Work with stored procedures for Apache Spark

This document is intended for data engineers, data scientists, and data analysts to create and call stored procedures for Spark in BigQuery.

Using BigQuery, you can create and run Spark stored procedures that are written in Python, Java, and Scala. You can then run these stored procedures in BigQuery using a GoogleSQL query, similar to running SQL stored procedures.

Before you begin

To create a stored procedure for Spark, ask your administrator to create a Spark connection and share it with you. Your administrator must also grant the service account associated with the connection the required Identity and Access Management (IAM) permissions.

Required roles

To get the permissions that you need to perform the tasks in this document, ask your administrator to grant you the following IAM roles:

• Create a stored procedure for Spark:
  • BigQuery Data Editor (roles/bigquery.dataEditor) on the dataset where you create the stored procedure
  • BigQuery Connection Admin (roles/bigquery.connectionAdmin) on the connection that the stored procedure uses
  • BigQuery Job User (roles/bigquery.jobUser) on your project
• Call a stored procedure for Spark:
  • BigQuery Metadata Viewer (roles/bigquery.metadataViewer) on the dataset where the stored procedure is stored
  • BigQuery Connection User (roles/bigquery.connectionUser) on the connection
  • BigQuery Job User (roles/bigquery.jobUser) on your project

For more information about granting roles, see Manage access to projects, folders, and organizations.

These predefined roles contain the permissions required to perform the tasks in this document. To see the exact permissions that are required, expand the Required permissions section:

You might also be able to get these permissions with custom roles or other predefined roles.

Location consideration

You must create a stored procedure for Spark in the same location as your connection because the stored procedure runs in the same location as the connection. For example, to create a stored procedure in the US multi-region, you use a connection located in the US multi-region.

Pricing

• Charges for running Spark procedures on BigQuery are similar to charges for running Spark procedures on Dataproc Serverless. For more information, see Dataproc Serverless pricing.

• Spark stored procedures can be used with the on-demand pricing model as well as with any of the BigQuery editions. Spark procedures are charged using the BigQuery Enterprise edition pay-as-you-go model in all cases, regardless of the compute pricing model used in your project.

• Spark stored procedures for BigQuery don't support the use of reservations or commitments. Existing reservations and commitments continue to be used for other supported queries and procedures. Charges for use of Spark stored procedures are added to your bill at Enterprise edition - pay-as-you-go cost. Your organization discounts are applied, where applicable.

• While Spark stored procedures use a Spark execution engine, you won't see separate charges for Spark execution. As noted, corresponding charges are reported as BigQuery Enterprise edition pay-as-you-go SKU.

• Spark stored procedures don't offer a free tier.

Create a stored procedure for Spark

You must create the stored procedure in the same location as the connection that you use.

If the body of your stored procedure is more than 1 MB, then we recommend that you put your stored procedure in a file in a Cloud Storage bucket instead of using inline code. BigQuery provides two methods to create a stored procedure for Spark using Python:

• If you want to use the CREATE PROCEDURE statement, use the SQL query editor.
• If you want to type in Python code directly, use the PySpark editor. You can save the code as a stored procedure.

Use SQL query editor

To create a stored procedure for Spark in the SQL query editor, follow these steps:

1. Go to the BigQuery page.

   Go to BigQuery

2. In the query editor, add the sample code for the CREATE PROCEDURE statement that appears.

   Alternatively, in the Explorer pane, click the connection in the project that you used to create the connection resource. Then, to create a stored procedure for Spark, click search Create stored procedure.

   Python

   To create a stored procedures for Spark in Python, use the following sample code:

   CREATE OR REPLACE PROCEDURE `PROJECT_ID`.DATASET.PROCEDURE_NAME(PROCEDURE_ARGUMENT)
    WITH CONNECTION `CONNECTION_PROJECT_ID.CONNECTION_REGION.CONNECTION_ID`
    OPTIONS (
        engine="SPARK", runtime_version="RUNTIME_VERSION",
        main_file_uri=["MAIN_PYTHON_FILE_URI"]);
    LANGUAGE PYTHON [AS PYSPARK_CODE]
   

   Java or Scala

   To create a stored procedure for Spark in Java or Scala with the main_file_uri option, use the following sample code:

   CREATE [OR REPLACE] PROCEDURE `PROJECT_ID`.DATASET.PROCEDURE_NAME(PROCEDURE_ARGUMENT)
    WITH CONNECTION `CONNECTION_PROJECT_ID.CONNECTION_REGION.CONNECTION_ID`
    OPTIONS (
        engine="SPARK", runtime_version="RUNTIME_VERSION",
        main_file_uri=["MAIN_JAR_URI"]);
    LANGUAGE JAVA|SCALA
   

   To create a stored procedure for Spark in Java or Scala with main_class and jar_uris options, use the following sample code:

   CREATE [OR REPLACE] PROCEDURE `PROJECT_ID`.DATASET.PROCEDURE_NAME(PROCEDURE_ARGUMENT)
    WITH CONNECTION `CONNECTION_PROJECT_ID.CONNECTION_REGION.CONNECTION_ID`
    OPTIONS (
        engine="SPARK", runtime_version="RUNTIME_VERSION",
        main_class=["CLASS_NAME"],
        jar_uris=["URI"]);
    LANGUAGE JAVA|SCALA
   

   Replace the following:

   • PROJECT_ID: the project in which you want to create the stored procedure—for example, myproject.
   • DATASET: the dataset in which you want to create the stored procedure—for example, mydataset.
   • PROCEDURE_NAME: the name of the stored procedure that you want to run in BigQuery—for example, mysparkprocedure.
   • PROCEDURE_ARGUMENT: a parameter to enter the input arguments.

     In this parameter, specify the following fields:

     • ARGUMENT_MODE: the mode of the argument.

       Valid values include IN, OUT, and INOUT. By default the value is IN.

     • ARGUMENT_NAME: the name of the argument.
     • ARGUMENT_TYPE: the type of the argument.
       

     For example: myproject.mydataset.mysparkproc(num INT64).

     For more information, see pass a value as an IN parameter or the OUT and INOUT parameters in this document.

   • CONNECTION_PROJECT_ID: the project that contains the connection to run the Spark procedure.
   • CONNECTION_REGION: the region that contains the connection to run the Spark procedure—for example, us.
   • CONNECTION_ID: the connection ID—for example, myconnection.

     When you view the connection details in the Google Cloud console, the connection ID is the value in the last section of the fully qualified connection ID that is shown in Connection ID—for example projects/myproject/locations/connection_location/connections/myconnection.

   • RUNTIME_VERSION: the runtime version of Spark—for example, 1.1.
   • MAIN_PYTHON_FILE_URI: the path to a PySpark file—for example, gs://mybucket/mypysparkmain.py.

     Alternatively, if you want to add the body of the stored procedure in the CREATE PROCEDURE statement, then add PYSPARK_CODE after LANGUAGE PYTHON AS as shown in the example in Use inline code in this document.

   • PYSPARK_CODE: the definition of a PySpark application in the CREATE PROCEDURE statement if you want to pass the body of the procedure inline.

     The value is a string literal. If the code includes quotation marks and backslashes, those must be either escaped or represented as a raw string. For example, the code return "\n"; can be represented as one of the following:

     • Quoted string: "return \"\\n\";". Both quotation marks and backslashes are escaped.
     • Triple-quoted string: """return "\\n";""". Backslashes are escaped while quotation marks are not.
     • Raw string: r"""return "\n";""". No escaping is needed.
     To learn how to add inline PySpark code, see Use inline code.
   • MAIN_JAR_URI: the path of the JAR file that contains the main class, for example, gs://mybucket/my_main.jar.
   • CLASS_NAME: the fully qualified name of a class in a JAR set with the jar_uris option, for example, com.example.wordcount.
   • URI: the path of the JAR file that contains the class specified in the main class, for example, gs://mybucket/mypysparkmain.jar.

