path: root/python-dagster.spec

%global _empty_manifest_terminate_build 0
Name:		python-dagster
Version:	1.2.6
Release:	1
Summary:	The data orchestration platform built for productivity.
License:	Apache-2.0
URL:		https://dagster.io
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/e7/36/50f856c3892ea03ead3a6913ef60e1c107bf84eb585d7a5282e28d2b3d96/dagster-1.2.6.tar.gz
BuildArch:	noarch

Requires:	python3-click
Requires:	python3-coloredlogs
Requires:	python3-Jinja2
Requires:	python3-PyYAML
Requires:	python3-alembic
Requires:	python3-croniter
Requires:	python3-grpcio-health-checking
Requires:	python3-packaging
Requires:	python3-pendulum
Requires:	python3-protobuf
Requires:	python3-dateutil
Requires:	python3-dotenv
Requires:	python3-pytz
Requires:	python3-requests
Requires:	python3-setuptools
Requires:	python3-tabulate
Requires:	python3-tomli
Requires:	python3-tqdm
Requires:	python3-typing-extensions
Requires:	python3-sqlalchemy
Requires:	python3-toposort
Requires:	python3-watchdog
Requires:	python3-docstring-parser
Requires:	python3-universal-pathlib
Requires:	python3-pydantic
Requires:	python3-psutil
Requires:	python3-pywin32
Requires:	python3-grpcio
Requires:	python3-contextvars
Requires:	python3-grpcio
Requires:	python3-black[jupyter]
Requires:	python3-docker
Requires:	python3-mypy
Requires:	python3-pyright
Requires:	python3-pandas-stubs
Requires:	python3-types-backports
Requires:	python3-types-certifi
Requires:	python3-types-chardet
Requires:	python3-types-croniter
Requires:	python3-types-cryptography
Requires:	python3-types-mock
Requires:	python3-types-paramiko
Requires:	python3-types-pkg-resources
Requires:	python3-types-pyOpenSSL
Requires:	python3-types-python-dateutil
Requires:	python3-types-PyYAML
Requires:	python3-types-pytz
Requires:	python3-types-requests
Requires:	python3-types-simplejson
Requires:	python3-types-six
Requires:	python3-types-sqlalchemy
Requires:	python3-types-tabulate
Requires:	python3-types-tzlocal
Requires:	python3-types-toml
Requires:	python3-ruff
Requires:	python3-docker
Requires:	python3-grpcio-tools
Requires:	python3-mock
Requires:	python3-objgraph
Requires:	python3-pytest-cov
Requires:	python3-pytest-dependency
Requires:	python3-pytest-mock
Requires:	python3-pytest-rerunfailures
Requires:	python3-pytest-runner
Requires:	python3-pytest-xdist
Requires:	python3-pytest
Requires:	python3-responses
Requires:	python3-snapshottest
Requires:	python3-tox
Requires:	python3-yamllint
Requires:	python3-buildkite-test-collector

