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Google BigQuery

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Google BigQuery is a serverless data warehouse that facilitates scalable analysis over petabytes of data. It is a Platform as a Service that supports querying using ANSI SQL. The BigQuery integration with Nobl9 empowers users to turn their big data into valuable business insights.

Scope of support​

Authentication​

Big Query authentication requires the user’s credentials to be entered in Nobl9. Users can retrieve their authentication credentials from the Google Cloud Platform as the Service account key file. For all necessary details on how to get the Service account key file, refer to the Getting Started with Authentication | BigQuery documentation.

For the direct connection, pass the contents of the downloaded Service account key file on the Nobl9 Web. This activates direct integration with the Big Query APIs to retrieve data, leveraging the SaaS-to-SaaS infrastructure in Nobl9.

Agent connection for the user to be granted with BigQuery permissions. The minimal set of permissions required for the BigQuery agent connection is:

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

Nobl9 agent can use Workload Identity in GCP (Google Cloud Platform) in GKE (Google Kubernetes Engine). For more information, refer to the Deploying BigQuery agent section.

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 addresses to add to your allowlist:
  • 18.159.114.21
  • 18.158.132.186
  • 3.64.154.26
⚠ Applies to app.nobl9.com only. In all other cases, contact Nobl9 support.

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 icon.
  4. Choose Direct.
  1. 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.

  2. 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.

  1. 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.
  2. Enter a Display Name.
    You can enter a user-friendly name with spaces in this field.
  3. 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.
  4. Enter a Description.
    Here you can add details such as who is responsible for the integration (team/owner) and the purpose of creating it.
  5. 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.
    info
    Changing the Query delay may affect your SLI data. For more details, check the Query delay documentation.
  6. Click Add Data Source.

sloctl​

The YAML for setting up a direct connection to BigQuery looks like this:

apiVersion: n9/v1alpha
kind: Direct
metadata:
  name: bigquery-direct
  displayName: BigQuery direct
  project: bigquery-direct
spec:
  description: Direct integration with BigQuery
  sourceOf:
    - Metrics
  releaseChannel: beta
  queryDelay:
      unit: Minute
      value: 720
  logCollectionEnabled: false
  bigQuery:
    serviceAccountKey: |-
     {
      # secret, copy and paste your credentials from the credentials.json file
     }
FieldTypeDescription
queryDelay.unit
mandatory
enumSpecifies 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
numericSpecifies 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
booleanOptional. Defaults to false. Set to true if you'd like your direct to collect event logs. Beta functionality available only through direct release channel. Reach out to support@nobl9.com to activate it.
releaseChannel
mandatory
enumSpecifies the release channel. Accepted values: beta | stable.
Source-specific fields
bigQuery.serviceAccountKey
mandatory
string You must embed the **Service Account Key File** content to authenticate with Google Cloud. See authentication for more details

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 icon.
  4. Choose Agent.
  1. 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.
  1. 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.
  2. Enter a Display Name.
    You can enter a user-friendly name with spaces in this field.
  3. 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.
  4. Enter a Description.
    Here you can add details such as who is responsible for the integration (team/owner) and the purpose of creating it.
  5. 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.
    info
    Changing the Query delay may affect your SLI data. For more details, check the Query delay documentation.
  6. Click Add Data Source.

sloctl​

The YAML for setting up an agent connection to BigQuery looks like this:

apiVersion: n9/v1alpha
kind: Agent
metadata:
  name: bigquery
  displayName: BigQuery Agent
  project: default
spec:
  description: BigQuery description
  sourceOf:
    - Metrics
  queryDelay:
      unit: Minute
      value: 720
  releaseChannel: beta
  bigQuery: {}
FieldTypeDescription
queryDelay.unit
mandatory
enumSpecifies 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
numericSpecifies 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
enumSpecifies the release channel. Accepted values: beta | stable.
warning

You can deploy only one Agent in one YAML file by using the sloctl apply command.

Agent deployment​

When you add the data source, Nobl9 automatically generates a Kubernetes configuration and a Docker command line for you to use to deploy the Agent. Both of these are available in the web UI, under the Agent Configuration section. Be sure to swap in your credentials (e.g., replace the <CREDENTIALS> comment with the contents of your credentials.json file encoded with base64).

warning

Nobl9 agent can use Workload Identity in GCP (Google Cloud Platform) in GKE (Google Kubernetes Engine). As such, the N9_GCP_CREDENTIALS_PATH environment variable has been changed to GOOGLE_APPLICATION_CREDENTIALS. For more information, refer to the Getting started with authentication | Google Cloud documentation.

