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Name

Data preparation using Hadoop MapReduce on YARN with Cloud Dataproc

Label

Cloud Dataproc, GCP, Cloud Storage, Hadoop, YARN, Apache, MapReduce

Summary

A Kubeflow Pipeline component to prepare data by submitting an Apache Hadoop MapReduce job on Apache Hadoop YARN to Cloud Dataproc.

Details

Intended use

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

Runtime arguments

Argument Description Optional Data type Accepted values Default
project_id The Google Cloud Platform (GCP) project ID that the cluster belongs to. No GCPProjectID
region The 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 containing the main class to execute. No List
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 hadoop_job.jarFileUris. No String
args The arguments to pass to the driver. Do not include arguments, such as -libjars or -Dfoo=bar, that can be set as job properties, since a collision may occur that causes an incorrect job submission. Yes List None
hadoop_job The payload of a HadoopJob. Yes Dict None
job The payload of a Dataproc job. Yes Dict None
wait_interval The number of seconds to pause between polling the operation. Yes Integer 30

Note: main_jar_file_uri: The examples for the files are :

  • gs://foo-bucket/analytics-binaries/extract-useful-metrics-mr.jar
  • hdfs:/tmp/test-samples/custom-wordcount.jarfile:///home/usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar

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 Hadoop job from 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_hadoop_job_op = comp.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/01a23ae8672d3b18e88adf3036071496aca3552d/components/gcp/dataproc/submit_hadoop_job/component.yaml')
help(dataproc_submit_hadoop_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 Hadoop job

Upload your Hadoop JAR file to a Cloud Storage bucket. In the sample, we will use a JAR file that is preinstalled in the main cluster, so there is no need to provide main_jar_file_uri.

Here is the WordCount example source code.

To package a self-contained Hadoop MapReduce application from the source code, follow the MapReduce Tutorial.

Set sample parameters





In [ ]:



    


PROJECT_ID = '<Please put your project ID here>'
CLUSTER_NAME = '<Please put your existing cluster name here>'
OUTPUT_GCS_PATH = '<Please put your output GCS path here>'
REGION = 'us-central1'
MAIN_CLASS = 'org.apache.hadoop.examples.WordCount'
INTPUT_GCS_PATH = 'gs://ml-pipeline-playground/shakespeare1.txt'
EXPERIMENT_NAME = 'Dataproc - Submit Hadoop Job'

Insepct Input Data

The input file is a simple text file:





In [ ]:



    


!gsutil cat $INTPUT_GCS_PATH

Clean up the existing output files (optional)

This is needed because the sample code requires the output folder to be a clean folder. To continue to run the sample, make sure that the service account of the notebook server has access to the OUTPUT_GCS_PATH.

CAUTION: This will remove all blob files under OUTPUT_GCS_PATH.





In [ ]:



    


!gsutil rm $OUTPUT_GCS_PATH/**

Example pipeline that uses the component





In [ ]:



    


import kfp.dsl as dsl
import json
@dsl.pipeline(
    name='Dataproc submit Hadoop job pipeline',
    description='Dataproc submit Hadoop job pipeline'
)
def dataproc_submit_hadoop_job_pipeline(
    project_id = PROJECT_ID, 
    region = REGION,
    cluster_name = CLUSTER_NAME,
    main_jar_file_uri = '',
    main_class = MAIN_CLASS,
    args = json.dumps([
        INTPUT_GCS_PATH,
        OUTPUT_GCS_PATH
    ]), 
    hadoop_job='', 
    job='{}', 
    wait_interval='30'
):
    dataproc_submit_hadoop_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, 
        hadoop_job=hadoop_job, 
        job=job, 
        wait_interval=wait_interval)

Compile the pipeline





In [ ]:



    


pipeline_func = dataproc_submit_hadoop_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)

Inspect the output

The sample in the notebook will count the words in the input text and save them in sharded files. The command to inspect the output is:





In [ ]:



    


!gsutil cat $OUTPUT_GCS_PATH/*

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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