RE: Simple DELETE on modest-size table runs 100% CPU forever
| От | Ravi Rai |
|---|---|
| Тема | RE: Simple DELETE on modest-size table runs 100% CPU forever |
| Дата | |
| Msg-id | BY5PR18MB31704159A63BC435E4888DB0A5700@BY5PR18MB3170.namprd18.prod.outlook.com обсуждение исходный текст |
| Ответ на | Re: Simple DELETE on modest-size table runs 100% CPU forever (Craig James <cjames@emolecules.com>) |
| Список | pgsql-performance |
Hey James,
Looking at your select query, if that’s taking forever STILL. Could it be because of the IN clause? If so, try using EXISTS instead of IN.. should give much better results.
From: Craig James <cjames@emolecules.com>
Sent: Friday, November 15, 2019 1:07 PM
To: Andres Freund <andres@anarazel.de>
Cc: pgsql-performance@lists.postgresql.org
Subject: Re: Simple DELETE on modest-size table runs 100% CPU forever
On Thu, Nov 14, 2019 at 2:29 PM Andres Freund <andres@anarazel.de> wrote:
Hi,
On 2019-11-14 14:19:51 -0800, Craig James wrote:
> I'm completely baffled by this problem: I'm doing a delete that joins three
> modest-sized tables, and it gets completely stuck: 100% CPU use forever.
> Here's the query:
I assume this is intended to be an equivalent SELECT? Because you did
mention DELETE, but I'm not seeing one here? Could you actually show
that query - surely that didn't include a count() etc... You can
EPLAIN DELETEs too.
Sorry, my explanation was misleading. It is a "delete ... where id in (select ...)". But I discovered that the select part itself never completes, whether you include it in the delete or not. So I only showed the select, which I converted to a "select count(1) ..." for simplicity.
> explain analyze
> select count(1) from registry.categories
> where category_id = 15 and id in
> (select c.id from registry.categories c
> left join registry.category_staging_15 st on (c.id = st.id) where
> c.category_id = 15 and st.id is null);
>
> If I leave out the "analyze", here's what I get (note that the
> categories_staging_N table's name changes every time; it's
> created on demand as "create table categories_staging_n(id integer)").
> Aggregate (cost=193.54..193.55 rows=1 width=8)
> -> Nested Loop Semi Join (cost=0.84..193.54 rows=1 width=0)
> Join Filter: (categories.id = c.id)
> -> Index Scan using i_categories_category_id on categories
> (cost=0.42..2.44 rows=1 width=4)
> Index Cond: (category_id = 23)
> -> Nested Loop Anti Join (cost=0.42..191.09 rows=1 width=4)
> Join Filter: (c.id = st.id)
> -> Index Scan using i_categories_category_id on categories c
> (cost=0.42..2.44 rows=1 width=4)
> Index Cond: (category_id = 23)
> -> Seq Scan on category_staging_23 st (cost=0.00..99.40
> rows=7140 width=4)
>
> The tables are small. From a debugging printout:
Is categories.category_id unique?
No, categories.category_id is not unique. It has a b-tree index.
Does the plan change if you ANALYZE
the tables?
No. No difference.
But interestingly, it changes as the process goes forward. And it's inconsistent. Here's an example: it's going through several "categories" to update each. The first plan works, and it typically uses this plan a few times. But when selects the second plan, it gets stuck.
----------------
15994 items in table registry.category_staging_15
245598 items in table registry.categories
309398 items in table registry.smiles
15994 items in joined registry.category_staging_15 / registry.categories
0 items to be inserted
inserted: 0E0
EXPLAIN: Aggregate (cost=3464.82..3464.83 rows=1 width=8)
EXPLAIN: -> Hash Semi Join (cost=2029.16..3464.05 rows=311 width=0)
EXPLAIN: Hash Cond: (categories.id = c.id)
EXPLAIN: -> Index Scan using i_categories_category_id on categories (cost=0.42..1405.28 rows=7900 width=4)
EXPLAIN: Index Cond: (category_id = 15)
EXPLAIN: -> Hash (cost=1933.44..1933.44 rows=7624 width=4)
EXPLAIN: -> Hash Anti Join (cost=431.28..1933.44 rows=7624 width=4)
EXPLAIN: Hash Cond: (c.id = st.id)
EXPLAIN: -> Index Scan using i_categories_category_id on categories c (cost=0.42..1405.28 rows=7900 width=4)
EXPLAIN: Index Cond: (category_id = 15)
EXPLAIN: -> Hash (cost=230.94..230.94 rows=15994 width=4)
EXPLAIN: -> Seq Scan on category_staging_15 st (cost=0.00..230.94 rows=15994 width=4)
0 items deleted
7997 items inserted
----------------
6250 items in table registry.category_staging_25
245598 items in table registry.categories
309398 items in table registry.smiles
6250 items in joined registry.category_staging_25 / registry.categories
6250 items to be inserted
inserted: 3125
EXPLAIN: Aggregate (cost=173.51..173.52 rows=1 width=8)
EXPLAIN: -> Nested Loop Semi Join (cost=0.84..173.51 rows=1 width=0)
EXPLAIN: Join Filter: (categories.id = c.id)
EXPLAIN: -> Index Scan using i_categories_category_id on categories (cost=0.42..2.44 rows=1 width=4)
EXPLAIN: Index Cond: (category_id = 25)
EXPLAIN: -> Nested Loop Anti Join (cost=0.42..171.06 rows=1 width=4)
EXPLAIN: Join Filter: (c.id = st.id)
EXPLAIN: -> Index Scan using i_categories_category_id on categories c (cost=0.42..2.44 rows=1 width=4)
EXPLAIN: Index Cond: (category_id = 25)
EXPLAIN: -> Seq Scan on category_staging_25 st (cost=0.00..90.50 rows=6250 width=4)
This plan doesn't look like it'd actually take long, if the estimates
are correct.
Another data point: during this query, Postgres is burning 100% CPU and doing no I/O. Pretty much for hours if I let it go.
Thanks for your help,
Craig
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