BigQuery integration

While mabl provides many in-app dashboards for reviewing test output and activity, we recognize that your application may benefit from bespoke reports that are unique to your business. Using the mabl integration for BigQuery, you can extract, transform, and view such reports in real time in tools like BigQuery and Looker Studio.

With the mabl BigQuery integration, you can enable real-time custom reporting and make deeper analyses of testing patterns. This article provides instructions for setting up the mabl BigQuery integration and an overview of tables in the mabl BigQuery dataset.

Setup

In order to use the mabl BigQuery integration, you must use a paid GCP BigQuery account.

The BigQuery Sandbox feature that is available to unpaid trial Google Cloud accounts cannot be used with the mabl BigQuery feed export because streaming inserts are not supported by the BigQuery Sandbox.

Create a service account for mabl

Add the service account mabl-feeds@mabl-prod.iam.gserviceaccount.com as a BigQuery Data Editor to the destination Google Cloud Platform project. Learn more.

Configure the BigQuery integration

In the mabl app, visit Settings > Integrations and click on the + Setup button for the BigQuery integration.

On the BigQuery integration page, add the destination project ID, dataset name, and a prefix for the export table. Optionally, you may toggle on React personal information fields. Click save to confirm your settings.

Redact personal information fields

If you toggle on React personal information fields, mabl excludes the following fields from the BigQuery Export Feed:

• trigger_user_email
• actor_email

mabl replaces the entries for these fields with the value PII_REDACTED on export. Other free text fields, such as plan_name or labels, are still included. mabl does not inspect the content of these fields.

BigQuery table layout

The BigQuery integration provides a streaming export of mabl data. Upon running the first mabl test through the BigQuery Export Feed, the mabl_export dataset and following tables are created:

• Plan runs: <table_prefix>_plan_run
• Test runs: <table_prefix>_journey_run
• Failure reasons: <table_prefix>_run_categorization
• Performance test runs: <table_prefix>_performance_test_run
• Activity feed: <table_prefix>_activity_feed

For example, if you used the default names when setting up the BigQuery integration in mabl, the following BigQuery tables are created:

• <your-project>:mabl_export.mabl_plan_run
• <your-project>:mabl_export.mabl_journey_run
• <your-project>:mabl_export.mabl_run_categorization
• <your-project>:mabl_export.mabl_performance_test_run
• <your-project>:mabl_export.mabl_activity_feed

Partitions

The tables are partitioned by day, based on the columns:

• <table_prefix>_plan_run on column start_time
• <table_prefix>_journey_run on column start_time
• <table_prefix>_run_categorization on column created_time
• <table_prefix>_performance-test_run on column start_time
• <table_prefix>_activity_feed on column created_time

If you want to discard test data after a set amount of time, configure a partition expiration in your BigQuery data warehouse.

Table schema

Plan run table

The *_plan_run table contains every plan run on the integrated workspace, written upon plan run completion.

Test run table

The *_journey_run table contains every test run in the integrated workspace, written upon test run completion. If the test is part of a plan run, the test run may be written to the test run table before the plan run is complete.

Failure reason table

The *_run_categorization table contains every test run failure reason for the integrated workspace, written when a user sets a failure reason on a given test run output.

Because BigQuery exports are done in a "data warehouse" fashion, tables are never updated or deleted. Instead, tables are only appended. As a result, a new row is written for each change to a test run's failure reason, which is stored in the category dimension.

Group by journey_run_id and take the latest create_time value to find the most recent category given to a test run. If a user clears the test run of a failure reason, the category is NULL.

You can use the following sample query to determine the latest failure reason. Replace my-project.mabl_export.mabl_run_categorization with the table name in your BigQuery system.

SELECT 
  failure_reason.* 

FROM ( 
  -- Get latest update time 
  SELECT 
    journey_run_id, 
    MAX(created_time) AS last_updated_time 
  FROM 
    `my-project.mabl_export.mabl_run_categorization` 
  GROUP BY 
    journey_run_id ) AS latest_categories 

JOIN `my-project.mabl_export.mabl_run_categorization` AS failure_reason 
ON failure_reason.created_time = latest_categories.last_updated_time 

-- Ignore removed failure reasons 
WHERE failure_reason.category IS NOT NULL

Performance test run table

The *_performance_test_run table contains metrics for every performance test run in the integrated workspace, written upon test completion. If the performance test is part of a plan run, the performance test run may be written to the performance test run table before the plan run is complete.

You may join the *_performance_test_run table with the *_journey_run table on id.

Activity feed table

The *_activity_feed table contains every activity feed entry in the integrated workspace, including activity data for all types supported by the activity feed in the mabl UI.

Next steps

After setting up the BigQuery integration, you can start querying the data or creating custom dashboards and charts using Looker Studio, Google Sheets, or another tool.