Can I run the BigQuery notebook on a Pub/Sub message or CRON?

[sysph]

Hi there,

Is it possible to run the BigQuery notebook on a Pub/Sub message or CRON? I presume that i should be using Cloud function and trigger by the pub/sub message? 

Many thanks in advance

Phoebe 

[ms4446 · Accepted solution]

Hi @sysph ,

You can trigger BigQuery notebooks to run with both Pub/Sub messages and CRON schedules by leveraging Google Cloud Functions. However, Cloud Functions are more ideal for this scenario because they are event-driven and serverless, scaling automatically and offering a cost-effective solution as you pay only for the time you use them.

Here's an breakdown of both approaches:

Triggering with Pub/Sub:

1. Create a Pub/Sub Topic: This topic will receive messages that trigger the notebook execution.

2. Develop a Cloud Function: This function, triggered by messages published to the topic, should contain the logic to execute the BigQuery notebook. Note that if your notebook is in a Jupyter format, you'll need to convert it into a script or use a tool/API that can programmatically execute Jupyter notebooks.

3. Deploy the Cloud Function: Bind the function to the Pub/Sub topic as a trigger.

Triggering with CRON:

1. Utilize Cloud Scheduler: Configure a Cloud Scheduler job with a CRON schedule.

2. Set the Cloud Scheduler Target to a Pub/Sub Topic: This topic will receive messages when the CRON job triggers.

3. Follow Steps 2 and 3 from the Pub/Sub Approach: Create and deploy a Cloud Function triggered by the topic to execute the notebook.

Additional Considerations:

• Notebook Execution: Ensure the Cloud Function's environment includes all necessary dependencies to run the notebook or script.

• Security and Permissions: Secure the Pub/Sub topics and ensure the Cloud Function has appropriate IAM roles and permissions for accessing BigQuery and other Google Cloud resources.

• Logging and Monitoring: Implement logging and monitoring for the Cloud Function to track its execution and troubleshoot issues.

• Testing and Validation: Thoroughly test the Cloud Function in a non-production environment to ensure it triggers and executes the notebook as expected.

• Scalability and Performance: Consider the scalability and performance, especially for resource-intensive notebooks or high volumes of Pub/Sub messages.

• Error Handling: Implement robust error handling in the Cloud Function to manage potential execution failures.

[ms4446 · Accepted solution]

Yes, there are several alternative methods to execute BigQuery notebooks using Pub/Sub without relying solely on Cloud Functions:

1. Cloud Run:

   • Deploy a containerized application on Cloud Run that listens to a Pub/Sub topic. This application can trigger the execution of the BigQuery notebook. This method offers more flexibility and control over the execution environment compared to Cloud Functions.

2. Cloud Scheduler with Cloud Functions or Cloud Run:

   • Use Cloud Scheduler to publish messages to a Pub/Sub topic based on a defined schedule. These messages can then trigger a service like Cloud Functions or Cloud Run, which in turn executes the BigQuery notebook. This approach is ideal for automating notebook execution at specific intervals.

3. Google Kubernetes Engine (GKE):

   • Set up a Kubernetes cluster in GKE to listen to Pub/Sub messages. You can configure it to trigger jobs, including the execution of notebooks. This approach is suitable for more complex workflows and offers greater scalability and control.

Each of these methods offers different levels of complexity and functionality. Factors like your desired level of control, the specific triggers required, and integration with other Google Cloud services should be considered when choosing the most suitable approach for your requirements.

[ms4446 · Accepted solution]

Below is a basic example of a Google Cloud Function in Python that is triggered by a Pub/Sub message:

import base64

from google.cloud import bigquery


def run_queries(event, context):
    """
    Cloud Function triggered by a Pub/Sub message to execute BigQuery queries.

    Args:
        event (dict): The Pub/Sub message event dictionary.
        context (google.cloud.functions.Context): The Cloud Function context.
    """

    # Decode the message and extract the SQL queries.
    message_data = base64.b64decode(event["data"]).decode("utf-8")
    queries = message_data.split(";")

    # **Caution:** Be cautious with this in production! Complex queries involving strings or stored procedures might require a more robust approach.

    # Initialize BigQuery client.
    bq_client = bigquery.Client()

    # Iterate over each query and execute it.
    executed_queries = 0
    for query in queries:
        if query.strip():
            try:
                # Execute the query and wait for it to complete.
                query_job = bq_client.query(query)
                query_job.result()

                # Print successful execution message.
                print(f"Executed query: {query}")
                executed_queries += 1
            except Exception as e:
                # Print error message.
                print(f"Error executing query: {e}")

    # Print summary message.
    print(f"Executed {executed_queries} BigQuery queries.")


# Define the entry point.
run = run_queries

[ms4446 · Accepted solution]

Hi @sysph ,

You can trigger BigQuery notebooks to run with both Pub/Sub messages and CRON schedules by leveraging Google Cloud Functions. However, Cloud Functions are more ideal for this scenario because they are event-driven and serverless, scaling automatically and offering a cost-effective solution as you pay only for the time you use them.

