Period-over-Period (PoP) Metrics¶

Period-over-Period metrics compare a measure against a prior time period. Common use cases include Year-over-Year (YoY) revenue growth, Month-over-Month (MoM) changes, and same-period-last-year comparisons.

OrionBelt implements PoP using a synthetical date pattern -- a 4-CTE architecture that:

Auto-discovers the date range from actual data (no hardcoded date range in OBML)
Generates a date spine with previous-period lookup
LEFT JOINs facts onto the spine
Self-joins for period comparison

This approach works across all eight supported dialects with no additional configuration beyond the metric definition itself.

OBML Syntax¶

Period-over-Period metrics are declared with type: period_over_period and a periodOverPeriod configuration block:

metrics:
  Revenue YoY Growth:
    type: period_over_period
    expression: '{[Revenue]}'
    periodOverPeriod:
      timeDimension: Order Date
      grain: month
      offset: -1
      offsetGrain: year
      comparison: percentChange

Properties¶

Property	Type	Default	Description
`timeDimension`	string	--	Name of time dimension used for period comparison (must be in SELECT)
`grain`	timeGrain	--	Time grain for the date spine (e.g. `month`, `quarter`, `year`)
`offset`	integer	`-1`	Number of periods to look back (negative = past)
`offsetGrain`	timeGrain	--	Unit for the offset (e.g. `year` for YoY, `month` for MoM)
`comparison`	enum	`percentChange`	How to compare: `ratio`, `difference`, `previousValue`, `percentChange`

Comparison Types¶

Type	Formula	Example
`percentChange`	`current / NULLIF(prev, 0) - 1`	Revenue grew 15% -- result is `0.15`
`ratio`	`current / NULLIF(prev, 0)`	Revenue is 1.15x previous
`difference`	`current - prev`	Revenue increased by $50k
`previousValue`	`prev`	Last year's revenue was $300k

Note

The NULLIF(prev, 0) guard prevents division-by-zero errors when the previous period has no data. The result is NULL in that case.

Examples¶

Year-over-Year (YoY) Growth¶

Compare each month's revenue against the same month one year earlier:

metrics:
  Revenue YoY Growth:
    type: period_over_period
    expression: '{[Revenue]}'
    periodOverPeriod:
      timeDimension: Order Date
      grain: month
      offset: -1
      offsetGrain: year
      comparison: percentChange

Month-over-Month (MoM) Difference¶

Compute the absolute change in revenue from one month to the next:

metrics:
  Revenue MoM Change:
    type: period_over_period
    expression: '{[Revenue]}'
    periodOverPeriod:
      timeDimension: Order Date
      grain: month
      offset: -1
      offsetGrain: month
      comparison: difference

Previous Year Value¶

Retrieve last year's revenue alongside the current period (no calculation, just the prior value):

metrics:
  Revenue Last Year:
    type: period_over_period
    expression: '{[Revenue]}'
    periodOverPeriod:
      timeDimension: Order Date
      grain: month
      offset: -1
      offsetGrain: year
      comparison: previousValue

Quarter-over-Quarter Ratio¶

Compare quarterly revenue as a ratio:

metrics:
  Revenue QoQ Ratio:
    type: period_over_period
    expression: '{[Revenue]}'
    periodOverPeriod:
      timeDimension: Order Date
      grain: quarter
      offset: -1
      offsetGrain: quarter
      comparison: ratio

How It Works (CTE Architecture)¶

OrionBelt compiles PoP metrics into a 4-CTE structure. Each CTE builds on the previous one to produce the final period comparison.

CTE	Purpose
`date_range`	Discover `MIN`/`MAX` date from fact tables with ALL query filters pushed down
`date_spine`	Generate date series with `spine_date` and `spine_date_prev` columns
`pop_base`	Aggregate measures using the spine as `FROM`, with facts LEFT JOINed
`pop_compare`	Self-join `pop_base` via `spine_date_prev` for period comparison

Filter Push-Down¶

Important

ALL query WHERE filters -- both time filters and dimension filters -- are pushed into the date_range CTE. This means that dimension filters like Country = 'Germany' correctly narrow the date range. If sales in Germany only started in 2024, the spine will not extend further back than that.

This design ensures the date spine is scoped to the actual data range relevant to the query, avoiding unnecessary NULL rows for periods with no matching data.

Generated SQL Example¶

The following shows the compiled Postgres SQL for a YoY growth query selecting Order Date (monthly) and Revenue YoY Growth:

PostgresSnowflakeBigQuery

WITH date_range AS (
  SELECT date_trunc('month', MIN("Orders"."ORDER_DATE")) AS min_date,
         date_trunc('month', MAX("Orders"."ORDER_DATE")) AS max_date
  FROM PUBLIC.ORDERS AS "Orders"
),
date_spine AS (
  SELECT d::date AS spine_date,
         CASE WHEN (d + INTERVAL '-1 year')::date >= date_range.min_date
              THEN (d + INTERVAL '-1 year')::date END AS spine_date_prev
  FROM date_range,
       generate_series(date_range.min_date::timestamp,
                       date_range.max_date::timestamp,
                       INTERVAL '1 month') AS t(d)
),
pop_base AS (
  SELECT date_spine.spine_date AS "Order Date",
         SUM("Orders"."AMOUNT") AS "Revenue"
  FROM date_spine
  LEFT JOIN PUBLIC.ORDERS AS "Orders"
    ON date_trunc('month', "Orders"."ORDER_DATE") = date_spine.spine_date
  GROUP BY 1
),
pop_compare AS (
  SELECT pop_base."Order Date",
         pop_base."Revenue",
         pop_base."Revenue" / NULLIF(prev."Revenue", 0) - 1
           AS "Revenue YoY Growth"
  FROM pop_base
  LEFT JOIN date_spine ON pop_base."Order Date" = date_spine.spine_date
  LEFT JOIN pop_base AS prev
    ON date_spine.spine_date_prev = prev."Order Date"
)
SELECT "Order Date", "Revenue", "Revenue YoY Growth"
FROM pop_compare
ORDER BY 1

