在SQL / PostgreSQL中使用加权过滤器随机选择行

Question

我有一个问题表，需要准备X个问题才能准备考试。 需要根据多个标准（主题，机构，区域等）对问题进行过滤，每个标准具有不同的权重。

过滤器权重是在查询外部动态设置和标准化的。 例如：

1. 主题1 — 0.4
2. 主题2 — 0.1
3. 主题3 — 0.5
4. 机构1 — 0.2
5. 机构2-0.04
6. 机构3 — 0.76
7. 区域1 — 1

其他一些要点：

• 今天，我有10个不同的过滤器（主题，机构，区域等），但是用户可以以多种方式（例如：10个主题，5个机构，30个区域等）进行选择以上。
• 问题表有约50万行；
• 筛选条件为N — N个问题；
• 过滤之后，我想限制返回的行；
• 如果某个过滤器不能再提供其他问题，则必须考虑其他问题（请记住：我要准备测试-如果我还有问题，则必须使用它们）
• 我非常关心此查询的性能。

为了说明这一点，如果我不想加权过滤器，我会做类似的事情：

SELECT
    *
FROM
    public.questions q
    INNER JOIN public.subjects_questions sq ON q.id = sq.question_id
    INNER JOIN public.subjects s ON s.id = sq.subject_id
    INNER JOIN public.institutions_questions iq ON iq.question_id = q.id
    INNER JOIN public.institutions i ON i.id = iq.institution_id
    INNER JOIN public.areas_questions aq ON aq.question_id = q.id
    INNER JOIN public.areas a ON a.id = aq.area_id
WHERE
    s.id IN :subjects
    AND a.id IN :areas
    AND i.id IN :institutions
ORDER BY
    random() limit 200

所需的输出：

Question — Subject — Institution — Area

我以为是这样：

1. 使用过滤器返回的问题创建CTE； 必须考虑到一个以上的过滤器可以返回相同的问题-我是否需要将每个过滤器分开评估，然后用UNION ALL来解决这个问题？ 还必须分配问题来自哪个过滤器；
2. 创建另一个具有权重和关联的过滤器的CTE；
3. 加入CTE，但此时必须对问题进行分组，并对权重求和。
4. 应用窗口函数并返回结果（限于X行（LIMIT X））。

您将如何编写此类查询/解决此问题？

Answer 1

那这样的事呢 这只是为了演示这个想法，我将详细信息留给您。 如果您不熟悉这种随机选择方法，则如果您随机生成一个介于0和1之间的数字，则它有40％的可能性低于.4。 因此rand（）<= .4将在40％的时间内返回true。

假设您拥有或可以创建一个看起来像这样的“过滤器”实体

CREATE TABLE Filters
  ( FieldName VARCHAR(100), 
    FieldValue VARCHAR(100),
    Prob Float -- probability of selection based on Name and Value
  );

SELECT DISTINCT TMP.* -- The fields you want. Distinct needed to get rid of 
                      -- records which pass multiple conditions.
  FROM (SELECT YRSWF.*,
               RAND() AS rnd
          FROM YourResultSetWithoutFilters YRSWF -- You can code the details
       ) TMP  
 INNER
  JOIN Filters F
    ON (
       TMP.Subject = F.FieldValue
   AND F.FieldName = 'Subject'
   AND TMP.rnd <= F.prob
       )
    OR (
       TMP.Institution = F.FieldValue
   AND F.FieldName = 'Institution'
   AND TMP.rnd <= F.prob
       )
    OR ( 
       TMP.Area = F.FieldValue
   AND F.FieldName = 'Area'
   AND TMP.rnd <= F.prob
       );

Answer 2

好。 设法解决它。 基本上，使用问题中已经概述的策略以及从此处获得的一些帮助-我以前已经看过这篇文章，但是我（并且现在）正试图以一种更优雅的方式解决问题-类似这样，但是对于多个行-无需手动创建“界限”。

让我们逐步尝试：

由于具有权重的过滤器来自架构外部，因此我们创建一个CTE：

WITH filters (type, id, weight) AS (
    SELECT 'subject', '148232e0-dece-40d9-81e0-0fa675f040e5'::uuid, 0.5
    UNION SELECT 'subject', '854431bb-18ee-4efb-803f-185757d25235'::uuid, 0.4
    UNION SELECT 'area', 'e12863fb-afb7-45cf-9198-f9f58ebc80cf'::uuid, 1
    UNION SELECT 'institution', '7f56c89f-705e-45c7-98fb-fee470550edf'::uuid, 0.5
    UNION SELECT 'institution', '0066257b-b2e3-4ee8-8075-517a2aa1379e'::uuid, 0.5
)

现在，让我们过滤行，而忽略权重（现在），因此以后我们不需要使用整个表：

WITH filtered_questions AS (
    SELECT
        q.id,
        s.id subject_id,
        a.id area_id,
        i.id institution_id
    FROM
        public.questions q
        INNER JOIN public.subjects_questions sq ON q.id = sq.question_id
        INNER JOIN public.subjects s ON s.id = sq.subject_id
        INNER JOIN public.institutions_questions iq ON iq.question_id = q.id
        INNER JOIN public.institutions i ON i.id = iq.institution_id
        INNER JOIN public.areas_questions aq ON aq.question_id = q.id
        INNER JOIN public.areas a ON a.id = aq.area_id
    WHERE
        subject_id IN (SELECT id from filters where type = 'subject')
        and institution_id IN (SELECT id from filters where type = 'institution')
        and area_id IN (SELECT id from filters where type = 'area')
)

