SQL functions are powerful tools that can help you manipulate and analyze data in various ways. One common use case is to rank data based on some criteria, such as sales, performance, or popularity. In this article, you will learn how to use SQL functions to rank data in different scenarios and how to optimize your queries for better performance and accuracy.
Ranking functions are a type of window function that assign a numerical value to each row in a result set based on its position relative to other rows. They can be used to sort, group, or filter data based on some ranking criteria. There are four main ranking functions in SQL: ROW_NUMBER, RANK, DENSE_RANK, and NTILE. Each of them has a different way of handling ties and partitions.
ROW_NUMBER assigns a sequential number to each row in a result set, starting from 1. It does not consider ties, so rows with the same value will have different numbers. You can use ROW_NUMBER to assign a unique identifier to each row or to select the top or bottom N rows based on some order. For example, you can use this query to select the top 10 best-selling products by category:
SELECT category, product, sales, ROW_NUMBER() OVER (PARTITION BY category ORDER BY sales DESC) AS rank
FROM products
WHERE rank <= 10;
Well, As you all are aware of the definition of this Oracle/SQL function. It is interesting that you can do so much with this function from generating a list of desired bracket range of items to the aggregated amount in a running total of any number column. You can use its partition function to divide the auto increment number at any sort of data change. This will reset the counter and starts from 1, partition may have values 1,2,3,4, and so on and the second partitions again starts the counter from 1…
Assigning sequence of numbers to a dataset is very useful in real-life DWH scenarios - whether the requirement is to find the first or last occurence of something, this function helps with that. It's also very useful with complex datasets which use joins using 1:N or N:M cardinality - leading to duplication of data. In such cases, where a single leading row is needed, Row_number analytical function can be utilized to filter out duplicated rows per key (controlled by "partition by" clause) and leave only one occurrence of the row.
RANK assigns a number to each row in a result set based on its position relative to other rows, but it considers ties. If two or more rows have the same value, they will have the same rank, and the next rank will be skipped. For example, if three rows have the rank 2, the next rank will be 5. You can use RANK to compare the performance or popularity of different items based on some metric. For example, you can use this query to rank the employees by their salary:
SELECT name, salary, RANK() OVER (ORDER BY salary DESC) AS rank
FROM employees;
DENSE_RANK is similar to RANK, but it does not skip any ranks when there are ties. If two or more rows have the same value, they will have the same rank, and the next rank will be the consecutive number. For example, if three rows have the rank 2, the next rank will be 3. You can use DENSE_RANK to create a more compact ranking system that does not leave any gaps. For example, you can use this query to rank the movies by their rating:
SELECT title, rating, DENSE_RANK() OVER (ORDER BY rating DESC) AS rank
FROM movies;
NTILE divides the result set into a specified number of groups of approximately equal size and assigns a number to each group. The number of groups is determined by a parameter that you provide. You can use NTILE to create percentile or quartile rankings or to distribute data into categories. For example, you can use this query to divide the customers into four groups based on their spending:
SELECT name, spending, NTILE(4) OVER (ORDER BY spending DESC) AS group
FROM customers;
Extremely useful when you want to split a large number of rows into buckets with from-to groups...for instance 1.000.000 with NTILE(20) will split all rows into 20 groups , assigning group # to each row. Then you can utilize some external algorithm, like for loop to process those batches of rows, instead of doing row-by-row processing.
To optimize ranking queries and improve database performance, it is important to use appropriate indexes to speed up sorting and partitioning operations. Additionally, using filters and joins can reduce the size of the result set before applying the ranking function. Avoid using ranking functions in subqueries or nested queries, as they can cause unnecessary calculations and repetitions. To limit the number of rows returned by the ranking function, use the FETCH clause instead of a WHERE clause on the rank column. By following these tips, you can ensure efficient ranking queries.
I would rank, dense_rank, then run and inverted dense_rank just to have the dense duplicates in a separate table for review. I couldn't imagine analyzing data without minding the gap.