Dane 的答案包括以引入平方律的方式进行自连接。(n*n/2)表中有 n 行的连接后的行。
更理想的是能够只解析一次表。
DECLARE @id int, @weight_sum int, @weight_point int
DECLARE @table TABLE (id int, weight int)
INSERT INTO @table(id, weight) VALUES(1, 50)
INSERT INTO @table(id, weight) VALUES(2, 25)
INSERT INTO @table(id, weight) VALUES(3, 25)
SELECT @weight_sum = SUM(weight)
FROM @table
SELECT @weight_point = FLOOR(((@weight_sum - 1) * RAND() + 1))
SELECT
@id = CASE WHEN @weight_point < 0 THEN @id ELSE [table].id END,
@weight_point = @weight_point - [table].weight
FROM
@table [table]
ORDER BY
[table].Weight DESC
这将遍历表格，设置@id到每条记录的id值同时递减@weight观点。最终，@weight_point将会变为负值。这意味着SUM所有先前权重的总和大于随机选择的目标值。这是我们想要的记录，所以从那时起我们就设定了@id到其自身（忽略表中的任何 ID）。
这仅运行一次表，但即使所选值是第一条记录，也必须运行整个表。因为平均位置是表格的一半（如果按权重升序排序则更少），所以编写循环可能会更快......（特别是如果权重位于公共组中）：
DECLARE @id int, @weight_sum int, @weight_point int, @next_weight int, @row_count int
DECLARE @table TABLE (id int, weight int)
INSERT INTO @table(id, weight) VALUES(1, 50)
INSERT INTO @table(id, weight) VALUES(2, 25)
INSERT INTO @table(id, weight) VALUES(3, 25)
SELECT @weight_sum = SUM(weight)
FROM @table
SELECT @weight_point = ROUND(((@weight_sum - 1) * RAND() + 1), 0)
SELECT @next_weight = MAX(weight) FROM @table
SELECT @row_count = COUNT(*) FROM @table WHERE weight = @next_weight
SET @weight_point = @weight_point - (@next_weight * @row_count)
WHILE (@weight_point > 0)
BEGIN
SELECT @next_weight = MAX(weight) FROM @table WHERE weight < @next_weight
SELECT @row_count = COUNT(*) FROM @table WHERE weight = @next_weight
SET @weight_point = @weight_point - (@next_weight * @row_count)
END
-- # Once the @weight_point is less than 0, we know that the randomly chosen record
-- # is in the group of records WHERE [table].weight = @next_weight
SELECT @row_count = FLOOR(((@row_count - 1) * RAND() + 1))
SELECT
@id = CASE WHEN @row_count < 0 THEN @id ELSE [table].id END,
@row_count = @row_count - 1
FROM
@table [table]
WHERE
[table].weight = @next_weight
ORDER BY
[table].Weight DESC
