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|
%global _empty_manifest_terminate_build 0
Name: python-apache-flink
Version: 1.17.0
Release: 1
Summary: Apache Flink Python API
License: https://www.apache.org/licenses/LICENSE-2.0
URL: https://flink.apache.org
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/79/d3/dee7bb8fd0aac3d91950e901dbbcc64e97cb2cbd054723a98a5976f9db48/apache-flink-1.17.0.tar.gz
BuildArch: noarch
Requires: python3-py4j
Requires: python3-dateutil
Requires: python3-apache-beam
Requires: python3-cloudpickle
Requires: python3-avro-python3
Requires: python3-pytz
Requires: python3-fastavro
Requires: python3-requests
Requires: python3-protobuf
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-pyarrow
Requires: python3-httplib2
Requires: python3-apache-flink-libraries
Requires: python3-pemja
%description
# Apache Flink
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.
Learn more about Flink at [https://flink.apache.org/](https://flink.apache.org/)
## Python Packaging
PyFlink is a Python API for Apache Flink that allows you to build scalable batch and streaming workloads,
such as real-time data processing pipelines, large-scale exploratory data analysis, Machine Learning (ML)
pipelines and ETL processes. If you’re already familiar with Python and libraries such as Pandas,
then PyFlink makes it simpler to leverage the full capabilities of the Flink ecosystem.
Depending on the level of abstraction you need, there are two different APIs that can be used in PyFlink: PyFlink Table API and PyFlink DataStream API.
The PyFlink Table API allows you to write powerful relational queries in a way that is similar to
using SQL or working with tabular data in Python. You can find more information about it via the tutorial
[https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/table_api_tutorial/](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/table_api_tutorial/)
The PyFlink DataStream API gives you lower-level control over the core building blocks of Flink,
state and time, to build more complex stream processing use cases.
Tutorial can be found at [https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/datastream_tutorial/](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/datastream_tutorial/)
You can find more information via the documentation at [https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/overview/](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/overview/)
The auto-generated Python docs can be found at [https://nightlies.apache.org/flink/flink-docs-stable/api/python/](https://nightlies.apache.org/flink/flink-docs-stable/api/python/)
## Python Requirements
Apache Flink Python API depends on Py4J (currently version 0.10.9.7), CloudPickle (currently version 2.2.0), python-dateutil(currently version >=2.8.0,<3), Apache Beam (currently version 2.43.0).
## Development Notices
### Protobuf Code Generation
Protocol buffer is used in file `flink_fn_execution_pb2.py` and the file is generated from `flink-fn-execution.proto`. Whenever `flink-fn-execution.proto` is updated, please re-generate `flink_fn_execution_pb2.py` by executing:
```
python pyflink/gen_protos.py
```
PyFlink depends on the following libraries to execute the above script:
1. grpcio-tools (>=1.29.0,<=1.46.3)
2. setuptools (>=37.0.0)
3. pip (>=20.3)
### Running Test Cases
Currently, we use conda and tox to verify the compatibility of the Flink Python API for multiple versions of Python and will integrate some useful plugins with tox, such as flake8.
We can enter the directory where this README.md file is located and run test cases by executing
```
./dev/lint-python.sh
```
To use your system conda environment, you can set `FLINK_CONDA_HOME` variable:
```shell
export FLINK_CONDA_HOME=$(dirname $(dirname $CONDA_EXE))
```
Create a virtual environment:
```shell
conda create -n pyflink_38 python=3.8
```
Then you can activate your environment and run tests, for example:
```shell
conda activate pyflink_38
pip install -r ./dev/dev-requirements.txt
./dev/lint-python.sh
```
%package -n python3-apache-flink
Summary: Apache Flink Python API
Provides: python-apache-flink
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-apache-flink
# Apache Flink
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.
Learn more about Flink at [https://flink.apache.org/](https://flink.apache.org/)
## Python Packaging
PyFlink is a Python API for Apache Flink that allows you to build scalable batch and streaming workloads,
such as real-time data processing pipelines, large-scale exploratory data analysis, Machine Learning (ML)
pipelines and ETL processes. If you’re already familiar with Python and libraries such as Pandas,
then PyFlink makes it simpler to leverage the full capabilities of the Flink ecosystem.
Depending on the level of abstraction you need, there are two different APIs that can be used in PyFlink: PyFlink Table API and PyFlink DataStream API.
The PyFlink Table API allows you to write powerful relational queries in a way that is similar to
using SQL or working with tabular data in Python. You can find more information about it via the tutorial
[https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/table_api_tutorial/](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/table_api_tutorial/)
The PyFlink DataStream API gives you lower-level control over the core building blocks of Flink,
state and time, to build more complex stream processing use cases.
