How to optimize join and sub-query in pgSQL
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I've 2 tables in PostgreSQL, Schema ==> sts
product_price_logcontains columnsproduct_name(character varying), K (numeric),sale_date(date)
product_transactioncontains columnsproduct_name(character varying),price(numeric),sale_date(date)
I want to get json data for sale_date = '2018-01-15' and '2018-01-01'.
Product price should be from product_price_log table and sale data from product_transaction table for summarized report.
this my query:
(SELECT
json_agg(j.*)
from(
select
a.sale_date, a.product_name, sum(a.quantity) as quantity, sum( a.quantity * b.price) as total_sale
from(
select
sale_date, product_name, quantity
from
sts.product_transaction p
where
sale_date = '2018-01-15' or sale_date = '2018-01-01' and quantity > 0
)a
inner join (
select * from(
select product_name, price, sale_date from sts.product_price_log where sale_date <= '2018-01-15' order by sale_date desc limit 1
union all
select product_name, price, sale_date from sts.product_price_log where sale_date <= '2018-01-01' order by sale_date desc limit 1
)a order by sale_date
)r on (a.product_name = r.product_name and a.sale_date = r.sale_date)
group by
sale_date,
product_name
)j)
;
QUERY PLAN
Aggregate (cost=230444.51..230444.52 rows=1 width=28)
-> Subquery Scan on j (cost=230290.98..230444.50 rows=1 width=28)
-> GroupAggregate (cost=230290.98..230444.49 rows=1 width=20)
How to I reduce time cost or optimize this query?
postgresql
edited Nov 12 at 10:45
a_horse_with_no_name
290k46443537
asked Mar 28 at 17:22
RKTUXYN
466415
add a comment |
up vote
1
down vote
favorite
1
I've 2 tables in PostgreSQL, Schema ==> sts
product_price_logcontains columnsproduct_name(character varying), K (numeric),sale_date(date)
product_transactioncontains columnsproduct_name(character varying),price(numeric),sale_date(date)
I want to get json data for sale_date = '2018-01-15' and '2018-01-01'.
Product price should be from product_price_log table and sale data from product_transaction table for summarized report.
this my query:
(SELECT
json_agg(j.*)
from(
select
a.sale_date, a.product_name, sum(a.quantity) as quantity, sum( a.quantity * b.price) as total_sale
from(
select
sale_date, product_name, quantity
from
sts.product_transaction p
where
sale_date = '2018-01-15' or sale_date = '2018-01-01' and quantity > 0
)a
inner join (
select * from(
select product_name, price, sale_date from sts.product_price_log where sale_date <= '2018-01-15' order by sale_date desc limit 1
union all
select product_name, price, sale_date from sts.product_price_log where sale_date <= '2018-01-01' order by sale_date desc limit 1
)a order by sale_date
)r on (a.product_name = r.product_name and a.sale_date = r.sale_date)
group by
sale_date,
product_name
)j)
;
QUERY PLAN
Aggregate (cost=230444.51..230444.52 rows=1 width=28)
-> Subquery Scan on j (cost=230290.98..230444.50 rows=1 width=28)
-> GroupAggregate (cost=230290.98..230444.49 rows=1 width=20)
How to I reduce time cost or optimize this query?
postgresql
edited Nov 12 at 10:45
a_horse_with_no_name
290k46443537
asked Mar 28 at 17:22
RKTUXYN
466415
That's not the full query plan, post the whole thing please. Since joining on thersubquery would seem quite fast, my guess is theaquery is slow - maybe it returns a huge amount of records, or it searches through a table with a lot of records without using indexes? But I'd need to see the actual query plan to determine that.
– 404
Mar 28 at 19:50
Indexes are present in all where Claus column. Yes, 'a' query returns huge row(s). sts.product_transaction contains 10m row(s) +. Filter: (((sale_date = '2018-01-15'::date) OR (sale_date = '2018-01-01'::date)) AND (quantity > '0'::numeric)). -> Seq Scan on product_price_log product_price_log_1 (cost=0.00..17756.29 rows=638411 width=19) Filter: (sale_date <= '2018-03-15'::date) May I've to use any another technique to optimize this query?
