How to calculate median in AWS Redshift?: 3 Methods + Performance Guide

# Quick Answer

Calculating the median in AWS Redshift can be achieved efficiently using several methods, primarily leveraging window functions. The most direct and recommended approach for modern Redshift versions is the

MEDIAN()

window function. For scenarios requiring more control or compatibility with older Redshift versions,

PERCENTILE_CONT()

NTILE()

offer robust alternatives.

Here are immediate copy-paste solutions for common scenarios:

Scenario 1: Simple Median of a Column To find the median of a single numeric column in your entire dataset.

-- Speed Seeker, Problem Solver
-- Calculates the median of the 'value_column' across the entire table.
-- The DISTINCT keyword is crucial to avoid duplicate median values for each row.
SELECT DISTINCT MEDIAN(value_column) OVER () AS overall_median
FROM your_table;

Scenario 2: Median Grouped by a Category To calculate the median for each distinct category within your data.

-- Speed Seeker, Problem Solver, Architecture Builder
-- Calculates the median of 'value_column' for each 'category_column'.
SELECT DISTINCT category_column, MEDIAN(value_column) OVER (PARTITION BY category_column) AS category_median
FROM your_table
ORDER BY category_column;

Scenario 3: Median using PERCENTILE_CONT (for continuous data) When you need a percentile-based median, especially for continuous data where interpolation is desired.

-- Learning Explorer, Architecture Builder
-- Calculates the 50th percentile (median) of 'value_column' across the entire table.
-- PERCENTILE_CONT interpolates between values if the median falls between two data points.
SELECT DISTINCT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY value_column) OVER () AS overall_median_cont
FROM your_table;

Scenario 4: Median using PERCENTILE_DISC (for discrete data) When you need a percentile-based median that picks an actual data point.

-- Learning Explorer, Architecture Builder
-- Calculates the 50th percentile (median) of 'value_column' across the entire table.
-- PERCENTILE_DISC picks an actual value from the dataset.
SELECT DISTINCT PERCENTILE_DISC(0.5) WITHIN GROUP (ORDER BY value_column) OVER () AS overall_median_disc
FROM your_table;

Scenario 5: Median using NTILE (approximation for large datasets) An approximation method, useful for very large datasets or older Redshift versions where other functions might not be available or perform optimally.

-- Legacy Maintainer, Problem Solver (for specific edge cases)
-- Approximates the median by dividing data into two groups and finding the minimum of the first group.
-- This method is less precise for small datasets but can be performant for very large ones.
WITH RankedData AS (
    SELECT
        value_column,
        NTILE(2) OVER (ORDER BY value_column DESC) AS ntile_group
    FROM your_table
)
SELECT MIN(value_column) AS approximate_median
FROM RankedData
WHERE ntile_group = 1;

# Choose Your Method

# Table of Contents

Quick Answer
Choose Your Method
Table of Contents
Ready-to-Use Code
Method Sections
- Method 1: Using the
```
MEDIAN()
```
  Window Function [Speed Seeker, Problem Solver, Architecture Builder]
- Method 2: Using
```
PERCENTILE_CONT()
```
  and
```
PERCENTILE_DISC()
```
  Window Functions [Learning Explorer, Architecture Builder, Legacy Maintainer]
- Method 3: Approximating with
```
NTILE()
```
  Window Function [Legacy Maintainer, Problem Solver (for specific edge cases)]
Performance Comparison
Amazon-Redshift Version Support
Common Problems & Solutions
Real-World Examples
Related Amazon-Redshift Functions
Summary
Frequently Asked Questions
Test Your Code

# Ready-to-Use Code

Here's a collection of practical, copy-paste ready code snippets for various median calculation scenarios in Redshift. We'll use a sample

sales_data

table for demonstration.

First, let's create a sample table and insert some data:

-- Create sample table
CREATE TABLE sales_data (
    sale_id INT IDENTITY(1,1),
    product_category VARCHAR(50),
    sale_amount DECIMAL(10, 2),
    sale_date DATE
);

