Edit

QUANTILE (Transact-SQL)

Applies to: SQL analytics endpoint in Microsoft Fabric and Warehouse in Microsoft Fabric

The QUANTILE function returns an exact continuous quantile of non-NULL numeric values. You can use it as both an aggregate function and a window (analytic) function:

  • Aggregate usage: Returns the requested quantile for an entire group.
  • Window usage: Returns the requested quantile for each partition while preserving row-level output.

Transact-SQL syntax conventions

Syntax

Aggregation function syntax:

QUANTILE ( numeric_literal , numeric_expression )

Analytic function syntax:

QUANTILE ( numeric_literal , numeric_expression ) OVER ( [ <partition_by_clause> ] )

Arguments

numeric_literal

The quantile to calculate. The value must be in the inclusive range from 0.0 through 1.0. For example, specify 0.5 to calculate the median or 0.95 to calculate the 95th percentile.

numeric_expression

The numeric expression whose quantile is calculated. Supported exact numeric types are int, bigint, smallint, tinyint, numeric, decimal, smallmoney, and money. Supported approximate numeric types are float and real.

OVER clause

The partition_by_clause divides the result set produced by the FROM clause into partitions, and the function is applied to each partition.

If you don't specify partition_by_clause, the function treats all rows of the query result set as a single partition.

The OVER clause doesn't support ORDER BY, ROWS, or RANGE for QUANTILE.

For more information, see SELECT - OVER clause (Transact-SQL).

Return types

Returns float(53).

Remarks

QUANTILE computes a continuous quantile over the ordered non-NULL input values. When the requested quantile falls between two values, the function interpolates between them. The result might not be a value that exists in the input rows.

The aggregate form of QUANTILE(p, numeric_expression) is equivalent to PERCENTILE_CONT(p) WITHIN GROUP (ORDER BY numeric_expression). The analytic form has equivalent percentile-continuous semantics within each partition.

NULL values are ignored. If all input values are NULL, or if no rows qualify, QUANTILE returns NULL. When ANSI_WARNINGS is ON, eliminating NULL values produces the standard aggregate warning.

For the same non-NULL input values and numeric_literal, QUANTILE returns the same result.

DISTINCT isn't supported. Character, date, time, and datetime expressions aren't supported.

The QUANTILE function isn't supported in SQL Server, Azure SQL Database, Azure SQL Managed Instance, or SQL database in Fabric.

Use case

Use QUANTILE to calculate quartiles, percentiles, and other exact distribution boundaries with concise syntax. For example, calculate p90 claim amounts by plan, p95 service latency by region, or Q1, median, and Q3 values for a business metric.

Examples

A. Calculate aggregate quantiles

This example calculates the first quartile, median, third quartile, and 95th percentile.

WITH Samples AS (
    SELECT *
    FROM (VALUES
        (1), (NULL), (2), (3), (4), (5), (6), (7), (8), (13)
    ) AS v(value)
)
SELECT
    QUANTILE(0.25, value) AS Q1_25thPercentile,
    QUANTILE(0.50, value) AS Median_50thPercentile,
    QUANTILE(0.75, value) AS Q3_75thPercentile,
    QUANTILE(0.95, value) AS P95
FROM Samples;

B. Calculate a quantile for each group

This example calculates the 90th percentile claim amount for each insurance plan.

WITH Claims AS (
    SELECT *
    FROM (VALUES
        ('Plan-A', 420.00),
        ('Plan-A', 500.00),
        ('Plan-A', 610.00),
        ('Plan-B', 250.00),
        ('Plan-B', 275.00),
        ('Plan-B', 310.00)
    ) AS v(plan_name, claim_amount)
)
SELECT
    plan_name,
    QUANTILE(0.90, claim_amount) AS P90ClaimAmount
FROM Claims
GROUP BY plan_name;

C. Return NULL for an all-NULL group

This example returns NULL because all qualifying values are NULL.

WITH CampaignDiscounts AS (
    SELECT *
    FROM (VALUES
        (9001, NULL),
        (9001, NULL),
        (9001, NULL),
        (9002, 10.00)
    ) AS v(campaign_id, discount_percent)
)
SELECT QUANTILE(0.90, discount_percent) AS P90DiscountPercent
FROM CampaignDiscounts
WHERE campaign_id = 9001;

D. Calculate a partitioned window quantile

This example adds the 95th-percentile latency for each service to every request row.

WITH ServiceLatency AS (
    SELECT *
    FROM (VALUES
        ('Checkout', 'req-001', 180),
        ('Checkout', 'req-002', 220),
        ('Checkout', 'req-003', 260),
        ('Search', 'req-010', 90),
        ('Search', 'req-011', 110),
        ('Search', 'req-012', 130)
    ) AS v(service, request_id, latency_ms)
)
SELECT
    service,
    request_id,
    latency_ms,
    QUANTILE(0.95, latency_ms) OVER (PARTITION BY service) AS ServiceP95Latency
FROM ServiceLatency;

Related content

  • Last updated on