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GCP Billing and BigQuery Audit

This repository contains two Looker blocks for analysing Google Cloud Platform logs. We provide a Looker model called GCP Billing which sits on top of GCP billing log exports. This model allows you to analyse billing data across projects and across services and resource types, allowing you to efficiently manage you GCP Billing account. We also provide projected monthly spend based on your current daily billing, so you can take actions to control spending across your organisation.

We also provide a model to anlayse BigQuery data access logs. The model sits on top of a Stackdriver Logging export of BigQuery queries and data access. This allows you to effectively monitor BigQuery performance and cost at a per user level. It can be used to set up alerts to long running or high cost queries. Below we also detail a method to analyse these logs across several projects, allowing you to visualise your BigQuery organisation in one place.

This block was updated in January 2020 to use the new BigQuery logs structure.

Getting Started

Let's run through the steps in both the Google Cloud Platform, and in Looker, to setup the logging exports and the Looker block.

GCP Setup

Create a BigQuery dataset for the billing and BigQuery audit logs. Go to the Google Cloud Platform console, and select BigQuery, or go to https://bigquery.cloud.google.com/. Click the drop down next to the project name and select Create New Dataset, set a location and click OK.

Optional: We recommend setting up a new GCP Project, purely for this purpose.

Setting up the Billing Export

To setup a billing export to BigQuery do the following:

  1. Go to the Google Cloud Platform console and select Billing
  2. Choose the appropriate billing account (if you have more than one) using Manage billing accounts
  3. Click Billing Export > BigQuery export
  4. Select the Project and Dataset you created earlier
  5. Click Enable BigQuery export

Billing data will now be exported to your dataset at regular intervals. The Billing export table is date partitioned, and will incur a small data storage charge.

Note: Recently the GCP Billing Export moved from Beta to v1. If you activated the billing export before the change, then there will be two tables in your export dataset. This model sits on top of the new v1 table, as the old table will soon be deprecated.

Setting up BigQuery audit logs export

To set up the BigQuery log export do the following in a project that contains BigQuery:

  1. Go to the Google Cloud Platform console and select Stackdriver Logging
  2. Click Exports and then Create Export
  3. Add a Sink Name and select Custom Destination as the Sink Service. The Sink Destination should be set to bigquery.googleapis.com/projects/<project-name>/datasets/<dataset-name>, adding the project and dataset names you created earlier.
  4. Click Create Sink

If you got a permission error then that is perfectly normal. It is because the project you have set up the export to is different to the project you have set up the logging export in. In this case the Service Account which writes the logs into the BigQuery dataset you have created will not have permission to do so. Follow the steps below to complete the setup:

  1. Go to BigQuery in the project the logs are exported to and click on the dropdown next to the dataset you have chosen. Click Share Dataset
  2. Get the name of the service account by going to Stackdriver Logging in the project where you set up the logging export, then Exports, and copy the Writer Identity
  3. Add this Writer Identity into the Share Dataset window in BigQuery from Step 1
  4. Give the account Can edit access, and click Add, and then Save Changes

The BigQuery audit log export should now be set up. The table will be updated throughout the day. The BigQuery audit log table is date sharded rather than date partitioned.

If you have more than one project using BigQuery, repeat the steps above. All logs from different projects will be added to the same table, allowing easy querying across projects.

Using the Google Cloud SDK

Alternatively, if you have the Google Cloud SDK installed, you can set up the BigQuery logging using the following command (make sure you in the project you want to set up the logging for by running gcloud config set project <project-name>)

gcloud beta logging sinks create <sink_name> bigquery.googleapis.com/projects/<project-name>/datasets/<dataset-name> --log-filter='resource.type="bigquery_resource"'

Looker Configuration

This block requires almost no configuration once added to your Looker instance. We will only need to change the billing export table name:

  1. Create a new Database Connection in Looker to connect to the BigQuery dataset: follow the steps here to create a service account in GCP and add a new connection to Looker, ensure you use BigQuery standard SQL
  2. Change the connection name in the model files.
  3. In the manifest file, change the connection name in the Looker model files to the connection name you chose in Step 1
  4. Change the schema and table names in the manifest file. The table name will likely resemble cloudaudit_googleapis_com_data_access.
  5. Also change the project name to the current project in the manifest file.

You should now be ready to start monitoring your BigQuery and GCP usage.