Question

以下查询在PostgreSQL 11.1中花费超过7分钟：

SELECT 
    '2019-01-19' as date, 
    '2019-01-19'::date - generate_series(first_observed, last_observed, interval '1 day')::date as days_to_date, 
    ROUND(AVG(price)) as price,
    area_id
FROM 
    table_example
GROUP BY 
    days_to_date, area_id;

table_example大约有 1500万行。
有什么方法可以优化它？我已经添加了以下索引：

CREATE INDEX ON table_example (first_observed, last_observed);
CREATE INDEX ON table_example (area_id);

这是从EXPLAIN (ANALYZE,BUFFERS)输出的：

GroupAggregate  (cost=3235559683.68..3377398628.68 rows=1418000 width=72) (actual time=334933.966..440096.869 rows=21688 loops=1)
  Group Key: (('2019-01-19'::date - ((generate_series((first_observed)::timestamp with time zone, (last_observed)::timestamp with time zone, '1 day'::interval)))::date)), area_id
  Buffers: local read=118167 dirtied=118167 written=117143, temp read=1634631 written=1635058
  ->  Sort  (cost=3235559683.68..3271009671.18 rows=14179995000 width=40) (actual time=334923.933..391690.184 rows=380203171 loops=1)
        Sort Key: (('2019-01-19'::date - ((generate_series((first_observed)::timestamp with time zone, (last_observed)::timestamp with time zone, '1 day'::interval)))::date)), area_id
        Sort Method: external merge  Disk: 9187584kB
        Buffers: local read=118167 dirtied=118167 written=117143, temp read=1634631 written=1635058
        ->  Result  (cost=0.00..390387079.39 rows=14179995000 width=40) (actual time=214.798..171717.941 rows=380203171 loops=1)
              Buffers: local read=118167 dirtied=118167 written=117143
              ->  ProjectSet  (cost=0.00..71337191.89 rows=14179995000 width=44) (actual time=214.796..102823.749 rows=380203171 loops=1)
                    Buffers: local read=118167 dirtied=118167 written=117143
                    ->  Seq Scan on table_example  (cost=0.00..259966.95 rows=14179995 width=44) (actual time=0.031..2449.511 rows=14179995 loops=1)
                          Buffers: local read=118167 dirtied=118167 written=117143
Planning Time: 0.409 ms
JIT:
  Functions: 18
  Options: Inlining true, Optimization true, Expressions true, Deforming true
  Timing: Generation 5.034 ms, Inlining 13.010 ms, Optimization 121.440 ms, Emission 79.996 ms, Total 219.480 ms
Execution Time: 441133.410 ms

这是table_example的样子：

column name        data type
'house_pk'         'integer'    
'date_in'          'date'   
'first_observed'   'date'   
'last_observed'    'date'   
'price'            'numeric'    
'area_id'          'integer'

有60个不同的area_id。

查询正在具有128 GB内存的多核计算机（24个核）上运行。但是，设置可能不是最佳的。

Answer 1

在处理整个表时，索引通常是无用的（如果表行比索引宽得多，则可能只有索引扫描例外）。

在处理整个表时，我认为查询本身的性能优化没有太大余地。一件小事：

SELECT d.the_date
     , generate_series(d.the_date - last_observed
                     , d.the_date - first_observed) AS days_to_date
     , round(avg(price)) AS price
     , area_id
FROM   table_example
     , (SELECT date '2019-01-19') AS d(the_date)
GROUP  BY days_to_date, area_id;

假设first_observed和last_observed为date NOT NULL，并且始终为< date '2019-01-19'。否则，您需要投放/做更多的事情。

这样，您只有两个减法，然后generate_series()使用整数（最快）。

添加的迷你子查询只是为了方便起见，仅提供一次日期。在准备好的语句或函数中，您可以使用参数，而无需这样做：

     , (SELECT date '2019-01-19') AS d(the_date)

除此之外，如果EXPLAIN (ANALYZE, BUFFERS)提到“磁盘”（例如：Sort Method: external merge Disk: 3240kB），那么work_mem的（临时）较高的设置应该会有所帮助。参见：

如果您负担不起更多的RAM，并且聚合和/或排序步骤仍散落到磁盘上，则可能有助于使用LATERAL连接之类的查询来划分和征服：

SELECT d.the_date, f.*, a.area_id
FROM   area a
     , (SELECT date '2019-01-19') AS d(the_date)
     , LATERAL (
   SELECT generate_series(d.the_date - last_observed
                        , d.the_date - first_observed) AS days_to_date
        , round(avg(price)) AS price
   FROM   table_example
   WHERE  area_id = a.area_id
   GROUP  BY 1
   ) f;

显然，假设有一个表area。

在执行generate_series（）的大表上优化查询

1 个答案: