我有以下查询，我想对其进行优化。
SELECT
a.household_id household_id,
age_of_youngest_woman,
b.number_of_children,
c.number_of_men,
fertility_cond_prob_number_of_children.cond_prob cond_prob_number_of_children,
fertility_cond_age.cond_prob cond_prob_age,
fertility_cond_prob_number_of_children.cond_prob * fertility_cond_age.cond_prob total_cond_prob,
random() <= (874. / 1703.) is_newborn_male
FROM
(
SELECT household_id, MIN(age) age_of_youngest_woman
FROM person
WHERE
(user_id = 1) and
(gender = 'FEMALE') and
(age >= 18)
GROUP BY household_id
) a
LEFT JOIN
(
SELECT household_id, COUNT(*) number_of_children
FROM person
WHERE
(user_id = 1) and
(gender = 'CHILD')
GROUP BY household_id
) b ON (a.household_id = b.household_id)
LEFT JOIN
(
SELECT household_id, COUNT(*) number_of_men
FROM person
WHERE
(user_id = 1) and
(gender = 'MALE') and
(age >= 18)
GROUP BY household_id
) c ON (a.household_id = c.household_id)
LEFT JOIN fertility_cond_prob_number_of_children ON (fertility_cond_prob_number_of_children.number_of_children = b.number_of_children)
LEFT JOIN fertility_cond_age ON (fertility_cond_age.age = age_of_youngest_woman)
WHERE
(c.number_of_men > 0) and
(random() <= (fertility_cond_prob_number_of_children.cond_prob * fertility_cond_age.cond_prob))
EXPLAIN ANALYZE返回以下信息：
Merge Join (cost=20366.67..853430.69 rows=34797455 width=44) (actual time=1330.609..1641.402 rows=224 loops=1)
Merge Cond: (c.household_id = public.person.household_id)
-> Sort (cost=4806.12..4829.66 rows=9416 width=16) (actual time=492.839..546.397 rows=25098 loops=1)
Sort Key: c.household_id
Sort Method: external merge Disk: 640kB
-> Subquery Scan on c (cost=3972.76..4184.62 rows=9416 width=16) (actual time=232.953..367.689 rows=25259 loops=1)
-> HashAggregate (cost=3972.76..4090.46 rows=9416 width=8) (actual time=232.946..288.922 rows=25259 loops=1)
Filter: (count(*) > 0)
-> Seq Scan on person (cost=0.00..3737.68 rows=31344 width=8) (actual time=7.366..137.853 rows=38497 loops=1)
Filter: ((age >= 18) AND (user_id = 1) AND ((gender)::text = 'MALE'::text))
Rows Removed by Filter: 64856
-> Materialize (cost=15560.55..67482.77 rows=739113 width=44) (actual time=836.591..1049.115 rows=352 loops=1)
-> Merge Join (cost=15560.55..65634.99 rows=739113 width=44) (actual time=836.577..1047.666 rows=352 loops=1)
Merge Cond: (public.person.household_id = b.household_id)
Join Filter: (random() <= (fertility_cond_prob_number_of_children.cond_prob * fertility_cond_age.cond_prob))
Rows Removed by Join Filter: 11054
-> Sort (cost=4728.64..4747.85 rows=7684 width=20) (actual time=451.992..506.614 rows=26755 loops=1)
Sort Key: public.person.household_id
Sort Method: external merge Disk: 888kB
-> Hash Join (cost=3912.57..4232.73 rows=7684 width=20) (actual time=208.538..357.160 rows=26755 loops=1)
Hash Cond: ((min(public.person.age)) = fertility_cond_age.age)
-> HashAggregate (cost=3908.20..4010.65 rows=10245 width=12) (actual time=208.048..263.094 rows=26755 loops=1)
-> Seq Scan on person (cost=0.00..3737.68 rows=34104 width=12) (actual time=1.612..111.773 rows=42369 loops=1)
Filter: ((age >= 18) AND (user_id = 1) AND ((gender)::text = 'FEMALE'::text))
Rows Removed by Filter: 60984
-> Hash (cost=2.50..2.50 rows=150 width=12) (actual time=0.464..0.464 rows=150 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 6kB
-> Seq Scan on fertility_cond_age (cost=0.00..2.50 rows=150 width=12) (actual time=0.019..0.233 rows=150 loops=1)
-> Materialize (cost=10831.91..11120.48 rows=57715 width=24) (actual time=380.522..455.086 rows=14412 loops=1)
-> Sort (cost=10831.91..10976.20 rows=57715 width=24) (actual time=380.504..411.816 rows=14412 loops=1)
Sort Key: b.household_id
Sort Method: external merge Disk: 480kB
-> Merge Join (cost=4205.69..5081.12 rows=57715 width=24) (actual time=221.294..301.093 rows=14412 loops=1)
Merge Cond: (fertility_cond_prob_number_of_children.number_of_children = b.number_of_children)
-> Sort (cost=135.34..140.19 rows=1940 width=12) (actual time=0.098..0.107 rows=7 loops=1)
Sort Key: fertility_cond_prob_number_of_children.number_of_children
Sort Method: quicksort Memory: 17kB
-> Seq Scan on fertility_cond_prob_number_of_children (cost=0.00..29.40 rows=1940 width=12) (actual time=0.015..0.051 rows=25 loops=1)
-> Sort (cost=4070.35..4085.23 rows=5950 width=16) (actual time=221.176..247.951 rows=14412 loops=1)
Sort Key: b.number_of_children
Sort Method: quicksort Memory: 819kB
-> Subquery Scan on b (cost=3578.32..3697.32 rows=5950 width=16) (actual time=118.096..193.664 rows=14412 loops=1)
-> HashAggregate (cost=3578.32..3637.82 rows=5950 width=8) (actual time=118.090..147.604 rows=14412 loops=1)
-> Seq Scan on person (cost=0.00..3479.30 rows=19806 width=8) (actual time=30.973..70.129 rows=20025 loops=1)
Filter: ((user_id = 1) AND ((gender)::text = 'CHILD'::text))
Rows Removed by Filter: 83328
我可以做些什么来提高查询的性能？
我试图添加索引，但这使事情变得更糟（没有索引，查询运行得更快）。
更新 1：
询问
SELECT
a.household_id household_id,
age_of_youngest_woman,
a.number_of_children,
a.number_of_men,
fertility_cond_prob_number_of_children.cond_prob cond_prob_number_of_children,
fertility_cond_age.cond_prob cond_prob_age,
fertility_cond_prob_number_of_children.cond_prob * fertility_cond_age.cond_prob total_cond_prob,
random() <= (874. / 1703.) is_newborn_male
FROM
(SELECT
household_id,
MIN(CASE WHEN
(gender = 'FEMALE') and
(age >= 18)
THEN age
END) age_of_youngest_woman,
COUNT(CASE WHEN (gender = 'CHILD')
THEN 1
END) number_of_children,
COUNT(CASE WHEN (gender = 'MALE') and
(age >= 18)
THEN 1
END) number_of_men
FROM person
WHERE user_id = 1
GROUP BY household_id) a
JOIN fertility_cond_prob_number_of_children ON (fertility_cond_prob_number_of_children.number_of_children = a.number_of_children)
JOIN fertility_cond_age ON (fertility_cond_age.age = a.age_of_youngest_woman)
WHERE
(a.number_of_men > 0) and
(random() <= (fertility_cond_prob_number_of_children.cond_prob * fertility_cond_age.cond_prob))
具有以下性能特点：
Hash Join (cost=21783.55..21871.65 rows=6 width=44) (actual time=701.418..3042.547 rows=247 loops=1)
Hash Cond: ((min(CASE WHEN (((person.gender)::text = 'FEMALE'::text) AND (person.age >= 18)) THEN person.age ELSE NULL::integer END)) = fertility_cond_age.age)
Join Filter: (random() <= (fertility_cond_prob_number_of_children.cond_prob * fertility_cond_age.cond_prob))
Rows Removed by Join Filter: 18741
-> Nested Loop (cost=21779.17..21866.82 rows=19 width=36) (actual time=696.983..2949.993 rows=25647 loops=1)
Join Filter: ((count(CASE WHEN ((person.gender)::text = 'CHILD'::text) THEN 1 ELSE NULL::integer END)) = fertility_cond_prob_number_of_children.number_of_children)
Rows Removed by Join Filter: 615528
-> Seq Scan on fertility_cond_prob_number_of_children (cost=0.00..29.40 rows=1940 width=12) (actual time=0.007..0.098 rows=25 loops=1)
-> Materialize (cost=21779.17..21779.23 rows=2 width=28) (actual time=27.894..76.814 rows=25647 loops=25)
-> HashAggregate (cost=21779.17..21779.20 rows=2 width=50) (actual time=696.954..764.681 rows=25647 loops=1)
Filter: (count(CASE WHEN (((person.gender)::text = 'MALE'::text) AND (person.age >= 18)) THEN 1 ELSE NULL::integer END) > 0)
Rows Removed by Filter: 8112
-> Seq Scan on person (cost=0.00..21648.46 rows=4357 width=50) (actual time=13.910..343.198 rows=106158 loops=1)
Filter: (user_id = 1)
-> Hash (cost=2.50..2.50 rows=150 width=12) (actual time=0.480..0.480 rows=150 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 6kB
-> Seq Scan on fertility_cond_age (cost=0.00..2.50 rows=150 width=12) (actual time=0.016..0.235 rows=150 loops=1)
Total runtime: 3045.405 ms
表定义：
CREATE TABLE fertility_cond_prob_number_of_children(number_of_children integer, cond_prob double precision);
CREATE TABLE fertility_cond_age(age integer, cond_prob double precision);
CREATE TABLE fertility_households(household_id bigint, user_id bigint, age_of_woman integer, number_of_children integer);
CREATE TABLE person (
id SERIAL,
user_id bigint NOT NULL,
age integer NOT NULL,
monthly_income double precision NOT NULL,
gender character varying(10),
household_id bigint);