postgres的generate_series性能上服务器慢然后膝上型

Question

我有一系列的观点，即建立过彼此的是这样的：

rpt_scn_appl_target_v - > rpt_scn_appl_target_unnest_v - > rpt_scn_appl_target_unnest_timeseries_v - > rpt_scn_appl_target_unnest_timeseries_ftprnt_v

在该视图中..rpt_scn_appl_target_unnest_timeseries_v，我使用generate_series函数在1/1/2015和12/31/2019之间生成每月行。

什么我注意到的是：

this one takes 10secs to run 
select * from rpt_scn_appl_target_unnest_timeseries_ftprnt_v where scenario_id = 202 

this one takes 9 secs to run: 
select * from rpt_scn_appl_target_unnest_timeseries_v where scenario_id = 202 

this one takes 219msecs to run: 
select * from rpt_scn_appl_target_unnest_v where scenario_id = 202 

this one takes <1sec to run: 
select * from rpt_scn_appl_target_v where scenario_id = 202 

我注意到注释掉在视图中generate_series代码，查询运行在一秒之内，但有了它，它需要10secs运行...

rpt_scn_appl_target_unnest_timeseries_v查看：

CREATE OR REPLACE VIEW public.rpt_scn_appl_target_unnest_timeseries_v AS 
SELECT a.scenario_id, 
    a.scenario_desc, 
    a.scenario_status, 
    a.scn_appl_lob_ownr_nm, 
    a.scn_appl_sub_lob_ownr_nm, 
    a.scenario_asv_id, 
    a.appl_ci_id, 
    a.appl_ci_nm, 
    a.appl_ci_comm_nm, 
    a.appl_lob_ownr_nm, 
    a.appl_sub_lob_ownr_nm, 
    a.cost, 
    a.agg_complexity, 
    a.srvc_lvl, 
    a.dc_loc, 
    a.start_dt, 
    a.end_dt, 
    a.decomm_dt, 
    a.asv_target_id, 
    a.asv_target_desc, 
    a.asv_target_master, 
    a.prod_qty_main_cloud, 
    a.prod_cost_main_cloud, 
    a.non_prod_qty_main_cloud, 
    a.non_prod_cost_main_cloud, 
    a.prod_qty_main_onprem, 
    a.prod_cost_main_onprem, 
    a.non_prod_qty_main_onprem, 
    a.non_prod_cost_main_onprem, 
    a.prod_qty_target_onprem, 
    a.prod_cost_target_onprem, 
    a.non_prod_qty_target_onprem, 
    a.non_prod_cost_target_onprem, 
    a.prod_qty_target_cloud, 
    a.prod_cost_target_cloud, 
    a.non_prod_qty_target_cloud, 
    a.non_prod_cost_target_cloud, 
    a.type, 
    a.cost_main, 
    a.qty_main, 
    a.cost_target, 
    a.qty_target, 
    a.dt, 
    a.mth_dt, 
     CASE 
      WHEN a.type ~~ '%onprem%'::text THEN 'On-Prem'::text 
      ELSE 'Cloud'::text 
     END AS env_stat, 
     CASE 
      WHEN a.type ~~ '%non_prod%'::text THEN 'Non-Prod'::text 
      ELSE 'Prod'::text 
     END AS env, 
     CASE 
      WHEN a.dt <= a.decomm_dt THEN COALESCE(a.cost_main, 0::double precision) 
      WHEN a.decomm_dt IS NULL AND a.end_dt IS NULL AND a.start_dt IS NULL THEN a.cost_main 
      ELSE 0::double precision 
     END AS cost_curr, 
     CASE 
      WHEN a.dt <= a.decomm_dt THEN COALESCE(a.qty_main, 0::bigint) 
      WHEN a.decomm_dt IS NULL AND a.end_dt IS NULL AND a.start_dt IS NULL THEN a.qty_main 
      ELSE 0::bigint 
     END AS qty_curr, 
     CASE 
      WHEN a.dt < a.start_dt THEN 0::bigint 
