Use Dataproc, BigQuery, and Apache Spark ML for Machine Learning

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The BigQuery Connector for Apache Spark allows Data Scientists to blend the power of BigQuery's seamlessly scalable SQL engine with Apache Spark’s Machine Learning capabilities. In this tutorial, we show how to use Dataproc, BigQuery and Apache Spark ML to perform machine learning on a dataset.

Objectives

Use linear regression to build a model of birth weight as a function of five factors:

• gestation weeks
• mother's age
• father's age
• mother's weight gain during pregnancy
• Apgar score

Use the following tools:

• BigQuery, to prepare the linear regression input table, which is written to your Google Cloud project
• Python, to query and manage data in BigQuery
• Apache Spark, to access the resulting linear regression table
• Spark ML, to build and evaluate the model
• Dataproc PySpark job, to invoke Spark ML functions

Costs

In this document, you use the following billable components of Google Cloud:

• Compute Engine
• Dataproc
• BigQuery

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

Before you begin

A Dataproc cluster has the Spark components, including Spark ML, installed. To set up a Dataproc cluster and run the code in this example, you will need to do (or have done) the following:

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Enable the Dataproc, BigQuery, Compute Engine APIs.

   Enable the APIs

4. Install the Google Cloud CLI.

5. To initialize the gcloud CLI, run the following command:

   gcloud init

6. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

7. Enable the Dataproc, BigQuery, Compute Engine APIs.

   Enable the APIs

8. Install the Google Cloud CLI.

9. To initialize the gcloud CLI, run the following command:

   gcloud init

10. Create a Dataproc cluster in your project. Your cluster should be running a Dataproc version with Spark 2.0 or higher, (includes machine learning libraries).

Create a Subset of BigQuery natality data

In this section, you create a dataset in your project, then create a table in the dataset to which you copy a subset of birth rate data from the publicly available natality BigQuery dataset. Later in this tutorial you will use the subset data in this table to predict birth weight as a function of maternal age, paternal age, and gestation weeks.

You can create the data subset using the Google Cloud console or running a Python script on your local machine.

Console

1. Create a dataset in your project.

   1. Go to the BigQuery Web UI.
   2. In the left navigation panel, click your project name, then click CREATE DATASET.
   3. In the Create dataset dialog:
      1. For Dataset ID, enter "natality_regression".
      2. For Data location, you can choose a location for the dataset. The default value location is US multi-region. After a dataset is created, the location cannot be changed.
      3. For Default table expiration, choose one of the following options:
         • Never (default): You must delete the table manually.
         • Number of days: The table will be deleted after the specified number days from its creation time.
      4. For Encryption, choose one of the following options:
         • Google-owned and Google-managed key (default).
         • Customer-managed key: See Protecting data with Cloud KMS keys.
      5. Click Create dataset.
         You cannot add a description or a label when you create a dataset using the Web UI. After the dataset is created, you can add a description, and add a label.

2. Run a query against the public natality dataset, then save the query results in a new table in your dataset.

   1. Copy and paste the following query into the Query Editor, then click Run.

      CREATE OR REPLACE TABLE natality_regression.regression_input as
      SELECT
      weight_pounds,
      mother_age,
      father_age,
      gestation_weeks,
      weight_gain_pounds,
      apgar_5min
      FROM
      `bigquery-public-data.samples.natality`
      WHERE
      weight_pounds IS NOT NULL
      AND mother_age IS NOT NULL
      AND father_age IS NOT NULL
      AND gestation_weeks IS NOT NULL
      AND weight_gain_pounds IS NOT NULL
      AND apgar_5min IS NOT NULL
      

   2. After the query completes (in approximately one minute), the results are saved as "regression_input" BigQuery table in the natality_regression dataset in your project.

      

Python

Before trying this sample, follow the Python setup instructions in the Dataproc quickstart using client libraries. For more information, see the Dataproc Python API reference documentation.

To authenticate to Dataproc, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

1. See Setting Up a Python Development Environment for instructions on installing Python and the Google Cloud Client Library for Python (needed to run the code). Installing and using a Python virtualenv is recommended.

