提高讨厌的嵌套视图连接的性能

Question

我有一个中等大小的数据库，分布在几个表上，粗略的架构是：

• 输入数据（数据 ID、会话 ID 和一些具有统计重要性的字段）
• 输入文件（数据 ID 和 blob）
• 阶段 1 输出文件（数据 ID 和 blob）
• 阶段 2 输出文件（数据 ID 和 blob）
• 类别 1 结果（数据 ID 和一些布尔值）
• 类别 2 结果（数据 ID 和一些整数）
• 第 3 类结果（数据 ID 和一些整数）

每个表有大约 200,000 行。

我还有一个视图，它基本上将所有这些粘合在一起，以便我可以SELECT使用一堆 ID（通常根据会话 ID 选择它们）并在一个页面上查看所有相关数据。

该视图有效，查询计划的索引利用率似乎正常，但结果并不快：

> EXPLAIN ANALYZE SELECT(*) FROM overlay WHERE test_session=12345;

                 QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Merge Right Join  (cost=7.19..74179.49 rows=10 width=305) (actual time=10680.129..10680.494 rows=4 loops=1)
   Merge Cond: (p.data_id = d.id)
   ->  Merge Join  (cost=7.19..75077.04 rows=183718 width=234) (actual time=0.192..10434.995 rows=173986 loops=1)
         Merge Cond: (p.data_id = input_file.data_id)
         ->  Merge Join  (cost=7.19..69917.74 rows=183718 width=222) (actual time=0.173..9255.653 rows=173986 loops=1)
               Merge Cond: (p.data_id = stage1_output_file.data_id)
               ->  Merge Join  (cost=5.50..62948.54 rows=183718 width=186) (actual time=0.153..8081.949 rows=173986 loops=1)
                     Merge Cond: (p.data_id = stage2_output_file.data_id)
                     ->  Merge Join  (cost=3.90..55217.36 rows=183723 width=150) (actual time=0.132..6918.814 rows=173986 loops=1)
                           Merge Cond: (p.data_id = stage3_output_file.data_id)
                           ->  Nested Loop  (cost=2.72..47004.01 rows=183723 width=114) (actual time=0.111..5753.105 rows=173986 loops=1)
                                 Join Filter: (p.impression = istr.id)
                                 ->  Merge Join  (cost=1.68..30467.90 rows=183723 width=102) (actual time=0.070..2675.733 rows=173986 loops=1)
                                       Merge Cond: (p.data_id = s.data_id)
                                       ->  Merge Join  (cost=1.68..19031.56 rows=183723 width=58) (actual time=0.049..1501.546 rows=173986 loops=1)
                                             Merge Cond: (p.data_id = t.data_id)
                                             ->  Index Scan using Category1_Results_pkey on Category1_Results p  (cost=0.00..7652.17 rows=183723 width=18) (actual time=0.025..315.531 rows=173986 loops=1)
                                             ->  Index Scan using Category3_Results_pkey on Category3_Results t  (cost=0.00..8624.43 rows=183787 width=40) (actual time=0.016..321.460 rows=173986 loops=1)
                                       ->  Index Scan using Category2_Results_pkey on Category2_Results s  (cost=0.00..8681.47 rows=183787 width=44) (actual time=0.014..320.794 rows=173986 loops=1)
                                 ->  Materialize  (cost=1.04..1.08 rows=4 width=20) (actual time=0.001..0.007 rows=4 loops=173986)
                                       ->  Seq Scan on Category1_impression_str istr  (cost=0.00..1.04 rows=4 width=20) (actual time=0.005..0.012 rows=4 loops=1)
                           ->  Index Scan using Stage3_Output_file_pkey on Stage3_Output_file stage3  (cost=0.00..8178.35 rows=183871 width=36) (actual time=0.015..317.698 rows=173986 loops=1)
                     ->  Index Scan using analysis_file_pkey on analysis_file Stage2_Output  (cost=0.00..8168.99 rows=183718 width=36) (actual time=0.014..317.776 rows=173986 loops=1)
               ->  Index Scan using Stage1_output_file_pkey on Stage1_output_file stg1  (cost=0.00..8199.07 rows=183856 width=36) (actual time=0.014..321.648 rows=173986 loops=1)
         ->  Index Scan using input_file_pkey on input_file input  (cost=0.00..8618.05 rows=183788 width=36) (actual time=0.014..328.968 rows=173986 loops=1)
   ->  Materialize  (cost=0.00..39.59 rows=10 width=75) (actual time=0.046..0.150 rows=4 loops=1)
         ->  Nested Loop Left Join  (cost=0.00..39.49 rows=10 width=75) (actual time=0.039..0.128 rows=4 loops=1)
               Join Filter: (t.id = d.input_quality)
               ->  Index Scan using input_data_exists_index on input_data d  (cost=0.00..28.59 rows=10 width=45) (actual time=0.013..0.025 rows=4 loops=1)
                     Index Cond: (test_session = 1040)
               ->  Seq Scan on quality_codes t  (cost=0.00..1.04 rows=4 width=38) (actual time=0.002..0.009 rows=4 loops=4)
 Total runtime: 10680.902 ms

