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|
%global _empty_manifest_terminate_build 0
Name: python-spark-etl
Version: 0.0.122
Release: 1
Summary: Generic ETL Pipeline Framework for Apache Spark
License: MIT
URL: https://github.com/stonezhong/spark_etl
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/9e/b4/940f4b3aea2b51b6358bfa552ea03c04001978cbd16694f666a129e5f97a/spark-etl-0.0.122.tar.gz
BuildArch: noarch
Requires: python3-requests
Requires: python3-Jinja2
Requires: python3-termcolor
%description
* [Overview](#overview)
* [Goal](#goal)
* [Benefit](#benefit)
* [Application](#application)
* [Build your application](#build_your_application)
* [Deploy your application](#deploy_your_application)
* [Run your application](#run_your_application)
* [Supported platforms](#supported_platforms)
* [Demos](#demos)
* [APIs](#apis)
* [Job Deployer](#job-deployer)
* [Job Submitter](#job-submitter)
# Overview
## Goal
There are many public clouds provide managed Apache Spark as service, such as databricks, AWS EMR, Oracle OCI DataFlow, see the table below for a detailed list.
However, the way to deploy Spark application and launch Spark application are incompatible among different cloud Spark platforms.
spark-etl is a python package, provides a standard way for building, deploying and running your Spark application that supports various cloud spark platforms.
## Benefit
Your application using `spark-etl` can be deployed and launched from different cloud spark platforms without changing the source code.
## Application
An application is a python program. It contains:
* A `main.py` file which contains the application entry
* A `manifest.json` file, which specify the metadata of the application.
* A `requirements.txt` file, which specify the application dependency.
### Application entry signature
In your application's `main.py`, you shuold have a `main` function with the following signature:
* `spark` is the spark session object
* `input_args` a dict, is the argument user specified when running this application.
* `sysops` is the system options passed, it is platform specific. Job submitter may inject platform specific object in `sysops` object.
* Your `main` function's return value should be a JSON object, it will be returned from the job submitter to the caller.
```
def main(spark, input_args, sysops={}):
# your code here
```
[Here](examples/apps/demo01) is an application example.
## Build your application
`etl -a build -c <config-filename> -p <application-name>`
## Deploy your application
`etl -a deploy -c <config-filename> -p <application-name> -f <profile-name>`
## Run your application
`etl -a run -c <config-filename> -p <application-name> -f <profile-name> --run-args <input-filename>`
## Supported platforms
<table>
<tr>
<td>
<img
src="https://upload.wikimedia.org/wikipedia/commons/thumb/f/f3/Apache_Spark_logo.svg/1200px-Apache_Spark_logo.svg.png"
width="120px"
/>
</td>
<td>You setup your own Apache Spark Cluster.
</td>
</tr>
<tr>
<td>
<img src="https://miro.medium.com/max/700/1*qgkjkj6BLVS1uD4mw_sTEg.png" width="120px" />
</td>
<td>
Use <a href="https://pypi.org/project/pyspark/">PySpark</a> package, fully compatible to other spark platform, allows you to test your pipeline in a single computer.