   For additional options that you can specify in OPTIONS, see the procedure option list.

Use PySpark editor

When creating a procedure using the PySpark editor, you don't need to use the CREATE PROCEDURE statement. Instead, add your Python code directly in the Pyspark editor and save or run your code.

To create a stored procedure for Spark in the PySpark editor, follow these steps:

1. Go to the BigQuery page.

   Go to BigQuery

2. If you want to type in the PySpark code directly, open the PySpark editor. To open the PySpark editor, click the arrow_drop_down menu next to add_box Create SQL query, and then select Create PySpark Procedure.

3. To set options, click More > PySpark Options, and then do the following:

   1. Specify the location where you want to run the PySpark code.

   2. In the Connection field, specify the Spark connection.

   3. In the Stored procedure invocation section, specify the dataset in which you want to store the temporary stored procedures that are generated. You can either set a specific dataset or allow for the use of a temporary dataset to invoke the PySpark code.

      The temporary dataset is generated with the location specified in the preceding step. If a dataset name is specified, ensure that the dataset and Spark connection must be in the same location.

   4. In the Parameters section, define parameters for the stored procedure. The value of the parameter is only used during in-session runs of the PySpark code, but the declaration itself is stored in the procedure.

   5. In the Advanced options section, specify the procedure options. For a detailed list of the procedure options, see the procedure option list.

   6. In the Properties section, add the key-value pairs to configure the job. You can use any of the key-value pairs from the Dataproc Serverless Spark properties.

   7. In Service account settings, specify the custom service account, CMEK, staging dataset, and staging Cloud Storage folder to be used during in-session runs of the PySpark code.

   8. Click Save.

Save a stored procedure for Spark

After you create the stored procedure by using the PySpark editor, you can save the stored procedure. To do so, follow these steps:

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the query editor, create a stored procedure for Spark using Python with PySpark editor.

3. Click Save > Save procedure.

4. In the Save stored procedure dialog, specify the dataset name where you want to store the stored procedure and the name of the stored procedure.

5. Click Save.

   If you only want to run the PySpark code instead of saving it as a stored procedure, you can click Run instead of Save.

Use custom containers

The custom container provides the runtime environment for the workload's driver and executor processes. To use custom containers, use the following sample code:

CREATE OR REPLACE PROCEDURE `PROJECT_ID`.DATASET.PROCEDURE_NAME(PROCEDURE_ARGUMENT)
  WITH CONNECTION `CONNECTION_PROJECT_ID.CONNECTION_REGION.CONNECTION_ID`
  OPTIONS (
      engine="SPARK", runtime_version="RUNTIME_VERSION",
      container_image="CONTAINER_IMAGE", main_file_uri=["MAIN_PYTHON_FILE_URI"]);
  LANGUAGE PYTHON [AS PYSPARK_CODE]

Replace the following:

• PROJECT_ID: the project in which you want to create the stored procedure—for example, myproject.
• DATASET: the dataset in which you want to create the stored procedure—for example, mydataset.
• PROCEDURE_NAME: the name of the stored procedure that you want to run in BigQuery—for example, mysparkprocedure.
• PROCEDURE_ARGUMENT: a parameter to enter the input arguments.

  In this parameter, specify the following fields:

  • ARGUMENT_MODE: the mode of the argument.

    Valid values include IN, OUT, and INOUT. By default the value is IN.

  • ARGUMENT_NAME: the name of the argument.
  • ARGUMENT_TYPE: the type of the argument.
    

  For example: myproject.mydataset.mysparkproc(num INT64).

  For more information, see pass a value as an IN parameter or the OUT and INOUT parameters in this document.

• CONNECTION_PROJECT_ID: the project that contains the connection to run the Spark procedure.
• CONNECTION_REGION: the region that contains the connection to run the Spark procedure—for example, us.
• CONNECTION_ID: the connection ID, for example, myconnection.

