Re: Bad plan chosen for union all

[Alex Reece]

One more thing. Given this:


> The difference here is that, from the perspective of the outer query,
> the WHERE condition is a restriction clause on the "cim" relation,
> not a join clause.  So it will get pushed down into the subquery
> without creating any join order constraints on the outer query.


I expected the lateral form of the query to properly use the indexes. Sure
enough, this correctly uses the index:

explain select cim.yield
from earnings
JOIN contributions on contributions.id = earnings.note_id
JOIN LATERAL
(
SELECT contribution_id,
   max(CASE metrics.name WHEN 'Yield'::text THEN projected ELSE
NULL::double precision END) AS yield
from contribution_metrics
JOIN  metrics ON metrics.id = metric WHERE contributions.id =
contribution_id
group by contribution_id
) cim ON true
WHERE earnings.id = '\x595400456c1f1400116b3843'

However, when I try to wrap that subquery query again (e.g. as I would need
to if it were a view), it doesn't restrict:

select cim.yield
from earnings

JOIN contributions on contributions.id = earnings.note_id
JOIN LATERAL
(
select * from
(
SELECT contribution_id,
   max(CASE metrics.name WHEN 'Yield'::text THEN projected ELSE
NULL::double precision END) AS yield
from contribution_metrics
JOIN  metrics ON metrics.id = metric

group by contribution_id
) my_view WHERE contribution_id = contributions.id
) cim ON true
WHERE earnings.id = '\x595400456c1f1400116b3843'

Is there a way I can get the restriction to be pushed down into my subquery
in this lateral form?

Best,
~Alex

Re: Bad plan chosen for union all

[Tom Lane]

Alex Reece  writes:
> I managed to reduce my test case: the following query does not take
> advantage of the index on contribution metrics.

Yeah.  What you're wishing is that the planner would push a join
condition down into a subquery, but it won't do that at present.
Doing so would require generating "parameterized paths" for subqueries.
While I do not think there's any fundamental technical reason anymore
that we couldn't do so, there's considerable risk of wasting a lot of
planner cycles chasing unprofitable plan alternatives.  Anyway it was
totally impractical before 9.6's upper-planner-pathification changes,
and not all of the dust has settled from that rewrite.

> But I expected it to be equivalent to the plan from this query:

The difference here is that, from the perspective of the outer query,
the WHERE condition is a restriction clause on the "cim" relation,
not a join clause.  So it will get pushed down into the subquery
without creating any join order constraints on the outer query.

regards, tom lane

Re: Bad plan chosen for union all

[Alex Reece]

I managed to reduce my test case: the following query does not take
advantage of the index on contribution metrics.

explain select cim.yield
from earnings
JOIN contributions on contributions.id = earnings.note_id
JOIN
(
SELECT contribution_id,
max(CASE metrics.name WHEN 'Yield'::text THEN projected ELSE
NULL::double precision END) AS yield
from contribution_metrics
JOIN  metrics ON metrics.id = metric
group by contribution_id
) cim ON cim.contribution_id = contributions.id
WHERE earnings.id = '\x595400456c1f1400116b3843';

I got this:

  Hash Join  (cost=125.02..147.03 rows=1 width=8) (actual time=4.781..4.906
rows=1 loops=1)
Hash Cond: (contribution_metrics.contribution_id = contributions.id)
->  HashAggregate  (cost=116.86..126.64 rows=3261 width=21) (actual
time=4.157..4.600 rows=3261 loops=1)
  Group Key: contribution_metrics.contribution_id
  ->  Hash Join  (cost=1.11..108.18 rows=5788 width=33) (actual
time=0.021..2.425 rows=5788 loops=1)
Hash Cond: (contribution_metrics.metric = metrics.id)
->  Seq Scan on contribution_metrics  (cost=0.00..85.36
rows=5788 width=34) (actual time=0.006..0.695 rows=5788 loops=1)
->  Hash  (cost=1.05..1.05 rows=17 width=25) (actual
time=0.009..0.009 rows=17 loops=1)
  ->  Seq Scan on metrics  (cost=0.00..1.05 rows=17
width=25) (actual time=0.002..0.005 rows=17 loops=1)
->  Hash  (cost=8.15..8.15 rows=1 width=26) (actual time=0.022..0.022
rows=1 loops=1)
  ->  Nested Loop  (cost=0.14..8.15 rows=1 width=26) (actual
time=0.019..0.020 rows=1 loops=1)
->  Index Scan using earnings_pkey on earnings
(cost=0.06..4.06 rows=1 width=13) (actual time=0.009..0.009 rows=1 loops=1)
  Index Cond: (id = '\x595400456c1f1400116b3843'::bytea)
->  Index Only Scan using contributions_pkey on
contributions  (cost=0.08..4.09 rows=1 width=13) (actual time=0.008..0.009
rows=1 loops=1)
  Index Cond: (id = earnings.note_id)
  Planning time: 0.487 ms
  Execution time: 4.975 ms

