Calling Tumult Analytics from a BigQuery stored procedure#

In this section, we will explain how to run a Tumult Analytics program directly from BigQuery. We will do so using the sample program from the second part of this topic guide.

You should also have data in BigQuery, and a Google Cloud Storage bucket to store intermediate results from the previous parts. Let’s assume that our table is as initialized in the BigQuery setup section tumult-labs.analytics_tutorial.library_members. We’ll use the public Tumult Labs image being hosted at


If you want to use your own image, you can find the instructions to do so in the Docker section of this topic guide.

PROJECT          = "tumult-labs"
BIGQUERY_DATASET = "analytics_tutorial"
BIGQUERY_TABLE   = "library_members"
IMAGE_REPOSITORY = "analytics"
IMAGE_NAME       = "tutorial"

In BigQuery, tables are used to store the data, and datasets are used to group tables and procedures together. To call external Spark-based programs from BigQuery, we must create a stored procedure, which is associated with a BigQuery dataset.

First, we need to construct an external data source pointing to Apache Spark.

  1. Press the “+ Add Data” button in the top left corner of the BigQuery console.

  2. Choose “Connections to external data sources”.

  3. Select Apache Spark as the connection type.

  4. Choose a name for the connection, and remember it. In our running example, we will call it bigspark.

  5. Create the connection.

After creating the connection, in the explorer to the left above our dataset, there is now an “External connections” section, in which we can see our Apache Spark connection. Its name is the connection name appended with the region. In our example, it is us.bigspark, as our connection name is bigspark and it is situated in the us region.

Another thing we need to to with the connection is to copy the service account ID that was generated for this connection. We will need to grant this service account the necessary permissions it needs to run our Tumult Analytics program.

To do so, we have to go to the IAM & Admin page, click “Grant access”, paste our service account ID in “New Principals”, and assign it the following roles.

  • BigQuery Data Editor

  • BigQuery Read Session User

  • BigQuery Job User

  • Storage Admin

  • Artifact Registry Reader

Now, we can navigate back to the BigQuery page to create the stored procedure directly from the BigQuery editor.

For this example, we can ignore the parameters, as our script does not take any. With the sample values used throughout this topic guide, and choosing count_members as the name of our stored procedure, we end up with the following query.

CREATE OR REPLACE PROCEDURE `tumult-labs.analytics_tutorial.count_members`()


When copy-pasting the procedure creation script, make sure you replace the procedure name and external connection name, to point to your own project dataset and connection.

This creates a stored procedure that exists in tumult-labs.analytics_tutorial.count_members, akin to defining a function. Finally you can run the remote procedure by calling it with the appropriate parameters.

CALL `tumult-labs.analytics_tutorial.count_members`()

If successful, our script should produce a BigQuery table, which we can see after a few minutes once we refresh the page. Otherwise, you can check Cloud Logging for the results. This does require you to enable the Cloud Logging API as well.

Congratulations! You have successfully created a stored procedure that runs a Tumult Analytics program in BigQuery. The next few parts of the topic guide will cover how to set your own parameters to the program passing it from the remote procedure, and creating a custom image to include the libraries necessary for your programs.