– RKTUXYN
Mar 29 at 16:42
add a comment |
up vote
1
down vote
favorite
1
up vote
1
down vote
favorite
1
1
I've 2 tables in PostgreSQL, Schema ==> sts
product_price_logcontains columnsproduct_name(character varying), K (numeric),sale_date(date)
product_transactioncontains columnsproduct_name(character varying),price(numeric),sale_date(date)
I want to get json data for sale_date = '2018-01-15' and '2018-01-01'.
Product price should be from product_price_log table and sale data from product_transaction table for summarized report.
this my query:
(SELECT
json_agg(j.*)
from(
select
a.sale_date, a.product_name, sum(a.quantity) as quantity, sum( a.quantity * b.price) as total_sale
from(
select
sale_date, product_name, quantity
from
sts.product_transaction p
where
sale_date = '2018-01-15' or sale_date = '2018-01-01' and quantity > 0
)a
inner join (
select * from(
select product_name, price, sale_date from sts.product_price_log where sale_date <= '2018-01-15' order by sale_date desc limit 1
union all
select product_name, price, sale_date from sts.product_price_log where sale_date <= '2018-01-01' order by sale_date desc limit 1
)a order by sale_date
)r on (a.product_name = r.product_name and a.sale_date = r.sale_date)
group by
sale_date,
product_name
)j)
;
QUERY PLAN
Aggregate (cost=230444.51..230444.52 rows=1 width=28)
-> Subquery Scan on j (cost=230290.98..230444.50 rows=1 width=28)
-> GroupAggregate (cost=230290.98..230444.49 rows=1 width=20)
How to I reduce time cost or optimize this query?
postgresql
edited Nov 12 at 10:45
a_horse_with_no_name
290k46443537
asked Mar 28 at 17:22
RKTUXYN
466415
I've 2 tables in PostgreSQL, Schema ==> sts
product_price_logcontains columnsproduct_name(character varying), K (numeric),sale_date(date)
product_transactioncontains columnsproduct_name(character varying),price(numeric),sale_date(date)
I want to get json data for sale_date = '2018-01-15' and '2018-01-01'.
Product price should be from product_price_log table and sale data from product_transaction table for summarized report.
this my query:
(SELECT
json_agg(j.*)
from(
select
a.sale_date, a.product_name, sum(a.quantity) as quantity, sum( a.quantity * b.price) as total_sale
from(
select
sale_date, product_name, quantity
from
sts.product_transaction p
where
sale_date = '2018-01-15' or sale_date = '2018-01-01' and quantity > 0
)a
inner join (
select * from(
select product_name, price, sale_date from sts.product_price_log where sale_date <= '2018-01-15' order by sale_date desc limit 1
union all
select product_name, price, sale_date from sts.product_price_log where sale_date <= '2018-01-01' order by sale_date desc limit 1
)a order by sale_date
)r on (a.product_name = r.product_name and a.sale_date = r.sale_date)
group by
sale_date,
product_name
)j)
;
QUERY PLAN
Aggregate (cost=230444.51..230444.52 rows=1 width=28)
-> Subquery Scan on j (cost=230290.98..230444.50 rows=1 width=28)
-> GroupAggregate (cost=230290.98..230444.49 rows=1 width=20)
How to I reduce time cost or optimize this query?
postgresql
postgresql
edited Nov 12 at 10:45
a_horse_with_no_name
290k46443537
asked Mar 28 at 17:22
RKTUXYN
466415
edited Nov 12 at 10:45
a_horse_with_no_name
290k46443537
asked Mar 28 at 17:22
RKTUXYN
466415
edited Nov 12 at 10:45
a_horse_with_no_name
290k46443537
edited Nov 12 at 10:45
a_horse_with_no_name
290k46443537
edited Nov 12 at 10:45
a_horse_with_no_name
290k46443537
290k46443537
asked Mar 28 at 17:22
RKTUXYN
466415
asked Mar 28 at 17:22
RKTUXYN
466415
asked Mar 28 at 17:22
RKTUXYN
466415
466415
That's not the full query plan, post the whole thing please. Since joining on thersubquery would seem quite fast, my guess is theaquery is slow - maybe it returns a huge amount of records, or it searches through a table with a lot of records without using indexes? But I'd need to see the actual query plan to determine that.