  When you view the connection details in the Google Cloud console, the connection ID is the value in the last section of the fully qualified connection ID that is shown in Connection ID—for example projects/myproject/locations/connection_location/connections/myconnection.

• RUNTIME_VERSION: the runtime version of Spark—for example, 1.1.
• MAIN_PYTHON_FILE_URI: the path to a PySpark file—for example, gs://mybucket/mypysparkmain.py.

  Alternatively, if you want to add the body of the stored procedure in the CREATE PROCEDURE statement, then add PYSPARK_CODE after LANGUAGE PYTHON AS as shown in the example in Use inline code in this document.

• PYSPARK_CODE: the definition of a PySpark application in the CREATE PROCEDURE statement if you want to pass the body of the procedure inline.

  The value is a string literal. If the code includes quotation marks and backslashes, those must be either escaped or represented as a raw string. For example, the code return "\n"; can be represented as one of the following:

  • Quoted string: "return \"\\n\";". Both quotation marks and backslashes are escaped.
  • Triple-quoted string: """return "\\n";""". Backslashes are escaped while quotation marks are not.
  • Raw string: r"""return "\n";""". No escaping is needed.
  To learn how to add inline PySpark code, see Use inline code.
• CONTAINER_IMAGE: path of image in artifacts registry. It must only contain libraries to use in your procedure. If not specified, the system default container image associated with the runtime version is used.

For more information about how to build a custom container image with Spark, see Build a custom container image.

Call a stored procedure for Spark

After you create a stored procedure, you can call it by using one the following options:

Use a custom service account

Instead of using Spark connection's service identity for data access, you can use a custom service account to access data within your Spark code.

To use a custom service account, specify the INVOKER security mode (using the EXTERNAL SECURITY INVOKER statement) when you create a Spark stored procedure, and specify the service account when you invoke the stored procedure.

If you want to access and use Spark code from Cloud Storage, you need to grant necessary permissions to the Spark connection's service identify. You need to grant the connection's service account the storage.objects.get IAM permission or the storage.objectViewer IAM role.

Optionally, you can grant the connection's service account access to Dataproc Metastore and Dataproc Persistent History Server if you have specified them in the connection. For more information, see Grant access to the service account.

CREATE OR REPLACE PROCEDURE `PROJECT_ID`.DATASET.PROCEDURE_NAME(PROCEDURE_ARGUMENT)
  EXTERNAL SECURITY INVOKER
  WITH CONNECTION `CONNECTION_PROJECT_ID.CONNECTION_REGION.CONNECTION_ID`
  OPTIONS (
      engine="SPARK", runtime_version="RUNTIME_VERSION",
      main_file_uri=["MAIN_PYTHON_FILE_URI"]);
  LANGUAGE PYTHON [AS PYSPARK_CODE]

SET @@spark_proc_properties.service_account='CUSTOM_SERVICE_ACCOUNT';
CALL PROJECT_ID.DATASET_ID.PROCEDURE_NAME();

Optionally, you can add the following arguments to the preceding code:

SET @@spark_proc_properties.staging_bucket='BUCKET_NAME';
SET @@spark_proc_properties.staging_dataset_id='DATASET';

Replace the following:

• CUSTOM_SERVICE_ACCOUNT: Required. A custom service account provided by you.
• BUCKET_NAME: Optional. The Cloud Storage bucket that is used as the default Spark application file system. If this is not provided, a default Cloud Storage bucket is created in your project and the bucket is shared by all jobs running under the same project.
• DATASET: Optional. The dataset to store the temporary data produced by invoking the procedure. The data is cleaned up after the job is completed. If this is not provided, a default temporary dataset is created for the job.

Your custom service account must have the following permissions:

• To read and write to the staging bucket used as the default Spark application file system:

  • storage.objects.* permissions or the roles/storage.objectAdmin IAM role on the staging bucket that you specify.
  • Additionally, the storage.buckets.* permissions or the roles/storage.Admin IAM role on the project if the staging bucket is not specified.