-- Insert sample data
INSERT INTO sales_data (product_category, sale_amount, sale_date) VALUES
('Electronics', 150.00, '2024-01-01'),
('Electronics', 200.00, '2024-01-02'),
('Electronics', 120.00, '2024-01-03'),
('Electronics', 300.00, '2024-01-04'),
('Electronics', 180.00, '2024-01-05'),
('Clothing', 50.00, '2024-01-01'),
('Clothing', 75.00, '2024-01-02'),
('Clothing', 60.00, '2024-01-03'),
('Clothing', 100.00, '2024-01-04'),
('Books', 25.00, '2024-01-01'),
('Books', 30.00, '2024-01-02'),
('Books', 40.00, '2024-01-03'),
('Books', 35.00, '2024-01-04'),
('Home Goods', 80.00, '2024-01-01'),
('Home Goods', 90.00, '2024-01-02'),
('Home Goods', 110.00, '2024-01-03'),
('Home Goods', 100.00, '2024-01-04'),
('Electronics', 220.00, '2024-01-06'),
('Clothing', 85.00, '2024-01-05'),
('Books', 28.00, '2024-01-05'),
('Electronics', 190.00, '2024-01-07');

1. Overall Median of

sale_amount

using
MEDIAN()

-- Calculates the median sale amount for all sales.
SELECT DISTINCT MEDIAN(sale_amount) OVER () AS overall_median_sale_amount
FROM sales_data;

2. Median

sale_amount

per
product_category
using
MEDIAN()

-- Calculates the median sale amount for each product category.
SELECT DISTINCT
    product_category,
    MEDIAN(sale_amount) OVER (PARTITION BY product_category) AS category_median_sale_amount
FROM sales_data
ORDER BY product_category;

3. Overall Median of

sale_amount

using
PERCENTILE_CONT()

-- Calculates the 50th percentile (median) of sale amounts, interpolating if necessary.
SELECT DISTINCT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY sale_amount) OVER () AS overall_median_cont
FROM sales_data;

4. Median

sale_amount

per
product_category
using
PERCENTILE_CONT()

-- Calculates the 50th percentile (median) of sale amounts for each category, interpolating.
SELECT DISTINCT
    product_category,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY sale_amount) OVER (PARTITION BY product_category) AS category_median_cont
FROM sales_data
ORDER BY product_category;

5. Overall Median of

sale_amount

using
PERCENTILE_DISC()

-- Calculates the 50th percentile (median) of sale amounts, picking an actual value.
SELECT DISTINCT PERCENTILE_DISC(0.5) WITHIN GROUP (ORDER BY sale_amount) OVER () AS overall_median_disc
FROM sales_data;

6. Median

sale_amount

per
product_category
using
PERCENTILE_DISC()

-- Calculates the 50th percentile (median) of sale amounts for each category, picking an actual value.
SELECT DISTINCT
    product_category,
    PERCENTILE_DISC(0.5) WITHIN GROUP (ORDER BY sale_amount) OVER (PARTITION BY product_category) AS category_median_disc
FROM sales_data
ORDER BY product_category;

7. Approximate Overall Median using

NTILE()

-- Approximates the median sale amount for all sales using NTILE.
WITH RankedSales AS (
    SELECT
        sale_amount,
        NTILE(2) OVER (ORDER BY sale_amount DESC) AS ntile_group
    FROM sales_data
)
SELECT MIN(sale_amount) AS approximate_overall_median
FROM RankedSales
WHERE ntile_group = 1;

8. Approximate Median

sale_amount

per
product_category
using
NTILE()

-- Approximates the median sale amount for each product category using NTILE.
WITH RankedCategorySales AS (
    SELECT
        product_category,
        sale_amount,
        NTILE(2) OVER (PARTITION BY product_category ORDER BY sale_amount DESC) AS ntile_group
    FROM sales_data
)
SELECT
    product_category,
    MIN(sale_amount) AS approximate_category_median
FROM RankedCategorySales
WHERE ntile_group = 1
GROUP BY product_category
ORDER BY product_category;

9. Median with Filtering (e.g., sales after a specific date)

-- Calculates the median sale amount for sales made after '2024-01-03'.
SELECT DISTINCT MEDIAN(sale_amount) OVER () AS median_after_date
FROM sales_data
WHERE sale_date > '2024-01-03';

10. Median with Multiple Partitioning Columns

-- Calculates the median sale amount for each product category per month.
-- (Assuming sale_date is granular enough to extract month)
SELECT DISTINCT
    product_category,
    EXTRACT(MONTH FROM sale_date) AS sale_month,
    MEDIAN(sale_amount) OVER (PARTITION BY product_category, EXTRACT(MONTH FROM sale_date)) AS monthly_category_median
FROM sales_data
ORDER BY product_category, sale_month;

11. Median of a derived value (e.g.,

sale_amount

/
quantity
)

-- Assuming a 'quantity' column exists, calculate median unit price.
-- For demonstration, let's assume quantity is always 1 for simplicity, or add it to the table.
-- Let's add a quantity column for a more realistic example.
ALTER TABLE sales_data ADD COLUMN quantity INT DEFAULT 1;
UPDATE sales_data SET quantity = FLOOR(RANDOM() * 5) + 1; -- Random quantities between 1 and 5

SELECT DISTINCT MEDIAN(sale_amount / quantity) OVER () AS median_unit_price
FROM sales_data
WHERE quantity > 0; -- Avoid division by zero

12. Median with NULL values handling

Redshift's

MEDIAN

PERCENTILE_CONT

, and

PERCENTILE_DISC

functions, like most aggregate and window functions, ignore

NULL

values by default. This example demonstrates this behavior.