      WHEN a.dt >= a.start_dt AND a.dt < a.end_dt AND a.type ~~ '%non_prod%'::text THEN COALESCE(a.qty_target, 0::bigint) 
      WHEN a.dt > a.end_dt THEN COALESCE(a.qty_target, 0::bigint) 
      ELSE 0::bigint 
     END AS qty_trgt, 
     CASE 
      WHEN a.dt < a.start_dt THEN 0::double precision 
      WHEN a.dt >= a.start_dt AND a.dt < a.end_dt AND a.type ~~ '%non_prod%'::text THEN COALESCE(a.cost_target, 0::double precision) 
      WHEN a.dt > a.end_dt THEN COALESCE(a.cost_target, 0::double precision) 
      ELSE 0::double precision 
     END AS cost_trgt 
    FROM (SELECT t1.scenario_id, 
      t1.scenario_desc, 
      t1.scenario_status, 
      t1.scn_appl_lob_ownr_nm, 
      t1.scn_appl_sub_lob_ownr_nm, 
      t1.scenario_asv_id, 
      t1.appl_ci_id, 
      t1.appl_ci_nm, 
      t1.appl_ci_comm_nm, 
      t1.appl_lob_ownr_nm, 
      t1.appl_sub_lob_ownr_nm, 
      t1.cost, 
      t1.agg_complexity, 
      t1.srvc_lvl, 
      t1.dc_loc, 
      t1.start_dt, 
      t1.end_dt, 
      t1.decomm_dt, 
      t1.asv_target_id, 
      t1.asv_target_desc, 
      t1.asv_target_master, 
      t1.prod_qty_main_cloud, 
      t1.prod_cost_main_cloud, 
      t1.non_prod_qty_main_cloud, 
      t1.non_prod_cost_main_cloud, 
      t1.prod_qty_main_onprem, 
      t1.prod_cost_main_onprem, 
      t1.non_prod_qty_main_onprem, 
      t1.non_prod_cost_main_onprem, 
      t1.prod_qty_target_onprem, 
      t1.prod_cost_target_onprem, 
      t1.non_prod_qty_target_onprem, 
      t1.non_prod_cost_target_onprem, 
      t1.prod_qty_target_cloud, 
      t1.prod_cost_target_cloud, 
      t1.non_prod_qty_target_cloud, 
      t1.non_prod_cost_target_cloud, 
      t1.type, 
      t1.cost_main, 
      t1.qty_main, 
      t1.cost_target, 
      t1.qty_target, 
      generate_series('2015-01-01 00:00:00'::timestamp without time zone, '2019-12-31 00:00:00'::timestamp without time zone, '1 mon'::interval)::date AS dt, 
      to_char(generate_series('2015-01-01 00:00:00'::timestamp without time zone, '2019-12-31 00:00:00'::timestamp without time zone, '1 mon'::interval)::date::timestamp with time zone, 'YYYY-MM'::text) AS mth_dt 
      FROM rpt_scn_appl_target_unnest_v t1) a; 

什么我也注意到也就是我的笔记本电脑和AWS数据库之间的性能具有相同数据，表格和视图的rds服务器在我的笔记本电脑上运行得更快，尽管它的内存和CPU更少。我在笔记本电脑上运行postgres 9.6，在AWS rds上运行9.6。我的笔记本电脑是MacBook Pro与16GB的内存和I7双核心。对于rds，我使用的是一个m4.4xlarge，它是16个内核和64gb的内存。

这里是AWS解释计划： https://explain.depesz.com/s/UGF

我的笔记本电脑解释计划： https://explain.depesz.com/s/zaWt

所以我想我的问题是：

1）为什么查询需要更长的时间来运行在AWS上我的笔记本电脑？

2.）任何人可以做的事情来加快generate_series功能？是否创建一个单独的日历表，然后加入该工作更快？

Answer 1

1）您的笔记本电脑行数较少。

AWS: (cost=17,814.33..7,931,812.83 rows=158,200,000 width=527) 
Laptop: (cost=15,238.52..4,002,252.94 rows=79,700,000 width=2,030) 

2）如果你打算多次使用该表，最好创建一个日历表。 10年只有3650行，100年36k行