Google BigQuery

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Nobl9's integration with Google BigQuery enables SLO measurement using data stored in BigQuery tables. This integration allows organizations to define service level objectives based on metrics queried directly from BigQuery datasets and get valuable business insights.

Google BigQuery parameters and supported features in Nobl9

General support:

Release channel: Stable, Beta

Connection method: Agent, Direct

Replay and SLI Analyzer: Not supported

Event logs: Supported

Query checker: Not supported

Query parameters retrieval: Supported

Timestamp cache persistence: Supported

---

Query parameters:

Query interval: 1 min

Query delay: 0 sec

Jitter: 15 sec

Timeout: 30 sec

---

Agent details and minimum required versions for supported features:

Plugin name: n9bigquery

Query delay environment variable: BIGQUERY_QUERY_DELAY

Query parameters retrieval: 0.73.2

Timestamp cache persistence: 0.65.0

Authentication

Nobl9 supports two authentication methods for BigQuery integration:

1. For the direct connection method:
   1. Obtain a Service account key from Google Cloud Platform.
   2. Provide the key when adding Google BigQuery with the direct connection method in Nobl9.
   3. Nobl9 establishes direct connection with BigQuery APIs.

Creating service account keys

For detailed instructions on creating service account keys, refer to the Getting Started with Authentication.

2. For the agent connection method, make sure your agent is granted with the following IAM permissions:

bigquery.datasets.get
bigquery.jobs.create
bigquery.jobs.list
bigquery.models.getData
bigquery.models.getMetadata
bigquery.tables.getData

For deployments in Google Kubernetes Engine (GKE), the Nobl9 agent supports Workload Identity authentication.
Read more about Nobl9 agent deployment.

Using Workload Identity in GCP

When using Workload Identity in GCP, ensure your GKE cluster has Workload Identity enabled and properly configured.

Adding Google BigQuery as a data source

To ensure data transmission between Nobl9 and Google BigQuery, it may be necessary to list Nobl9 IP addresses as trusted.

💻ip allowlist

IP addresses to include in your allowlist for secure access:

If you're using app.nobl9.com instance:

• 18.159.114.21
• 18.158.132.186
• 3.64.154.26

If you're using us1.nobl9.com instance:

• 34.121.54.120
• 34.123.193.191
• 34.134.71.10
• 35.192.105.150
• 35.225.248.37
• 35.226.78.175
• 104.198.44.161

You can add the BigQuery data source using the direct or agent connection methods.

Direct connection method

Direct connection to BigQuery requires users to enter their credentials which Nobl9 stores safely.

Nobl9 Web

To set up this type of connection:

1. Navigate to Integrations > Sources.
2. Click .
3. Click the required Source button.
4. Choose Direct.

5. Select one of the following Release Channels:

   • The stable channel is fully tested by the Nobl9 team. It represents the final product; however, this channel does not contain all the new features of a beta release. Use it to avoid crashes and other limitations.
   • The beta channel is under active development. Here, you can check out new features and improvements without the risk of affecting any viable SLOs. Remember that features in this channel can change.

6. Upload Service Account Key File to authenticate with Google Cloud.
   The file needs to be in JSON format. Refer to the Authentication section for more details.

7. Select a Project.
   Specifying a project is helpful when multiple users are spread across multiple teams or projects. When the Project field is left blank, Nobl9 uses the default project.
8. Enter a Display Name.
   You can enter a user-friendly name with spaces in this field.
9. Enter a Name.
   The name is mandatory and can only contain lowercase, alphanumeric characters, and dashes (for example, my-project-1). Nobl9 duplicates the display name here, transforming it into the supported format, but you can edit the result.
10. Enter a Description.
    Here you can add details such as who is responsible for the integration (team/owner) and the purpose of creating it.
11. Specify the Query delay to set a customized delay for queries when pulling the data from the data source.

    • The default value in BigQuery integration for Query delay is 0 seconds.

    Changing the query delay

    Changing the query delay can affect your SLI data.
    Learn more about query delay and its impact.
12. Click Add Data Source.

sloctl

1. Create a YAML definition to set up a direct connection with Google BigQuery. For this, refer to the following example:

YAML definition for the direct connection method

apiVersion: n9/v1alpha
kind: Direct
metadata:
  name: big-query
  displayName: BigQuery Direct
  project: default
  annotations:
    area: latency
    env: prod
    region: us
    team: sales
spec:
  description: Example BigQuery Direct
  releaseChannel: stable
  bigQuery:
    serviceAccountKey: "{\n  \"type\": \"service_account\",\n  \"project_id\": \"prod-app\",\n  \"private_key_id\": \"669180ba44964eddba9e2f6623721381\",\n  \"private_key\": \"-----BEGIN PRIVATE KEY-----\\nSECRET_KEY_GOES_HERE\\n-----END PRIVATE KEY-----\\n\",\n  \"client_email\": \"nobl9@nobl9.iam.gserviceaccount.com\",\n  \"client_id\": \"eddba9e2f66237213812\",\n  \"auth_uri\": \"https://accounts.google.com/o/oauth2/auth\",\n  \"token_uri\": \"https://oauth2.googleapis.com/token\",\n  \"auth_provider_x509_cert_url\": \"https://www.googleapis.com/oauth2/v1/certs\",\n  \"client_x509_cert_url\": \"https://www.googleapis.com/robot/v1/metadata/x509/nobl9%40nobl9.iam.gserviceaccount.com\"\n}"
  queryDelay:
    value: 1
    unit: Second