• (Optional) To read and write data from and to BigQuery:

  • bigquery.tables.* on your BigQuery tables.
  • bigquery.readsessions.* on your project.
  • The roles/bigquery.admin IAM role includes the previous permissions.

• (Optional) To read and write data from and to Cloud Storage:

  • storage.objects.* permissions or the roles/storage.objectAdmin IAM role on your Cloud Storage objects.

• (Optional) To read and write to the staging dataset used for INOUT/OUT parameters:

  • bigquery.tables.* or roles/bigquery.dataEditor IAM role on the staging dataset that you specify.
  • Additionally, the bigquery.datasets.create permission or the roles/bigquery.dataEditor IAM role on the project if the staging dataset is not specified.

Examples of stored procedures for Spark

This section shows examples of how you can create a stored procedure for Apache Spark.

Use a PySpark or a JAR file in Cloud Storage

The following example shows how to create a stored procedure for Spark by using the my-project-id.us.my-connection connection and a PySpark or a JAR file that's stored in a Cloud Storage bucket:

CREATE PROCEDURE my_bq_project.my_dataset.spark_proc()
WITH CONNECTION `my-project-id.us.my-connection`
OPTIONS(engine="SPARK", runtime_version="1.1", main_file_uri="gs://my-bucket/my-pyspark-main.py")
LANGUAGE PYTHON

CREATE PROCEDURE my_bq_project.my_dataset.scala_proc_wtih_main_jar()
WITH CONNECTION `my-project-id.us.my-connection`
OPTIONS(engine="SPARK", runtime_version="1.1", main_file_uri="gs://my-bucket/my-scala-main.jar")
LANGUAGE SCALA

CREATE PROCEDURE my_bq_project.my_dataset.scala_proc_with_main_class()
WITH CONNECTION `my-project-id.us.my-connection`
OPTIONS(engine="SPARK", runtime_version="1.1",
main_class="com.example.wordcount", jar_uris=["gs://my-bucket/wordcount.jar"])
LANGUAGE SCALA

Use inline code

The following example shows how to create a stored procedure for Spark by using the connection my-project-id.us.my-connection and inline PySpark code:

CREATE OR REPLACE PROCEDURE my_bq_project.my_dataset.spark_proc()
WITH CONNECTION `my-project-id.us.my-connection`
OPTIONS(engine="SPARK", runtime_version="1.1")
LANGUAGE PYTHON AS R"""
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("spark-bigquery-demo").getOrCreate()

# Load data from BigQuery.
words = spark.read.format("bigquery") \
  .option("table", "bigquery-public-data:samples.shakespeare") \
  .load()
words.createOrReplaceTempView("words")

# Perform word count.
word_count = words.select('word', 'word_count').groupBy('word').sum('word_count').withColumnRenamed("sum(word_count)", "sum_word_count")
word_count.show()
word_count.printSchema()

# Saving the data to BigQuery
word_count.write.format("bigquery") \
  .option("writeMethod", "direct") \
  .save("wordcount_dataset.wordcount_output")
"""

Pass a value as an input parameter

The following examples display the two methods to pass a value as an input parameter in Python:

Method 1: Use environment variables

In the PySpark code, you can obtain the input parameters of the stored procedure for Spark through environment variables in the Spark driver and executors. The name of the environment variable has the format of BIGQUERY_PROC_PARAM.PARAMETER_NAME, where PARAMETER_NAME is the name of the input parameter. For example, if the name of the input parameter is var, the name of the corresponding environment variable is BIGQUERY_PROC_PARAM.var. The input parameters are JSON encoded. In your PySpark code, you can get the input parameter value in a JSON string from the environment variable and decode it to a Python variable.

