![]() ![]() The window function sums all sales values for the ax it then goes to the next window partition, resets, and sums all sales values for the hammer. This means the ax is one window partition, while the hammer is another window partition. The partition in this example is the product. Here, I have sales per product as in the previous example, but I also have sales data for every product and date. For now, take a look at the result of the query above: product Don’t worry later on, I’ll show you example queries and explain them in detail. This query is just to show you what PARTITION BY does. SUM(sales) OVER (PARTITION BY product) AS sales_per_product However, if I want to see aggregate values for each group while preserving row-level data, I need to use PARTITION BY. ![]() GROUP BY works fine if I only want to see aggregate values for each group. If I want to see total sales by product, using GROUP BY comes to mind. Let’s use a small example dataset to see how it works: id ![]() When PARTITION BY is omitted, the window function is applied to the whole dataset. It partitions a dataset into smaller segments called windows. This SQL clause lets you define the window.
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