%description
<div align="center">
  <!-- Note: Do not try adding the dark mode version here with the `picture` element, it will break formatting in PyPI -->
  <a target="_blank" href="https://dagster.io" style="background:none">
    <img alt="dagster logo" src=".github/dagster-readme-header.svg" width="auto" height="100%">
  </a>
<p style="text-align: center;">Remember to <a target="_blank" href="https://github.com/dagster-io/dagster">star the Dagster GitHub repo</a> for future reference.</p>
  <a target="_blank" href="https://github.com/dagster-io/dagster" style="background:none">
    <img src="https://img.shields.io/github/stars/dagster-io/dagster?labelColor=4F43DD&color=163B36&logo=github">
  </a>
  <a target="_blank" href="https://github.com/dagster-io/dagster/blob/master/LICENSE" style="background:none">
    <img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg?label=license&labelColor=4F43DD&color=163B36">
  </a>
  <a target="_blank" href="https://pypi.org/project/dagster/" style="background:none">
    <img src="https://img.shields.io/pypi/v/dagster?labelColor=4F43DD&color=163B36">
  </a>
  <a target="_blank" href="https://pypi.org/project/dagster/" style="background:none">
    <img src="https://img.shields.io/pypi/pyversions/dagster?labelColor=4F43DD&color=163B36">
  </a>
  <a target="_blank" href="https://twitter.com/dagster" style="background:none">
    <img src="https://img.shields.io/badge/twitter-dagster-blue.svg?labelColor=4F43DD&color=163B36&logo=twitter" />
  </a>
  <a target="_blank" href="https://dagster.io/slack" style="background:none">
    <img src="https://img.shields.io/badge/slack-dagster-blue.svg?labelColor=4F43DD&color=163B36&logo=slack" />
  </a>
  <a target="_blank" href="https://linkedin.com/showcase/dagster" style="background:none">
    <img src="https://img.shields.io/badge/linkedin-dagster-blue.svg?labelColor=4F43DD&color=163B36&logo=linkedin" />
  </a>
</div>

__Dagster is a cloud-native data pipeline orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability.__

It is designed for **developing and maintaining data assets**, such as tables, data sets, machine learning models, and reports.

With Dagster, you declare—as Python functions—the data assets that you want to build. Dagster then helps you run your functions at the right time and keep your assets up-to-date.

Here is an example of a graph of three assets defined in Python:

```python
from dagster import asset
from pandas import DataFrame, read_html, get_dummies
from sklearn.linear_model import LinearRegression

@asset
def country_populations() -> DataFrame:
    df = read_html("https://tinyurl.com/mry64ebh")[0]
    df.columns = ["country", "continent", "rg", "pop2018", "pop2019", "change"]
    df["change"] = df["change"].str.rstrip("%").str.replace("−", "-").astype("float")
    return df

@asset
def continent_change_model(country_populations: DataFrame) -> LinearRegression:
    data = country_populations.dropna(subset=["change"])
    return LinearRegression().fit(get_dummies(data[["continent"]]), data["change"])

@asset
def continent_stats(country_populations: DataFrame, continent_change_model: LinearRegression) -> DataFrame:
    result = country_populations.groupby("continent").sum()
    result["pop_change_factor"] = continent_change_model.coef_
    return result
```
The graph loaded into Dagster's web UI:

<p align="center">
  <img width="400px" alt="An example asset graph as rendered in the Dagster UI" src="https://user-images.githubusercontent.com/654855/183537484-48dde394-91f2-4de0-9b17-a70b3e9a3823.png">
</p>

Dagster is built to be used at every stage of the data development lifecycle - local development, unit tests, integration tests, staging environments, all the way up to production.

## Quick Start:

If you're new to Dagster, we recommend reading about its [core concepts](https://docs.dagster.io/concepts) or learning with the hands-on [tutorial](https://docs.dagster.io/tutorial).

Dagster is available on PyPI and officially supports Python 3.7+.

```bash
pip install dagster dagit
```

This installs two modules:

- **Dagster**: The core programming model.
- **Dagit**: The web interface for developing and operating Dagster jobs and assets.

Running on Using a Mac with an M1 or M2 chip? Check the [install details here](https://docs.dagster.io/getting-started/install#installing-dagster-into-an-existing-python-environment).

## Documentation

You can find the full Dagster documentation [here](https://docs.dagster.io), including the ['getting started' guide](https://docs.dagster.io/getting-started).

<hr/>

## Key Features:

  <p align="center">
    <img width="100%" alt="image" src=".github/key-features-cards.svg">
  </p>

### Dagster as a productivity platform
Identify the key assets you need to create using a declarative approach, or you can focus on running basic tasks. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.

### Dagster as a robust orchestration engine
Put your pipelines into production with a robust multi-tenant, multi-tool engine that scales technically and organizationally.