2. Copy and paste the natality_tutorial.py code, below, into a python shell on your local machine. Press the <return> key in the shell to run the code to create a "natality_regression" BigQuery dataset in your default Google Cloud project with a "regression_input" table that is populated with a subset of the public natality data.

   Setting up to run the application code locally:

   • Run gcloud config list project to see the name of your default project.
   • Run gcloud config set project project-name to change the default project.
   • Run gcloud auth application-default login to set application credentials to your user account.

   """Create a Google BigQuery linear regression input table.
   
   In the code below, the following actions are taken:
   * A new dataset is created "natality_regression."
   * A query is run against the public dataset,
       bigquery-public-data.samples.natality, selecting only the data of
       interest to the regression, the output of which is stored in a new
       "regression_input" table.
   * The output table is moved over the wire to the user's default project via
       the built-in BigQuery Connector for Spark that bridges BigQuery and
       Cloud Dataproc.
   """
   
   from google.cloud import bigquery
   
   # Create a new Google BigQuery client using Google Cloud Platform project
   # defaults.
   client = bigquery.Client()
   
   # Prepare a reference to a new dataset for storing the query results.
   dataset_id = "natality_regression"
   dataset_id_full = f"{client.project}.{dataset_id}"
   
   dataset = bigquery.Dataset(dataset_id_full)
   
   # Create the new BigQuery dataset.
   dataset = client.create_dataset(dataset)
   
   # Configure the query job.
   job_config = bigquery.QueryJobConfig()
   
   # Set the destination table to where you want to store query results.
   # As of google-cloud-bigquery 1.11.0, a fully qualified table ID can be
   # used in place of a TableReference.
   job_config.destination = f"{dataset_id_full}.regression_input"
   
   # Set up a query in Standard SQL, which is the default for the BigQuery
   # Python client library.
   # The query selects the fields of interest.
   query = """
       SELECT
           weight_pounds, mother_age, father_age, gestation_weeks,
           weight_gain_pounds, apgar_5min
       FROM
           `bigquery-public-data.samples.natality`
       WHERE
           weight_pounds IS NOT NULL
           AND mother_age IS NOT NULL
           AND father_age IS NOT NULL
           AND gestation_weeks IS NOT NULL
           AND weight_gain_pounds IS NOT NULL
           AND apgar_5min IS NOT NULL
   """
   
   # Run the query.
   client.query_and_wait(query, job_config=job_config)  # Waits for the query to finish

3. Confirm the creation of the natality_regression dataset and the regression_input table.

   

Run a linear regression

In this section, you'll run a PySpark linear regression by submitting the job to the Dataproc service using the Google Cloud console or by running the gcloud command from a local terminal.

Console

1. Copy and paste the following code into a new natality_sparkml.py file on your local machine.

   """Run a linear regression using Apache Spark ML.
   
   In the following PySpark (Spark Python API) code, we take the following actions:
   
     * Load a previously created linear regression (BigQuery) input table
       into our Cloud Dataproc Spark cluster as an RDD (Resilient
       Distributed Dataset)
     * Transform the RDD into a Spark Dataframe
     * Vectorize the features on which the model will be trained
     * Compute a linear regression using Spark ML
   
   """
   from pyspark.context import SparkContext
   from pyspark.ml.linalg import Vectors
   from pyspark.ml.regression import LinearRegression
   from pyspark.sql.session import SparkSession
   # The imports, above, allow us to access SparkML features specific to linear
   # regression as well as the Vectors types.
   