WITH date_range AS (
  SELECT DATE_TRUNC('month', MIN("Orders"."ORDER_DATE")) AS min_date,
         DATE_TRUNC('month', MAX("Orders"."ORDER_DATE")) AS max_date
  FROM WAREHOUSE.PUBLIC.ORDERS AS "Orders"
),
date_spine AS (
  SELECT DATEADD('month', ROW_NUMBER() OVER (ORDER BY SEQ4()) - 1,
                 date_range.min_date)::DATE AS spine_date,
         CASE WHEN DATEADD('year', -1,
                 DATEADD('month', ROW_NUMBER() OVER (ORDER BY SEQ4()) - 1,
                         date_range.min_date))::DATE >= date_range.min_date
              THEN DATEADD('year', -1,
                 DATEADD('month', ROW_NUMBER() OVER (ORDER BY SEQ4()) - 1,
                         date_range.min_date))::DATE END AS spine_date_prev
  FROM date_range,
       TABLE(GENERATOR(ROWCOUNT => DATEDIFF('month', date_range.min_date,
                                             date_range.max_date) + 1))
),
pop_base AS (
  SELECT date_spine.spine_date AS "Order Date",
         SUM("Orders"."AMOUNT") AS "Revenue"
  FROM date_spine
  LEFT JOIN WAREHOUSE.PUBLIC.ORDERS AS "Orders"
    ON DATE_TRUNC('month', "Orders"."ORDER_DATE") = date_spine.spine_date
  GROUP BY 1
),
pop_compare AS (
  SELECT pop_base."Order Date",
         pop_base."Revenue",
         pop_base."Revenue" / NULLIF(prev."Revenue", 0) - 1
           AS "Revenue YoY Growth"
  FROM pop_base
  LEFT JOIN date_spine ON pop_base."Order Date" = date_spine.spine_date
  LEFT JOIN pop_base AS prev
    ON date_spine.spine_date_prev = prev."Order Date"
)
SELECT "Order Date", "Revenue", "Revenue YoY Growth"
FROM pop_compare
ORDER BY 1

WITH date_range AS (
  SELECT DATE_TRUNC(MIN(`Orders`.`ORDER_DATE`), MONTH) AS min_date,
         DATE_TRUNC(MAX(`Orders`.`ORDER_DATE`), MONTH) AS max_date
  FROM `PROJECT.DATASET.ORDERS` AS `Orders`
),
date_spine AS (
  SELECT d AS spine_date,
         CASE WHEN DATE_SUB(d, INTERVAL 1 YEAR) >= date_range.min_date
              THEN DATE_SUB(d, INTERVAL 1 YEAR) END AS spine_date_prev
  FROM date_range,
       UNNEST(GENERATE_DATE_ARRAY(date_range.min_date,
                                   date_range.max_date,
                                   INTERVAL 1 MONTH)) AS d
),
pop_base AS (
  SELECT date_spine.spine_date AS `Order Date`,
         SUM(`Orders`.`AMOUNT`) AS `Revenue`
  FROM date_spine
  LEFT JOIN `PROJECT.DATASET.ORDERS` AS `Orders`
    ON DATE_TRUNC(`Orders`.`ORDER_DATE`, MONTH) = date_spine.spine_date
  GROUP BY 1
),
pop_compare AS (
  SELECT pop_base.`Order Date`,
         pop_base.`Revenue`,
         pop_base.`Revenue` / NULLIF(prev.`Revenue`, 0) - 1
           AS `Revenue YoY Growth`
  FROM pop_base
  LEFT JOIN date_spine ON pop_base.`Order Date` = date_spine.spine_date
  LEFT JOIN pop_base AS prev
    ON date_spine.spine_date_prev = prev.`Order Date`
)
SELECT `Order Date`, `Revenue`, `Revenue YoY Growth`
FROM pop_compare
ORDER BY 1

Dialect Support¶

Each dialect uses a different technique to generate the date spine:

Dialect	Date Spine Technique
Postgres	`generate_series(min, max, INTERVAL)`
DuckDB	`generate_series(min, max, INTERVAL)`
Snowflake	`TABLE(GENERATOR(ROWCOUNT => ...))` + `DATEADD`
BigQuery	`UNNEST(GENERATE_DATE_ARRAY(min, max, INTERVAL))`
Databricks	`EXPLODE(SEQUENCE(min, max, INTERVAL))`
MySQL	Recursive CTE: `WITH RECURSIVE dates AS (...)`
ClickHouse	`arrayJoin(range(...))` + date arithmetic
Dremio	Recursive CTE: `WITH RECURSIVE dates AS (...)`

Recursive CTE fallback

MySQL and Dremio lack built-in series-generating functions, so the date spine is generated using a recursive CTE that starts at min_date and increments by the grain interval until max_date is reached.

Constraints¶

Current limitations

All PoP metrics in a single query must share the same timeDimension and grain.
The timeDimension must be included in the query's selected dimensions.
PoP metrics require an expression referencing at least one measure.