LayersQL is as a derivative of ShopifyQL but is deliberately narrower in scope. It removes complexity in favor of a small, consistent set of clauses and functions that make metrics easy to define, reuse, and apply directly in merchandising workflows.

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What you can do with LayersQL

Use LayersQL to create metrics and logic that can be reused in:
  • Merchandising rules — e.g., sort products by recent sales, views, or conversions.
  • Custom scoring — combine metrics into weighted scores to power advanced sort orders.
  • Performance tracking — define the same metrics you use in sort orders so they are visible in dashboards.
  • Segmentation — break down performance by geography, marketing channel, or other dimensions to support localized merchandising.

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Basic query structure

LayersQL uses a strict clause order to keep queries simple and predictable: FROM → SHOW → GROUP BY → [WHERE] → SINCE → [SEGMENT BY]
  • FROM: Selects the dataset to build metrics from (e.g., products).
  • SHOW: Defines which metrics or expressions to calculate (e.g., SUM(total_sales)).
  • GROUP BY: Groups results by dimensions or identifiers (e.g., product_id).
  • WHERE (optional): Filters rows before aggregation (e.g., geo_country = 'US').
  • SINCE: Sets the time window for the metric.
  • SEGMENT BY (optional): Adds an additional dimension to further break down each group.

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Example

This query creates a metric for 7-day product sales segmented by country: 
FROM products
SHOW SUM(total_sales)
GROUP BY product_id
SINCE -7d
SEGMENT BY geo_country
The resulting metric can be:
  • displayed directly in the merchandising dashboard, and
  • referenced in sort orders to rank products by recent sales.

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Next steps

  • Learn the full grammar in Syntax.
  • Explore Functions to see available aggregations and expressions.
  • Review Datasets to understand which metrics and dimensions you can query.