-- Create a table with NULLs
CREATE TABLE sales_with_nulls (
    id INT,
    value DECIMAL(10,2)
);

INSERT INTO sales_with_nulls (id, value) VALUES
(1, 10.00),
(2, 20.00),
(3, NULL),
(4, 30.00),
(5, 40.00),
(6, NULL),
(7, 50.00);

-- Calculate median, NULLs are ignored
SELECT DISTINCT MEDIAN(value) OVER () AS median_ignoring_nulls
FROM sales_with_nulls;

-- If you wanted to treat NULLs as 0 (or another value), you'd use COALESCE
SELECT DISTINCT MEDIAN(COALESCE(value, 0)) OVER () AS median_treating_nulls_as_zero
FROM sales_with_nulls;

# Method Sections

# Method 1: Using the

MEDIAN()

Window Function

[Speed Seeker, Problem Solver, Architecture Builder]

The

MEDIAN()

window function is the most straightforward and generally recommended way to calculate the median in AWS Redshift. It was introduced in Redshift around October 2014, making it available in most modern Redshift clusters. This function directly computes the median value within a specified window (or the entire dataset if no

PARTITION BY

clause is used).

Technical Deep Dive: The

MEDIAN()

function is an ordered-set aggregate function, but in Redshift, it's implemented as a window function. This means it operates on a set of rows defined by the

OVER()

clause. When used without

PARTITION BY

, it treats the entire result set as a single window. When

PARTITION BY

is included, it calculates the median independently for each partition.

The

MEDIAN()

function handles both odd and even numbers of elements in a set. For an odd number of elements, it returns the middle value. For an even number of elements, it calculates the average of the two middle values. This behavior aligns with the standard definition of a median for continuous data.

NULL

values are ignored in the calculation.

Best Practices:

Use
DISTINCT
with
OVER()
: When calculating a median over the entire dataset (
```
OVER ()
```
), the
```
MEDIAN()
```
window function will return the same median value for every row in your table. To get a single result, always wrap it in a
```
SELECT DISTINCT
```
or an outer aggregate function like
```
MIN()
```
or
```
MAX()
```
.
```
SELECT DISTINCT
```
is generally preferred for clarity.
Partitioning: Leverage
```
PARTITION BY
```
to calculate medians for subgroups (e.g., median sales per product category, median age per demographic). This is highly efficient as it processes partitions in parallel.
Performance: For very large datasets,
```
MEDIAN()
```
is optimized within Redshift and generally performs well. However, complex
```
PARTITION BY
```
clauses on high-cardinality columns can impact performance due to data redistribution.

Working Examples:

Example 1.1: Overall Median of a Numeric Column Find the median

sale_amount

for all sales in the

sales_data

table.

-- Method 1.1: Overall Median
SELECT DISTINCT MEDIAN(sale_amount) OVER () AS overall_median_sale_amount
FROM sales_data;

Example 1.2: Median per Category Calculate the median

sale_amount

for each

product_category

-- Method 1.2: Median per Category
SELECT DISTINCT
    product_category,
    MEDIAN(sale_amount) OVER (PARTITION BY product_category) AS category_median_sale_amount
FROM sales_data
ORDER BY product_category;

Example 1.3: Median with Multiple Partitioning Keys Find the median

sale_amount

for each

product_category

per

sale_date

-- Method 1.3: Median with Multiple Partitioning Keys
SELECT DISTINCT
    product_category,
    sale_date,
    MEDIAN(sale_amount) OVER (PARTITION BY product_category, sale_date) AS daily_category_median
FROM sales_data
ORDER BY product_category, sale_date;

Example 1.4: Median of a Derived Value Calculate the median of

sale_amount

divided by

quantity

(median unit price).

