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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.

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
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

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 Service.
    It will be the location for your SLO in Nobl9.

  4. Select your BigQuery data source.

  5. Enter Project ID: a unique identifier of your required Google Cloud project.
    Project ID can contain 6-30 lowercase letters, digits, or hyphens.
    For example, bigquery://project

  6. Select a Location of the BigQuery dataset that contains the data you need to read.

  7. Select the Metric type:

    • Threshold metric: a single time series is evaluated against a threshold.
    • Ratio metric: two-time series for comparison for good events and total events.
      For ratio metrics, select the Data count method:
      • Non-incremental counts incoming data points one-by-one. As a result, the SLI chart is pike-shaped.
      • Incremental counts incoming data points incrementally, adding every next value to the previous values. It results in a constantly increasing SLI chart.

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

    • Sample threshold metric query:
      SELECT response_time AS n9value, created AS n9date FROM my-project-id WHERE created BETWEEN DATETIME(@n9date_from) AND DATETIME(@n9date_to)`ORDER BY n9date

    • Sample ratio metric queries:
      Good SELECT http_code AS n9value, created AS n9date FROM my-project-id WHERE http_code = 200 AND created BETWEEN DATETIME(@n9date_from) AND DATETIME(@n9date_to) ORDER BY n9date
      Total SELECT http_code AS n9value, created AS n9date FROM my-project-id WHERE created BETWEEN DATETIME(@n9date_from) AND DATETIME(@n9date_to) ORDER BY n9date

    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.
  1. Define the Time window for your SLO:
  2. Configure the Error budget calculation method and Objectives:
    • Occurrences method counts good attempts against the count of total attempts.
    • Time Slices method 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.

      • Similar threshold values for objectives
      To use similar threshold values for different objectives in your SLO, we recommend differentiating them by setting varying decimal points for each objective.
      For example, if you want to use threshold value 1 for two objectives, set it to 1.0000001 for the first objective and to 1.0000002 for the second one.
      Learn more about threshold value uniqueness.
  3. Add the Display name, Name, and other settings for your SLO:
    • Name identifies your SLO in Nobl9. After you save the SLO, its name becomes read-only.
      Use only lowercase letters, numbers, and dashes.
    • Create composite SLO: with this option selected, you create a composite SLO 1.0. Composite SLOs 1.0 are deprecated. They're fully operable; however, we encourage you to create new composite SLOs 2.0.
      You can create composite SLOs 2.0 with sloctl using the provided template. Alternatively, you can create a composite SLO 2.0 with Nobl9 Terraform provider.
    • Set Notifications on data. With it, Nobl9 will notify you in the cases when SLO won't be reporting data for more than 15 minutes.
    • Add alert policies, labels, and links, if required.
      Up to 20 items of each type per SLO is allowed.
  4. Click CREATE SLO.

    5. SLO configuration use case
    Check the SLO configuration use case for a real-life SLO example.

sloctl​

Sample Google BigQuery threshold SLO
apiVersion: n9/v1alpha
kind: SLO
metadata:
  name: api-server-slo
  displayName: API Server SLO
  project: default
  labels:
    area:
      - latency
      - slow-check
    env:
      - prod
      - dev
    region:
      - us
      - eu
    team:
      - green
      - sales
  annotations:
    area: latency
    env: prod
    region: us
    team: sales
spec:
  description: Example BigQuery SLO
  indicator:
    metricSource:
      name: big-query
      project: default
      kind: Agent
  budgetingMethod: Occurrences
  objectives:
    - displayName: Good response (200)
      value: 200
      name: ok
      target: 0.95
      rawMetric:
        query:
          bigQuery:
            query: >-
              SELECT response_time AS n9value, created AS n9date FROM
              `api-server-256112.metrics.http_response` WHERE created BETWEEN
              DATETIME(@n9date_from) AND DATETIME(@n9date_to)`
            projectId: api-server-256112
            location: US
      op: lte
      primary: true
  service: api-server
  timeWindows:
    - unit: Month
      count: 1
      isRolling: false
      calendar:
        startTime: 2022-12-01T00:00:00.000Z
        timeZone: UTC
  alertPolicies:
    - fast-burn-5x-for-last-10m
  attachments:
    - url: https://docs.nobl9.com
      displayName: Nobl9 Documentation
  anomalyConfig:
    noData:
      alertMethods:
        - name: slack-notification
          project: default

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.

  • To optimize your BigQuery plugin performance, consider including ORDER BY n9date in the query. With the sorting order defined, your BigQuery plugin can more efficiently retrieve and process data in batches, reducing overall query execution time.

Query samples​

Threshold metric sample:

SELECT
response_time AS n9value,
created AS n9date
FROM `my-google-cloud-project`
WHERE created
BETWEEN DATETIME(@n9date_from)
AND DATETIME(@n9date_to)
ORDER BY n9date

The n9value must be an alias for a numeric field. It's the DATETIME format representation of a date. Conditions are required.

For example, a WHERE or HAVING clause narrows the query to a DATETIME(@n9date_from) and DATETIME(@n9date_to) timeframe.

The queries are validated against columns or aliases.

Data type consistency

When narrowing the query to the interval by the DATETIME(@n9date_from) and DATETIME(@n9date_to) parameters, the data type of the value you're comparing must be the same. For example,

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

Since the parameters are DATETIME, the created value must also be 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:

  • 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.

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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