The following example shows how to get the value of an input parameter of type INT64 into your PySpark code:

CREATE OR REPLACE PROCEDURE my_bq_project.my_dataset.spark_proc(num INT64)
WITH CONNECTION `my-project-id.us.my-connection`
OPTIONS(engine="SPARK", runtime_version="1.1")
LANGUAGE PYTHON AS R"""
from pyspark.sql import SparkSession
import os
import json

spark = SparkSession.builder.appName("spark-bigquery-demo").getOrCreate()
sc = spark.sparkContext

# Get the input parameter num in JSON string and convert to a Python variable
num = int(json.loads(os.environ["BIGQUERY_PROC_PARAM.num"]))

"""

Method 2: Use a built-in library

In the PySpark code, you can simply import a built-in library and use it to populate all types of parameters. To pass the parameters to executors, populate the parameters in a Spark driver as Python variables and pass the values to executors. The built-in library supports most of the BigQuery data types except INTERVAL, GEOGRAPHY, NUMERIC, and BIGNUMERIC.

The following example shows how to import the built-in library and use it to populate an input parameter of type INT64 and an input parameter of type ARRAY<STRUCT<a INT64, b STRING>> into your PySpark code:

CREATE OR REPLACE PROCEDURE my_bq_project.my_dataset.spark_proc(num INT64, info ARRAY<STRUCT<a INT64, b STRING>>)
WITH CONNECTION `my-project-id.us.my-connection`
OPTIONS(engine="SPARK", runtime_version="1.1")
LANGUAGE PYTHON AS R"""
from pyspark.sql import SparkSession
from bigquery.spark.procedure import SparkProcParamContext

def check_in_param(x, num):
  return x['a'] + num

def main():
  spark = SparkSession.builder.appName("spark-bigquery-demo").getOrCreate()
  sc=spark.sparkContext
  spark_proc_param_context = SparkProcParamContext.getOrCreate(spark)

  # Get the input parameter num of type INT64
  num = spark_proc_param_context.num

  # Get the input parameter info of type ARRAY<STRUCT<a INT64, b STRING>>
  info = spark_proc_param_context.info

  # Pass the parameter to executors
  df = sc.parallelize(info)
  value = df.map(lambda x : check_in_param(x, num)).sum()

main()
"""

In the Java or Scala code, you can obtain the input parameters of the stored procedure for Spark through environment variables in the Spark driver and executors. The name of the environment variable has the format of BIGQUERY_PROC_PARAM.PARAMETER_NAME, where PARAMETER_NAME is the name of the input parameter. For example, if the name of the input parameter is var, the name of the corresponding environment variable is BIGQUERY_PROC_PARAM.var. In your Java or Scala code, you can get the input parameter value from the environment variable.

The following example shows how to get the value of an input parameter from environment variables into your Scala code:

val input_param = sys.env.get("BIGQUERY_PROC_PARAM.input_param").get

The following example shows getting input parameters from environment variables into your Java code:

String input_param = System.getenv("BIGQUERY_PROC_PARAM.input_param");

Pass values as OUT and INOUT parameters

Output parameters return the value from the Spark procedure, whereas the INOUT parameter accepts a value for the procedure and returns a value from the procedure. To use the OUT and INOUT parameters, add the OUT or INOUT keyword before the parameter name when creating the Spark procedure. In the PySpark code, you use the built-in library to return a value as an OUT or an INOUT parameter. Same as input parameters, the built-in library supports most of the BigQuery data types except INTERVAL, GEOGRAPHY, NUMERIC, and BIGNUMERIC. The TIME and DATETIME type values are converted to the UTC timezone when returning as the OUT or INOUT parameters.

