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Merge pull request #189 from dagster-io/0.2.0.develop
Upgrade master to 0.2.0
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*.db | ||
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docs/_build | ||
python_modules/dagster/docs/_build |
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- repo: https://github.com/pre-commit/mirrors-yapf | ||
sha: v0.22.0 | ||
hooks: | ||
- id: yapf |
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./python_modules/dagster |
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Setup for developing the Dagit UI | ||
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1. Install Python 3.6. | ||
* You can't use Python 3.7+ yet because of https://github.com/apache/arrow/issues/1125 | ||
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2. Create a virtual environment | ||
``` | ||
python3 -m venv ~/venvs/dagit/ | ||
source ~/venvs/dagit/bin/activate | ||
pip | ||
``` | ||
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3. Install all the dependencies, and make the dagit and dagster packages available by name: | ||
``` | ||
cd ./dagster | ||
pip install -r dev-requirements.txt | ||
pip install -e . | ||
cd ../dagit | ||
pip install -r dev-requirements.txt | ||
pip install -e . | ||
``` | ||
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4. Run the GraphQL server from a directory that contains a repository.yml file. | ||
For example: | ||
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``` | ||
cd ./dagster/dagster/dagster_examples | ||
python3.6 /path/to/python_modules/dagit/bin/dagit -p 3333 | ||
``` | ||
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5. Run the JS component of the Dagit UI, pointing it to the GraphQL server: | ||
``` | ||
cd ./dagit/dagit/webapp | ||
yarn install | ||
REACT_APP_GRAPHQL_URI="http://localhost:3333/graphql" yarn start | ||
``` | ||
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6. You can run tests for python by running `tox` in `python_modules/dagit` or `python_modules/dagster`. | ||
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7. Inside webapp directory, you can run tests too. `yarn test` runs all tests. In development it's handy to run `yarn run jest --watch` to have interactive test runner. | ||
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8. Some webapp tests use *snapshots* - auto-generated results to which the test render tree is compared. Those tests are supposed to break when you change something, check that the change is sensible and run `yarn run jest -u` to update snapshot to the new result. You can also update snapshots interactively when you are in `--watch` mode. |
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.. image:: https://user-images.githubusercontent.com/28738937/44878798-b6e17e00-ac5c-11e8-8d25-2e47e5a53418.png | ||
:align: center | ||
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.. docs-include | ||
============ | ||
Introduction | ||
============ | ||
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Dagster is an opinionated system and programming model for data pipelines. This process goes by | ||
many names -- ETL (extract-load-transform), ELT (extract-transform-load), model production, data | ||
integration, and so on -- but in essence they all describe the same activity: Performing a set of | ||
computations structured as a DAG (directed, acyclic graph) that end up producing data assets, | ||
whether those assets be tables, files, machine-learning models, etc. | ||
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There are a few tools in this repo | ||
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This repo has a few primary components: | ||
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- **Dagster**: The core programming model and abstraction stack; a stateless single-node and -process execution engine; and a CLI tool for driving that engine. | ||
* **Dagit**: Dagit is a rich viewer for Dagster assets. | ||
* **Dagster GE**: A Dagster integration with Great Expectations. (see https://github.com/great-expectations/great_expectations) | ||
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Go to https://dagster.readthedocs.io/en/latest/ for documentation! |
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