在SQL / PostgreSQL中使用加權過濾器隨機選擇行

Question

我有一個問題表，需要准備X個問題才能准備考試。 需要根據多個標准（主題，機構，區域等）對問題進行過濾，每個標准具有不同的權重。

過濾器權重是在查詢外部動態設置和標准化的。 例如：

1. 主題1 — 0.4
2. 主題2 — 0.1
3. 主題3 — 0.5
4. 機構1 — 0.2
5. 機構2-0.04
6. 機構3 — 0.76
7. 區域1 — 1

其他一些要點：

• 今天，我有10個不同的過濾器（主題，機構，區域等），但是用戶可以以多種方式（例如：10個主題，5個機構，30個區域等）進行選擇以上。
• 問題表有約50萬行；
• 篩選條件為N — N個問題；
• 過濾之后，我想限制返回的行；
• 如果某個過濾器不能再提供其他問題，則必須考慮其他問題（請記住：我要准備測試-如果我還有問題，則必須使用它們）
• 我非常關心此查詢的性能。

為了說明這一點，如果我不想加權過濾器，我會做類似的事情：

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

所需的輸出：

Question — Subject — Institution — Area

我以為是這樣：

1. 使用過濾器返回的問題創建CTE； 必須考慮到一個以上的過濾器可以返回相同的問題-我是否需要將每個過濾器分開評估，然后用UNION ALL來解決這個問題？ 還必須分配問題來自哪個過濾器；
2. 創建另一個具有權重和關聯的過濾器的CTE；
3. 加入CTE，但此時必須對問題進行分組，並對權重求和。
4. 應用窗口函數並返回結果（限於X行（LIMIT X））。

您將如何編寫此類查詢/解決此問題？

Answer 1

那這樣的事呢 這只是為了演示這個想法，我將詳細信息留給您。 如果您不熟悉這種隨機選擇方法，則如果您隨機生成一個介於0和1之間的數字，則它有40％的可能性低於.4。 因此rand（）<= .4將在40％的時間內返回true。

假設您擁有或可以創建一個看起來像這樣的“過濾器”實體

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。 設法解決它。 基本上，使用問題中已經概述的策略以及從此處獲得的一些幫助-我以前已經看過這篇文章，但是我（並且現在）正試圖以一種更優雅的方式解決問題-類似這樣，但是對於多個行-無需手動創建“界限”。

讓我們逐步嘗試：

由於具有權重的過濾器來自架構外部，因此我們創建一個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
)

現在，讓我們過濾行，而忽略權重（現在），因此以后我們不需要使用整個表：

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')
)

可以通過多個過濾器選擇同一問題，從而增加了選擇它的機會。 我們必須更新權重以解決此問題。

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
)

產生界限，就像暴露在這里一樣 。

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
)

生成隨機序列。 必須重新規范化（ random() * (SELECT SUM(weight_sum) ），因為權random() * (SELECT SUM(weight_sum)上一步中已更新。10是我們要返回的行數。

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, '(]')

我們得到以下分布：

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, '(]')