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Using dbt Metrics with PipeRider
From dbt 1.6, support for the dbt_metrics package was deprecated and replaced by the Semantic Layer. Likewise, from version 0.33.0 of PipeRider, support for the legacy metric config has also been discontinued. Metrics defined using the old configuration will now be automatically skipped by PipeRider.
PipeRider supports querying dbt metrics and analyzing the impact on metrics between two branches.

How to use

  1. 1.
    Define semantic models and metrics in your dbt project
  2. 2.
    Validate metric configuration
  3. 3.
    Run PipeRider

1. Define semantic models and metrics

For full details on how to define metrics, please refer to the official dbt documentation on the semantic model and creating metrics.
The following serves as a basic example on how to define a metric. Here, we define a metric revenue, which is the sum of the amount column with a status of completed.
- name: orders
agg_time_dimension: order_date
model: ref('orders')
- name: order_id
type: primary
- name: customer
type: foreign
expr: customer_id
- name: order_date
expr: order_date
type: time
time_granularity: day
- name: status
type: categorical
- name: revenue
description: "The total revenue of our jaffle business"
agg: sum
expr: amount
- name: revenue
description: "The total revenue of our jaffle business"
type: simple
label: Revenue
measure: revenue
filter: |
{{ Dimension('order_id__status') }} = 'completed'

2. Validate metric configuration

The fastest way to validate the configuration of your dbt project is the dbt-parse command:
$ dbt-parse
dbt has also released a powerful tool, MetricFlow CLI that, among other features, can also validate your metric configuration:
$ mf validate-configs
MetricFlow CLI can also query the metric to ensure it is functional:
$ mf query --metrics revenue --group-by metric_time__month
✔ Success 🦄 - query completed after 0.12 seconds
| metric_time__month | revenue |
| 2023-05-01 | 69.00 |
| 2023-06-01 | 420.00 |
| 2023-07-01 | 478.00 |
| 2023-08-01 | 136.00 |

3. Run PipeRider

Once your metrics are correctly configured, run PipeRider:
$ piperider run
View metrics in your PipeRider Report by selecting the metrics page form the project sidebar:

More about metric queries

How PipeRider queries the dbt metric

PipeRider can run daily, monthly, and yearly queries for your metrics. If the time_granularity is defined, the queries will adhere to these settings. For example, if the time_granularity is month, then PipeRider only run the monthly and yearly queries.
For each time grains, PipeRider only queries the last n periods
metric query
daily result for last 30 days
monthly result for last 12 months
yearly result for last 10 years
The behavior is identical to the following command in MetricFlow.
$ mf query --metrics revenue --group-by metric_time__day --start-time <30d ago>
$ mf query --metrics revenue --group-by metric_time__month --start-time <12m ago>
$ mf query --metrics revenue --group-by metric_time__year --start-time <10y ago>

Limited Support of DBT Metrics

  • PipeRider no longer supports metric specification prior to dbt v1.6. dbt provides official documentation on how to migrate metrics configs to the new spec.
  • PipeRider doesn't support metrics that use measures with agg , such as sum_boolean, median, and percentile
  • PipeRider doesn't support metrics that use filters with jinja macros other than Dimension
  • PipeRider doesn't support metrics that use filters with Dimension macros that require joins. Referencing the above metrics example:
    • {{ Dimension('order_id__status') }} = 'completed' is supported because order_id is type of primary
    • {{ Dimension('customer__type') }} = 'vip' is not supported because customer is type of foreign, and so requires additional join
  • PipeRider doesn't support derived metrics with offset_window
  • PipeRider doesn't support cumulative metrics.

Query single metric

When developing a new metric, it is time-consuming to run through all the piperider run to test the metric result. PipeRider support dbt node selection to run on single metric
$ piperider run -s 'metric:jaffle_shop.revenue'

Compare metrics

With PipeRider's compare feature, you're also able to compare metrics between reports, which is particularly useful when analyzing the impact of data model or metric definition changes.
Metric comparison in a PipeRider Impact Report