> ## Documentation Index
> Fetch the complete documentation index at: https://lightdash-mintlify-cccf65ca.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Aggregate functions

> Built-in functions for referencing column and row totals in your table calculations.

Aggregate functions let you reference grand totals and row totals in your table calculations. Unlike [row functions](/references/table-calculation-functions/row-functions) and [pivot functions](/references/table-calculation-functions/pivot-functions) which compile to SQL window functions, aggregate functions generate separate CTEs that compute totals from the underlying data.

This means `total()` correctly handles all metric types — including `count_distinct` and `average` — by re-running the metric's native aggregation rather than naively summing grouped values.

<Note>
  Aggregate functions only accept **metric** field references — dimensions and other table calculations are not valid arguments. Passing a dimension (e.g. `total(${orders.customer_id})`) returns the error *"Tried to reference metric with unknown field id"*.
</Note>

## total

Returns the grand total of a metric across all rows, computed from the raw data using the metric's native aggregation.

```
total(${table.metric})
```

| Parameter | Type             | Description                               |
| :-------- | :--------------- | :---------------------------------------- |
| `metric`  | metric reference | The metric to compute the grand total for |

**Example**

Calculate each row's share of total revenue:

```
${orders.total_revenue} / total(${orders.total_revenue})
```

With percent formatting applied, this gives you each row as a percentage of the overall total.

<img src="https://mintcdn.com/lightdash-mintlify-cccf65ca/xnj0RWoRyoyarXfO/images/references/table-calculation-functions/aggregate-functions-total-example.png?fit=max&auto=format&n=xnj0RWoRyoyarXfO&q=85&s=168a5fc6a5c76b18da28bb7e4441f5d6" alt="Edit Table Calculation dialog with a percent_of_total formula using total()" width="800" height="476" data-path="images/references/table-calculation-functions/aggregate-functions-total-example.png" />

<Accordion title="How it works">
  Lightdash generates a `column_totals` CTE that re-aggregates the metric from the raw data with no `GROUP BY`, producing a single grand-total value. This value is then cross-joined into the main query.

  For a `sum` metric, the CTE computes `SUM(...)`. For `count_distinct`, it computes `COUNT(DISTINCT ...)`. For `average`, it computes `AVG(...)`. Each metric type uses its own native aggregation, so the total is always mathematically correct.
</Accordion>

***

## row\_total

Returns the sum of a metric's values across all pivot columns for the current row.

```
row_total(${table.metric})
```

| Parameter | Type             | Description                            |
| :-------- | :--------------- | :------------------------------------- |
| `metric`  | metric reference | The metric to sum across pivot columns |

<Note>
  `row_total` is only available when your query includes a pivoted dimension. If no pivot is configured, `row_total(${table.metric})` falls back to the metric's value directly.
</Note>

**Example**

In a pivot table with revenue broken out by region, calculate each region's share of the row total:

```
${orders.total_revenue} / row_total(${orders.total_revenue})
```

<Accordion title="How it works">
  Lightdash generates a `row_totals` CTE that reads from the already-grouped results and computes `SUM(metric)` grouped by the non-pivot dimensions. This gives one total per row, which is then joined back into the main query.

  Unlike `total()`, `row_total()` always uses `SUM` regardless of the metric type, since it's summing pre-aggregated values across the pivot columns within each row.
</Accordion>