</td>
</tr>
<tr>
<td>
<img src="https://databricks.com/wp-content/uploads/2019/02/databricks-generic-tile.png" width="120px">
</td>
<td>You host your spark cluster in <a href="https://databricks.com/">databricks </a></td>
</tr>
<tr>
<td>
<img
src="https://blog.ippon.tech/content/images/2019/06/emrlogogo.png"
width="120px"
/>
</td>
<td>You host your spark cluster in <a href="https://aws.amazon.com/emr/">Amazon AWS EMR</a>
</td>
</tr>
<tr>
<td>
<img
src="https://d15shllkswkct0.cloudfront.net/wp-content/blogs.dir/1/files/2020/07/100-768x402.jpeg"
width="120px"
/>
</td>
<td>You host your spark cluster in <a href="https://cloud.google.com/dataproc">Google Cloud</a></td>
</tr>
<tr>
<td>
<img
src="https://apifriends.com/wp-content/uploads/2018/05/HDInsightsDetails.png"
width="120px"
/>
</td>
<td>You host your spark cluster in <a href="https://azure.microsoft.com/en-us/services/hdinsight/">Microsoft Azure HDInsight</a></td>
</tr>
<tr>
<td>
<img
src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRajQVuckGogS3c8Yxa4M-OPd7yFCyWSj4Cpg&usqp=CAU"
width="120px"
/>
</td>
<td>
You host your spark cluster in <a href="https://www.oracle.com/big-data/data-flow/">Oracle Cloud Infrastructure, Data Flow Service</a>
</td>
</tr>
<tr>
<td>
<img
src="https://upload.wikimedia.org/wikipedia/commons/2/24/IBM_Cloud_logo.png"
width="120px"
/>
</td>
<td>You host your spark cluster in <a href="https://www.ibm.com/products/big-data-and-analytics">IBM Cloud</a></td>
</tr>
</table>
# Demos
* [Using local pyspark, access data on local disk](examples/pyspark_local/readme.md)
* [Using local pyspark, access data on AWS S3](examples/pyspark_s3/readme.md)
* [Using on-premise spark, access data on HDFS](examples/livy_hdfs1/readme.md)
* [Using on-premise spark, access data on AWS S3](examples/livy_hdfs2/readme.md)
* [Using AWS EMR's spark, access data on AWS S3](examples/aws_emr/readme.md)
* [Using Oracle OCI's Dataflow with API key, access data on Object Storage](examples/oci_dataflow1/readme.md)
* [Using Oracle OCI's Dataflow with instance principal, access data on Object Storage](examples/oci_dataflow2/readme.md)
# APIs
[pydocs for APIs](https://stonezhong.github.io/spark_etl/pydocs/spark_etl.html)
## Job Deployer
For job deployers, please check the [wiki](https://github.com/stonezhong/spark_etl/wiki#job-deployer-classes) .
## Job Submitter
For job submitters, please check the [wiki](https://github.com/stonezhong/spark_etl/wiki#job-submitter-classes)
%package -n python3-spark-etl
Summary: Generic ETL Pipeline Framework for Apache Spark
Provides: python-spark-etl
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-spark-etl
* [Overview](#overview)
* [Goal](#goal)
* [Benefit](#benefit)
* [Application](#application)
* [Build your application](#build_your_application)
* [Deploy your application](#deploy_your_application)
* [Run your application](#run_your_application)
* [Supported platforms](#supported_platforms)
* [Demos](#demos)
* [APIs](#apis)
* [Job Deployer](#job-deployer)
* [Job Submitter](#job-submitter)
# Overview
## Goal
There are many public clouds provide managed Apache Spark as service, such as databricks, AWS EMR, Oracle OCI DataFlow, see the table below for a detailed list.
However, the way to deploy Spark application and launch Spark application are incompatible among different cloud Spark platforms.
spark-etl is a python package, provides a standard way for building, deploying and running your Spark application that supports various cloud spark platforms.
## Benefit
Your application using `spark-etl` can be deployed and launched from different cloud spark platforms without changing the source code.
## Application
An application is a python program. It contains:
* A `main.py` file which contains the application entry
* A `manifest.json` file, which specify the metadata of the application.
* A `requirements.txt` file, which specify the application dependency.
### Application entry signature
In your application's `main.py`, you shuold have a `main` function with the following signature:
* `spark` is the spark session object
* `input_args` a dict, is the argument user specified when running this application.
* `sysops` is the system options passed, it is platform specific. Job submitter may inject platform specific object in `sysops` object.
* Your `main` function's return value should be a JSON object, it will be returned from the job submitter to the caller.
```
def main(spark, input_args, sysops={}):
# your code here
```
[Here](examples/apps/demo01) is an application example.
## Build your application
`etl -a build -c <config-filename> -p <application-name>`
## Deploy your application
`etl -a deploy -c <config-filename> -p <application-name> -f <profile-name>`
## Run your application
`etl -a run -c <config-filename> -p <application-name> -f <profile-name> --run-args <input-filename>`
## Supported platforms
<table>
<tr>
<td>
<img
src="https://upload.wikimedia.org/wikipedia/commons/thumb/f/f3/Apache_Spark_logo.svg/1200px-Apache_Spark_logo.svg.png"
width="120px"
/>
</td>
<td>You setup your own Apache Spark Cluster.
</td>
</tr>
<tr>
<td>
<img src="https://miro.medium.com/max/700/1*qgkjkj6BLVS1uD4mw_sTEg.png" width="120px" />
</td>
<td>
Use <a href="https://pypi.org/project/pyspark/">PySpark</a> package, fully compatible to other spark platform, allows you to test your pipeline in a single computer.
