Metrics

Query the metric

A metric is an aggregation over a table. In general, it consists
The model to aggregate. (e.g. Events)
The column to aggregate (e.g. user_id)
The aggregation method (e.g. distinct count)
The time column to group (e.g. event_time)
The time granularity for grouping (e.g. group by date_trunc('DAY', event_time)
The dimension for grouping (e.g. group by country)
The filters
So if we can define a daily active users metric as
The daily active user is the distinct count of the user_id in the Events table, and group the result by the date of the event_time
The dbt metrics provide a way to define the metrics, and PipeRider implements the query of the metrics according to the dbt metrics definition.
How to use
There are three steps to
define metrics in your dbt project
update the dbt manfests
run piperider
Define a metric
To define a metric, please see the dbt metric document to see how to define a metric in your dbt project
models/marts/metrics.yml
metrics:
  - name: active_users
    label: Active Users
    model: ref('stg_events')
    description: "The active user"
​
    calculation_method: count_distinct
    expression: user_id 
​
    timestamp: event_time
    time_grains: [day, week, month, year]
+   tags: ['piperider']
To tell PipeRider to query your metrics, you must add the piperider tag. The tag name can be changed in the config.yml
Check if the metric is well-configured
The following command will list the metrics that you have just modified.
dbt list -s tag:piperider --resource-type metric
Run PipeRider
Now, you can try if the metric is successfully queried
piperider run
View metrics in your PipeRider Report. Metric query results can is shown in the Metrics page
More about metric queries
How PipeRider query the dbt metric
PipeRider queries each time_grain except all_time. This results in a maximum of 5 queries per metric. For instance, given the following metric, PipeRider would perform four queries: daily, weekly, monthly, and yearly active users.
metrics:
  - name: active_users
    label: Active Users
    model: ref('stg_events')
    description: "The active user"
​
    calculation_method: count_distinct
    expression: user_id 
​
    timestamp: event_time
    time_grains: [day, week, month, year]
    tags: ['piperider']
For each time grains, PipeRider only queries the last n periods of the table data
time_grain
metric query
day
daily result for last 30 days
week
weekly result for last 12 week
month
monthly result for last 12 months
quarter
quarterly result for last 10 quarters
year
yearly result for last 10 years
How about dimensions?
PipeRider currently supports only the default queries. Queries for specific dimensions and other query customization will be added in the future. Tell us if you consider it is super useful for us. It will help us to prioritize them earlier.
Run on single metric
When developing a new metric, it is time-consuming to run through all the piperider run to test the metric result. A tip is that use the dbt list to shorten the development cycle
1.Update the dbt yaml file
2.Update the dbt manifest by dbt compile
3.Run piperider by dbt list output
dbt list -s metric:active_users | piperider run --dbt-list     
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.
Dbt metrics compatibility
PipeRider supports the follow dbt metric properties.
Field
Support
name
yes
model
yes
label
yes
description
yes
calculation_method
count, count_distinct, sum, average, min, max, derived
expression
yes
timestamp
yes
time_grains
yes
dimensions
Dimensions are currently not shown on metric charts
window
No. Support for window is coming soon
Metrics that use the 'window' property are currently not supported and will not be queried. Support for 'window' is will be added in an upcoming release.
Derived metric
PipeRider supports querying derived metrics. It's useful for
ratios
subtractions
any arbitrary calculation
models/marts/<derived-metric>.yml
metrics:
  - name: average_revenue_per_customer
    label: Average Revenue Per Customer
    description: "The average revenue received per customer"
​
    calculation_method: derived
    expression: "{{metric('total_revenue')}} / {{metric('count_of_customers')}}"
    tags: ['piperider']

time_grain	metric query
day	daily result for last 30 days
week	weekly result for last 12 week
month	monthly result for last 12 months
quarter	quarterly result for last 10 quarters
year	yearly result for last 10 years

Field	Support
name	yes
model	yes
label	yes
description	yes
calculation_method	count, count_distinct, sum, average, min, max, derived
expression	yes
timestamp	yes
time_grains	yes
dimensions	Dimensions are currently not shown on metric charts
window	No. Support for `window` is coming soon

Profiling

Assertions (deprecated)

Last modified 10d ago