Unlock SQL power: Essential guide to analytic functions

A theoretical introduction

Analytic functions (often referred to as window functions) are a cornerstone of modern SQL and data processing. However, despite their power and wide support by SQL-compatible databases – such as PostgreSQL, Oracle, SQL Server and others – they are underutilised by many developers and analysts.

In this introductory article, we explain the theory behind analytic functions, their evolution and the key use cases that show why every data scientist should master them.

 What are analytic functions?

Analytic functions extend the capabilities of SQL by enabling complex calculations — such as rankings, cumulative totals and moving averages – without changing row-level detail. This is an important distinction from aggregate functions, which combine multiple rows into a single result.

With analytic functions you can:

• Analyse trends and compare values within a data set
• Calculate metrics using dynamic subsets (so-called windows)
• Perform advanced reports and time series analyses

 All this is achieved with the powerful OVER() clause, which determines how the data is partitioned and organised.

A brief history

The concept was developed by Oracle, which introduced the analytic functions in Oracle 8i in 1999 as part of OLAP support. They became part of the SQL:1999 standard (also known as SQL3) under the name window functions.

PostgreSQL followed in 2009 with version 8.4, which adapted to SQL:2003 and offered functions such as ROW_NUMBER(), RANK() and aggregates with OVER(). Today, this function is supported by most large databases, even if the behaviour can vary slightly between systems – especially with frame definitions and optimisation strategies.

 Why use analytic functions?

Analytic functions offer numerous advantages in terms of performance and clarity:

• they eliminate the need for multiple joins or subqueries
• they perform calculations in a single query run
• they use native database optimisations and indexing
• they combine detailed and summary views.

They are particularly valuable for:

• financial reporting
• time-based analysis
• tracking user behaviour
• preparing data for machine learning

In addition, their performance is often better than external processing in Python, R or Java.

 Core syntax and concepts

At the heart of analytic functions is the OVER() clause, which can contain the following

• PARTITION BY: Groups rows for comparison (e.g. per customer)
• ORDER BY: Defines the sort order within each partition
• FRAME: (Optional) Restricts the range of rows used for the calculation

Example:

SUM(sales) OVER (PARTITION BY region ORDER BY date)

This calculates a running total of sales per region, sorted by date.

Categories of analytic functions

Here are the most common types of analytic functions:

1. Ranking functions

• LINENUMBER(): Unique sequential number per line
• RANK(): Ranking list with gaps for ties
• DENSE_RANK(): Ranking list without gaps
• NTILE(n): Divides rows into n ranges

2. Offset functions

• LAG() / LEAD(): Access to preceding or following rows
• FIRST_VALUE() / LAST_VALUE() / NTH_VALUE (): Access to values within a frame

3. Aggregation functions

• SUM(), AVG(), MIN(), MAX(), COUNT() — extended by OVER() for windowed calculations

4. Statistical functions

• PERCENT_RANK(), CUME_DIST(), PERCENTILE_CONT(), PERCENTILE_DISC()

Different databases may implement percentile functions differently or impose different syntax restrictions — always consult the platform-specific documentation.

Working with frames

Frames determine which rows are taken into account when calculating a function. SQL supports various frame types:

• ROWS: Based on physical row positions
• RANGE: Based on value ranges
• GROUPS: Based on equal-ranking groups with the same ORDER BY value

Example:

ROWS BETWEEN 1 PREVIOUS AND CURRENT ROW

This function calculates over two rows: the current and the previous row.

Note: Some functions such as FIRST_VALUE() require an explicit frame clause in certain databases (e.g. Oracle), while others such as RANK() use the entire partition by default.

Why analytic functions are underutilised

Despite their power, many developers avoid analytic functions due to:

• unfamiliar or complex syntax
• lack of formal training on the subject
• preference for procedural logic in Python/Java
• confusion about OVER() and frame behaviour

This leads to unnecessarily complex and less powerful queries.

When should you replace aggregates with analytic functions?

Use analytic functions when:

• you need both detailed and aggregated views
• you want to avoid collapsing rows
• you need comparisons between rows (e.g. differences from monthto month)

 Example:

-- Ranking list of employees by turnover without collapsing the row 

SELECT employees, turnover,  
RANK() OVER (ORDER BY sales DESC) AS rank 
FROM sales_data;

Compatibility and optimisation

All large databases support analytic functions, but the performance features are different:

 Summary

Analytic functions are important tools for modern SQL development:

• Maintain row-level detail while performing advanced calculations 
• Boost performance with efficient, native processing 
• Replace complex joins and subqueries 
• Supports most large databases

Coming up next: Practical examples

In our next article, we’ll move from theory to practical application — with SQL examples, performance comparisons and best practises for real-world use cases.

Do you need SQL expert advice?

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