在数据库日志中发现慢查询

贡献 功能
1

我在 Discourse 中运行测试，并在查询日志中发现了一些低效的查询，具体如下：

  1. 不必要的 DISTINCT 导致查询变慢 site_settings_controller.rb#L165，示例如下：
SELECT 
  DISTINCT users.id 
FROM 
  "users" CROSS 
  JOIN categories c 
  LEFT JOIN category_users cu ON users.id = cu.user_id 
  AND c.id = cu.category_id 
WHERE 
  (
    c.id = '3613'
    AND cu.notification_level IS NULL
  )

当 categories.id 和 notification_level 指定具体值时，由于 categories_users 表存在 UNIQUE (category_id, user_id) 约束，且 categories 表存在 PRIMARY KEY(id) 约束，CROSS JOIN 和 LEFT JOIN 都不会产生重复记录。因此，我们可以移除 DISTINCT 以加速查询，优化后的查询如下：
优化后的查询耗时为 4532166 纳秒（性能提升 30%）。

  1. 子查询中不必要的 DISTINCT 导致查询变慢 search.rb#L523：
SELECT 
  "posts".* 
FROM 
  "posts" 
  JOIN (
    SELECT 
      *, 
      row_number() over() row_number 
    FROM 
      (
        SELECT 
          topics.id, 
          min(posts.post_number) post_number 
        FROM 
          "posts" 
          INNER JOIN "post_search_data" ON "post_search_data"."post_id" = "posts"."id" 
          INNER JOIN "topics" ON "topics"."id" = "posts"."topic_id" 
          AND ("topics"."deleted_at" IS NULL) 
          LEFT JOIN categories ON categories.id = topics.category_id 
        WHERE 
          ("posts"."deleted_at" IS NULL) 
          AND "posts"."post_type" IN (1, 2, 3) 
          AND (topics.visible) 
          AND (
            topics.archetype <> 'private_message'
          ) 
          AND (
            topics.id IN (
              SELECT 
                DISTINCT(tt.topic_id) 
              FROM 
                topic_tags tt 
              WHERE 
                tt.tag_id in (
                  SELECT 
                    tag_id 
                  FROM 
                    tag_group_memberships 
                  WHERE 
                    tag_group_id = 504
                )
            )
          ) 
          AND (
            categories.id NOT IN (
              SELECT 
                categories.id 
              WHERE 
                categories.search_priority = 1
            )
          ) 
          AND (
            (categories.id IS NULL) 
            OR (NOT categories.read_restricted)
          ) 
        GROUP BY 
          topics.id 
        ORDER BY 
          MAX(posts.created_at) DESC 
        LIMIT 
          6 OFFSET 0
      ) xxx
  ) x ON x.id = posts.topic_id 
  AND x.post_number = posts.post_number 
WHERE 
  ("posts"."deleted_at" IS NULL) 
ORDER BY 
  row_number

DISTINCT(tt.topic_id) 是冗余的，我们可以将其移除以加速查询，优化后的查询如下：
该优化可将查询性能从 12655768 纳秒提升至 5005154 纳秒（性能提升 60%）。

  1. 子查询中不必要的 DISTINCT 导致查询变慢 [search.rb#L642]：
SELECT 
  "posts".* 
FROM 
  "posts" 
  JOIN (
    SELECT 
      *, 
      row_number() over() row_number 
    FROM 
      (
        SELECT 
          topics.id, 
          posts.post_number 
        FROM 
          "posts" 
          INNER JOIN "post_search_data" ON "post_search_data"."post_id" = "posts"."id" 
          INNER JOIN "topics" ON "topics"."id" = "posts"."topic_id" 
          AND ("topics"."deleted_at" IS NULL) 
          LEFT JOIN categories ON categories.id = topics.category_id 
        WHERE 
          ("posts"."deleted_at" IS NULL) 
          AND "posts"."post_type" IN (1, 2, 3) 
          AND (topics.visible) 
          AND (
            topics.archetype <> 'private_message'
          ) 
          AND (
            topics.category_id IN (3715)
          ) 
          AND (
            topics.id IN (
              SELECT 
                DISTINCT(tt.topic_id) 
              FROM 
                topic_tags tt, 
                tags 
              WHERE 
                tt.tag_id = tags.id 
                AND lower(tags.name) IN ('lunch')
            )
          ) 
          AND (
            (categories.id IS NULL) 
            OR (NOT categories.read_restricted)
          ) 
        ORDER BY 
          posts.like_count DESC 
        LIMIT 
          6 OFFSET 0
      ) xxx
  ) x ON x.id = posts.topic_id 
  AND x.post_number = posts.post_number 
WHERE 
  ("posts"."deleted_at" IS NULL) 
ORDER BY 
  row_number

与上一个案例类似（源代码不同），DISTINCT(tt.topic_id) 是冗余的，我们可以将其移除以加速查询：
该优化可将查询性能从 23659762 纳秒提升至 21030593 纳秒（性能提升 10%）。