### Dagster as a unified control plane
Maintain control over your data as the complexity scales. Centralize your metadata in one tool with built-in observability, diagnostics, cataloging, and lineage. Spot any issues and identify performance improvement opportunities.

<hr />

## Master the Modern Data Stack with integrations

Dagster provides a growing library of integrations for today’s most popular data tools. Integrate with the tools you already use, and deploy to your infrastructure.

<br/>
<p align="center">
    <a target="_blank" href="https://dagster.io/integrations" style="background:none">
        <img width="100%" alt="image" src=".github/integrations-bar-for-readme.png">
    </a>
</p>

## Community

Connect with thousands of other data practitioners building with Dagster. Share knowledge, get help,
and contribute to the open-source project. To see featured material and upcoming events, check out
our [Dagster Community](https://dagster.io/community) page.

Join our community here:

- 🌟 [Star us on Github](https://github.com/dagster-io/dagster)
- 📥 [Subscribe to our Newsletter](https://dagster.io/newsletter-signup)
- 🐦 [Follow us on Twitter](https://twitter.com/dagster)
- 🕴️ [Follow us on LinkedIn](https://linkedin.com/showcase/dagster)
- 📺 [Subscribe to our YouTube channel](https://www.youtube.com/channel/UCfLnv9X8jyHTe6gJ4hVBo9Q)
- 📚 [Read our blog posts](https://dagster.io/blog)
- 👋 [Join us on Slack](https://dagster.io/slack)
- 🗃 [Browse Slack archives](https://discuss.dagster.io)
- ✏️ [Start a Github Discussion](https://github.com/dagster-io/dagster/discussions)

## Contributing

For details on contributing or running the project for development, check out our [contributing
guide](https://docs.dagster.io/community/contributing/).

## License

Dagster is [Apache 2.0 licensed](https://github.com/dagster-io/dagster/blob/master/LICENSE).


%package -n python3-dagster
Summary:	The data orchestration platform built for productivity.
Provides:	python-dagster
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-dagster
<div align="center">
  <!-- Note: Do not try adding the dark mode version here with the `picture` element, it will break formatting in PyPI -->
  <a target="_blank" href="https://dagster.io" style="background:none">
    <img alt="dagster logo" src=".github/dagster-readme-header.svg" width="auto" height="100%">
  </a>
<p style="text-align: center;">Remember to <a target="_blank" href="https://github.com/dagster-io/dagster">star the Dagster GitHub repo</a> for future reference.</p>
  <a target="_blank" href="https://github.com/dagster-io/dagster" style="background:none">
    <img src="https://img.shields.io/github/stars/dagster-io/dagster?labelColor=4F43DD&color=163B36&logo=github">
  </a>
  <a target="_blank" href="https://github.com/dagster-io/dagster/blob/master/LICENSE" style="background:none">
    <img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg?label=license&labelColor=4F43DD&color=163B36">
  </a>
  <a target="_blank" href="https://pypi.org/project/dagster/" style="background:none">
    <img src="https://img.shields.io/pypi/v/dagster?labelColor=4F43DD&color=163B36">
  </a>
  <a target="_blank" href="https://pypi.org/project/dagster/" style="background:none">
    <img src="https://img.shields.io/pypi/pyversions/dagster?labelColor=4F43DD&color=163B36">
  </a>
  <a target="_blank" href="https://twitter.com/dagster" style="background:none">
    <img src="https://img.shields.io/badge/twitter-dagster-blue.svg?labelColor=4F43DD&color=163B36&logo=twitter" />
  </a>
  <a target="_blank" href="https://dagster.io/slack" style="background:none">
    <img src="https://img.shields.io/badge/slack-dagster-blue.svg?labelColor=4F43DD&color=163B36&logo=slack" />
  </a>
  <a target="_blank" href="https://linkedin.com/showcase/dagster" style="background:none">
    <img src="https://img.shields.io/badge/linkedin-dagster-blue.svg?labelColor=4F43DD&color=163B36&logo=linkedin" />
  </a>
</div>

__Dagster is a cloud-native data pipeline orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability.__

It is designed for **developing and maintaining data assets**, such as tables, data sets, machine learning models, and reports.

