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vars.yml
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vars.yml
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self_version: "v8"
base_version: "v5"
versions:
- airflow: ["2.1"]
spark: ["3.1.3"]
hadoop: ["3.2.0"]
scala: ["2.12"]
java: ["8", "11"]
python: ["3.8", "3.9"]
sqlalchemy: ["1.3", "1.4"]
- airflow: ["2.1"]
spark: ["3.2.2"]
hadoop: ["3.3.1"]
scala: ["2.12", "2.13"]
java: ["8", "11"]
python: ["3.8", "3.9"]
sqlalchemy: ["1.3", "1.4"]
- airflow: ["2.1"]
spark: ["3.3.0"]
hadoop: ["3.3.2"]
scala: ["2.12", "2.13"]
java: ["8", "11"]
python: ["3.8", "3.9"]
sqlalchemy: ["1.3", "1.4"]
- airflow: ["2.2"]
spark: ["3.1.3"]
hadoop: ["3.2.0"]
scala: ["2.12"]
java: ["8", "11"]
python: ["3.7", "3.8", "3.9"]
sqlalchemy: ["1.3", "1.4"]
- airflow: ["2.2"]
spark: ["3.2.2"]
hadoop: ["3.3.1"]
scala: ["2.12", "2.13"]
java: ["8", "11"]
python: ["3.7", "3.8", "3.9"]
sqlalchemy: ["1.3", "1.4"]
- airflow: ["2.2"]
spark: ["3.3.0"]
hadoop: ["3.3.2"]
scala: ["2.12", "2.13"]
java: ["8", "11"]
python: ["3.7", "3.8", "3.9"]
sqlalchemy: ["1.3", "1.4"]
- airflow: ["2.3"]
spark: ["3.1.3"]
hadoop: ["3.2.0"]
scala: ["2.12"]
java: ["8", "11"]
python: ["3.7", "3.8", "3.9"]
sqlalchemy: ["1.4"]
- airflow: ["2.3"]
spark: ["3.2.2"]
hadoop: ["3.3.1"]
scala: ["2.12", "2.13"]
java: ["8", "11"]
python: ["3.7", "3.8", "3.9"]
sqlalchemy: ["1.4"]
- airflow: ["2.3"]
spark: ["3.3.0"]
hadoop: ["3.3.2"]
scala: ["2.12", "2.13"]
java: ["8", "11"]
python: ["3.7", "3.8", "3.9"]
sqlalchemy: ["1.4"]
- airflow: ["2.3"]
spark: ["3.4.1"]
hadoop: ["3.3.4"]
scala: ["2.12", "2.13"]
java: ["8", "11"]
python: ["3.8", "3.9"]
sqlalchemy: ["1.4"]
# Somehow when Airflow 2.1 is paired with Python 3.7, poetry cannot resolve the dependencies
# So we drop 3.7 for this above combination
# Also, apache-airflow (2.3.0) depends on sqlalchemy (>=1.4,<1.4.10)