The usage of window functions mainly includes grouping operations, sorting operations, aggregation operations, window range and window sorting, etc. Detailed introduction: 1. Grouping operation, the windowing function can group the query result set according to the specified grouping key. By using the PARTITION BY clause in the windowing function, the result set can be divided into multiple groups, and each grouping Apply the corresponding calculation logic; 2. Sorting operation, the window function can sort the query result set, by using the ORDER BY clause in the window function, etc.
Window Function is a function used to analyze and process data in a relational database. It can group, sort, and aggregate query result sets, and can apply specific calculation logic to each grouping. Windowing functions provide a flexible way to perform calculations on query results. They can be calculated on each row in the result set without changing the query result set.
The usage of the windowing function mainly includes the following aspects:
1. Grouping operation: The windowing function can group the query result set according to the specified grouping key. By using the PARTITION BY clause in the windowing function, the result set can be divided into multiple groups and the corresponding calculation logic is applied to each group. For example, you can use windowing functions to calculate the sum, average, maximum, minimum, etc. of each group.
2. Sorting operation: The windowing function can sort the query result set. By using the ORDER BY clause in a windowing function, the result set can be sorted based on a specified sort key. Sorting operations can be performed within groups or on the entire result set. For example, you can use a windowing function to calculate the ranking, row number, etc. within each grouping.
3. Aggregation operation: The windowing function can perform aggregation calculations on the query result set. By using aggregate functions (such as SUM, AVG, COUNT, etc.) in windowing functions, you can perform aggregation operations on specified columns. Aggregation operations can be performed within groups or on the entire result set. For example, you can use windowing functions to calculate the sum, average, etc. of each group.
4. Window range: The windowing function can specify the window range for calculation. The window range can be the N lines before and after the current line, or all the lines in the group where the current line is located. By using the ROWS BETWEEN clause in the windowing function, you can specify the starting and ending positions of the window. For example, you can use windowing functions to calculate cumulative sums, moving averages, etc. within each grouping.
5. Window sorting: The windowing function can specify the sorting method within the window. By using the ORDER BY clause in the windowing function, the rows within the window can be sorted. Window sorting can be performed according to the specified sort key and sorting method. For example, you can use a windowing function to calculate the cumulative sum within each group and sort by a specified sort key.
In short, the windowing function is a powerful tool for data analysis and processing in relational databases. It can implement grouping, sorting and aggregation operations on query result sets, and can flexibly specify the window range and sorting method. By rationally using windowing functions, the efficiency and flexibility of data analysis can be improved to meet the data processing needs in different scenarios.
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