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APPROX_QUANTILE (Transact-SQL)

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

The APPROX_QUANTILE function returns an approximate 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 approximate quantile for an entire group.
  • Window usage: Returns the requested approximate quantile for each partition while preserving row-level output.

Transact-SQL syntax conventions

Syntax

Aggregation function syntax:

APPROX_QUANTILE ( numeric_literal , numeric_expression )

Analytic function syntax:

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

Arguments

numeric_literal

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

numeric_expression

The numeric expression whose approximate 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 APPROX_QUANTILE.

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

Return types

Returns float(53).

Remarks

APPROX_QUANTILE estimates a continuous quantile over the ordered non-NULL input values. Use QUANTILE when you require an exact result.

Approximate results can differ slightly from exact QUANTILE results and can vary across executions because of execution plans and parallel merge paths. APPROX_QUANTILE is nondeterministic. For more information, see Deterministic and nondeterministic functions.

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

APPROX_QUANTILE is intended to reduce latency and memory usage compared with exact QUANTILE calculations on large datasets.

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

The APPROX_QUANTILE function is available in Fabric Data Warehouse and the SQL analytics endpoint of Fabric items. The APPROX_QUANTILE function isn't supported in SQL Server, Azure SQL Database, Azure SQL Managed Instance, or SQL database in Fabric.

Use case

Use APPROX_QUANTILE for large-scale dashboards, exploratory analysis, and grouped reporting where a close estimate is sufficient. For example, estimate p90 claim amounts, p95 service latency, or p99 telemetry values without the cost of an exact quantile calculation.

Examples

A. Calculate aggregate approximate quantiles

This example estimates 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
    APPROX_QUANTILE(0.25, value) AS ApproxQ1,
    APPROX_QUANTILE(0.50, value) AS ApproxMedian,
    APPROX_QUANTILE(0.75, value) AS ApproxQ3,
    APPROX_QUANTILE(0.95, value) AS ApproxP95
FROM Samples;

B. Calculate an approximate quantile for each group

This example estimates 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,
    APPROX_QUANTILE(0.90, claim_amount) AS ApproxP90ClaimAmount
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 APPROX_QUANTILE(0.90, discount_percent) AS ApproxP90DiscountPercent
FROM CampaignDiscounts
WHERE campaign_id = 9001;

D. Calculate a partitioned window approximate quantile

This example adds the approximate 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,
    APPROX_QUANTILE(0.95, latency_ms) OVER (PARTITION BY service) AS ServiceApproxP95Latency
FROM ServiceLatency;

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