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Rank and Dense Rank
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Rank and dense rank are calculations similar to row number. But unlike row number, which has a large variety of practical applications, rank and dense rank are typically used for ranking and scoring applications.
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RANK and DENSE_RANK Functions in SQL Server 2005
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SQL Server 2005 provides you with built-in RANK and DENSE_RANK functions that are similar to the ROW_NUMBER function. The difference between these functions and ROW_NUMBER is that, as I described earlier, ROW_NUMBER is not deterministic when the ORDER BY list is not unique. RANK
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and DENSE_RANK are always deterministicthat is, the same ranking values are assigned to rows with the same sort values. The difference between RANK and DENSE_RANK is that RANK might have gaps in the ranking values, but allows you to know how many rows have lower sort values. DENSE_RANK values have no gaps. As an example, the following query (which produces the output shown in Table 4-31) returns both rank and dense rank values for the sales rows based on an ordering by quantity: SELECT empid, qty, RANK() OVER(ORDER BY qty) AS rnk, DENSE_RANK() OVER(ORDER BY qty) AS drnk FROM dbo.Sales ORDER BY qty;
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Table 4-31. Rank and Dense Rank
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empid qty rnk drnk B G J H C D K E I A F 100 1 100 1 100 1 150 4 200 5 200 5 200 5 250 8 250 8 300 10 300 10 1 1 1 2 3 3 3 4 4 5 5
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Here's a short quiz: what's the difference between the results of ROW_NUMBER, RANK and DENSE_RANK given a unique ORDER BY list For the answer, run the following code: SELECT REVERSE('!gnihton yletulosbA');
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Set-Based Solutions prior to SQL Server 2005
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Rank and dense rank calculations using a set-based technique from preSQL Server 2005 are very similar to row number calculations. To calculate rank, use a subquery that counts the number of rows with a smaller sort value, and add one. To calculate dense rank, use a subquery that counts the distinct number of smaller sort values, and add one. SELECT empid, qty,
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(SELECT COUNT(*) FROM dbo.Sales AS S2 WHERE S2.qty < S1.qty) + 1 AS rnk, (SELECT COUNT(DISTINCT qty) FROM dbo.Sales AS S2 WHERE S2.qty < S1.qty) + 1 AS drnk FROM dbo.Sales AS S1 ORDER BY qty;
Of course, you can add a correlation to return partitioned calculations just like you did with row numbers.
NTILE
The NTILE function distributes rows into a specified number of tiles (or groups). The tiles are numbered 1 and on. Each row is assigned with the tile number to which it belongs. NTILE is based on row number calculationnamely, it is based on a requested order and can optionally be partitioned. Based on the number of rows in the table (or partition), the number of requested tiles, and the row number, you can determine the tile number for each row. For example, for a table with 10 rows, the value of NTILE(2) OVER (ORDER BY c) would be 1 for the first 5 rows in column c order, and 2 for the 6th through 10th rows. Typically, NTILE calculations are used for analytical purposes such as calculating percentiles or arranging items in groups. The task of "tiling" has more than one solution, and SQL Server 2005 implements a specific solution, called ANSI NTILE. I will describe the NTILE function implementation in SQL Server 2005, and then cover other solutions.
NTILE Function in SQL Server 2005
Calculating NTILE in SQL Server 2005 is as simple as calculating any of the other ranking valuesin this case, using the NTILE function. The only difference is that NTILE accepts an input, the number of tiles, while the other ranking functions have no input. Because NTILE calculations are based on row numbers, NTILE has exactly the same issues regarding determinism that I described in the row numbers section. As an example, the following query calculates NTILE values for the rows from the Sales table, producing three tiles, based on the order of qty and empid: SELECT empid, qty, NTILE(3) OVER(ORDER BY qty, empid) AS tile FROM dbo.Sales ORDER BY qty, empid;
The output of this query is shown in Table 4-32.
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