CREATE OR REPLACE PROCEDURE my_bq_project.my_dataset.pyspark_proc(IN int INT64, INOUT datetime DATETIME,OUT b BOOL, OUT info ARRAY<STRUCT<a INT64, b STRING>>, OUT time TIME, OUT f FLOAT64, OUT bs BYTES, OUT date DATE, OUT ts TIMESTAMP, OUT js JSON)
WITH CONNECTION `my_bq_project.my_dataset.my_connection`
OPTIONS(engine="SPARK", runtime_version="1.1") LANGUAGE PYTHON AS
R"""
from pyspark.sql.session import SparkSession
import datetime
from bigquery.spark.procedure import SparkProcParamContext

spark = SparkSession.builder.appName("bigquery-pyspark-demo").getOrCreate()
spark_proc_param_context = SparkProcParamContext.getOrCreate(spark)

# Reading the IN and INOUT parameter values.
int = spark_proc_param_context.int
dt = spark_proc_param_context.datetime
print("IN parameter value: ", int, ", INOUT parameter value: ", dt)

# Returning the value of the OUT and INOUT parameters.
spark_proc_param_context.datetime = datetime.datetime(1970, 1, 1, 0, 20, 0, 2, tzinfo=datetime.timezone.utc)
spark_proc_param_context.b = True
spark_proc_param_context.info = [{"a":2, "b":"dd"}, {"a":2, "b":"dd"}]
spark_proc_param_context.time = datetime.time(23, 20, 50, 520000)
spark_proc_param_context.f = 20.23
spark_proc_param_context.bs = b"hello"
spark_proc_param_context.date = datetime.date(1985, 4, 12)
spark_proc_param_context.ts = datetime.datetime(1970, 1, 1, 0, 20, 0, 2, tzinfo=datetime.timezone.utc)
spark_proc_param_context.js = {"name": "Alice", "age": 30}
""";

Read from a Hive Metastore table and write results to BigQuery

The following example shows how to transform a Hive Metastore table and write the results to BigQuery:

CREATE OR REPLACE PROCEDURE my_bq_project.my_dataset.spark_proc()
WITH CONNECTION `my-project-id.us.my-connection`
OPTIONS(engine="SPARK", runtime_version="1.1")
LANGUAGE PYTHON AS R"""
from pyspark.sql import SparkSession

spark = SparkSession \
   .builder \
   .appName("Python Spark SQL Dataproc Hive Metastore integration test example") \
   .enableHiveSupport() \
   .getOrCreate()

spark.sql("CREATE DATABASE IF NOT EXISTS records")

spark.sql("CREATE TABLE IF NOT EXISTS records.student (eid int, name String, score int)")

spark.sql("INSERT INTO records.student VALUES (1000000, 'AlicesChen', 10000)")

df = spark.sql("SELECT * FROM records.student")

df.write.format("bigquery") \
  .option("writeMethod", "direct") \
  .save("records_dataset.student")
"""

View log filters

After you call a stored procedure for Spark, you can view the log information. To obtain the Cloud Logging filter information and the Spark History Cluster endpoint, use the bq show command. The filter information is available under the SparkStatistics field of the child job. To get log filters, follow these steps:

1. Go to the BigQuery page.

   Go to BigQuery

2. In the query editor, list child jobs of the stored procedure's script job:

   bq ls -j --parent_job_id=$parent_job_id

   To learn how to get the job ID, see View job details.

   The output is similar to the following:

                   jobId                         Job Type     State       Start Time         Duration
   ---------------------------------------------- ---------   ---------  ---------------  ----------------
   script_job_90fb26c32329679c139befcc638a7e71_0   query      SUCCESS   07 Sep 18:00:27   0:05:15.052000

3. Identify the jobId for your stored procedure and use the bq show command to view details of the job:

   bq show --format=prettyjson --job $child_job_id

   Copy the sparkStatistics field because you need it in another step.

   The output is similar to the following:

   {
   "configuration": {...}
   …
   "statistics": {
    …
     "query": {
       "sparkStatistics": {
         "loggingInfo": {
           "projectId": "myproject",
           "resourceType": "myresource"
         },
         "sparkJobId": "script-job-90f0",
         "sparkJobLocation": "us-central1"
       },
       …
     }
   }
   }

4. For Logging, generate log filters with the SparkStatistics fields:

   resource.type = sparkStatistics.loggingInfo.resourceType
   resource.labels.resource_container=sparkStatistics.loggingInfo.projectId
   resource.labels.spark_job_id=sparkStatistics.sparkJobId
   resource.labels.location=sparkStatistics.sparkJobLocation

   The logs are written in the bigquery.googleapis.com/SparkJob monitored resource. The logs are labeled by the INFO, DRIVER, and EXECUTOR components. To filter logs from the Spark driver, add the labels.component = "DRIVER" component to the log filters. To filter logs from the Spark executor, add the labels.component = "EXECUTOR" component to the log filters.

