Создает ли возвращаемая таблица, определенная внутри хранимой процедуры, временную таблицу?

Question

Определена хранимая процедура, которая принимает некоторые параметры, внутри нее запускается цикл. Проблема заключается в том, что всякий раз, когда я выполняю этот запрос, этот отдельный процесс / функция требует 100% загрузки ЦП и около 2 минут, чтобы выполнить, я увеличивал количество ядер, но он по-прежнему требует 100% загрузки ЦП. Я думаю, что, возможно, он создает временные таблицы, и Postgresql не может autovaccum.

Я пробовал несколько конфигураций, но безрезультатно.

create
or replace
function some_function(offers numeric[],
list character varying[],
startdate date,
enddate date) returns table
  (prod character varying,
  offer numeric,
  dates date,
  baseline double precision,
  promoincremental double precision,
  couponincremental double precision,
  affinityquantity double precision,
  affinitymargin double precision,
  affinityrevenue double precision,
  cannibalisationquantity double precision,
  cannibalisationrevenue double precision,
  cannibalisationmargin double precision
    ) language plpgsql as $function$ declare a numeric[] := offers;

i numeric;
begin
foreach i in array a loop return QUERY select
  models.prod,
  i as offer,
  date(models.transdate_dt) as dates,
  greatest(0,(sum(models.unitretailprice) * sum(coefficients.unit_retail_price)) + (sum(models.flag::int) * sum(coefficients.flag::int)) + (sum(models.mc_baseline) * sum(coefficients.mc_baseline)) + (sum(models.mc_day_avg) * sum(coefficients.mc_day_avg)) + (sum(models.mc_day_normal) * sum(coefficients.mc_day_normal)) + (sum(models.mc_week_avg) * sum(coefficients.mc_week_avg)) + (sum(models.mc_week_normal) * sum(coefficients.mc_week_normal)) + (sum(models.sku_day_avg) * sum(coefficients.sku_day_avg)) + (sum(models.sku_month_avg) * sum(coefficients.sku_month_avg)) + (sum(models.sku_month_normal)* sum(coefficients.sku_month_normal)) + (sum(models.sku_moving_avg) * sum(coefficients.sku_moving_avg)) + (sum(models.sku_week_avg) * sum(coefficients.sku_week_avg)) + (sum(models.sku_week_normal)* sum(coefficients.sku_week_normal))) as baseline,
  greatest(0, ((0 * sum(coefficients.f)) + (0 * sum(coefficients.p)) + (i * sum(coefficients.a)))) as promoIncremental,
  greatest(0, (sum(models.basket_dollar_off) * sum(coefficients.basket_dollar_off)) + (sum(models.basket_per_off) * sum(coefficients.basket_per_off)) + (sum(models.category_dollar_off) * sum(coefficients.category_dollar_off)) + (sum(models.category_per_off) * sum(coefficients.category_per_off)) + (sum(models.disc_per) * sum(coefficients.disc_per))) as couponIncremnetal,
  greatest(0, (0 * sum(coefficients.f)) + (0 * sum(coefficients.p)) + (i * sum(coefficients.a))) * sum(affinity.pull) * sum(affinity.confidence) as affinityquantity,
  greatest(0, (0 * sum(coefficients.f)) + (0 * sum(coefficients.p)) + (i * sum(coefficients.a))) * sum(affinity.margin_lift) * sum(affinity.confidence) as affinitymargin,
  greatest(0, (0 * sum(coefficients.f)) + (0 * sum(coefficients.p)) + (i * sum(coefficients.a))) * sum(affinity.revenue_lift) * sum(affinity.confidence) as affinityrevenue,
  greatest(0, (0 * sum(coefficients.f)) + (0 * sum(coefficients.p)) + (i * sum(coefficients.a)))/(NULLIF( greatest(0,(sum(models.unitretailprice) * sum(coefficients.unit_retail_price)) + (sum(models.flag::int) * sum(coefficients.flag::int)) + (sum(models.mc_baseline) * sum(coefficients.mc_baseline)) + (sum(models.mc_day_avg) * sum(coefficients.mc_day_avg)) + (sum(models.mc_day_normal) * sum(coefficients.mc_day_normal)) + (sum(models.mc_week_avg) * sum(coefficients.mc_week_avg)) + (sum(models.mc_week_normal) * sum(coefficients.mc_week_normal)) + (sum(models.sku_day_avg) * sum(coefficients.sku_day_avg)) + (sum(models.sku_month_avg) * sum(coefficients.sku_month_avg)) + (sum(models.sku_month_normal)* sum(coefficients.sku_month_normal)) + (sum(models.sku_moving_avg) * sum(coefficients.sku_moving_avg)) + (sum(models.sku_week_avg) * sum(coefficients.sku_week_avg)) + (sum(models.sku_week_normal)* sum(coefficients.sku_week_normal))) + greatest(0, (0 * sum(coefficients.f)) + (0 * sum(coefficients.p)) + (i * sum(coefficients.a))) + greatest(0, (sum(models.basket_dollar_off) * sum(coefficients.basket_dollar_off)) + (sum(models.basket_per_off) * sum(coefficients.basket_per_off)) + (sum(models.category_dollar_off) * sum(coefficients.category_dollar_off)) + (sum(models.category_per_off) * sum(coefficients.category_per_off)) + (sum(models.disc_per) * sum(coefficients.disc_per))), 0)) * sum(cannibalisation.effect) * (sum(products.price) - (sum(products.price) - i )) as cannibalisationquantity,
