Gentle Introduction to Window Functions in PostgreSQL

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2024-07-08 07:30:03

Understanding the relationship between data points is crucial. For instance, you might need to identify the most recent orders for each customer or track changes in sensor readings over time. Unlike aggregate functions, which summarise data into a single row, it is window functions that allow you to analyse data while preserving each row’s details. This is the core of the logic, but don’t worry if you struggle to imagine the difference, as we will cover all of it in this article.

PostgreSQL supports SQL window functions, facilitating complex calculations across related rows within a table. These functions are particularly useful for tasks such as ranking entries, calculating running totals, finding moving averages, and comparing individual entries. Mastering window functions can significantly enhance your data analysis capabilities.

This reiterates the fundamental difference between the two sets of functions. As mentioned earlier, while the results for sensor_id are the same in both cases, the aggregate function summarised it into a single row (grouped by sensor_id), whereas the window function provides the value for the set of rows in the partition defined by sensor_id.

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