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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
|