  ((sum(products.price) - i) * sum(models.disc_per) - (sum(products.price) - i) ) * (   greatest(0, (0 * sum(coefficients.f)) + (0 * sum(coefficients.p)) + (i * sum(coefficients.a)))/(NULLIF( greatest(0,(sum(models.unitretailprice) * sum(coefficients.unit_retail_price)) + (sum(models.flag::int) * sum(coefficients.flag::int)) + (sum(models.mc_baseline) * sum(coefficients.mc_baseline)) + (sum(models.mc_day_avg) * sum(coefficients.mc_day_avg)) + (sum(models.mc_day_normal) * sum(coefficients.mc_day_normal)) + (sum(models.mc_week_avg) * sum(coefficients.mc_week_avg)) + (sum(models.mc_week_normal) * sum(coefficients.mc_week_normal)) + (sum(models.sku_day_avg) * sum(coefficients.sku_day_avg)) + (sum(models.sku_month_avg) * sum(coefficients.sku_month_avg)) + (sum(models.sku_month_normal)* sum(coefficients.sku_month_normal)) + (sum(models.sku_moving_avg) * sum(coefficients.sku_moving_avg)) + (sum(models.sku_week_avg) * sum(coefficients.sku_week_avg)) + (sum(models.sku_week_normal)* sum(coefficients.sku_week_normal))) + greatest(0, (0 * sum(coefficients.f)) + (0 * sum(coefficients.p)) + (i * sum(coefficients.a))) + greatest(0, (sum(models.basket_dollar_off) * sum(coefficients.basket_dollar_off)) + (sum(models.basket_per_off) * sum(coefficients.basket_per_off)) + (sum(models.category_dollar_off) * sum(coefficients.category_dollar_off)) + (sum(models.category_per_off) * sum(coefficients.category_per_off)) + (sum(models.disc_per) * sum(coefficients.disc_per))), 0)) * sum(cannibalisation.effect) * (sum(products.price) -  (sum(products.price) - i ) )) as cannibalisationrevenue,
  (((sum(products.price) - i) * sum(models.disc_per) - (sum(products.price) - i) ) - sum(products.cost)) * (    greatest(0, (0 * sum(coefficients.f)) + (0 * sum(coefficients.p)) + (i * sum(coefficients.a)))/(NULLIF(greatest(0,(sum(models.unitretailprice) * sum(coefficients.unit_retail_price)) + (sum(models.flag::int) * sum(coefficients.flag::int)) + (sum(models.mc_baseline) * sum(coefficients.mc_baseline)) + (sum(models.mc_day_avg) * sum(coefficients.mc_day_avg)) + (sum(models.mc_day_normal) * sum(coefficients.mc_day_normal)) + (sum(models.mc_week_avg) * sum(coefficients.mc_week_avg)) + (sum(models.mc_week_normal) * sum(coefficients.mc_week_normal)) + (sum(models.sku_day_avg) * sum(coefficients.sku_day_avg)) + (sum(models.sku_month_avg) * sum(coefficients.sku_month_avg)) + (sum(models.sku_month_normal)* sum(coefficients.sku_month_normal)) + (sum(models.sku_moving_avg) * sum(coefficients.sku_moving_avg)) + (sum(models.sku_week_avg) * sum(coefficients.sku_week_avg)) + (sum(models.sku_week_normal)* sum(coefficients.sku_week_normal))) + greatest(0, (0 * sum(coefficients.f)) + (0 * sum(coefficients.p)) + (i * sum(coefficients.a))) + greatest(0, (sum(models.basket_dollar_off) * sum(coefficients.basket_dollar_off)) + (sum(models.basket_per_off) * sum(coefficients.basket_per_off)) + (sum(models.category_dollar_off) * sum(coefficients.category_dollar_off)) + (sum(models.category_per_off) * sum(coefficients.category_per_off)) + (sum(models.disc_per) * sum(coefficients.disc_per))), 0)) * sum(cannibalisation.effect) * (sum(products.price) - (sum(products.price) - i )  )) as cannibalisationmargin 
from
  models
join coefficients on
  models.prod = coefficients.prod
  and models.si_type = coefficients.si_type
  and models.model_type = coefficients.model_type
left join products on
  products.unique_id1 = models.prod
left join affinity on
  affinity.prod = models.prod
left join cannibalisation on
  cannibalisation.prod = models.prod
where
  coefficients.prod = any(skusList)
  and models.transdate_dt >= startDate
  and models.transdate_dt <= endDate
group by
  models.prod,
  dates,
  offer ;
end loop;
end;
$function$ ;

Здесь я вызываю функцию с этими параметрами :::::

select
  *
from
  some_function(array[1.5,
  2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5 ],
  '{12841276, 11873916,07915473, 01504273,10405843,11231446,12224242,11249380,08604365, 11248952, 11230018,10447621,11229820,10406916,09578733,11280161, 01503697, 11923554, 10406460,11295219,01458421,09053893,11224409, 06755789, 11317377, 11275047,12231817,11309507,10447522,10406296, 10406338, 01460658, 12272811,11318870,11248838,10406130,11248812, 11223682, 11276748, 10447605, 11232451, 10405827,10447670,08177743, 10405231, 02326791, 12224226,12231650,11929197,01504380 }',
  '2018-01-02'::date,
  '2018-01-06'::date );

Я ожидал, что максимальная загрузка ЦП составит около 30%, поскольку он выполняет некоторые вычисления, но снижает нагрузку на 100%. Может ли кто-нибудь помочь в этом отношении?