-- Method 1.4: Median of a Derived Value
SELECT DISTINCT MEDIAN(sale_amount / quantity) OVER () AS median_unit_price
FROM sales_data
WHERE quantity > 0; -- Ensure no division by zero

Example 1.5: Median with Filtering Calculate the median

sale_amount

only for sales made in 'Electronics' category.

-- Method 1.5: Median with Filtering
SELECT DISTINCT MEDIAN(sale_amount) OVER () AS median_electronics_sales
FROM sales_data
WHERE product_category = 'Electronics';

Example 1.6: Median with Ordering within Partition (though not strictly needed for MEDIAN) While

MEDIAN()

doesn't explicitly require an

ORDER BY

within its

OVER()

clause (it orders internally), it's good to understand how

ORDER BY

works with window functions. For

MEDIAN()

ORDER BY

within

OVER()

is ignored.

-- Method 1.6: Median with Ordering (ORDER BY within OVER() is ignored for MEDIAN)
-- This query will produce the same result as Example 1.2
SELECT DISTINCT
    product_category,
    MEDIAN(sale_amount) OVER (PARTITION BY product_category ORDER BY sale_amount) AS category_median_sale_amount_ordered
FROM sales_data
ORDER BY product_category;

# Method 2: Using

PERCENTILE_CONT()

and

PERCENTILE_DISC()

Window Functions

[Learning Explorer, Architecture Builder, Legacy Maintainer]

PERCENTILE_CONT()

(continuous percentile) and

PERCENTILE_DISC()

(discrete percentile) are powerful window functions that can calculate any percentile, including the median (which is the 50th percentile, or

0.5

). These functions were available in Redshift prior to the introduction of the dedicated

MEDIAN()

function (from 2014-06-30 version onwards), making them useful for legacy systems or when more granular percentile analysis is needed.

Technical Deep Dive: Both

PERCENTILE_CONT()

and

PERCENTILE_DISC()

require an

ORDER BY

clause within their

WITHIN GROUP

specification to define the order of values for percentile calculation.

PERCENTILE_CONT(percentile_value) WITHIN GROUP (ORDER BY sort_expression)
: This function calculates a percentile by interpolating between adjacent values. For the median (0.5), if there's an even number of values, it will average the two middle values. If there's an odd number, it returns the middle value. This is generally preferred for continuous data where interpolation makes sense.
PERCENTILE_DISC(percentile_value) WITHIN GROUP (ORDER BY sort_expression)
: This function calculates a percentile by picking an actual value from the dataset. For the median (0.5), if there's an even number of values, it will pick one of the two middle values based on the ordering and the exact percentile calculation logic (typically the lower one for 0.5). This is often preferred for discrete data or when you need a value that exists in the dataset.

MEDIAN()

, these are window functions and will return the percentile value for every row in the window. You'll need

DISTINCT

or an outer aggregate to get a single result per window/partition.

NULL

values are ignored.

Best Practices:

Choose
CONT
vs.
DISC
wisely:
```
PERCENTILE_CONT
```
is suitable for continuous data (e.g., temperatures, prices) where interpolation is meaningful.
```
PERCENTILE_DISC
```
is better for discrete data (e.g., number of items, ranks) where you want an actual data point. For median,
```
PERCENTILE_CONT
```
often matches the behavior of
```
MEDIAN()
```
.
WITHIN GROUP (ORDER BY ...)
is mandatory: Always specify the ordering for percentile calculation.
Partitioning: Use
```
PARTITION BY
```
in the
```
OVER()
```
clause to calculate percentiles for subgroups.

Working Examples:

Example 2.1: Overall Median using

PERCENTILE_CONT()

Find the median

sale_amount

for all sales, interpolating if necessary.

-- Method 2.1: Overall Median using PERCENTILE_CONT
SELECT DISTINCT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY sale_amount) OVER () AS overall_median_cont
FROM sales_data;

Example 2.2: Median per Category using

PERCENTILE_CONT()

Calculate the median

sale_amount

for each

product_category

using

PERCENTILE_CONT()

-- Method 2.2: Median per Category using PERCENTILE_CONT
SELECT DISTINCT
    product_category,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY sale_amount) OVER (PARTITION BY product_category) AS category_median_cont
FROM sales_data
ORDER BY product_category;

Example 2.3: Overall Median using

PERCENTILE_DISC()

Find the median

sale_amount

for all sales, picking an actual value from the dataset.

