pipelines/components/gcp/dataproc/submit_spark_job

Name

Data preparation using Spark on YARN with Cloud Dataproc

Label

Cloud Dataproc, GCP, Cloud Storage, Spark, Kubeflow, pipelines, components, YARN

Summary

A Kubeflow Pipeline component to prepare data by submitting a Spark job on YARN to Cloud Dataproc.

Details

Intended use

Use the component to run an Apache Spark 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_jar_file_uri	The Hadoop Compatible Filesystem (HCFS) URI of the JAR file that contains the main class.	No	GCSPath
main_class	The name of the driver's main class. The JAR file that contains the class must be either in the default CLASSPATH or specified in spark_job.jarFileUris.	No
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
spark_job	The payload of a SparkJob.	Yes
job	The payload of a Dataproc job.	Yes
wait_interval	The number of seconds to wait between polling the operation.	Yes			30

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 Spark job from 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_spark_job_op = comp.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/e598176c02f45371336ccaa819409e8ec83743df/components/gcp/dataproc/submit_spark_job/component.yaml')
help(dataproc_submit_spark_job_op)

Sample

Note: The following sample code works in an IPython notebook or directly in Python code.

Set up a Dataproc cluster

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

Prepare a Spark job

Upload your Spark JAR file to a Cloud Storage bucket. In the sample, we use a JAR file that is preinstalled in the main cluster: file:///usr/lib/spark/examples/jars/spark-examples.jar.

Here is the source code of the sample.

To package a self-contained Spark application, follow these instructions.

Set sample parameters

PROJECT_ID = '<Please put your project ID here>'
CLUSTER_NAME = '<Please put your existing cluster name here>'
REGION = 'us-central1'
SPARK_FILE_URI = 'file:///usr/lib/spark/examples/jars/spark-examples.jar'
MAIN_CLASS = 'org.apache.spark.examples.SparkPi'
ARGS = ['1000']
EXPERIMENT_NAME = 'Dataproc - Submit Spark Job'

Example pipeline that uses the component

import kfp.dsl as dsl
import kfp.gcp as gcp
import json
@dsl.pipeline(
    name='Dataproc submit Spark job pipeline',
    description='Dataproc submit Spark job pipeline'
)
def dataproc_submit_spark_job_pipeline(
    project_id = PROJECT_ID, 
    region = REGION,
    cluster_name = CLUSTER_NAME,
    main_jar_file_uri = '',
    main_class = MAIN_CLASS,
    args = json.dumps(ARGS), 
    spark_job=json.dumps({ 'jarFileUris': [ SPARK_FILE_URI ] }), 
    job='{}', 
    wait_interval='30'
):
    dataproc_submit_spark_job_op(
        project_id=project_id, 
        region=region, 
        cluster_name=cluster_name, 
        main_jar_file_uri=main_jar_file_uri, 
        main_class=main_class,
        args=args, 
        spark_job=spark_job, 
        job=job, 
        wait_interval=wait_interval).apply(gcp.use_gcp_secret('user-gcp-sa'))
    

Compile the pipeline

pipeline_func = dataproc_submit_spark_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

• Component Python code
• Component Docker file
• Sample notebook
• Dataproc SparkJob

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.