a set of bloated, technically challenged and slow tools/flows. What we were looking for was a more hacker'ish way to look at data, so we built one.
Redash was built to allow fast and easy access to billions of records, that we process and collect using Amazon Redshift ("petabyte scale data warehouse" that "speaks" PostgreSQL). Today Redash has support for querying multiple databases, including: Redshift, Google BigQuery, PostgreSQL, MySQL, Graphite, Presto, Google Spreadsheets, Cloudera Impala, Hive and custom scripts.
Redash consists of two parts:
- Query Editor: think of JS Fiddle for SQL queries. It's your way to share data in the organization in an open way, by sharing both the dataset and the query that generated it. This way everyone can peer review not only the resulting dataset but also the process that generated it. Also it's possible to fork it and generate new datasets and reach new insights.
- Dashboards/Visualizations: once you have a dataset, you can create different visualizations out of it, and then combine several visualizations into a single dashboard. Currently it supports charts, pivot table and cohorts.
Demo
You can try out the demo instance: http://demo.redash.io/ (login with any Google account).
Getting Started
- Setting up Redash instance (includes links to ready made AWS/GCE images).
- Documentation.
Getting Help
- Issues: https://github.com/getredash/redash/issues
- Discussion Forum: https://discuss.redash.io/
- Slack: http://slack.redash.io/
- Gitter (chat): https://gitter.im/getredash/redash
Reporting Bugs and Contributing Code
- Want to report a bug or request a feature? Please open an issue.
- Want to help us build Redash? Fork the project, edit in a dev environment, and make a pull request. We need all the help we can get!
License
BSD-2-Clause.