dbt MetricFlow metrics

Connect Lightdash to your dbt MetricFlow semantic layer metrics

If your dbt project defines metrics using MetricFlow (dbt’s semantic layer), the Lightdash CLI can translate them into Lightdash metrics automatically when you deploy. You keep a single source of truth for metric definitions in MetricFlow format, and Lightdash reads them directly from your compiled dbt project. You don’t need a dbt Cloud subscription or the dbt Semantic Layer API, and you don’t need to duplicate the definitions.

How it works

When you run lightdash deploy (or lightdash compile / lightdash preview), the CLI reads your project’s compiled manifest.json, which contains everything MetricFlow knows about your project: the semantic_models (entities, dimensions, measures) and the metrics defined on top of them.

These metrics can then be used everywhere Lightdash metrics work (charts, dashboards, the API, AI agents).

dbt parse                # or dbt compile — writes target/manifest.json
lightdash deploy         # translates MetricFlow metrics as part of the deploy

If you define a metric with the same name in both MetricFlow and your model’s meta.metrics, the meta.metrics (Lightdash YAML) definition will take precedence.

Use the latest MetricFlow spec

Lightdash supports the latest and legacy spec, though it’s recommended to use the latest spec.

• Legacy spec (dbt Core 1.6+): top-level semantic_models: and metrics: blocks with type_params.
• Latest spec (dbt Fusion engine, dbt platform, dbt Core 1.12+): semantic_model: enabled inline on the model, with entities/dimensions on columns and metrics using top-level agg / expr keys.

Supported features

simple metrics, and measures flagged create_metric: true, translate into Lightdash:

MetricFlow	Lightdash metric type
agg: sum	sum
agg: count	count
agg: count_distinct	count_distinct
agg: average	average
agg: median	median
agg: min / agg: max	min / max
agg: percentile	percentile

Also carried over:

• Measure expr: bare column references and SQL expressions both become the metric’s SQL.
• Labels and descriptions: from the metric, falling back to the measure’s.

What’s not supported yet

These are skipped with a warning on deploy (details under --verbose):

MetricFlow feature	Notes
ratio metrics	numerator/denominator over other metrics
derived metrics	expressions over other metrics
cumulative metrics	require time-spine semantics
conversion metrics	require entity-journey semantics
Metric or measure filter:	MetricFlow where-filter templates (e.g. {{ Dimension('order__status') }} = 'completed') don’t map to Lightdash metric filters
agg: sum_boolean	no Lightdash equivalent
percentile_type: discrete	Lightdash percentiles always compile to PERCENTILE_CONT
join_to_timespine, fill_nulls_with, non_additive_dimension	no equivalents

And these parts of the semantic model are currently skipped:

• Entities / joins. MetricFlow joins semantic models implicitly at query time through shared entity keys. Lightdash joins are explicit and authored per-explore (joining tables).
• Dimensions and agg_time_dimension. Lightdash generates dimensions from your model’s real columns, so all of your columns are already available as dimensions, and metrics can be grouped by any of them — the MetricFlow dimension definitions aren’t needed.

Example

A complete working example (both specs, with a reproducible test) lives in the Lightdash repo under examples/metricflow-demo. The short version, in the legacy spec:

semantic_models:
  - name: orders
    model: ref('orders')
    defaults:
      agg_time_dimension: ordered_at
    entities:
      - name: order
        type: primary
        expr: order_id
    dimensions:
      - name: ordered_at
        type: time
        type_params:
          time_granularity: day
    measures:
      - name: total_revenue
        agg: sum
        expr: amount
      # create_metric: true auto-creates a metric — also translated
      - name: unique_customers
        agg: count_distinct
        expr: customer_id
        create_metric: true

metrics:
  - name: total_revenue
    label: Total revenue
    type: simple
    type_params:
      measure: total_revenue

Deploying this project gives the orders explore two metrics, Total revenue (SUM("orders".amount)) and unique_customers (COUNT(DISTINCT "orders".customer_id)), with no Lightdash-specific YAML.