对此的基本视图是我们的“完整结果”视图，定义为：

 SELECT p.data_id, p.x2, istr.str AS impression, input.h, p.x3, p.x3, p.x4, s.x5,
        s.x6, s.x7, s.x8, s.x9, s.x10, s.x11, s.x12, s.x13, s.x14, t.x15,
        t.x16, t.x17, t.x18, t.x19, t.x20, t.x21, t.x22, t.x23,
        input.data AS input, stage1_output_file.data AS stage1, 
        stage2_output_file.data AS stage2, stage3_output_file.data AS stage3
FROM category1_results p, category1_impression_str istr, input_file input,
     stage1_output_file, stage2_output_file, stage3_output_file, 
     category2_results s, category3_results t
 WHERE p.impression = istr.id AND p.data_id = input.data_id AND p.data_id = stage1_output_file.data_id
       AND p.data_id = stage2_output_file.data_id AND p.data_id = stage3_output_file.data_id AND p.data_id = s.data_id AND p.data_id = t.data_id;                                  

以及生成上述查询计划的覆盖视图，定义为：

 SELECT d.data_id, d.test_session, d.a, d.b, t.c, d.d, d.e, d.f, r.*
 FROM input_data d LEFT JOIN quality_codes t ON t.id = d.input_quality
      LEFT JOIN full_results r ON r.data_id = d.data_id  
 WHERE NOT d.deleted;

我们似乎在链中的大部分时间都加入了我们的整个数据集，我非常确信这是我们的性能问题——有没有人对优化这头猪的方法有什么建议？

Answer 1

我在这里推测，但我猜你LEFT JOIN到视图的事实使计划器在加入查询的第一部分之前从整体上计算视图的结果。

尝试从视图内联查询并将其JOIN设为 LEFT JOIN 而不是 LEFT JOIN，只是为了看看规划器现在是否找到了更快的方法：

SELECT d.data_id, d.test_session, d.a, d.b, t.c, d.d, d.e, d.f
     , p.data_id AS p_data_id, p.x2, c.str AS impression, i.h
     , p.x3, p.x3, p.x4
     , s.x5, s.x6, s.x7, s.x8, s.x9, s.x10, s.x11, s.x12, s.x13, s.x14
     , t.x15, t.x16, t.x17, t.x18, t.x19, t.x20, t.x21, t.x22, t.x23
     , i.data AS input
     , s1.data AS stage1, s2.data AS stage2, s3.data AS stage3
FROM   input_data d
JOIN   category1_results        p ON p.data_id = d.data_id
JOIN   input_file               i USING (data_id)
JOIN   stage1_output_file      s1 USING (data_id)
JOIN   stage2_output_file      s2 USING (data_id)
JOIN   stage3_output_file      s3 USING (data_id)
JOIN   category2_results        s USING (data_id)
JOIN   category3_results        t USING (data_id)
JOIN   category1_impression_str c ON p.impression = c.id 
LEFT   JOIN quality_codes       t ON t.id = d.input_quality
WHERE  NOT d.deleted;

我清理了您的语法，使其更易于管理。为第二data_id列添加了别名，因此它可以执行。

如果这会导致相当快的执行时间，您可以尝试添加丢失的行，原因INNER JOIN如下：

SELECT DISTINCT ON (1,2,3,4,5,6,7,8) *
FROM (
    <<query>>
    ) x
UNION ALL
SELECT d.data_id, d.test_session, d.a, d.b, t.c, d.d, d.e, d.f
      ,NULL,NULL,NULL,NULL,NULL,NULL,NULL,NULL,NULL,NULL
      ,NULL,NULL,NULL,NULL,NULL,NULL,NULL,NULL,NULL,NULL
      ,NULL,NULL,NULL,NULL,NULL,NULL,NULL,NULL,NULL,NULL
FROM   input_data d
LEFT   JOIN quality_codes t ON t.id = d.input_quality
WHERE  NOT d.deleted;
ORDER  BY 1,2,3,4,5,6,7,8, 9 NULLS LAST; -- p.data_id is otherwise not null