-- Method 2.3: Overall Median using PERCENTILE_DISC
SELECT DISTINCT PERCENTILE_DISC(0.5) WITHIN GROUP (ORDER BY sale_amount) OVER () AS overall_median_disc
FROM sales_data;

Example 2.4: Median per Category using

PERCENTILE_DISC()

Calculate the median

sale_amount

for each

product_category

using

PERCENTILE_DISC()

-- Method 2.4: Median per Category using PERCENTILE_DISC
SELECT DISTINCT
    product_category,
    PERCENTILE_DISC(0.5) WITHIN GROUP (ORDER BY sale_amount) OVER (PARTITION BY product_category) AS category_median_disc
FROM sales_data
ORDER BY product_category;

Example 2.5: Calculating other percentiles (e.g., 25th and 75th) Demonstrate the flexibility of these functions to calculate quartiles.

-- Method 2.5: Calculating Quartiles using PERCENTILE_CONT
SELECT DISTINCT
    product_category,
    PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY sale_amount) OVER (PARTITION BY product_category) AS q1_sale_amount,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY sale_amount) OVER (PARTITION BY product_category) AS median_sale_amount,
    PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY sale_amount) OVER (PARTITION BY product_category) AS q3_sale_amount
FROM sales_data
ORDER BY product_category;

Example 2.6: Median with Filtering using

PERCENTILE_CONT()

Calculate the median

sale_amount

for sales made in 'Books' category using

PERCENTILE_CONT()

-- Method 2.6: Median with Filtering using PERCENTILE_CONT
SELECT DISTINCT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY sale_amount) OVER () AS median_books_sales_cont
FROM sales_data
WHERE product_category = 'Books';

# Method 3: Approximating with

NTILE()

Window Function

[Legacy Maintainer, Problem Solver (for specific edge cases)]

The

NTILE()

function divides an ordered set of rows into a specified number of groups (tiles) and assigns a tile number to each row. While not a direct median function, it can be used to approximate the median, especially in older Redshift versions where

MEDIAN()

PERCENTILE_CONT/DISC()

might not have been available, or for very large datasets where a precise median is less critical than performance.

Technical Deep Dive:

NTILE(N) OVER (ORDER BY sort_expression)

divides the rows into

groups. To approximate the median, we divide the data into 2 groups (

NTILE(2)

). The median will be the minimum value in the first group (the "upper half" when ordered descending) or the maximum value in the first group (the "lower half" when ordered ascending).

The approximation works best for large datasets. For small datasets, especially those with an odd number of elements,

NTILE(2)

will put one more element in the first group than the second. If you order

DESC

, the median will be the

MIN()

of the first group. If you order

ASC

, the median will be the

MAX()

of the first group. This method is less precise than

MEDIAN()

PERCENTILE_CONT()

because it doesn't interpolate and might not pick the exact middle value for even-sized sets.

Best Practices:

Use with caution: This is an approximation. Only use it if precision is not paramount or if other methods are unavailable/too slow for your specific Redshift version/dataset size.
Ordering is critical: The
```
ORDER BY
```
clause within
```
OVER()
```
determines how the data is divided. For median, ordering by the value itself is necessary.
Subquery/CTE: Typically requires a subquery or CTE to first assign the
```
NTILE
```
group, then filter and aggregate.

Working Examples:

Example 3.1: Approximate Overall Median using

NTILE(2)

(Descending Order) Find the approximate median

sale_amount

for all sales. We order descending and take the minimum of the first group.

-- Method 3.1: Approximate Overall Median (NTILE DESC)
WITH RankedSales AS (
    SELECT
        sale_amount,
        NTILE(2) OVER (ORDER BY sale_amount DESC) AS ntile_group
    FROM sales_data
)
SELECT MIN(sale_amount) AS approximate_overall_median
FROM RankedSales
WHERE ntile_group = 1;

Example 3.2: Approximate Overall Median using

NTILE(2)

(Ascending Order) Find the approximate median

sale_amount

for all sales. We order ascending and take the maximum of the first group. This can yield a slightly different approximation depending on data distribution.

-- Method 3.2: Approximate Overall Median (NTILE ASC)
WITH RankedSalesAsc AS (
    SELECT
        sale_amount,
        NTILE(2) OVER (ORDER BY sale_amount ASC) AS ntile_group
    FROM sales_data
)
SELECT MAX(sale_amount) AS approximate_overall_median_asc
FROM RankedSalesAsc
WHERE ntile_group = 1;

Example 3.3: Approximate Median per Category using

NTILE(2)

Calculate the approximate median

sale_amount

for each

product_category

-- Method 3.3: Approximate Median per Category (NTILE DESC)
WITH RankedCategorySales AS (
    SELECT
        product_category,
        sale_amount,
        NTILE(2) OVER (PARTITION BY product_category ORDER BY sale_amount DESC) AS ntile_group
    FROM sales_data
)
SELECT
    product_category,
    MIN(sale_amount) AS approximate_category_median
FROM RankedCategorySales
WHERE ntile_group = 1
GROUP BY product_category
ORDER BY product_category;

Example 3.4: Approximate Median with Filtering using

NTILE(2)

Calculate the approximate median

sale_amount

for sales made after '2024-01-03'.