</td>
</tr>
<tr>
<td>
<img src="https://databricks.com/wp-content/uploads/2019/02/databricks-generic-tile.png" width="120px">
</td>
<td>You host your spark cluster in <a href="https://databricks.com/">databricks </a></td>
</tr>
<tr>
<td>
<img
src="https://blog.ippon.tech/content/images/2019/06/emrlogogo.png"
width="120px"
/>
</td>
<td>You host your spark cluster in <a href="https://aws.amazon.com/emr/">Amazon AWS EMR</a>
</td>
</tr>
<tr>
<td>
<img
src="https://d15shllkswkct0.cloudfront.net/wp-content/blogs.dir/1/files/2020/07/100-768x402.jpeg"
width="120px"
/>
</td>
<td>You host your spark cluster in <a href="https://cloud.google.com/dataproc">Google Cloud</a></td>
</tr>
<tr>
<td>
<img
src="https://apifriends.com/wp-content/uploads/2018/05/HDInsightsDetails.png"
width="120px"
/>
</td>
<td>You host your spark cluster in <a href="https://azure.microsoft.com/en-us/services/hdinsight/">Microsoft Azure HDInsight</a></td>
</tr>
<tr>
<td>
<img
src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRajQVuckGogS3c8Yxa4M-OPd7yFCyWSj4Cpg&usqp=CAU"
width="120px"
/>
</td>
<td>
You host your spark cluster in <a href="https://www.oracle.com/big-data/data-flow/">Oracle Cloud Infrastructure, Data Flow Service</a>
</td>
</tr>
<tr>
<td>
<img
src="https://upload.wikimedia.org/wikipedia/commons/2/24/IBM_Cloud_logo.png"
width="120px"
/>
</td>
<td>You host your spark cluster in <a href="https://www.ibm.com/products/big-data-and-analytics">IBM Cloud</a></td>
</tr>
</table>
# Demos
* [Using local pyspark, access data on local disk](examples/pyspark_local/readme.md)
* [Using local pyspark, access data on AWS S3](examples/pyspark_s3/readme.md)
* [Using on-premise spark, access data on HDFS](examples/livy_hdfs1/readme.md)
* [Using on-premise spark, access data on AWS S3](examples/livy_hdfs2/readme.md)
* [Using AWS EMR's spark, access data on AWS S3](examples/aws_emr/readme.md)
* [Using Oracle OCI's Dataflow with API key, access data on Object Storage](examples/oci_dataflow1/readme.md)
* [Using Oracle OCI's Dataflow with instance principal, access data on Object Storage](examples/oci_dataflow2/readme.md)
# APIs
[pydocs for APIs](https://stonezhong.github.io/spark_etl/pydocs/spark_etl.html)
## Job Deployer
For job deployers, please check the [wiki](https://github.com/stonezhong/spark_etl/wiki#job-deployer-classes) .
## Job Submitter
For job submitters, please check the [wiki](https://github.com/stonezhong/spark_etl/wiki#job-submitter-classes)
%package help
Summary: Development documents and examples for spark-etl
Provides: python3-spark-etl-doc
%description help
* [Overview](#overview)
* [Goal](#goal)
* [Benefit](#benefit)
* [Application](#application)
* [Build your application](#build_your_application)
* [Deploy your application](#deploy_your_application)
* [Run your application](#run_your_application)
* [Supported platforms](#supported_platforms)
* [Demos](#demos)
* [APIs](#apis)
* [Job Deployer](#job-deployer)
* [Job Submitter](#job-submitter)
# Overview
## Goal
There are many public clouds provide managed Apache Spark as service, such as databricks, AWS EMR, Oracle OCI DataFlow, see the table below for a detailed list.
However, the way to deploy Spark application and launch Spark application are incompatible among different cloud Spark platforms.
spark-etl is a python package, provides a standard way for building, deploying and running your Spark application that supports various cloud spark platforms.
## Benefit
Your application using `spark-etl` can be deployed and launched from different cloud spark platforms without changing the source code.
## Application
An application is a python program. It contains:
* A `main.py` file which contains the application entry
* A `manifest.json` file, which specify the metadata of the application.
* A `requirements.txt` file, which specify the application dependency.
### Application entry signature
In your application's `main.py`, you shuold have a `main` function with the following signature:
* `spark` is the spark session object
* `input_args` a dict, is the argument user specified when running this application.
