SQLite Window Functions

Window functions are one of the most powerful yet underutilized features in SQLite. Business analysts need robust tools to derive insights as companies collect more data. Window functions enable complex data analysis that is difficult to achieve with vanilla SQL.

In this comprehensive guide, you’ll learn:

  • What are window functions, and why do they matter
  • Core concepts like partitions, ordering, and framing
  • How to use common window functions for rankings, percentiles, and offsets
  • Advanced topics like window chaining and custom aggregates

So, let’s slice and dice some data!

Why SQLite Window Functions Matter

Let’s first understand what window functions are and what types of analysis they facilitate.

In a nutshell, window functions perform calculations across sets of rows related to the current row. For example, they can calculate a running total or average, a row number based on ordering rules, fetch values from adjacent rows, and more.

Unlike regular aggregates and scalar functions, window functions retain the original granularity of the rows. So you can analyze subgroups and drill down to individual rows.

This combination of row-level granularity and grouped computation unlocks valuable analytics like:

  • Rankings and distributions
  • Time series smoothing, trends, and offsets
  • Segmented metrics across accounts, regional data etc.

Things regular SQL struggles at!

Window functions lend superpowers to business analysts by simplifying complex reporting. Let’s see them in action.

Also read: SQLite BLOB Data Type: Storing Binary Data in SQLite

Core Concepts of SQLite Window Functions

Before using window functions, you need to understand a few key concepts:


A partition is a subset of rows grouped based on one or more columns. Computations happen within partitions, independently of other partitions.

For example, if you partition data by region, averages will be calculated separately for each region.


The ORDER BY clause defines sorting rules within each partition. This controls the order in which rows are processed.

For example, values could be sorted by date or aggregated by account ID.


The frame is the subset of rows in a partition accessed during computation. For example, a running total depends on the current row and all preceding rows. So the frame spans from the partition start to the current row.

Frame bounds can be based on physical rows or values. More on this later.

Got it? Partitions divide, ordering arranges rows, and framing creates slideable computation subsets per row. With these core concepts, you’re ready to use window functions!

Also read: SQLite UNIQUE Constraint

Window Function Types and Examples

It’s easiest to understand window functions through examples. We will use a sample database of regional product sales. First, let’s create and populate the sample table:

  region TEXT,
  country TEXT, 
  product TEXT,
  units INTEGER,
  revenue FLOAT 


This table captures regional sales data – units sold and revenues for different products. Let’s analyze this data using window functions!

1. Ranking Functions

Ranking window functions assign a rank value to each row based on the ORDER BY clause:

ROW_NUMBER() - Unique ranks based on order  
RANK() - Ranks with gaps if values repeat
DENSE_RANK() - Ranks without gaps  
PERCENT_RANK() - Percentile ranks from 0 to 1

Example: Rank countries by total revenue

  SUM(revenue) AS total_revenue,
  ROW_NUMBER() OVER(ORDER BY SUM(revenue) DESC) AS revenue_rank 
FROM sales
GROUP BY country;



China ranks first based on total sales revenue, followed by Japan and USA. The ranks are assigned based on the ordering rule.

Example: Rank products by units sold per country

Let’s rank product performance within each country using partitions:

  RANK() OVER(PARTITION BY country ORDER BY units DESC) AS product_rank
FROM sales;



Now phones are ranked first in China, while tablets lead in Japan. The ranks get assigned independently per country partition.

2. Aggregate Functions

Aggregate window functions work like normal aggregates, except they return values for every row by partitioning/ordering subsets of rows.

Some common examples:

SUM() OVER() - Running total
AVG() OVER() - Running average 
MIN() OVER() - Running minimum
MAX() OVER() - Running maximum  

Example: Calculate 7-day moving average of daily revenue

    AS ma7
FROM daily_sales; 

This sums up the revenue values from the past 6 days and the current day and is divided by 7 to calculate a centered 7-day moving average.

Also read: SQLite String Concatenation: Joining Strings in Queries

The result contains the moving average alongside the original daily revenue data.

3. Offset Functions

Offset window functions access rows before/after the current row using physical offsets or value-based conditions.

Some examples are:

LAG(column) - Value from previous row
LEAD(column) - Value from next row  
FIRST_VALUE(column) - First value in partition
LAST_VALUE(column) - Last value in partition

Example: Compare revenue with previous year

  LAG(revenue, 1) OVER(ORDER BY year) AS prev_year  
FROM yearly_revenue;

Accessing the previous year’s revenue allows you to calculate year-over-year growth. You can customize framing bounds and offsets to suit analytical needs.

Window Function Chaining

Defining the full partition/order logic within every window function can get tedious.

Window chaining allows reusing the definition cleanly:

WINDOW weekly_sales AS (
  PARTITION BY region 
  ORDER BY date

  SUM(sales) OVER weekly_sales AS weekly_sales
FROM all_sales;

Here weekly_sales gets defined once and reused across queries.

Going Beyond Inbuilt Functions

So far we used some common built-in window functions. But the possibilities don’t end here.

SQLite lets you create custom aggregate window functions using C/C++. They work like regular aggregates, with added callbacks to handle framing, offsets etc.

For example, here’s how a custom SUM() can be defined:

// Callback to process each row 
void sumStep(FunctionContext* ctx, int argc, sqlite3_value** argv) {
  double* sum = sqlite3_aggregate_context(ctx, sizeof(*sum));
  *sum += sqlite3_value_double(argv[0]); 

// Callback to return current value
void sumValue(FunctionContext* ctx) {
  double sum = *((double*)sqlite3_aggregate_context(ctx, 0));
  sqlite3_result_double(ctx, sum);

// Register the function
sqlite3_create_window_function(db, "custom_sum", 1, SQLITE_UTF8, 0, 
  &sumStep, NULL, &sumValue, NULL, NULL);  

So, in addition to out-of-the-box capabilities, you can build specialized logic.

Best Practices for Productive Analysis

While window functions open up new avenues for analytics, watch out for some nuances:

  • Ordering determines results – An omitted or incorrect ORDER BY clause can return unexpected outputs
  • Frames impact calculations – Are aggregates calculated over the whole partition or a sliding subset of rows?
  • Custom functions involve C/C++ – Reusable logic needs programming expertise
  • Performance varies by use case – Certain window operations like ordering can get slow for large datasets

Here are some tips:

  • Test thoroughly with simple examples – Confirm window framing and ordering work as expected
  • Partition narrower datasets where possible – Group by year instead of dates, account instead of transactions, etc.
  • Index appropriately – Ordering and partitioning work faster with indexes
  • Check execution plans – Diagnose and optimize expensive window operations


There you have it – a comprehensive guide to unlocking the power of window functions in SQLite!

You learned:

✅ What window functions and why do they matter
✅ Core concepts like partitioning, ordering, and framing
✅ Examples of ranking, aggregate, and offset window functions
✅ Advanced topics like chaining and custom aggregates
✅ Tips to use window functions effectively

These techniques will level up your SQL analytics and simplify complex reporting.

Window functions democratize sophisticated business intelligence across teams. So go ahead, slice and dice your data at will!