Aggregations

Aggregations turn raw field values into Pivot measures. A value field points to a source field, and an aggregator describes how that field should be summarized inside each Pivot cell.

Dimension vs Measure

• A dimension groups facts.
• A measure summarizes facts.

In RevoGrid Pivot, both are built from the same field model. The difference is how you place them:

• rows and columns use a field as a grouping dimension.
• values uses a field as a measure source.

Basic Value Configuration

values: [
  { prop: 'sales', aggregator: 'sum' },
  { prop: 'sales', aggregator: 'avg' },
  { prop: 'orders', aggregator: 'count' },
]

Each value becomes a separate measure in the generated Pivot output.

How Aggregator Resolution Works

values[].aggregator is a string id. Pivot resolves that id against the matching dimension’s aggregators map. If you use the configurator, those same registered aggregator ids populate the measure selector.

dimensions: [
  {
    prop: 'sales',
    name: 'Sales',
    aggregators: {
      sum: commonAggregators.sum,
      avg: commonAggregators.avg,
    },
  },
],
values: [{ prop: 'sales', aggregator: 'avg' }]

If the dimension does not expose the aggregator id, the measure cannot be resolved correctly.

Built-In Summary Functions

The public remote/server-side summary model supports these stable ids:

• sum
• min
• max
• avg
• count

Client-side Pivot can also use custom aggregator functions through the dimension model.

Multiple Measures

When you define multiple values, Pivot generates one measure per configured entry. That is useful for layouts like:

• Sales sum by quarter
• Margin avg by quarter
• Order count by quarter

On the client side, each generated value column uses the measure’s prop and aggregator. On the remote side, result ids are stable summary ids such as SalesAmount_sum and SalesAmount_avg.

Formatting Measures

Formatting belongs in the dimension metadata used by the generated columns. For example, attach a numeric column type or a custom template to the measure dimension so generated cells inherit the right rendering behavior.

Custom Aggregators

Custom aggregators are useful when:

• you need a domain-specific summary
• the display value is not one of the common numeric summaries
• you are staying on the client-side path

Keep them lightweight. Client-side aggregators are run while materializing Pivot output, so very expensive functions will slow down large pivots.

Average And Derived Measures

avg deserves special attention in OLAP-style systems:

• It is not additive.
• Correct remote planning often requires sum + count internally.
• Totals must be computed from underlying facts, not from already-averaged child cells.

The remote planner layer in this repository already models avg as a derived summary when planning server-side requests.

Best Practices

• Prefer additive measures like sum and count when possible.
• Only expose aggregators that make business sense for the field.
• Keep stable aggregator ids across saved states and remote APIs.
• Name measures clearly in examples and UI text so users know whether they are looking at a sum, avg, or count.

Next

Continue with Configuration Reference for the full PivotConfig surface.