If you use Kubernetes, you can apply the supplied YAML config file to a Kubernetes cluster to deploy the Agent. It will look something like this:

# DISCLAIMER: This deployment description contains only the fields necessary for the purpose of this demo.
# It is not a ready-to-apply k8s deployment description, and the client_id and client_secret are only exemplary values.

apiVersion: v1
kind: Secret
metadata:
  name: nobl9-agent-nobl9-dev-bigquery-agent
  namespace: default
type: Opaque
stringData:
  client_id: "unique_client_id"
  client_secret: "unique_client_secret"
data:
  credentials.json: |-
#  <CREDENTIALS>
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: nobl9-agent-nobl9-dogfood-test-mybq
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      nobl9-agent-name: mybq
      nobl9-agent-project: test
  template:
    metadata:
      labels:
        nobl9-agent-name: mybq
        nobl9-agent-project: test
    spec:
      containers:
        - name: agent-container
          image: nobl9/agent:0.80.0
          resources:
            requests:
              memory: "350Mi"
              cpu: "0.1"
          env:
            - name: N9_CLIENT_ID
              valueFrom:
                secretKeyRef:
                  key: client_id
                  name: nobl9-agent-nobl9-dogfood-test-mybq
            - name: N9_CLIENT_SECRET
              valueFrom:
                secretKeyRef:
                  key: client_secret
                  name: nobl9-agent-nobl9-dogfood-test-mybq
            # The N9_METRICS_PORT is a variable specifying the port to which the /metrics and /health endpoints are exposed.
            # The 9090 is the default value and can be changed.
            # If you don’t want the metrics to be exposed, comment out or delete the N9_METRICS_PORT variable.
            - name: N9_METRICS_PORT
              value: "9090"
            # To use Workload Identity in Kubernetes Cluster in Google Cloud Platform,
            # comment out or delete the GOOGLE_APPLICATION_CREDENTIALS environment variable
            # and follow the instructions described here https://cloud.google.com/kubernetes-engine/docs/how-to/workload-identity
             - name: GOOGLE_APPLICATION_CREDENTIALS
              value: "/var/gcp/credentials.json"
            # N9_ALLOWED_URLS is an optional security parameter that limits the URLs that an Agent can query
            # for metrics. URLs defined in the Nobl9 app are prefix-compared against the N9_ALLOWED_URLS list of
            # comma separated URLs.
            # - name: N9_ALLOWED_URLS
            #   value: "http://172.16.0.2/api/v1/query,http://172.16.0.3"
          volumeMounts:
          - name: gcp-credentials
            mountPath: "/var/gcp"
            readOnly: true
      volumes:
      - name: gcp-credentials
        secret:
          secretName: nobl9-agent-nobl9-dogfood-test-mybq

Creating SLOs with Google BigQuery​

Nobl9 Web​

Follow the instructions below to create your SLOs with BigQuery in the UI:

  1. Navigate to Service Level Objectives.

  2. Click .

  3. Select a Data Source from the drop-down list.

  4. Enter a Project ID.
    The Project ID is a unique identifier of the Google Cloud project. For more details, refer to Creating and Managing Projects | BigQuery documentation. The Project ID must be a unique string of 6-30 lowercase letters, digits, or hyphens. Example: bigquery://project

  5. Enter a Location of the BigQuery dataset from where the data is read.
    See Supported Locations | BigQuery documentation.

  6. Specify the Metric. You can choose either a Threshold Metric, where a single time series is evaluated against a threshold or a Ratio Metric, which allows you to enter two time series to compare (for example, a count of good requests and total requests).

    1. Choose the Data Count Method for your ratio metric:
      • Non-incremental: counts incoming metric values one-by-one. So the resulting SLO graph is pike-shaped.
      • Incremental: counts the incoming metric values incrementally, adding every next value to previous values. It results in a constantly increasing SLO graph.

  7. Enter an SQL query or SQL query for the good counter, and an SQL query for the total counter for the metric you selected.

    • Threshold metric for BigQuery:
      SELECT response_time AS n9value, created AS n9date FROM `bdwtest-256112.metrics.http_response` WHERE created BETWEEN DATETIME(@n9date_from) AND DATETIME(@n9date_to)

    • Ratio metric for BigQuery:
      Good query: SELECT response_time AS n9value, created AS n9date FROM `test-123.metrics.http_response`
      Total query: SELECT response_time AS n9value, created AS n9date FROM `test-123.metrics.http_response`

    SLI values for good and total
    When choosing the query for the ratio SLI (countMetrics), keep in mind that the values ​​resulting from that query for both good and total:
    • Must be positive.
    • While we recommend using integers, fractions are also acceptable.
      • If using fractions, we recommend them to be larger than 1e-4 = 0.0001.
    • Shouldn't be larger than 1e+20.

  8. In step 3, define a Time Window for the SLO.

  • Rolling time windows are better for tracking the recent user experience of a service.

  • Calendar-aligned windows are best suited for SLOs that are intended to map to business metrics measured on a calendar-aligned basis, such as every calendar month or every quarter.

  1. In step 4, specify the Error Budget Calculation Method and your Objective(s).

    • Occurrences method counts good attempts against the count of total attempts.
    • Time Slicesmethod measures how many good minutes were achieved (when a system operates within defined boundaries) during a time window.
    • You can define up to 12 objectives for an SLO.

    See the use case example and the SLO calculations guide for more information on the error budget calculation methods.