Use the customer-managed encryption key

BigQuery Spark procedure uses the customer-managed encryption key (CMEK) to protect your content, along with the default encryption provided by BigQuery. To use the CMEK in the Spark procedure, first trigger creation of the BigQuery encryption service account and grant the required permissions. Spark procedure also supports the CMEK organization policies if they are applied to your project.

If your stored procedure is using the INVOKER security mode, your CMEK should be specified through the SQL system variable when calling the procedure. Otherwise, your CMEK can be specified through the connection associated with the stored procedure.

To specify the CMEK through the connection when you create a Spark stored procedure, use the following sample code:

bq mk --connection --connection_type='SPARK' \
 --properties='{"kms_key_name"="projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING_NAME/cryptoKeys/KMS_KEY_NAME"}' \
 --project_id=PROJECT_ID \
 --location=LOCATION \
 CONNECTION_NAME

To specify CMEK through the SQL system variable when calling the procedure, use the following sample code:

SET @@spark_proc_properties.service_account='CUSTOM_SERVICE_ACCOUNT';
SET @@spark_proc_properties.kms_key_name='projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING_NAME/cryptoKeys/KMS_KEY_NAME;
CALL PROJECT_ID.DATASET_ID.PROCEDURE_NAME();

Use VPC Service Controls

VPC Service Controls lets you set up a secure perimeter to guard against data exfiltration. To use VPC Service Controls with a Spark procedure for additional security, first create a service perimeter.

To fully protect your Spark procedure jobs, add the following APIs to the service perimeter:

• BigQuery API (bigquery.googleapis.com)
• Cloud Logging API (logging.googleapis.com)
• Cloud Storage API (storage.googleapis.com), if you use Cloud Storage
• Artifact Registry API (artifactregistry.googleapis.com) or Container Registry API (containerregistry.googleapis.com), if you use a custom container
• Dataproc Metastore API (metastore.googleapis.com) and Cloud Run Admin API (run.googleapis.com), if you use Dataproc Metastore

Add the spark procedure's query project into the perimeter. Add other projects that host your Spark code or data into the perimeter.

Best practices

• When you use a connection in your project for the first time, it takes about an extra minute to provision. To save time, you can reuse an existing Spark connection when you create a stored procedure for Spark.

• When you create a Spark procedure for production use, Google recommends specifying a runtime version. For a list of supported runtime versions, see Dataproc Serverless runtime versions. We recommended to use the Long-Time-Support (LTS) version.

• When you specify a custom container in a Spark procedure, we recommend using Artifact Registry and image streaming.

• For better performance, you can specify resource allocation properties in the Spark procedure. Spark stored procedures support a list of resource allocation properties same as Dataproc Serverless.

Limitations

• You can only use gRPC endpoint protocol to connect to Dataproc Metastore. Other types of Hive Metastore are not yet supported.
• Customer-managed encryption keys (CMEK) are only available when customers create single-region Spark procedures. Global region CMEK keys and multi-region CMEK keys, for example, EU or US, are not supported.
• Passing output parameters is only supported for PySpark.
• If the dataset associated with the stored procedure for Spark is replicated to a destination region through cross-region dataset replication, the stored procedure can only be queried in the region that it was created in.
• Spark doesn't support accessing HTTP endpoints in your private VPC Service Controls network.

Quotas and limits

For information about quotas and limits, see stored procedures for Spark quotas and limits.

What's next

• Learn how to view a stored procedure.
• Learn how to delete a stored procedure.
• Learn how to work with a SQL stored procedure.