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Name

Data preparation using PySpark on Cloud Dataproc

Label

Cloud Dataproc, GCP, Cloud Storage,PySpark, Kubeflow, pipelines, components

Summary

A Kubeflow Pipeline component to prepare data by submitting a PySpark job to Cloud Dataproc.

Details

Intended use

Use the component to run an Apache PySpark job as one preprocessing step in a Kubeflow Pipeline.

Runtime arguments

Argument Description Optional Data type Accepted values Default
project_id The ID of the Google Cloud Platform (GCP) project that the cluster belongs to. No GCPProjectID
region The Cloud Dataproc region to handle the request. No GCPRegion
cluster_name The name of the cluster to run the job. No String
main_python_file_uri The HCFS URI of the Python file to use as the driver. This must be a .py file. No GCSPath
args The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission. Yes List None
pyspark_job The payload of a PySparkJob. Yes Dict None
job The payload of a Dataproc job. Yes Dict None

Output

Name Description Type
job_id The ID of the created job. String

Cautions & requirements

To use the component, you must:

Detailed description

This component creates a PySpark job from the Dataproc submit job REST API.

Follow these steps to use the component in a pipeline:

  1. Install the Kubeflow Pipeline SDK:




In [ ]:



    


%%capture --no-stderr

KFP_PACKAGE = 'https://storage.googleapis.com/ml-pipeline/release/0.1.14/kfp.tar.gz'
!pip3 install $KFP_PACKAGE --upgrade
  1. Load the component using KFP SDK




In [ ]:



    


import kfp.components as comp

dataproc_submit_pyspark_job_op = comp.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/01a23ae8672d3b18e88adf3036071496aca3552d/components/gcp/dataproc/submit_pyspark_job/component.yaml')
help(dataproc_submit_pyspark_job_op)

Sample

Note: The following sample code works in an IPython notebook or directly in Python code. See the sample code below to learn how to execute the template.

Setup a Dataproc cluster

Create a new Dataproc cluster (or reuse an existing one) before running the sample code.

Prepare a PySpark job

Upload your PySpark code file to a Cloud Storage bucket. For example, this is a publicly accessible hello-world.py in Cloud Storage:





In [ ]:



    


!gsutil cat gs://dataproc-examples-2f10d78d114f6aaec76462e3c310f31f/src/pyspark/hello-world/hello-world.py

Set sample parameters





In [ ]:



    


PROJECT_ID = '<Please put your project ID here>'
CLUSTER_NAME = '<Please put your existing cluster name here>'
REGION = 'us-central1'
PYSPARK_FILE_URI = 'gs://dataproc-examples-2f10d78d114f6aaec76462e3c310f31f/src/pyspark/hello-world/hello-world.py'
ARGS = ''
EXPERIMENT_NAME = 'Dataproc - Submit PySpark Job'

Example pipeline that uses the component





In [ ]:



    


import kfp.dsl as dsl
import json
@dsl.pipeline(
    name='Dataproc submit PySpark job pipeline',
    description='Dataproc submit PySpark job pipeline'
)
def dataproc_submit_pyspark_job_pipeline(
    project_id = PROJECT_ID, 
    region = REGION,
    cluster_name = CLUSTER_NAME,
    main_python_file_uri = PYSPARK_FILE_URI, 
    args = ARGS, 
    pyspark_job='{}', 
    job='{}', 
    wait_interval='30'
):
    dataproc_submit_pyspark_job_op(
        project_id=project_id, 
        region=region, 
        cluster_name=cluster_name, 
        main_python_file_uri=main_python_file_uri, 
        args=args, 
        pyspark_job=pyspark_job, 
        job=job, 
        wait_interval=wait_interval)

Compile the pipeline





In [ ]:



    


pipeline_func = dataproc_submit_pyspark_job_pipeline
pipeline_filename = pipeline_func.__name__ + '.zip'
import kfp.compiler as compiler
compiler.Compiler().compile(pipeline_func, pipeline_filename)

Submit the pipeline for execution





In [ ]:



    


#Specify pipeline argument values
arguments = {}

#Get or create an experiment and submit a pipeline run
import kfp
client = kfp.Client()
experiment = client.create_experiment(EXPERIMENT_NAME)

#Submit a pipeline run
run_name = pipeline_func.__name__ + ' run'
run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, arguments)

References

License

By deploying or using this software you agree to comply with the AI Hub Terms of Service and the Google APIs Terms of Service. To the extent of a direct conflict of terms, the AI Hub Terms of Service will control.

Content source: kubeflow/kfp-tekton-backend

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