* `sysops` is the system options passed, it is platform specific. Job submitter may inject platform specific object in `sysops` object.
* Your `main` function's return value should be a JSON object, it will be returned from the job submitter to the caller.
```
def main(spark, input_args, sysops={}):
# your code here
```
[Here](examples/apps/demo01) is an application example.
## Build your application
`etl -a build -c <config-filename> -p <application-name>`
## Deploy your application
`etl -a deploy -c <config-filename> -p <application-name> -f <profile-name>`
## Run your application
`etl -a run -c <config-filename> -p <application-name> -f <profile-name> --run-args <input-filename>`
## Supported platforms
<table>
<tr>
<td>
<img
src="https://upload.wikimedia.org/wikipedia/commons/thumb/f/f3/Apache_Spark_logo.svg/1200px-Apache_Spark_logo.svg.png"
width="120px"
/>
</td>
<td>You setup your own Apache Spark Cluster.
</td>
</tr>
<tr>
<td>
<img src="https://miro.medium.com/max/700/1*qgkjkj6BLVS1uD4mw_sTEg.png" width="120px" />
</td>
<td>
Use <a href="https://pypi.org/project/pyspark/">PySpark</a> package, fully compatible to other spark platform, allows you to test your pipeline in a single computer.
</td>
</tr>
<tr>
<td>
<img src="https://databricks.com/wp-content/uploads/2019/02/databricks-generic-tile.png" width="120px">
</td>
<td>You host your spark cluster in <a href="https://databricks.com/">databricks </a></td>
</tr>
<tr>
<td>
<img
src="https://blog.ippon.tech/content/images/2019/06/emrlogogo.png"
width="120px"
/>
</td>
<td>You host your spark cluster in <a href="https://aws.amazon.com/emr/">Amazon AWS EMR</a>
</td>
</tr>
<tr>
<td>
<img
src="https://d15shllkswkct0.cloudfront.net/wp-content/blogs.dir/1/files/2020/07/100-768x402.jpeg"
width="120px"
/>
</td>
<td>You host your spark cluster in <a href="https://cloud.google.com/dataproc">Google Cloud</a></td>
</tr>
<tr>
<td>
<img
src="https://apifriends.com/wp-content/uploads/2018/05/HDInsightsDetails.png"
width="120px"
/>
</td>
<td>You host your spark cluster in <a href="https://azure.microsoft.com/en-us/services/hdinsight/">Microsoft Azure HDInsight</a></td>
</tr>
<tr>
<td>
<img
src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRajQVuckGogS3c8Yxa4M-OPd7yFCyWSj4Cpg&usqp=CAU"
width="120px"
/>
</td>
<td>
You host your spark cluster in <a href="https://www.oracle.com/big-data/data-flow/">Oracle Cloud Infrastructure, Data Flow Service</a>
</td>
</tr>
<tr>
<td>
<img
src="https://upload.wikimedia.org/wikipedia/commons/2/24/IBM_Cloud_logo.png"
width="120px"
/>
</td>
<td>You host your spark cluster in <a href="https://www.ibm.com/products/big-data-and-analytics">IBM Cloud</a></td>
</tr>
</table>
# Demos
* [Using local pyspark, access data on local disk](examples/pyspark_local/readme.md)
* [Using local pyspark, access data on AWS S3](examples/pyspark_s3/readme.md)
* [Using on-premise spark, access data on HDFS](examples/livy_hdfs1/readme.md)
* [Using on-premise spark, access data on AWS S3](examples/livy_hdfs2/readme.md)
* [Using AWS EMR's spark, access data on AWS S3](examples/aws_emr/readme.md)
* [Using Oracle OCI's Dataflow with API key, access data on Object Storage](examples/oci_dataflow1/readme.md)
* [Using Oracle OCI's Dataflow with instance principal, access data on Object Storage](examples/oci_dataflow2/readme.md)
# APIs
[pydocs for APIs](https://stonezhong.github.io/spark_etl/pydocs/spark_etl.html)
## Job Deployer
For job deployers, please check the [wiki](https://github.com/stonezhong/spark_etl/wiki#job-deployer-classes) .
## Job Submitter
For job submitters, please check the [wiki](https://github.com/stonezhong/spark_etl/wiki#job-submitter-classes)
%prep
%autosetup -n spark-etl-0.0.122
%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-spark-etl -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.122-1
- Package Spec generated
|
