pipelines/components/gcp/bigquery/query

Name

Gather training data by querying BigQuery

Labels

GCP, BigQuery, Kubeflow, Pipeline

Summary

A Kubeflow Pipeline component to submit a query to BigQuery and store the result in a Cloud Storage bucket.

Details

Intended use

Use this Kubeflow component to:

• Select training data by submitting a query to BigQuery.
• Output the training data into a Cloud Storage bucket as CSV files.

Runtime arguments:

Argument	Description	Optional	Data type	Accepted values	Default
query	The query used by BigQuery to fetch the results.	No	String
project_id	The project ID of the Google Cloud Platform (GCP) project to use to execute the query.	No	GCPProjectID
dataset_id	The ID of the persistent BigQuery dataset to store the results of the query. If the dataset does not exist, the operation will create a new one.	Yes	String		None
table_id	The ID of the BigQuery table to store the results of the query. If the table ID is absent, the operation will generate a random ID for the table.	Yes	String		None
output_gcs_path	The path to the Cloud Storage bucket to store the query output.	Yes	GCSPath		None
dataset_location	The location where the dataset is created. Defaults to US.	Yes	String		US
job_config	The full configuration specification for the query job. See QueryJobConfig for details.	Yes	Dict	A JSONobject which has the same structure as QueryJobConfig	None

Input data schema

The input data is a BigQuery job containing a query that pulls data f rom various sources.

Output:

Name	Description	Type
output_gcs_path	The path to the Cloud Storage bucket containing the query output in CSV format.	GCSPath

Cautions & requirements

To use the component, the following requirements must be met:

• The BigQuery API is enabled.

• The component is running under a secret Kubeflow user service account in a Kubeflow Pipeline cluster. For example:

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

• The Kubeflow user service account is a member of the roles/bigquery.admin role of the project.

• The Kubeflow user service account is a member of the roles/storage.objectCreator role of the Cloud Storage output bucket.

Detailed description

This Kubeflow Pipeline component is used to:

• Submit a query to BigQuery.

  • The query results are persisted in a dataset table in BigQuery.
  • An extract job is created in BigQuery to extract the data from the dataset table and output it to a Cloud Storage bucket as CSV files.

  Use the code below as an example of how to run your BigQuery job.

Sample

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

Set sample parameters

%%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

bigquery_query_op = comp.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/e598176c02f45371336ccaa819409e8ec83743df/components/gcp/bigquery/query/component.yaml')
help(bigquery_query_op)

Sample

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

In this sample, we send a query to get the top questions from stackdriver public data and output the data to a Cloud Storage bucket. Here is the query:

QUERY = 'SELECT * FROM `bigquery-public-data.stackoverflow.posts_questions` LIMIT 10'

Set sample parameters

# Required Parameters
PROJECT_ID = '<Please put your project ID here>'
GCS_WORKING_DIR = 'gs://<Please put your GCS path here>' # No ending slash

# Optional Parameters
EXPERIMENT_NAME = 'Bigquery -Query'
OUTPUT_PATH = '{}/bigquery/query/questions.csv'.format(GCS_WORKING_DIR)

Run the component as a single pipeline

import kfp.dsl as dsl
import kfp.gcp as gcp
import json
@dsl.pipeline(
    name='Bigquery query pipeline',
    description='Bigquery query pipeline'
)
def pipeline(
    query=QUERY, 
    project_id = PROJECT_ID, 
    dataset_id='', 
    table_id='', 
    output_gcs_path=OUTPUT_PATH, 
    dataset_location='US', 
    job_config=''
):
    bigquery_query_op(
        query=query, 
        project_id=project_id, 
        dataset_id=dataset_id, 
        table_id=table_id, 
        output_gcs_path=output_gcs_path, 
        dataset_location=dataset_location, 
        job_config=job_config).apply(gcp.use_gcp_secret('user-gcp-sa'))

Compile the pipeline

pipeline_func = 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)

Inspect the output

!gsutil cat OUTPUT_PATH

References

• Component python code
• Component docker file
• Sample notebook
• BigQuery query REST API

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.