   
   # Define a function that collects the features of interest
   # (mother_age, father_age, and gestation_weeks) into a vector.
   # Package the vector in a tuple containing the label (`weight_pounds`) for that
   # row.
   def vector_from_inputs(r):
     return (r["weight_pounds"], Vectors.dense(float(r["mother_age"]),
                                               float(r["father_age"]),
                                               float(r["gestation_weeks"]),
                                               float(r["weight_gain_pounds"]),
                                               float(r["apgar_5min"])))
   
   sc = SparkContext()
   spark = SparkSession(sc)
   
   # Read the data from BigQuery as a Spark Dataframe.
   natality_data = spark.read.format("bigquery").option(
       "table", "natality_regression.regression_input").load()
   # Create a view so that Spark SQL queries can be run against the data.
   natality_data.createOrReplaceTempView("natality")
   
   # As a precaution, run a query in Spark SQL to ensure no NULL values exist.
   sql_query = """
   SELECT *
   from natality
   where weight_pounds is not null
   and mother_age is not null
   and father_age is not null
   and gestation_weeks is not null
   """
   clean_data = spark.sql(sql_query)
   
   # Create an input DataFrame for Spark ML using the above function.
   training_data = clean_data.rdd.map(vector_from_inputs).toDF(["label",
                                                                "features"])
   training_data.cache()
   
   # Construct a new LinearRegression object and fit the training data.
   lr = LinearRegression(maxIter=5, regParam=0.2, solver="normal")
   model = lr.fit(training_data)
   # Print the model summary.
   print("Coefficients:" + str(model.coefficients))
   print("Intercept:" + str(model.intercept))
   print("R^2:" + str(model.summary.r2))
   model.summary.residuals.show()
   
   
   

2. Copy the local natality_sparkml.py file to a Cloud Storage bucket in your project.

   gcloud storage cp natality_sparkml.py gs://bucket-name
   

   Instead of copying the file to a user bucket in your project, you can copy it to the staging bucket that Dataproc created when you created your cluster.

3. Run the regression from the Dataproc Submit a job page.

   1. In the Main python file field, insert the gs:// URI of the Cloud Storage bucket where your copy of the natality_sparkml.py file is located.

   2. Select PySpark as the Job type.

   3. Insert gs://spark-lib/bigquery/spark-bigquery-latest_2.12.jar in the Jar files field. This makes the spark-bigquery-connector available to the PySpark application at runtime to allow it to read BigQuery data into a Spark DataFrame.

      The 2.12 jar is compatible with Dataproc clusters created with the 1.5 or later image. If your Dataproc cluster was created with the 1.3 or 1.4 image, specify the 2.11 jar instead (gs://spark-lib/bigquery/spark-bigquery-latest_2.11.jar).

   4. Fill in the Job ID, Region, and Cluster fields.

   5. Click Submit to run the job on your cluster.

When the job completes, the linear regression output model summary appears in the Dataproc Job details window.

Model Summary Terminology:

• R^2: a measure for how much of the "signal" the model captures.
• Residuals: the difference between the prediction of the model and the actual value for each point that was used to fit the model.

gcloud

1. Copy and paste the following code into a new natality_sparkml.py file on your local machine.

   """Run a linear regression using Apache Spark ML.
   
   In the following PySpark (Spark Python API) code, we take the following actions:
   
     * Load a previously created linear regression (BigQuery) input table
       into our Cloud Dataproc Spark cluster as an RDD (Resilient
       Distributed Dataset)
     * Transform the RDD into a Spark Dataframe
     * Vectorize the features on which the model will be trained
     * Compute a linear regression using Spark ML
   
   """
   from pyspark.context import SparkContext
   from pyspark.ml.linalg import Vectors
   from pyspark.ml.regression import LinearRegression
   from pyspark.sql.session import SparkSession
   # The imports, above, allow us to access SparkML features specific to linear
   # regression as well as the Vectors types.
   
   
   # Define a function that collects the features of interest
   # (mother_age, father_age, and gestation_weeks) into a vector.
   # Package the vector in a tuple containing the label (`weight_pounds`) for that
   # row.
   def vector_from_inputs(r):
     return (r["weight_pounds"], Vectors.dense(float(r["mother_age"]),
                                               float(r["father_age"]),
                                               float(r["gestation_weeks"]),
                                               float(r["weight_gain_pounds"]),
                                               float(r["apgar_5min"])))
   
   sc = SparkContext()
   spark = SparkSession(sc)
   
