Consumption

ℹ️

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Overview

The credits your account consumed, per monitor, warehouse, charge type and grouping.

What it answers

• How many credits the account consumed and where, by day, week, month or quarter, by warehouse, monitor type, charge type, monitor, agent charges, monitors on deleted assets, and by domain, data product, asset tag (monitors on their assets), monitor domain, monitor tag (team) and audience (their monitors).

🚧

Not covered here

• Money, contract rates, commits, invoices or remaining balance.
• Cost per table or column: a monitor covers many tables and its credits are not split; cost per monitor, monitor type, warehouse, domain or data product is available.
• Credits divided between domains, data products or tags so that they add up to the account's credits: a monitor's credits are not divided between the members it covers. Per monitor, monitor type, warehouse and charge type, credits add up to the account's.
• Coverage, health, alerts and incidents: Coverage and Data quality metrics.
• Spend older than 13 full months.
• Forecasts.

Try asking

How many credits did we use this quarter, by charge type?

The query your agent runs:

• Metrics: consumed_credits
• Grouped by: consumption__consumption_charge_type
• Filter: account__is_account_on_credit_pricing is true
• Filter: metric_time__day on or after the start of this quarter

{
  "metrics": ["consumed_credits"],
  "group_by": ["consumption__consumption_charge_type"],
  "filters": [
    {
      "field": "account__is_account_on_credit_pricing",
      "op": "in",
      "values": [true]
    },
    {
      "field": "metric_time__day",
      "op": "gte",
      "values": ["<start of this quarter>"]
    }
  ]
}

Metrics

Every metric here groups by Consumption, Monitor, Warehouse, and time.

Good to know

Grouping and filtering for all metrics, including why the numbers per domain, data product or tag do not add up to the account's: Before you group or filter.

• Default window: This quarter.
• Pricing: Spend is the credits of credit-based pricing; filter account__is_account_on_credit_pricing is true. There are no dollar amounts, contract rates or invoices, and agent usage from before agents were billed is included, so a month can be higher than the bill.
• Not a split of the bill: Credits per domain, data product and asset tag are the full credits of the monitors on their tables; per monitor domain, monitor tag and audience, of the monitors assigned to them (domain_consumed_credits and the like). A monitor counts in full in every member, so see Totals across members under Before you group or filter.

Examples

How much of this quarter's spend went to monitors on assets that no longer exist?

• Metrics: consumed_credits
• Grouped by: monitor__is_monitor_on_deleted_assets_only
• Filter: account__is_account_on_credit_pricing is true
• Filter: metric_time__day on or after the start of this quarter

{
  "metrics": ["consumed_credits"],
  "group_by": ["monitor__is_monitor_on_deleted_assets_only"],
  "filters": [
    {
      "field": "account__is_account_on_credit_pricing",
      "op": "in",
      "values": [true]
    },
    {
      "field": "metric_time__day",
      "op": "gte",
      "values": ["<start of this quarter>"]
    }
  ]
}

Spend per data product over the last 90 days

• Metrics: data_product_consumed_credits
• Grouped by: data_product, data_product__data_product_name
• Filter: account__is_account_on_credit_pricing is true
• Filter: data_product__is_data_product_deleted is false
• Filter: metric_time__day on or after 90 days ago

{
  "metrics": ["data_product_consumed_credits"],
  "group_by": [
    "data_product",
    "data_product__data_product_name"
  ],
  "filters": [
    {
      "field": "account__is_account_on_credit_pricing",
      "op": "in",
      "values": [true]
    },
    {
      "field": "data_product__is_data_product_deleted",
      "op": "in",
      "values": [false]
    },
    {
      "field": "metric_time__day",
      "op": "gte",
      "values": ["<90 days ago>"]
    }
  ]
}

Which monitors consumed the most credits in the last 30 days?

• Metrics: consumed_credits
• Grouped by: monitor, monitor__monitor_name, monitor__monitor_type
• Filter: account__is_account_on_credit_pricing is true
• Filter: metric_time__day on or after 30 days ago
• Sorted by: consumed_credits, highest first
• At most 20 rows

{
  "metrics": ["consumed_credits"],
  "group_by": [
    "monitor",
    "monitor__monitor_name",
    "monitor__monitor_type"
  ],
  "filters": [
    {
      "field": "account__is_account_on_credit_pricing",
      "op": "in",
      "values": [true]
    },
    {
      "field": "metric_time__day",
      "op": "gte",
      "values": ["<30 days ago>"]
    }
  ],
  "order_by": [
    {"field": "consumed_credits", "descending": true}
  ],
  "limit": 20
}

More questions these metrics answer:

• How many credits did we use this quarter?
• Which monitor types cost the most?
• Top 10 monitors by credits last 30 days.
• How much of this quarter's spend went to monitors on assets that no longer exist?
• Spend per data product.

See also

• Dimensions these metrics group by: Asset tag, Audience, Consumption, Data product, Domain, Monitor, Monitor domain, Monitor tag, Warehouse
• All metrics: Metrics; all dimensions: Dimensions; overview and guides: Composable dashboards; product words: Glossary