Time Dimensions

While every metrics view has a primary timeseries column that powers the main time series chart, you can define additional time dimensions using type: time. This enables users to filter and analyze data across multiple temporal columns.

When to Use Time Dimensions

Time dimensions are useful when your data contains multiple date or timestamp columns that users may want to filter by:

• Order date vs. Ship date: Filter orders by when they were placed or when they were shipped
• Created vs. Updated timestamps: Analyze records by creation date or last modification
• Event time vs. Processing time: Distinguish between when events occurred and when they were recorded
• Multiple business dates: Handle scenarios like invoice date, due date, and payment date

Adding a Time Dimension

To create a time dimension, add type: time to your dimension definition:

version: 1
type: metrics_view

model: orders_model
timeseries: order_date  # Primary time series for charts

dimensions:
  - name: order_date
    display_name: Order Date
    column: order_date
    type: time

  - name: ship_date
    display_name: Ship Date
    column: ship_date
    type: time

  - name: customer_region
    column: region

Time Dimension vs. Timeseries

Understanding the difference between these two concepts is important:

Feature	timeseries (top-level)	type: time (dimension)
Purpose	Powers main time series chart	Enables time-based filtering
Chart display	Shows trends over time	Used in filter panel
Required	No (but recommended)	No
Multiple allowed	No (one per metrics view)	Yes

The primary timeseries column determines which dates appear on the x-axis of your time series visualizations. Time dimensions provide additional temporal filtering options in the dashboard filter panel.

Full Example

Here's a complete example with multiple time dimensions:

version: 1
type: metrics_view

model: sales_model
timeseries: transaction_date

dimensions:
  # Time dimensions
  - name: transaction_date
    display_name: Transaction Date
    column: transaction_date
    type: time

  - name: fulfillment_date
    display_name: Fulfillment Date
    column: fulfillment_date
    type: time

  - name: return_date
    display_name: Return Date
    column: return_date
    type: time

  # Categorical dimensions
  - name: product_category
    display_name: Product Category
    column: category

  - name: store_location
    display_name: Store Location
    column: store_id

measures:
  - name: total_sales
    display_name: Total Sales
    expression: SUM(amount)

  - name: order_count
    display_name: Order Count
    expression: COUNT(*)

Using Expressions

You can also create time dimensions using expressions to transform or derive time values:

dimensions:
  - name: order_month
    display_name: Order Month
    expression: DATE_TRUNC('month', order_date)
    type: time

  - name: fiscal_quarter_start
    display_name: Fiscal Quarter Start
    expression: DATE_TRUNC('quarter', order_date + INTERVAL '3 months') - INTERVAL '3 months'
    type: time

tip

Time dimensions work with columns of type TIMESTAMP, TIME, or DATE. If your source data stores dates in a different format (like strings), use an expression to convert them to a proper date type.