  2. In step 5, add the Display name, Name, and other settings for your SLO:

    • Create a composite SLO
    • Set notification on data, if this option is available for your data source.
      When activated, Nobl9 notifies you if your SLO hasn't received data or received incomplete data for more than 15 minutes.
    • Add alert policies, labels, and links, if required.
      You can add up to 20 links per SLO.

  3. Click Create SLO.

sloctl​

Here’s an example of BiqQuery using rawMetric (threshold metric):

apiVersion: n9/v1alpha
kind: SLO
metadata:
  name: bigquery-test
  project: default
spec:
  service: bq-service
  indicator:
    metricSource:
      name: bigquery
  timeWindows:
    - unit: Day
      count: 7
      calendar:
        startTime: 2020-03-09 00:00:00
        timeZone: Europe/Warsaw
  budgetingMethod: Occurrences
  objectives:
    - displayName: Fatal
      op: lte
      rawMetric:
        query:
          bigQuery:
            projectId: "bdwtest-256112"
            location: "EU"
            query: "SELECT response_time AS n9value, created AS n9date FROM `bdwtest-256112.metrics.http_response`"
      value: 6.50
      target: 0.6
    - displayName: Poor
      op: lte
      rawMetric:
        query:
          bigQuery:
            projectId: "bdwtest-256112"
            location: "EU"
            query: "SELECT response_time AS n9value, created AS n9date FROM `bdwtest-256112.metrics.http_response`"
      value: 6.00
      target: 0.6
    - displayName: Good
      op: lte
      rawMetric:
        query:
          bigQuery:
            projectId: "bdwtest-256112"
            location: "EU"
            query: "SELECT response_time AS n9value, created AS n9date FROM `bdwtest-256112.metrics.http_response`"
      value: 5.55
      target: 0.7
    - displayName: Perfect
      op: lte
      rawMetric:
        query:
          bigQuery:
            projectId: "bdwtest-256112"
            location: "EU"
            query: "SELECT response_time AS n9value, created AS n9date FROM `bdwtest-256112.metrics.http_response`"
      value: 5.00
      target: 0.8
    - displayName: TooPerfect
      op: lte
      rawMetric:
        query:
          bigQuery:
            projectId: "bdwtest-256112"
            location: "EU"
            query: "SELECT response_time AS n9value, created AS n9date FROM `bdwtest-256112.metrics.http_response`"
      value: 4
      target: 0.8

Important notes:

The BigQuery SLO requires the following fields:

  • The location is the BigQuery dataset from where the data is read.

  • The projectID is a unique identifier of Google Cloud project. The projectID must be a unique string of 6-30 lowercase letters, digits, or hyphens.

  • The query.

Query samples​

Threshold metric sample:

SELECT
response_time AS n9value,
created AS n9date
FROM `bdwtest-256112.metrics.http_response`
WHERE created
BETWEEN DATETIME(@n9date_from)
AND DATETIME(@n9date_to)

The n9value must be an alias for a numeric field. The n9date represents a date in DATETIME format. Conditions are required. For example, a WHERE or HAVING clause narrows the query to a DATETIME(@n9date_from) and DATETIME(@n9date_to) timeframe. A validation checks if the columns or aliases are present in the queries.

warning

When you narrow the query to the time interval defined by the DATETIME(@n9date_from) and DATETIME(@n9date_to) parameters, then, the value you are comparing it with must be of the same type. For example, in this part of a query:

WHERE created
BETWEEN DATETIME(@n9date_from)
AND DATETIME(@n9date_to)

the created value must match the parameter’s type, meaning, DATETIME.

Sample query results:

n9value | n9date
256     | 2021-06-15T01:00:47.754070
259     | 2021-06-14T16:35:36.754070
250     | 2021-06-14T17:27:15.754070

Google BigQuery API rate limits​

The following rate limits apply to the BigQuery API:

  • The rate limits apply to query jobs. For detailed information, refer to the Quotas and Limits | BigQuery documentation.

  • The BigQuery rate limit applies also to the point density. If the point density fetched from database per one minute is greater than 1000, an error occurs. Then, you must rewrite the query with point aggregation.


Google BigQuery cost optimization

BigQuery pricing is based on bytes read by the query. You can find detailed instruction on how to estimate costs in the Estimate storage and query costs | BigQuery documentation.

As the Nobl9 agent requires BigQuery queries to contain the where clause with date between filter, for example:

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

you can use partitioning on the date_col column to reduce the number of bytes read and, therefore, the costs of running the BigQuery Agent.

For more detailed information on partitioning, refer to the Introduction to partitioned tables | BigQuery documentation.

For a more in-depth look, consult additional resources:
Getting StartedBigQuery docsQueriesBigQuery docsCreating and Managing ProjectsBigQuery docsQuotas and LimitsBigQuery docsGetting started with authenticationGoogle Cloud documentationAgent metricsAgentCreating SLOs via TerraformTerraformCreating via TerraformTerraform
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