automatic import of python-databricks-connectopeneuler20.03

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diff --git a/.gitignore b/.gitignore
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+/databricks-connect-11.3.7.tar.gz
diff --git a/python-databricks-connect.spec b/python-databricks-connect.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-databricks-connect
+Version:	11.3.7
+Release:	1
+Summary:	Databricks Connect Client
+License:	Databricks Proprietary License
+URL:		https://pypi.org/project/databricks-connect/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/c1/14/e9fdd8338b501d266eecc42ce4949eb3d0e6dc492e86707e4b4553b53693/databricks-connect-11.3.7.tar.gz
+BuildArch:	noarch
+
+
+%description
+Databricks Connect allows you to write 
+jobs using Spark native APIs and have them execute remotely on a Databricks 
+cluster instead of in the local Spark session.
+For example, when you run the DataFrame command ``spark.read.parquet(...).
+groupBy(...).agg(...).show()`` using Databricks Connect, the parsing and 
+planning of the job runs on your local machine. Then, the logical 
+representation of the job is sent to the Spark server running in Databricks 
+for execution in the cluster.
+With Databricks Connect, you can:
+- Run large-scale Spark jobs from any Python, Java, Scala, or R application. 
+Anywhere you can ``import pyspark``, ``import org.apache.spark``, or 
+``require(SparkR)``, you can now run Spark jobs directly from your 
+application, without needing to install any IDE plugins or use Spark 
+submission scripts.
+- Step through and debug code in your IDE even when working with a remote 
+cluster.
+- Iterate quickly when developing libraries. You do not need to restart the 
+cluster after changing Python or Java library dependencies in Databricks 
+Connect, because each client session is isolated from each other in the 
+cluster.
+- Shut down idle clusters without losing work. Because the client session is 
+decoupled from the cluster, it is unaffected by cluster restarts or upgrades, 
+which would normally cause you to lose all the variables, RDDs, and DataFrame 
+objects defined in a notebook.
+
+%package -n python3-databricks-connect
+Summary:	Databricks Connect Client
+Provides:	python-databricks-connect
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-databricks-connect
+Databricks Connect allows you to write 
+jobs using Spark native APIs and have them execute remotely on a Databricks 
+cluster instead of in the local Spark session.
+For example, when you run the DataFrame command ``spark.read.parquet(...).
+groupBy(...).agg(...).show()`` using Databricks Connect, the parsing and 
+planning of the job runs on your local machine. Then, the logical 
+representation of the job is sent to the Spark server running in Databricks 
+for execution in the cluster.
+With Databricks Connect, you can:
+- Run large-scale Spark jobs from any Python, Java, Scala, or R application. 
+Anywhere you can ``import pyspark``, ``import org.apache.spark``, or 
+``require(SparkR)``, you can now run Spark jobs directly from your 
+application, without needing to install any IDE plugins or use Spark 
+submission scripts.
+- Step through and debug code in your IDE even when working with a remote 
+cluster.
+- Iterate quickly when developing libraries. You do not need to restart the 
+cluster after changing Python or Java library dependencies in Databricks 
+Connect, because each client session is isolated from each other in the 
+cluster.
+- Shut down idle clusters without losing work. Because the client session is 
+decoupled from the cluster, it is unaffected by cluster restarts or upgrades, 
+which would normally cause you to lose all the variables, RDDs, and DataFrame 
+objects defined in a notebook.
+
+%package help
+Summary:	Development documents and examples for databricks-connect
+Provides:	python3-databricks-connect-doc
+%description help
+Databricks Connect allows you to write 
+jobs using Spark native APIs and have them execute remotely on a Databricks 
+cluster instead of in the local Spark session.
+For example, when you run the DataFrame command ``spark.read.parquet(...).
+groupBy(...).agg(...).show()`` using Databricks Connect, the parsing and 
+planning of the job runs on your local machine. Then, the logical 
+representation of the job is sent to the Spark server running in Databricks 
+for execution in the cluster.
+With Databricks Connect, you can:
+- Run large-scale Spark jobs from any Python, Java, Scala, or R application. 
+Anywhere you can ``import pyspark``, ``import org.apache.spark``, or 
+``require(SparkR)``, you can now run Spark jobs directly from your 
+application, without needing to install any IDE plugins or use Spark 
+submission scripts.
+- Step through and debug code in your IDE even when working with a remote 
+cluster.
+- Iterate quickly when developing libraries. You do not need to restart the 
+cluster after changing Python or Java library dependencies in Databricks 
+Connect, because each client session is isolated from each other in the 
+cluster.
+- Shut down idle clusters without losing work. Because the client session is 
+decoupled from the cluster, it is unaffected by cluster restarts or upgrades, 
+which would normally cause you to lose all the variables, RDDs, and DataFrame 
+objects defined in a notebook.
+
+%prep
+%autosetup -n databricks-connect-11.3.7
+
+%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-databricks-connect -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 11.3.7-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..d1a3fdb
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+5309ce049af3cdf659a292fa0a90be98  databricks-connect-11.3.7.tar.gz