%global _empty_manifest_terminate_build 0 Name: python-pysparkling Version: 0.6.2 Release: 1 Summary: Pure Python implementation of the Spark RDD interface. License: MIT URL: https://github.com/svenkreiss/pysparkling Source0: https://mirrors.nju.edu.cn/pypi/web/packages/96/6f/d66fcd96ed26f7526248ba11a4d09a0cd9d2d164f93ff709e3f72a8f425e/pysparkling-0.6.2.tar.gz BuildArch: noarch %description **Pysparkling** provides a faster, more responsive way to develop programs for PySpark. It enables code intended for Spark applications to execute entirely in Python, without incurring the overhead of initializing and passing data through the JVM and Hadoop. The focus is on having a lightweight and fast implementation for small datasets at the expense of some data resilience features and some parallel processing features. **How does it work?** To switch execution of a script from PySpark to pysparkling, have the code initialize a pysparkling Context instead of a SparkContext, and use the pysparkling Context to set up your RDDs. The beauty is you don't have to change a single line of code after the Context initialization, because pysparkling's API is (almost) exactly the same as PySpark's. Since it's so easy to switch between PySpark and pysparkling, you can choose the right tool for your use case. **When would I use it?** Say you are writing a Spark application because you need robust computation on huge datasets, but you also want the same application to provide fast answers on a small dataset. You're finding Spark is not responsive enough for your needs, but you don't want to rewrite an entire separate application for the *small-answers-fast* problem. You'd rather reuse your Spark code but somehow get it to run fast. Pysparkling bypasses the stuff that causes Spark's long startup times and less responsive feel. Here are a few areas where pysparkling excels: * Small to medium-scale exploratory data analysis * Application prototyping * Low-latency web deployments * Unit tests %package -n python3-pysparkling Summary: Pure Python implementation of the Spark RDD interface. Provides: python-pysparkling BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pysparkling **Pysparkling** provides a faster, more responsive way to develop programs for PySpark. It enables code intended for Spark applications to execute entirely in Python, without incurring the overhead of initializing and passing data through the JVM and Hadoop. The focus is on having a lightweight and fast implementation for small datasets at the expense of some data resilience features and some parallel processing features. **How does it work?** To switch execution of a script from PySpark to pysparkling, have the code initialize a pysparkling Context instead of a SparkContext, and use the pysparkling Context to set up your RDDs. The beauty is you don't have to change a single line of code after the Context initialization, because pysparkling's API is (almost) exactly the same as PySpark's. Since it's so easy to switch between PySpark and pysparkling, you can choose the right tool for your use case. **When would I use it?** Say you are writing a Spark application because you need robust computation on huge datasets, but you also want the same application to provide fast answers on a small dataset. You're finding Spark is not responsive enough for your needs, but you don't want to rewrite an entire separate application for the *small-answers-fast* problem. You'd rather reuse your Spark code but somehow get it to run fast. Pysparkling bypasses the stuff that causes Spark's long startup times and less responsive feel. Here are a few areas where pysparkling excels: * Small to medium-scale exploratory data analysis * Application prototyping * Low-latency web deployments * Unit tests %package help Summary: Development documents and examples for pysparkling Provides: python3-pysparkling-doc %description help **Pysparkling** provides a faster, more responsive way to develop programs for PySpark. It enables code intended for Spark applications to execute entirely in Python, without incurring the overhead of initializing and passing data through the JVM and Hadoop. The focus is on having a lightweight and fast implementation for small datasets at the expense of some data resilience features and some parallel processing features. **How does it work?** To switch execution of a script from PySpark to pysparkling, have the code initialize a pysparkling Context instead of a SparkContext, and use the pysparkling Context to set up your RDDs. The beauty is you don't have to change a single line of code after the Context initialization, because pysparkling's API is (almost) exactly the same as PySpark's. Since it's so easy to switch between PySpark and pysparkling, you can choose the right tool for your use case. **When would I use it?** Say you are writing a Spark application because you need robust computation on huge datasets, but you also want the same application to provide fast answers on a small dataset. You're finding Spark is not responsive enough for your needs, but you don't want to rewrite an entire separate application for the *small-answers-fast* problem. You'd rather reuse your Spark code but somehow get it to run fast. Pysparkling bypasses the stuff that causes Spark's long startup times and less responsive feel. Here are a few areas where pysparkling excels: * Small to medium-scale exploratory data analysis * Application prototyping * Low-latency web deployments * Unit tests %prep %autosetup -n pysparkling-0.6.2 %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-pysparkling -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Sun Apr 23 2023 Python_Bot - 0.6.2-1 - Package Spec generated