What the data for this category looks like
Usage statistics data is sourced from the platform's built-in call chain logs and resource monitoring probes. It covers dimensions including API call counts, compute consumption, and storage usage. Statistical snapshots are generated via minute-level aggregation updates, balancing data timeliness and storage costs. Each statistical document uses a fixed field structure: stat_time (statistical timestamp), tenant_id (tenant identifier), resource_type (resource type), usage_value (consumption value), unit (statistical unit). Different resource types use dedicated statistical units. For example, API call counts use "times", GPU compute consumption uses "core-hours", and storage usage uses "GB". No additional aggregated percentage fields are included.
What Constraints These Characteristics Impose on Workflow Orchestration
These characteristics impose clear constraints on workflow orchestration. Data updates at a minute-level aggregation cadence. Workflows must match this rhythm. Sub-minute trigger intervals cannot be configured, otherwise invalid unaggregated data will be pulled. The data includes the tenant ID field. Add tenant dimension filtering rules during orchestration to avoid cross-tenant data leaks and access errors. Resource types and statistical units vary. Configure field extraction and format conversion nodes to ensure downstream nodes receive uniformly formatted statistical data. Statistical data has a fixed field structure. Strictly bind extraction rules for corresponding fields during orchestration. Missing key fields will cause workflow execution failures. Pulling usage statistics data relies on native platform interfaces. Directly connect to built-in data sources during orchestration. Do not use custom external interfaces, otherwise data inconsistency will occur.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
trigger_interval | 60 seconds | Matches the minute-level aggregation update cadence of usage statistics data, avoids pulling incomplete temporary data |
filter_field | tenant_id, resource_type | Splits statistical data by tenant and resource type, achieves permission isolation and accurate statistics |
data_mapping | usage_value→workflow_input_var | Maps statistical values to standard input variables of the workflow, adapts to parameter requirements of downstream nodes |
unit_convert_node | Unify conversion to core-hours/times/GB | Standardizes statistical units for different resource types, eliminates format compatibility issues for downstream nodes |
alert_threshold | Calibrated based on business requirements | Usage thresholds vary significantly across tenants, adjust trigger conditions based on actual business scenarios |
data_source_node | Built-in usage statistics interface | Directly connects to the platform's native monitoring data, ensures accuracy and consistency of data sources |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Usage statistics variables referenced in the workflow only load fixed values on the first execution, with no updates in subsequent cycles. Cause: The
trigger_intervalis not set to match the update cadence of statistical data, and the output variables of the trigger node are not bound to downstream nodes. - Symptom: The workflow returns a
400 Bad Requesterror, prompting that thetenant_idfield is missing. Cause: The tenant identifier field is not added to thefilter_fieldconfiguration, so the pulled statistical data does not include parameters required for permission verification. - Symptom: Unit confusion appears in the stitched statistical report, for example, both "times" and "GB" are displayed. Cause: The
unit_convert_nodeis not configured to standardize the units of statistical values for different resource types, causing downstream display nodes to fail to unify formats.
How to Verify Proper Configuration
- Manually trigger the workflow once, check if the output node includes complete usage statistics fields, and verify that the field names match the configuration of the
data_mappingrule. - View the workflow run logs, confirm that the trigger interval matches the
trigger_intervalsetting, and that new statistical data is pulled each time the trigger runs. - Simulate trigger requests from different tenants, check if the workflow only returns statistical data for the corresponding tenant, and verify the filtering effect of
filter_field. - Modify the configuration of the
unit_convert_node, observe whether the input format of the downstream node meets expectations, and confirm that the unit conversion takes effect.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.