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abelanger5abelanger5
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feat: local setup
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‎.gitignore‎

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# Python
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.venv
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*.pyc
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.pyo
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*.pyd
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*.pyw
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*.pyz
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*.pywz
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*.ipynb_checkpoints/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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# TypeScript React
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node_modules/

‎Caddyfile‎

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http://localhost:8080 {
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handle /api/* {
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reverse_proxy hatchet-api:8080
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}
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handle /* {
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reverse_proxy hatchet-frontend:80
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}
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}

‎README.md‎

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## Hatchet Python Quickstart
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The following is a template repo to get started with the Hatchet Python SDK. It includes instructions for getting started with Hatchet cloud along with a locally running instance of Hatchet.
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### Cloud Version
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Navigate to your settings tab in the Hatchet dashboard. You should see a section called "API Keys". Click "Create API Key", input a name for the key and copy the key. Then set the following environment variables:
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```
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HATCHET_CLIENT_TOKEN="<token>"
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```
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Then start a worker via:
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```
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poetry run python worker.py
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```
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**Next steps:** see [Running Workflows](#running-workflows) to trigger your first Hatchet workflow.
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### Local Version
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Run the following command to start the Hatchet instance:
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```
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docker compose up
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```
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This will start a Hatchet instance on port `8080`. You should be able to navigate to [localhost:8080](localhost:8080) and use the following credentials to log in:
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```
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Email: admin@example.com
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Password: Admin123!!
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```
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Next, navigate to your settings tab in the Hatchet dashboard. You should see a section called "API Keys". Click "Create API Key", input a name for the key and copy the key. Then set the following environment variables:
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```
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HATCHET_CLIENT_TOKEN="<token>"
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```
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**Next steps:** see [Running Workflows](#running-workflows) to trigger your first Hatchet workflow.
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## Running Workflows
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## Using the Playground

‎docker-compose.yml‎

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version: "3.8"
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services:
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postgres:
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image: postgres:latest
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command: postgres -c 'max_connections=200'
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restart: always
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hostname: "postgres"
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environment:
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- POSTGRES_USER=hatchet
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- POSTGRES_PASSWORD=hatchet
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- POSTGRES_DB=hatchet
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ports:
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- "5435:5432"
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volumes:
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- hatchet_postgres_data:/var/lib/postgresql/data
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healthcheck:
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test: ["CMD-SHELL", "pg_isready", "-d", "hatchet"]
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interval: 10s
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timeout: 10s
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retries: 5
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start_period: 10s
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rabbitmq:
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image: "rabbitmq:3-management"
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hostname: "rabbitmq"
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ports:
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- "5673:5672" # RabbitMQ
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- "15673:15672" # Management UI
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environment:
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RABBITMQ_DEFAULT_USER: "user"
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RABBITMQ_DEFAULT_PASS: "password"
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volumes:
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- "hatchet_rabbitmq_data:/var/lib/rabbitmq"
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- "hatchet_rabbitmq.conf:/etc/rabbitmq/rabbitmq.conf" # Configuration file mount
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healthcheck:
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test: ["CMD", "rabbitmqctl", "status"]
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interval: 10s
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timeout: 10s
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retries: 5
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migration:
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image: ghcr.io/hatchet-dev/hatchet/hatchet-migrate:latest
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environment:
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DATABASE_URL: "postgres://hatchet:hatchet@postgres:5432/hatchet"
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depends_on:
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postgres:
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condition: service_healthy
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setup-config:
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image: ghcr.io/hatchet-dev/hatchet/hatchet-admin:latest
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command: /hatchet/hatchet-admin quickstart --skip certs --generated-config-dir /hatchet/config --overwrite=false
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environment:
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DATABASE_URL: "postgres://hatchet:hatchet@postgres:5432/hatchet"
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DATABASE_POSTGRES_PORT: "5432"
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DATABASE_POSTGRES_HOST: "postgres"
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SERVER_TASKQUEUE_RABBITMQ_URL: amqp://user:password@rabbitmq:5672/
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SERVER_AUTH_COOKIE_DOMAIN: localhost:8080
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SERVER_AUTH_COOKIE_INSECURE: "t"
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SERVER_GRPC_BIND_ADDRESS: "0.0.0.0"
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SERVER_GRPC_INSECURE: "t"
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SERVER_GRPC_BROADCAST_ADDRESS: localhost:7077
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volumes:
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- hatchet_certs:/hatchet/certs
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- hatchet_config:/hatchet/config
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depends_on:
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migration:
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condition: service_completed_successfully
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rabbitmq:
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condition: service_healthy
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postgres:
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condition: service_healthy
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hatchet-engine:
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image: ghcr.io/hatchet-dev/hatchet/hatchet-engine:latest
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command: /hatchet/hatchet-engine --config /hatchet/config
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restart: on-failure
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depends_on:
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setup-config:
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condition: service_completed_successfully
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migration:
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condition: service_completed_successfully
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ports:
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- "7077:7070"
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environment:
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DATABASE_URL: "postgres://hatchet:hatchet@postgres:5432/hatchet"
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SERVER_GRPC_BIND_ADDRESS: "0.0.0.0"
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SERVER_GRPC_INSECURE: "t"
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volumes:
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- hatchet_certs:/hatchet/certs
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- hatchet_config:/hatchet/config
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hatchet-api:
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image: ghcr.io/hatchet-dev/hatchet/hatchet-api:latest
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command: /hatchet/hatchet-api --config /hatchet/config
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restart: on-failure
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depends_on:
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setup-config:
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condition: service_completed_successfully
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migration:
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condition: service_completed_successfully
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environment:
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DATABASE_URL: "postgres://hatchet:hatchet@postgres:5432/hatchet"
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volumes:
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- hatchet_certs:/hatchet/certs
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- hatchet_config:/hatchet/config
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hatchet-frontend:
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image: ghcr.io/hatchet-dev/hatchet/hatchet-frontend:latest
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caddy:
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image: caddy:2.7.6-alpine
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ports:
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- 8080:8080
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volumes:
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- ./Caddyfile:/etc/caddy/Caddyfile
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volumes:
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hatchet_postgres_data:
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hatchet_rabbitmq_data:
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hatchet_rabbitmq.conf:
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hatchet_config:
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hatchet_certs:

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