pipelines/components/gcp/dataproc/submit_pyspark_job

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:

• Set up a GCP project by following this guide.

• Create a new cluster.

• Run the component under a secret Kubeflow user service account in a Kubeflow cluster. For example:

  component_op(...).apply(gcp.use_gcp_secret('user-gcp-sa'))
  

• Grant the Kubeflow user service account the role roles/dataproc.editor on the project.

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:

%%capture --no-stderr

KFP_PACKAGE = 'https://storage.googleapis.com/ml-pipeline/release/0.1.14/kfp.tar.gz'
!pip3 install $KFP_PACKAGE --upgrade

2. Load the component using KFP SDK

import kfp.components as comp

dataproc_submit_pyspark_job_op = comp.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/e598176c02f45371336ccaa819409e8ec83743df/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:

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

Set sample parameters

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

import kfp.dsl as dsl
import kfp.gcp as gcp
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).apply(gcp.use_gcp_secret('user-gcp-sa'))
    

Compile the pipeline

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

#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

• Create a new Dataproc cluster
• PySparkJob
• Dataproc job

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.