Field	Type	Description
queryDelay.unit mandatory	enum	Specifies the unit for the query delay. Possible values: Second | Minute. • Check query delay documentation for default unit of query delay for each source.
queryDelay.value mandatory	numeric	Specifies the value for the query delay. • Must be a number less than 1440 minutes (24 hours). • Check query delay documentation for default unit of query delay for each source.
logCollectionEnabled optional	boolean	Optional. Defaults to false. Set to true if you'd like your direct to collect event logs. Contact us to activate it.
releaseChannel mandatory	enum	Specifies the release channel. Accepted values: beta | stable.
Source-specific fields
bigQuery.serviceAccountKey mandatory	string	Provide your service account key to authenticate with Google Cloud. Read more about authentication with Google BigQuery

2. Apply your YAML definition using the sloctl apply command.

Agent connection method

Nobl9 Web

Follow the instructions below to create your BigQuery agent connection.

1. Navigate to Integrations > Sources.
2. Click .
3. Click the required Source button.
4. Choose Agent.

5. Select one of the following Release Channels:

   • The stable channel is fully tested by the Nobl9 team. It represents the final product; however, this channel does not contain all the new features of a beta release. Use it to avoid crashes and other limitations.
   • The beta channel is under active development. Here, you can check out new features and improvements without the risk of affecting any viable SLOs. Remember that features in this channel can change.

6. Select a Project.
   Specifying a project is helpful when multiple users are spread across multiple teams or projects. When the Project field is left blank, Nobl9 uses the default project.
7. Enter a Display Name.
   You can enter a user-friendly name with spaces in this field.
8. Enter a Name.
   The name is mandatory and can only contain lowercase, alphanumeric characters, and dashes (for example, my-project-1). Nobl9 duplicates the display name here, transforming it into the supported format, but you can edit the result.
9. Enter a Description.
   Here you can add details such as who is responsible for the integration (team/owner) and the purpose of creating it.
10. Specify the Query delay to set a customized delay for queries when pulling the data from the data source.

    • The default value in BigQuery integration for Query delay is 0 seconds.

    Changing the query delay

    Changing the query delay can affect your SLI data.
    Learn more about query delay and its impact.
11. Click Add Data Source.
Deploy your agent in a Kubernetes cluster or Docker container.

sloctl

1. Create a YAML definition to set up an agent connection with Google BigQuery. For this, refer to the following example:

YAML definition for the agent connection method

apiVersion: n9/v1alpha
kind: Agent
metadata:
  name: big-query
  displayName: BigQuery Agent
  project: default
  annotations:
    area: latency
    env: prod
    region: us
    team: sales
spec:
  description: Example BigQuery Agent
  releaseChannel: stable
  bigQuery: {}
  queryDelay:
    value: 1
    unit: Second

Field	Type	Description
queryDelay.unit mandatory	enum	Specifies the unit for the query delay. Possible values: Second | Minute. • Check query delay documentation for default unit of query delay for each source.
queryDelay.value mandatory	numeric	Specifies the value for the query delay. • Must be a number less than 1440 minutes (24 hours). • Check query delay documentation for default unit of query delay for each source.
releaseChannel mandatory	enum	Specifies the release channel. Accepted values: beta | stable.

1. Apply your YAML definition using the sloctl apply command.
2. Deploy your agent in a Kubernetes cluster or Docker container.

Google BigQuery API rate limits

The following rate limits apply to the BigQuery API:

• Query jobs. See Quotas and Limits reference.

• Point density. Point density greater than 1000 data points per minute leads to errors. To address this, add point aggregation to your query.

Google BigQuery cost optimization

BigQuery pricing is based on bytes read by the query.

Since BigQuery queries must contain the where clause with date between filter, as required by the Nobl9 agent, your can use partitioning on the date_col column to reduce the number of bytes read. For example,

WHERE
{date_col} BETWEEN
DATETIME(@n9date_from)
AND DATETIME(@n9date_to)

Learn more about storage and query costs estimation and partitioned tables.

Useful links

For a more in-depth look, consult additional resources:

Create SLOss with Google BigQueryCreating SLOsGetting StartedBigQuery docsCreating and Managing ProjectsBigQuery docsQuotas and LimitsBigQuery docsGetting started with authenticationGoogle Cloud documentationAgent metricsAgentCreating agents via TerraformTerraform