GROUP BY vs PARTITION BY in SQL

Differences between GROUP BY and PARTITION BY in SQL, including definitions and explanations for each.

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1. GROUP BY

Definition:

The GROUP BY clause is used to group rows that have the same values in specified columns into summary rows, typically with aggregate functions applied to each group (such as SUM, COUNT, AVG, etc.). It reduces the number of rows in the result by creating one row per unique combination of values in the grouping columns.

Explanation:

GROUP BY is primarily used when we want to summarize data. It groups rows based on the specified columns and calculates aggregate values for each group.

It combines all rows with the same values in the specified columns into a single row, making it useful for generating summary statistics, like the total count, average, or sum for each group.

Example:

Imagine a table with values like this:

Using GROUP BY to count occurrences of each unique value:

SELECT Value, COUNT(*) AS count_per_value
FROM example_table
GROUP BY Value;

Output:

In this example, GROUP BY combines all rows with the same Value and shows a single result per unique Value, summarizing the count of each value.

Effect on Rows: GROUP BY reduces the number of rows by creating one row per group.

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1. PARTITION BY

Definition:

The PARTITION BY clause is used with analytic (window) functions to divide the result set into partitions or logical groups, where calculations are independently performed for each partition. Unlike GROUP BY, PARTITION BY does not aggregate or collapse rows; it simply allows calculations within each partition while keeping all original rows.

Explanation:

PARTITION BY is commonly used with analytic functions such as ROW_NUMBER, RANK, SUM, and COUNT, where calculations need to be done over a subset of data, but without reducing the row count.

It "partitions" the data into logical subsets (groups) based on specified columns and performs calculations within each subset. Each row retains the same information plus the calculated value from the analytic function.

Example:

Using the same data, we can count the occurrences of each value in the Value column within each partition:

SELECT Value, COUNT(*) OVER (PARTITION BY Value) AS count_per_partition
FROM example_table;

Output:

Here, PARTITION BY divides the rows into partitions based on the Value. Each row within the same partition displays the count of rows for that partition, without collapsing or removing rows.

Effect on Rows: PARTITION BY keeps all rows intact but adds a calculated value for each row based on the partition.

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Key Differences

Summary

GROUP BY: Used to summarize and aggregate data by reducing rows to a single result for each group.

PARTITION BY: Used within analytic functions to perform calculations on partitions while keeping all original rows intact.