With Dagster, you declare—as Python functions—the data assets that you want to build. Dagster then helps you run your functions at the right time and keep your assets up-to-date.

Here is an example of a graph of three assets defined in Python:

```python
from dagster import asset
from pandas import DataFrame, read_html, get_dummies
from sklearn.linear_model import LinearRegression

@asset
def country_populations() -> DataFrame:
    df = read_html("https://tinyurl.com/mry64ebh")[0]
    df.columns = ["country", "continent", "rg", "pop2018", "pop2019", "change"]
    df["change"] = df["change"].str.rstrip("%").str.replace("−", "-").astype("float")
    return df

@asset
def continent_change_model(country_populations: DataFrame) -> LinearRegression:
    data = country_populations.dropna(subset=["change"])
    return LinearRegression().fit(get_dummies(data[["continent"]]), data["change"])

@asset
def continent_stats(country_populations: DataFrame, continent_change_model: LinearRegression) -> DataFrame:
    result = country_populations.groupby("continent").sum()
    result["pop_change_factor"] = continent_change_model.coef_
    return result
```
The graph loaded into Dagster's web UI:

<p align="center">
  <img width="400px" alt="An example asset graph as rendered in the Dagster UI" src="https://user-images.githubusercontent.com/654855/183537484-48dde394-91f2-4de0-9b17-a70b3e9a3823.png">
</p>

Dagster is built to be used at every stage of the data development lifecycle - local development, unit tests, integration tests, staging environments, all the way up to production.

## Quick Start:

If you're new to Dagster, we recommend reading about its [core concepts](https://docs.dagster.io/concepts) or learning with the hands-on [tutorial](https://docs.dagster.io/tutorial).

Dagster is available on PyPI and officially supports Python 3.7+.

```bash
pip install dagster dagit
```

This installs two modules:

- **Dagster**: The core programming model.
- **Dagit**: The web interface for developing and operating Dagster jobs and assets.

Running on Using a Mac with an M1 or M2 chip? Check the [install details here](https://docs.dagster.io/getting-started/install#installing-dagster-into-an-existing-python-environment).

## Documentation

You can find the full Dagster documentation [here](https://docs.dagster.io), including the ['getting started' guide](https://docs.dagster.io/getting-started).

<hr/>

## Key Features:

  <p align="center">
    <img width="100%" alt="image" src=".github/key-features-cards.svg">
  </p>

### Dagster as a productivity platform
Identify the key assets you need to create using a declarative approach, or you can focus on running basic tasks. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.

### Dagster as a robust orchestration engine
Put your pipelines into production with a robust multi-tenant, multi-tool engine that scales technically and organizationally.

### Dagster as a unified control plane
Maintain control over your data as the complexity scales. Centralize your metadata in one tool with built-in observability, diagnostics, cataloging, and lineage. Spot any issues and identify performance improvement opportunities.

<hr />

## Master the Modern Data Stack with integrations

Dagster provides a growing library of integrations for today’s most popular data tools. Integrate with the tools you already use, and deploy to your infrastructure.

<br/>
<p align="center">
    <a target="_blank" href="https://dagster.io/integrations" style="background:none">
        <img width="100%" alt="image" src=".github/integrations-bar-for-readme.png">
    </a>
</p>

## Community

Connect with thousands of other data practitioners building with Dagster. Share knowledge, get help,
and contribute to the open-source project. To see featured material and upcoming events, check out
our [Dagster Community](https://dagster.io/community) page.