Here's an breakdown of both approaches:

Triggering with Pub/Sub:

1. Create a Pub/Sub Topic: This topic will receive messages that trigger the notebook execution.

2. Develop a Cloud Function: This function, triggered by messages published to the topic, should contain the logic to execute the BigQuery notebook. Note that if your notebook is in a Jupyter format, you'll need to convert it into a script or use a tool/API that can programmatically execute Jupyter notebooks.

3. Deploy the Cloud Function: Bind the function to the Pub/Sub topic as a trigger.

Triggering with CRON:

1. Utilize Cloud Scheduler: Configure a Cloud Scheduler job with a CRON schedule.

2. Set the Cloud Scheduler Target to a Pub/Sub Topic: This topic will receive messages when the CRON job triggers.

3. Follow Steps 2 and 3 from the Pub/Sub Approach: Create and deploy a Cloud Function triggered by the topic to execute the notebook.

Additional Considerations:

• Notebook Execution: Ensure the Cloud Function's environment includes all necessary dependencies to run the notebook or script.

• Security and Permissions: Secure the Pub/Sub topics and ensure the Cloud Function has appropriate IAM roles and permissions for accessing BigQuery and other Google Cloud resources.

• Logging and Monitoring: Implement logging and monitoring for the Cloud Function to track its execution and troubleshoot issues.

• Testing and Validation: Thoroughly test the Cloud Function in a non-production environment to ensure it triggers and executes the notebook as expected.

• Scalability and Performance: Consider the scalability and performance, especially for resource-intensive notebooks or high volumes of Pub/Sub messages.

• Error Handling: Implement robust error handling in the Cloud Function to manage potential execution failures.

[sysph]

Hi @ms4446 ,

Thanks again for all of the info.  It's so helpful! 

Does any other ways to execute the bigQuery  notebooks by pub/sub if not via Cloud function?

Thanks 

[ms4446 · Accepted solution]

Yes, there are several alternative methods to execute BigQuery notebooks using Pub/Sub without relying solely on Cloud Functions:

1. Cloud Run:

   • Deploy a containerized application on Cloud Run that listens to a Pub/Sub topic. This application can trigger the execution of the BigQuery notebook. This method offers more flexibility and control over the execution environment compared to Cloud Functions.

2. Cloud Scheduler with Cloud Functions or Cloud Run:

   • Use Cloud Scheduler to publish messages to a Pub/Sub topic based on a defined schedule. These messages can then trigger a service like Cloud Functions or Cloud Run, which in turn executes the BigQuery notebook. This approach is ideal for automating notebook execution at specific intervals.

3. Google Kubernetes Engine (GKE):

   • Set up a Kubernetes cluster in GKE to listen to Pub/Sub messages. You can configure it to trigger jobs, including the execution of notebooks. This approach is suitable for more complex workflows and offers greater scalability and control.

Each of these methods offers different levels of complexity and functionality. Factors like your desired level of control, the specific triggers required, and integration with other Google Cloud services should be considered when choosing the most suitable approach for your requirements.

[sysph]

Does any code snippet for the Cloud Function?  

[ms4446 · Accepted solution]

Below is a basic example of a Google Cloud Function in Python that is triggered by a Pub/Sub message:

import base64

from google.cloud import bigquery


def run_queries(event, context):
    """
    Cloud Function triggered by a Pub/Sub message to execute BigQuery queries.

    Args:
        event (dict): The Pub/Sub message event dictionary.
        context (google.cloud.functions.Context): The Cloud Function context.
    """

    # Decode the message and extract the SQL queries.
    message_data = base64.b64decode(event["data"]).decode("utf-8")
    queries = message_data.split(";")

    # **Caution:** Be cautious with this in production! Complex queries involving strings or stored procedures might require a more robust approach.

    # Initialize BigQuery client.
    bq_client = bigquery.Client()

    # Iterate over each query and execute it.
    executed_queries = 0
    for query in queries:
        if query.strip():
            try:
                # Execute the query and wait for it to complete.
                query_job = bq_client.query(query)
                query_job.result()

                # Print successful execution message.
                print(f"Executed query: {query}")
                executed_queries += 1
            except Exception as e:
                # Print error message.
                print(f"Error executing query: {e}")

    # Print summary message.
    print(f"Executed {executed_queries} BigQuery queries.")


# Define the entry point.
run = run_queries

[sysph · Accepted solution]

Thank you so much, @ms4446.  This is super helpful.