-- Method 3.4: Approximate Median with Filtering (NTILE DESC)
WITH FilteredRankedSales AS (
    SELECT
        sale_amount,
        NTILE(2) OVER (ORDER BY sale_amount DESC) AS ntile_group
    FROM sales_data
    WHERE sale_date > '2024-01-03'
)
SELECT MIN(sale_amount) AS approximate_median_after_date
FROM FilteredRankedSales
WHERE ntile_group = 1;

Example 3.5: Understanding NTILE distribution with an odd number of rows Let's use a small dataset to illustrate how

NTILE(2)

distributes rows.

-- Method 3.5: NTILE distribution with odd rows
CREATE TEMPORARY TABLE temp_odd_data (num INT);
INSERT INTO temp_odd_data VALUES (1), (2), (3), (4), (5);

SELECT
    num,
    NTILE(2) OVER (ORDER BY num DESC) AS ntile_desc,
    NTILE(2) OVER (ORDER BY num ASC) AS ntile_asc
FROM temp_odd_data;

-- Result:
-- num | ntile_desc | ntile_asc
-- ----+------------+-----------
--   5 |          1 |         2
--   4 |          1 |         2
--   3 |          1 |         1  <-- Middle value
--   2 |          2 |         1
--   1 |          2 |         1

-- MIN of ntile_desc=1 is 3
SELECT MIN(num) FROM (SELECT num, NTILE(2) OVER (ORDER BY num DESC) AS ntile_group FROM temp_odd_data) WHERE ntile_group = 1; -- Result: 3

-- MAX of ntile_asc=1 is 3
SELECT MAX(num) FROM (SELECT num, NTILE(2) OVER (ORDER BY num ASC) AS ntile_group FROM temp_odd_data) WHERE ntile_group = 1; -- Result: 3

# Performance Comparison

| Feature / Method |

MEDIAN()

Window Function |

PERCENTILE_CONT()

PERCENTILE_DISC()

Window Functions |

NTILE()

Approximation | | :---------------------- | :------------------------------------------------------- | :--------------------------------------------------------- | :------------------------------------------------------- | | Precision | High (exact median) | High (exact percentile,

CONT

interpolates,

DISC

MEDIAN()

WITHIN GROUP (ORDER BY ...)

) | Medium (requires CTE/subquery and

MIN

MAX

PARTITION BY

clauses. | Requires data redistribution for

PARTITION BY

clauses. | Requires data redistribution for

PARTITION BY

Key Takeaways:

For most modern Redshift users,
```
MEDIAN()
```
is the go-to choice due to its simplicity and optimized performance for its specific task.
```
PERCENTILE_CONT()
```
is a strong alternative, especially if you need to calculate other percentiles alongside the median, or if you prefer its explicit interpolation behavior. It often yields the same result as
```
MEDIAN()
```
for continuous data.
```
NTILE()
```
should be considered a last resort for median calculation, primarily for very large datasets where an approximation is acceptable, or if you are constrained by an extremely old Redshift version. Its precision is lower.

All window functions in Redshift, when used with

PARTITION BY

, involve data redistribution across compute nodes. The efficiency of this redistribution depends on your cluster configuration, data distribution, and the cardinality of your partitioning keys. High-cardinality partitioning keys can lead to more data movement and potentially slower queries.

# Amazon-Redshift Version Support

AWS Redshift continuously updates its SQL engine. Here's a general overview of when these functions became available:

| Function | Initial Redshift Version Support | Notes

How to calculate median in AWS Redshift?: 3 Methods + Performance...

How to calculate median in AWS Redshift?: 3 Methods + Performance Guide

# Quick Answer

# Choose Your Method

# Table of Contents

# Ready-to-Use Code

# Method Sections

# Method 1: Using the
`MEDIAN()`
Window Function

# Method 2: Using
`PERCENTILE_CONT()`
and
`PERCENTILE_DISC()`
Window Functions

# Method 3: Approximating with
`NTILE()`
Window Function

# Performance Comparison

# Amazon-Redshift Version Support