Join our community here:

- 🌟 [Star us on Github](https://github.com/dagster-io/dagster)
- 📥 [Subscribe to our Newsletter](https://dagster.io/newsletter-signup)
- 🐦 [Follow us on Twitter](https://twitter.com/dagster)
- 🕴️ [Follow us on LinkedIn](https://linkedin.com/showcase/dagster)
- 📺 [Subscribe to our YouTube channel](https://www.youtube.com/channel/UCfLnv9X8jyHTe6gJ4hVBo9Q)
- 📚 [Read our blog posts](https://dagster.io/blog)
- 👋 [Join us on Slack](https://dagster.io/slack)
- 🗃 [Browse Slack archives](https://discuss.dagster.io)
- ✏️ [Start a Github Discussion](https://github.com/dagster-io/dagster/discussions)

## Contributing

For details on contributing or running the project for development, check out our [contributing
guide](https://docs.dagster.io/community/contributing/).

## License

Dagster is [Apache 2.0 licensed](https://github.com/dagster-io/dagster/blob/master/LICENSE).


%package help
Summary:	Development documents and examples for dagster
Provides:	python3-dagster-doc
%description help
<div align="center">
  <!-- Note: Do not try adding the dark mode version here with the `picture` element, it will break formatting in PyPI -->
  <a target="_blank" href="https://dagster.io" style="background:none">
    <img alt="dagster logo" src=".github/dagster-readme-header.svg" width="auto" height="100%">
  </a>
<p style="text-align: center;">Remember to <a target="_blank" href="https://github.com/dagster-io/dagster">star the Dagster GitHub repo</a> for future reference.</p>
  <a target="_blank" href="https://github.com/dagster-io/dagster" style="background:none">
    <img src="https://img.shields.io/github/stars/dagster-io/dagster?labelColor=4F43DD&color=163B36&logo=github">
  </a>
  <a target="_blank" href="https://github.com/dagster-io/dagster/blob/master/LICENSE" style="background:none">
    <img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg?label=license&labelColor=4F43DD&color=163B36">
  </a>
  <a target="_blank" href="https://pypi.org/project/dagster/" style="background:none">
    <img src="https://img.shields.io/pypi/v/dagster?labelColor=4F43DD&color=163B36">
  </a>
  <a target="_blank" href="https://pypi.org/project/dagster/" style="background:none">
    <img src="https://img.shields.io/pypi/pyversions/dagster?labelColor=4F43DD&color=163B36">
  </a>
  <a target="_blank" href="https://twitter.com/dagster" style="background:none">
    <img src="https://img.shields.io/badge/twitter-dagster-blue.svg?labelColor=4F43DD&color=163B36&logo=twitter" />
  </a>
  <a target="_blank" href="https://dagster.io/slack" style="background:none">
    <img src="https://img.shields.io/badge/slack-dagster-blue.svg?labelColor=4F43DD&color=163B36&logo=slack" />
  </a>
  <a target="_blank" href="https://linkedin.com/showcase/dagster" style="background:none">
    <img src="https://img.shields.io/badge/linkedin-dagster-blue.svg?labelColor=4F43DD&color=163B36&logo=linkedin" />
  </a>
</div>

__Dagster is a cloud-native data pipeline orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability.__

It is designed for **developing and maintaining data assets**, such as tables, data sets, machine learning models, and reports.

With Dagster, you declare—as Python functions—the data assets that you want to build. Dagster then helps you run your functions at the right time and keep your assets up-to-date.

Here is an example of a graph of three assets defined in Python:

```python
from dagster import asset
from pandas import DataFrame, read_html, get_dummies
from sklearn.linear_model import LinearRegression

@asset
def country_populations() -> DataFrame:
    df = read_html("https://tinyurl.com/mry64ebh")[0]
    df.columns = ["country", "continent", "rg", "pop2018", "pop2019", "change"]
    df["change"] = df["change"].str.rstrip("%").str.replace("−", "-").astype("float")
    return df

@asset
def continent_change_model(country_populations: DataFrame) -> LinearRegression:
    data = country_populations.dropna(subset=["change"])
    return LinearRegression().fit(get_dummies(data[["continent"]]), data["change"])

@asset
def continent_stats(country_populations: DataFrame, continent_change_model: LinearRegression) -> DataFrame:
    result = country_populations.groupby("continent").sum()
    result["pop_change_factor"] = continent_change_model.coef_
    return result
```
The graph loaded into Dagster's web UI:

<p align="center">
  <img width="400px" alt="An example asset graph as rendered in the Dagster UI" src="https://user-images.githubusercontent.com/654855/183537484-48dde394-91f2-4de0-9b17-a70b3e9a3823.png">
</p>

Dagster is built to be used at every stage of the data development lifecycle - local development, unit tests, integration tests, staging environments, all the way up to production.

## Quick Start:

If you're new to Dagster, we recommend reading about its [core concepts](https://docs.dagster.io/concepts) or learning with the hands-on [tutorial](https://docs.dagster.io/tutorial).

Dagster is available on PyPI and officially supports Python 3.7+.

```bash
pip install dagster dagit
```

This installs two modules:

- **Dagster**: The core programming model.
- **Dagit**: The web interface for developing and operating Dagster jobs and assets.

Running on Using a Mac with an M1 or M2 chip? Check the [install details here](https://docs.dagster.io/getting-started/install#installing-dagster-into-an-existing-python-environment).

## Documentation

You can find the full Dagster documentation [here](https://docs.dagster.io), including the ['getting started' guide](https://docs.dagster.io/getting-started).

<hr/>

## Key Features:

  <p align="center">
    <img width="100%" alt="image" src=".github/key-features-cards.svg">
  </p>

### Dagster as a productivity platform
Identify the key assets you need to create using a declarative approach, or you can focus on running basic tasks. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.

### Dagster as a robust orchestration engine
Put your pipelines into production with a robust multi-tenant, multi-tool engine that scales technically and organizationally.

### Dagster as a unified control plane
Maintain control over your data as the complexity scales. Centralize your metadata in one tool with built-in observability, diagnostics, cataloging, and lineage. Spot any issues and identify performance improvement opportunities.

<hr />

## Master the Modern Data Stack with integrations

Dagster provides a growing library of integrations for today’s most popular data tools. Integrate with the tools you already use, and deploy to your infrastructure.

<br/>
<p align="center">
    <a target="_blank" href="https://dagster.io/integrations" style="background:none">
        <img width="100%" alt="image" src=".github/integrations-bar-for-readme.png">
    </a>
</p>

## Community

Connect with thousands of other data practitioners building with Dagster. Share knowledge, get help,
and contribute to the open-source project. To see featured material and upcoming events, check out
our [Dagster Community](https://dagster.io/community) page.

Join our community here:

- 🌟 [Star us on Github](https://github.com/dagster-io/dagster)
- 📥 [Subscribe to our Newsletter](https://dagster.io/newsletter-signup)
- 🐦 [Follow us on Twitter](https://twitter.com/dagster)
- 🕴️ [Follow us on LinkedIn](https://linkedin.com/showcase/dagster)
- 📺 [Subscribe to our YouTube channel](https://www.youtube.com/channel/UCfLnv9X8jyHTe6gJ4hVBo9Q)
- 📚 [Read our blog posts](https://dagster.io/blog)
- 👋 [Join us on Slack](https://dagster.io/slack)
- 🗃 [Browse Slack archives](https://discuss.dagster.io)
- ✏️ [Start a Github Discussion](https://github.com/dagster-io/dagster/discussions)

## Contributing

For details on contributing or running the project for development, check out our [contributing
guide](https://docs.dagster.io/community/contributing/).

## License

Dagster is [Apache 2.0 licensed](https://github.com/dagster-io/dagster/blob/master/LICENSE).


%prep
%autosetup -n dagster-1.2.6

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-dagster -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.6-1
- Package Spec generated