spark-etl 0.0.70

Generic ETL Pipeline Framework for Apache Spark

Index

• Overview
  • Goal
  • Benefit
  • Supported platforms
• APIs
  • Application
    • Application entry signature

See https://stonezhong.github.io/spark_etl/ for more informaion

Overview

Goal

spark_etl provide a platform independent way of building spark application.

Benefit

Your application deployed and running using spark-etl is spark provider agnostic. Which means, for example, you can move your application from Azure HDInsight to AWS EMR without changing your application's code.

Supported platforms

	You setup your own Apache Spark Cluster.
	Use PySpark package, fully compatible to other spark platform, allows you to test your pipeline in a single computer.
	You host your spark cluster in databricks
	You host your spark cluster in Amazon AWS EMR
	You host your spark cluster in Google Cloud
	You host your spark cluster in Microsoft Azure HDInsight
	You host your spark cluster in Oracle Cloud Infrastructure, Data Flow Service
	You host your spark cluster in IBM Cloud

APIs

Application

An application is a pyspark application, so far we only support pyspark, Java and Scala support will be added latter. An application contains:

• A main.py file which contain the application entry
• A manifest.json file, which specify the metadata of the application.
• A requirements.txt file, which specify the application dependency.

Application class:

• You can create an application via Application(app_location)
• You can build an application via app.build(destination_location)

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 job.
• 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 will be returned from the job submitter to the caller.

def main(spark, input_args, sysops={}):
    # your code here

Here is an example.

Job Deployer

• A job deployer has method deploy(build_dir, destination_location), it deploy the application to the destination location
• spark_etl support the following deployer
  • spark_etl.vendors.local.LocalDeployer
  • spark_etl.deployers.HDFSDeployer
  • spark_etl.vendors.oracle.DataflowDeployer

Job Submitter

• A job submitter has method run(deployment_location, options={}, args={}, handlers=[], on_job_submitted=None), it submit a deployed job

• spark_etl support the following job submitter

  • spark_etl.vendors.local.PySparkJobSubmitter
  • spark_etl.job_submitters.livy_job_submitter.LivyJobSubmitter
  • spark_etl.vendors.oracle.DataflowJobSubmitter

• Job summiter's run function returns the retrun value from job's main function.

Tool: etl.py

Build an application

To build an application, run

./etl.py -a build --app-dir <app-dir> --build-dir <build-dir>

• <app_dir> is the directory where your application is located.

• <build-dir> is the directory where you want your build to be deployed

  • Your build actually located at <build-dir>/<version>, where <version> is specified by application's manifest file

• Build is mostly platform independent. You need to depend on package oci-core if you intent to use oci Dataflow

Deplay an application

./etl.py -a deploy \
    -c <config-filename> \
    --build-dir <build-dir> \
    --deploy-dir <deploy-dir>

• -c <config-filename>: this option specify the config file to use for the deployment
• --build-dir <build-dir>: this option specify where to look for the build bits to deploy
• --deolpy-dir <deploy-dir>: this option specify what is the destination for the deployment

Run a job

./etl.py -a run \
    -c <config-filename> \
    --deploy-dir <deploy-dir> \
    --version <version> \
    --args <input-json-file>

• -c <config-filename>: this option specify the config file
• --build-dir <build-dir>: this option specify where to look for the build bits to run
• --version <version>: this option specify which version of the app to run
• --args <input-json-file>: optional parameter for input variable for the job. The <input-json-file> points to a json file, the value of the file will be passed to job's main function in input_args parameter. If this option is missing, the input_args will be set to {} when calling the main function of the job.
• It prints the return value of the main function of the job

Examples

• Build, deploy and run in local spark
• Build, deploy and run in spark cluster
• Build, deploy and run in OCI Dataflow