Tutorial can be found at [https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/datastream_tutorial/](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/datastream_tutorial/)
You can find more information via the documentation at [https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/overview/](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/overview/)
The auto-generated Python docs can be found at [https://nightlies.apache.org/flink/flink-docs-stable/api/python/](https://nightlies.apache.org/flink/flink-docs-stable/api/python/)
## Python Requirements
Apache Flink Python API depends on Py4J (currently version 0.10.9.7), CloudPickle (currently version 2.2.0), python-dateutil(currently version >=2.8.0,<3), Apache Beam (currently version 2.43.0).
## Development Notices
### Protobuf Code Generation
Protocol buffer is used in file `flink_fn_execution_pb2.py` and the file is generated from `flink-fn-execution.proto`. Whenever `flink-fn-execution.proto` is updated, please re-generate `flink_fn_execution_pb2.py` by executing:
```
python pyflink/gen_protos.py
```
PyFlink depends on the following libraries to execute the above script:
1. grpcio-tools (>=1.29.0,<=1.46.3)
2. setuptools (>=37.0.0)
3. pip (>=20.3)
### Running Test Cases
Currently, we use conda and tox to verify the compatibility of the Flink Python API for multiple versions of Python and will integrate some useful plugins with tox, such as flake8.
We can enter the directory where this README.md file is located and run test cases by executing
```
./dev/lint-python.sh
```
To use your system conda environment, you can set `FLINK_CONDA_HOME` variable:
```shell
export FLINK_CONDA_HOME=$(dirname $(dirname $CONDA_EXE))
```
Create a virtual environment:
```shell
conda create -n pyflink_38 python=3.8
```
Then you can activate your environment and run tests, for example:
```shell
conda activate pyflink_38
pip install -r ./dev/dev-requirements.txt
./dev/lint-python.sh
```
%package help
Summary: Development documents and examples for apache-flink
Provides: python3-apache-flink-doc
%description help
# Apache Flink
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.
Learn more about Flink at [https://flink.apache.org/](https://flink.apache.org/)
## Python Packaging
PyFlink is a Python API for Apache Flink that allows you to build scalable batch and streaming workloads,
such as real-time data processing pipelines, large-scale exploratory data analysis, Machine Learning (ML)
pipelines and ETL processes. If you’re already familiar with Python and libraries such as Pandas,
then PyFlink makes it simpler to leverage the full capabilities of the Flink ecosystem.
Depending on the level of abstraction you need, there are two different APIs that can be used in PyFlink: PyFlink Table API and PyFlink DataStream API.
The PyFlink Table API allows you to write powerful relational queries in a way that is similar to
using SQL or working with tabular data in Python. You can find more information about it via the tutorial
[https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/table_api_tutorial/](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/table_api_tutorial/)
The PyFlink DataStream API gives you lower-level control over the core building blocks of Flink,
state and time, to build more complex stream processing use cases.
Tutorial can be found at [https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/datastream_tutorial/](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/datastream_tutorial/)
You can find more information via the documentation at [https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/overview/](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/overview/)
The auto-generated Python docs can be found at [https://nightlies.apache.org/flink/flink-docs-stable/api/python/](https://nightlies.apache.org/flink/flink-docs-stable/api/python/)
## Python Requirements
Apache Flink Python API depends on Py4J (currently version 0.10.9.7), CloudPickle (currently version 2.2.0), python-dateutil(currently version >=2.8.0,<3), Apache Beam (currently version 2.43.0).
## Development Notices
### Protobuf Code Generation
Protocol buffer is used in file `flink_fn_execution_pb2.py` and the file is generated from `flink-fn-execution.proto`. Whenever `flink-fn-execution.proto` is updated, please re-generate `flink_fn_execution_pb2.py` by executing:
```
python pyflink/gen_protos.py
```
PyFlink depends on the following libraries to execute the above script:
1. grpcio-tools (>=1.29.0,<=1.46.3)
2. setuptools (>=37.0.0)
3. pip (>=20.3)
### Running Test Cases
Currently, we use conda and tox to verify the compatibility of the Flink Python API for multiple versions of Python and will integrate some useful plugins with tox, such as flake8.
We can enter the directory where this README.md file is located and run test cases by executing
```
./dev/lint-python.sh
```
To use your system conda environment, you can set `FLINK_CONDA_HOME` variable:
```shell
export FLINK_CONDA_HOME=$(dirname $(dirname $CONDA_EXE))
```
Create a virtual environment:
```shell
conda create -n pyflink_38 python=3.8
```
Then you can activate your environment and run tests, for example:
```shell
conda activate pyflink_38
pip install -r ./dev/dev-requirements.txt
./dev/lint-python.sh
```
%prep
%autosetup -n apache-flink-1.17.0
%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-apache-flink -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 1.17.0-1
- Package Spec generated
|