– 404
Mar 28 at 19:50
Indexes are present in all where Claus column. Yes, 'a' query returns huge row(s). sts.product_transaction contains 10m row(s) +. Filter: (((sale_date = '2018-01-15'::date) OR (sale_date = '2018-01-01'::date)) AND (quantity > '0'::numeric)). -> Seq Scan on product_price_log product_price_log_1 (cost=0.00..17756.29 rows=638411 width=19) Filter: (sale_date <= '2018-03-15'::date) May I've to use any another technique to optimize this query?
– RKTUXYN
Mar 29 at 16:42
add a comment |
That's not the full query plan, post the whole thing please. Since joining on thersubquery would seem quite fast, my guess is theaquery is slow - maybe it returns a huge amount of records, or it searches through a table with a lot of records without using indexes? But I'd need to see the actual query plan to determine that.
– 404
Mar 28 at 19:50
Indexes are present in all where Claus column. Yes, 'a' query returns huge row(s). sts.product_transaction contains 10m row(s) +. Filter: (((sale_date = '2018-01-15'::date) OR (sale_date = '2018-01-01'::date)) AND (quantity > '0'::numeric)). -> Seq Scan on product_price_log product_price_log_1 (cost=0.00..17756.29 rows=638411 width=19) Filter: (sale_date <= '2018-03-15'::date) May I've to use any another technique to optimize this query?
– RKTUXYN
Mar 29 at 16:42
That's not the full query plan, post the whole thing please. Since joining on the
r subquery would seem quite fast, my guess is the a query is slow - maybe it returns a huge amount of records, or it searches through a table with a lot of records without using indexes? But I'd need to see the actual query plan to determine that.– 404
Mar 28 at 19:50
That's not the full query plan, post the whole thing please. Since joining on the
r subquery would seem quite fast, my guess is the a query is slow - maybe it returns a huge amount of records, or it searches through a table with a lot of records without using indexes? But I'd need to see the actual query plan to determine that.– 404
Mar 28 at 19:50
Indexes are present in all where Claus column. Yes, 'a' query returns huge row(s). sts.product_transaction contains 10m row(s) +. Filter: (((sale_date = '2018-01-15'::date) OR (sale_date = '2018-01-01'::date)) AND (quantity > '0'::numeric)). -> Seq Scan on product_price_log product_price_log_1 (cost=0.00..17756.29 rows=638411 width=19) Filter: (sale_date <= '2018-03-15'::date) May I've to use any another technique to optimize this query?
– RKTUXYN
Mar 29 at 16:42
Indexes are present in all where Claus column. Yes, 'a' query returns huge row(s). sts.product_transaction contains 10m row(s) +. Filter: (((sale_date = '2018-01-15'::date) OR (sale_date = '2018-01-01'::date)) AND (quantity > '0'::numeric)). -> Seq Scan on product_price_log product_price_log_1 (cost=0.00..17756.29 rows=638411 width=19) Filter: (sale_date <= '2018-03-15'::date) May I've to use any another technique to optimize this query?
– RKTUXYN
Mar 29 at 16:42
add a comment |
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That's not the full query plan, post the whole thing please. Since joining on the
rsubquery would seem quite fast, my guess is theaquery is slow - maybe it returns a huge amount of records, or it searches through a table with a lot of records without using indexes? But I'd need to see the actual query plan to determine that.– 404
Mar 28 at 19:50
Indexes are present in all where Claus column. Yes, 'a' query returns huge row(s). sts.product_transaction contains 10m row(s) +. Filter: (((sale_date = '2018-01-15'::date) OR (sale_date = '2018-01-01'::date)) AND (quantity > '0'::numeric)). -> Seq Scan on product_price_log product_price_log_1 (cost=0.00..17756.29 rows=638411 width=19) Filter: (sale_date <= '2018-03-15'::date) May I've to use any another technique to optimize this query?
– RKTUXYN
Mar 29 at 16:42