48 lines
1.7 KiB
Markdown
48 lines
1.7 KiB
Markdown
# csv_to_postgres
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This is a [Dagster](https://dagster.io/) project scaffolded with [`dagster project scaffold`](https://docs.dagster.io/guides/build/projects/creating-a-new-project).
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## Getting started
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First, install your Dagster code location as a Python package. By using the --editable flag, pip will install your Python package in ["editable mode"](https://pip.pypa.io/en/latest/topics/local-project-installs/#editable-installs) so that as you develop, local code changes will automatically apply.
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```bash
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pip install -e ".[dev]"
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```
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Then, start the Dagster UI web server:
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```bash
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dagster dev
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```
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Open http://localhost:3000 with your browser to see the project.
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You can start writing assets in `csv_to_postgres/assets.py`. The assets are automatically loaded into the Dagster code location as you define them.
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## Development
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### Adding new Python dependencies
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You can specify new Python dependencies in `setup.py`.
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### Unit testing
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Tests are in the `csv_to_postgres_tests` directory and you can run tests using `pytest`:
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```bash
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pytest csv_to_postgres_tests
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```
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### Schedules and sensors
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If you want to enable Dagster [Schedules](https://docs.dagster.io/guides/automate/schedules/) or [Sensors](https://docs.dagster.io/guides/automate/sensors/) for your jobs, the [Dagster Daemon](https://docs.dagster.io/guides/deploy/execution/dagster-daemon) process must be running. This is done automatically when you run `dagster dev`.
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Once your Dagster Daemon is running, you can start turning on schedules and sensors for your jobs.
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## Deploy on Dagster+
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The easiest way to deploy your Dagster project is to use Dagster+.
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Check out the [Dagster+ documentation](https://docs.dagster.io/dagster-plus/) to learn more.
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