LATERAL結合で低速アグリゲートを最適化する

これは、LATERALを使用したより高速なバリアントである必要があります サブクエリ。未テスト。

SELECT s.record_id, s.security_id, s.date
     , s.price / l.pmax   AS price_to_peak_earnings
     , s.price / l.pmin   AS price_to_minimum_earnings
  -- , ...
     , s.price / l.cape1  AS cape1
     , s.price / l.cape2  AS cape2
  -- , ...
     , s.price / l.cape10 AS cape10
     , s.price / l.capb1  AS capb1
     , s.price / l.capb2  AS capb2
  -- , ...
     , s.price / l.capb10 AS capb10
  -- , ...
FROM  (
   SELECT *
        , (date - interval  '1 y')::date AS date1
        , (date - interval  '2 y')::date AS date2
        -- ...
        , (date - interval '10 y')::date AS date10
   FROM  (
      SELECT *, min(date) OVER (PARTITION BY security_id) AS min_date
      FROM   security_data
      ) s1
   ) s
LEFT   JOIN LATERAL (
   SELECT CASE WHEN s.date10 >= s.min_date THEN NULLIF(max(earnings)                               , 0) END AS pmax
        , CASE WHEN s.date10 >= s.min_date THEN NULLIF(min(earnings)                               , 0) END AS pmin
        -- ...
        ,                                       NULLIF(avg(earnings) FILTER (WHERE date >= s.date1), 0)     AS cape1   -- no case
        , CASE WHEN s.date2  >= s.min_date THEN NULLIF(avg(earnings) FILTER (WHERE date >= s.date2), 0) END AS cape2
        -- ...
        , CASE WHEN s.date10 >= s.min_date THEN NULLIF(avg(earnings)                               , 0) END AS cape10  -- no filter

        ,                                       NULLIF(avg(book)     FILTER (WHERE date >= s.date1), 0)     AS capb1
        , CASE WHEN s.date2  >= s.min_date THEN NULLIF(avg(book)     FILTER (WHERE date >= s.date2), 0) END AS capb2
        -- ...
        , CASE WHEN s.date10 >= s.min_date THEN NULLIF(avg(book)                                   , 0) END AS capb10
        -- ...
   FROM   security_data 
   WHERE  security_id = s.security_id
   AND    date >= s.date10
   AND    date <  s.date
   ) l ON s.date1 >= s.min_date  -- no computations if < 1 year of trailing data
ORDER  BY s.security_id, s.date;

すべての行に複数の個別の集計が必要なため、それでも驚くほど高速になることはありません。ここでのボトルネックはCPUです。

別のアプローチ（生成されたカレンダー+ウィンドウ関数に参加）のフォローアップも参照してください：

• ウィンドウ関数は現在の行をフィルタリングします