可以通过多个过滤器选择同一问题，从而增加了选择它的机会。 我们必须更新权重以解决此问题。

WITH filtered_questions_weights_sum AS (
    SELECT
        q.id,
        SUM(filters.weight) weight_sum
    FROM filtered_questions q
    INNER JOIN filters
    ON (filters.type = 'subject' AND q.subject_id IN(filters.id))
    OR (filters.type = 'area' AND q.area_id IN(filters.id))
    OR (filters.type = 'institution' AND q.institution_id IN(filters.id))
    GROUP BY q.id
)

产生界限，就像暴露在这里一样 。

WITH cumulative_prob AS (
    SELECT
        id,
        SUM(weight_sum) OVER (ORDER BY id) AS cum_prob
    FROM filtered_questions_weights_sum
),
cumulative_bounds AS (
    SELECT
        id,
        COALESCE( lag(cum_prob) OVER (ORDER BY cum_prob, id), 0 ) AS lower_cum_bound,
        cum_prob AS upper_cum_bound
    FROM cumulative_prob
)

生成随机序列。 必须重新规范化（ random() * (SELECT SUM(weight_sum) ），因为权random() * (SELECT SUM(weight_sum)上一步中已更新。10是我们要返回的行数。

WITH random_series AS (
    SELECT generate_series (1,10),random() * (SELECT SUM(weight_sum) FROM filtered_questions_weights_sum) AS R
)

最后：

SELECT
      id, lower_cum_bound, upper_cum_bound, R
FROM random_series
JOIN cumulative_bounds
ON R::NUMERIC <@ numrange(lower_cum_bound::NUMERIC, upper_cum_bound::NUMERIC, '(]')

我们得到以下分布：

id                                   lower_cum_bound upper_cum_bound r                   
------------------------------------ --------------- --------------- ------------------- 
380f46e9-f373-4b89-a863-05f484e6b3b6 0               2.0             0.41090718149207534 
42bcb088-fc19-4272-8c49-e77999edd01c 2.0             3.9             3.4483200465794654  
46a97f1d-789f-46e7-9d3b-bd881a22a32e 3.9             5.9             5.159445870062337   
46a97f1d-789f-46e7-9d3b-bd881a22a32e 3.9             5.9             5.524481557868421   
972d0296-acc3-4b44-b67d-928049d5e9c2 5.9             7.8             6.842470594821498   
bdcc26f7-ccaf-4f8f-9e0b-81b9a6d29cdb 11.6            13.5            12.207371663767844  
bdcc26f7-ccaf-4f8f-9e0b-81b9a6d29cdb 11.6            13.5            12.674184153741226  
c935e3de-f1b6-4399-b5eb-ed3a9194eb7b 15.5            17.5            17.16804686235264   
e5061aeb-53b7-4247-8404-87508c5ac723 21.4            23.4            22.622627633158118  
f8c37700-0c3a-457e-8882-7c65269482ea 25.4            27.3            26.841821723571048  

放在一起：

WITH filters (type, id, weight) AS (
        SELECT 'subject', '148232e0-dece-40d9-81e0-0fa675f040e5'::uuid, 0.5
        UNION SELECT 'subject', '854431bb-18ee-4efb-803f-185757d25235'::uuid, 0.4
        UNION SELECT 'area', 'e12863fb-afb7-45cf-9198-f9f58ebc80cf'::uuid, 1
        UNION SELECT 'institution', '7f56c89f-705e-45c7-98fb-fee470550edf'::uuid, 0.5
        UNION SELECT 'institution', '0066257b-b2e3-4ee8-8075-517a2aa1379e'::uuid, 0.5
        )
    ,
    filtered_questions AS
    (
        SELECT
            q.id,
            SUM(filters.weight) weight_sum
        FROM
        public.questions q
        INNER JOIN public.subjects_questions sq ON q.id = sq.question_id
        INNER JOIN public.subjects s ON s.id = sq.subject_id
        INNER JOIN public.institutions_questions iq ON iq.question_id = q.id
        INNER JOIN public.institutions i ON i.id = iq.institution_id
        INNER JOIN public.activity_areas_questions aq ON aq.question_id = q.id
        INNER JOIN public.activity_areas a ON a.id = aq.activity_area_id
        INNER JOIN filters
            ON (filters.type = 'subject' AND s.id IN(filters.id))
            OR (filters.type = 'area' AND a.id IN(filters.id))
            OR (filters.type = 'institution' AND i.id IN(filters.id))
        WHERE
            s.id IN (SELECT id from filters where type = 'subject')
            and i.id IN (SELECT id from filters where type = 'institution')
            and a.id IN (SELECT id from filters where type = 'area')
        GROUP BY q.id
    )
    ,
    cumulative_prob AS (
        SELECT
            id,
            SUM(weight_sum) OVER (ORDER BY id) AS cum_prob
        FROM filtered_questions
    )
    ,
    cumulative_bounds AS (
        SELECT
            id,
            COALESCE( lag(cum_prob) OVER (ORDER BY cum_prob, id), 0 ) AS lower_cum_bound,
            cum_prob AS upper_cum_bound
        FROM cumulative_prob
    )
    ,
    random_series AS
    (
        SELECT generate_series (1,14),random() * (SELECT SUM(weight_sum) FROM filtered_questions) AS R
    )
SELECT id, lower_cum_bound, upper_cum_bound, R
FROM random_series
JOIN cumulative_bounds
ON R::NUMERIC <@ numrange(lower_cum_bound::NUMERIC, upper_cum_bound::NUMERIC, '(]')