   # Read the data from BigQuery as a Spark Dataframe.
   natality_data = spark.read.format("bigquery").option(
       "table", "natality_regression.regression_input").load()
   # Create a view so that Spark SQL queries can be run against the data.
   natality_data.createOrReplaceTempView("natality")
   
   # As a precaution, run a query in Spark SQL to ensure no NULL values exist.
   sql_query = """
   SELECT *
   from natality
   where weight_pounds is not null
   and mother_age is not null
   and father_age is not null
   and gestation_weeks is not null
   """
   clean_data = spark.sql(sql_query)
   
   # Create an input DataFrame for Spark ML using the above function.
   training_data = clean_data.rdd.map(vector_from_inputs).toDF(["label",
                                                                "features"])
   training_data.cache()
   
   # Construct a new LinearRegression object and fit the training data.
   lr = LinearRegression(maxIter=5, regParam=0.2, solver="normal")
   model = lr.fit(training_data)
   # Print the model summary.
   print("Coefficients:" + str(model.coefficients))
   print("Intercept:" + str(model.intercept))
   print("R^2:" + str(model.summary.r2))
   model.summary.residuals.show()
   
   
   

2. Copy the local natality_sparkml.py file to a Cloud Storage bucket in your project.

   gcloud storage cp natality_sparkml.py gs://bucket-name
   

   Instead of copying the file to a user bucket in your project, you can copy it to the staging bucket that Dataproc created when you created your cluster.

3. Submit the Pyspark job to the Dataproc service by running the gcloud command, shown below, from a terminal window on your local machine.

   1. The --jars flag value makes the spark-bigquery-connector available to the PySpark jobv at runtime to allow it to read BigQuery data into a Spark DataFrame.

      gcloud dataproc jobs submit pyspark \
          gs://your-bucket/natality_sparkml.py \
          --cluster=cluster-name \
          --region=region \
          --jars=gs://spark-lib/bigquery/spark-bigquery-with-dependencies_SCALA_VERSION-CONNECTOR_VERSION.jar
      

      The 2.12 jar is compatible with Dataproc clusters created with the 1.5 or later image. If your Dataproc cluster was created with the 1.3 or 1.4 image, specify the 2.11 jar instead. See Make the connector available to your application for more information.

The linear regression output (model summary) appears in the terminal window when the job completes.

Model Summary Terminology:

• R^2: a measure for how much of the "signal" the model captures.
• Residuals: the difference between the prediction of the model and the actual value for each point that was used to fit the model.

<<< # Print the model summary.
... print "Coefficients:" + str(model.coefficients)
Coefficients:[0.0166657454602,-0.00296751984046,0.235714392936,0.00213002070133,-0.00048577251587]
<<< print "Intercept:" + str(model.intercept)
Intercept:-2.26130330748
<<< print "R^2:" + str(model.summary.r2)
R^2:0.295200579035
<<< model.summary.residuals.show()
+--------------------+
|           residuals|
+--------------------+
| -0.7234737533344147|
|  -0.985466980630501|
| -0.6669710598385468|
|  1.4162434829714794|
|-0.09373154375186754|
|-0.15461747949235072|
| 0.32659061654192545|
|  1.5053877697929803|
|  -0.640142797263989|
|   1.229530260294963|
|-0.03776160295256...|
| -0.5160734239126814|
| -1.5165972740062887|
|  1.3269085258245008|
|  1.7604670124710626|
|  1.2348130901905972|
|   2.318660276655887|
|  1.0936947030883175|
|  1.0169768511417363|
| -1.7744915698181583|
+--------------------+
only showing top 20 rows.

  

Clean up

After you finish the tutorial, you can clean up the resources that you created so that they stop using quota and incurring charges. The following sections describe how to delete or turn off these resources.

Deleting the project

The easiest way to eliminate billing is to delete the project that you created for the tutorial.

To delete the project:

Caution: Deleting a project has the following effects:

• Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

1. In the Google Cloud console, go to the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut down to delete the project.

Deleting the Dataproc cluster

See Delete a cluster.

What's next

• See Spark job tuning tips