Set-Based SQL for Range-Driven Joins: Apply Rule Tables as Dynamic Filters to Aggregate Population

Learn how to use SQL range-and-equality joins to apply rule tables as dynamic filters and aggregate measures set-wise, with LEFT JOIN, BETWEEN, and scale.

When a fact table encodes filtering rules as values, you often need to “apply” each row as a dynamic filter to another dataset and aggregate the result. In this case, one table provides the ranges and categories, and another stores the counts you want to sum. The key is expressing those rules as a compound range-and-equality join, so every row in the rule table drives a corresponding aggregation of the measure table.

Single-row aggregation example

The following query demonstrates the goal for a single set of constraints: aggregate population by year, given explicit filters across ranges and categories.

SELECT cal_year, SUM(headcount) AS total_headcount
FROM census_pop
WHERE (cal_year BETWEEN 2018 AND 2018)
  AND (age_val BETWEEN 18 AND 85)
  AND region = 'Pennsylvania'
  AND sex IN ('Male', 'Female')
  AND ethnicity IN ('Multiracial')
  AND origin IN ('Not Hispanic')
GROUP BY cal_year
ORDER BY cal_year ASC

This works for one row’s worth of parameters. The challenge is doing the same for every row in the rule-bearing table without rewriting the WHERE clause over and over.

What’s really happening

You don’t have natural join keys. Instead, the linkage is defined by ranges and categorical rules: a year window, an age window, and equality on geography and demographics with allowances for catch-all values like both or All. That means your join is necessarily “squishy,” composed of BETWEEN and OR conditions. It’s valid SQL, just not a single-key equi-join.

Because this type of join evaluates multiple predicates row by row, it’s heavier than a key-based join. If your tables are small, it can be acceptable; as volume grows, so will the cost.

Set-based solution that scales to all rows

The practical approach is to join the rule table to the population table using all of the range and category constraints in the ON clause, then group by the rule columns and sum the measure. That way, each rule row drives its own result.

SELECT
  r.yr_start,
  r.yr_end,
  r.loc_label,
  r.age_min,
  r.age_max,
  r.sex,
  r.ethnicity,
  r.origin,
  SUM(p.headcount) AS total_headcount
FROM metrics_src AS r
LEFT JOIN census_pop AS p
  ON p.cal_year BETWEEN r.yr_start AND r.yr_end
 AND (r.sex = p.sex OR r.sex = 'both')
 AND p.age_val BETWEEN p.rng_age_from AND CASE WHEN p.rng_age_to = 'infinity' THEN 1000 ELSE p.rng_age_to END
 AND r.loc_label = p.region
 AND (r.ethnicity = p.ethnicity OR r.ethnicity = 'All')
 AND (r.origin = p.origin OR r.origin = 'Both')
GROUP BY 1,2,3,4,5,6,7,8
ORDER BY 9 DESC

This query applies every rule row in metrics_src against census_pop at once. It handles the year window, the optional both sex value, an age range that treats an upper bound of infinity as open-ended, and category matching for location, ethnicity, and origin with appropriate fallbacks to All and Both. The LEFT JOIN ensures all rule rows are preserved even when no matching population exists.

Why this works

Each predicate in the ON clause mirrors the intended filter logic. The BETWEEN checks capture ranges; the OR checks allow catch-all values; the equality checks bind geography. Grouping by the rule columns rolls up one aggregate per rule row, exactly like running the single-row query many times, but done set-wise in a single pass with ANSI SQL.

An alternative when you need a tighter link

If you prefer a more explicit linkage, one option is to transform the rule table by expanding its year and age ranges into atomic values and duplicating rows accordingly. After that, you can derive a composite identifier from all relevant fields and do the same in the population table, then join on those identifiers. This normalization style makes the relationship more direct and can simplify the join predicate. Whether it improves runtime depends on data and execution, but it can make the logic easier to reason about.

Why this matters

Range-driven joins are common in analytics: eligibility windows, demographic bands, rolling periods. Knowing how to encode those rules directly in the join lets you translate business logic into an auditable, repeatable SQL pattern. It also keeps the solution within standard SQL, which is portable and works well in engines like DuckDB.

Takeaways

When your rules live in a fact-like table, treat them as first-class join conditions. Put the full logic in the ON clause, aggregate by the rule columns, and be mindful that such joins are heavier than key-based joins. If you share your work, a Minimal Reproducible Example helps others reason about the logic, and avoiding screenshots of code or data makes your case clearer and easier to test.