But I expected it to be equivalent to the plan from this query:

select cim.yield from (
select contribution_id,
max(CASE metrics.name WHEN 'Yield'::text THEN projected ELSE NULL::double
precision END) AS yield
from contribution_metrics JOIN  metrics ON metrics.id = metric group by
contribution_id
) cim where cim.contribution_id = (
select contributions.id from contributions
join earnings on earnings.note_id = contributions.id
where earnings.id = '\x595400456c1f1400116b3843')

Which gives me _this_ plan, that correctly uses the index on
contribution_metrics.

  Subquery Scan on cim  (cost=9.32..14.23 rows=2 width=8) (actual
time=0.108..0.108 rows=1 loops=1)
InitPlan 1 (returns $1)
  ->  Nested Loop  (cost=0.14..8.15 rows=1 width=13) (actual
time=0.054..0.055 rows=1 loops=1)
->  Index Scan using earnings_pkey on earnings
(cost=0.06..4.06 rows=1 width=13) (actual time=0.025..0.026 rows=1 loops=1)
  Index Cond: (id = '\x595400456c1f1400116b3843'::bytea)
->  Index Only Scan using contributions_pkey on contributions
(cost=0.08..4.09 rows=1 width=13) (actual time=0.026..0.026 rows=1 loops=1)
  Index Cond: (id = earnings.note_id)
->  GroupAggregate  (cost=1.17..6.07 rows=2 width=21) (actual
time=0.108..0.108 rows=1 loops=1)
  Group Key: contribution_metrics.contribution_id
  ->  Hash Join  (cost=1.17..6.07 rows=2 width=33) (actual
time=0.100..0.101 rows=2 loops=1)
Hash Cond: (contribution_metrics.metric = metrics.id)
->  Index Scan using
contribution_metrics_contribution_id_metric_last_update_on_idx1 on
contribution_metrics  ( cost=0.06..4.95 rows=2 width=34) (actual time
  Index Cond: (contribution_id = $1)
->  Hash  (cost=1.05..1.05 rows=17 width=25) (actual
time=0.012..0.012 rows=17 loops=1)
  ->  Seq Scan on metrics  (cost=0.00..1.05 rows=17
width=25) (actual time=0.004..0.006 rows=17 loops=1)
  Planning time: 0.396 ms
  Execution time: 0.165 ms

schema here:
https://gist.github.com/awreece/aeacbc818277c7c6d99477645e7fcd03

Best,
~Alex



On Tue, Nov 28, 2017 at 2:13 AM Alex Reece  wrote:

> I'm on PostgreSQL 9.6.5 and getting an awkwardly bad plan chosen for my
> query.
>
> I want to do:
>
> select investments.id, cim.yield
> FROM contributions
> JOIN investments ON contributions.investment_id = investments.id
> JOIN contribution_investment_metrics_view cim ON cim.investment_id =
> investments.id
> WHERE contributions.id IN ('\x58c9c0d3ee944c48b32f814d', '\x11')
> Where contribution_investment_metrics_view is morally
>
> select investment_id, first(val) from (select * from contribution_metrics
> UNION ALL select * from investment_metrics) group by id
>
> Typically, querying this view is very fast since I have indexes in both
> component queries, leading to a 

Bad plan chosen for union all

[Alex Reece]

I'm on PostgreSQL 9.6.5 and getting an awkwardly bad plan chosen for my
query.

I want to do:

select investments.id, cim.yield
FROM contributions
JOIN investments ON contributions.investment_id = investments.id
JOIN contribution_investment_metrics_view cim ON cim.investment_id =
investments.id
WHERE contributions.id IN ('\x58c9c0d3ee944c48b32f814d', '\x11')
Where contribution_investment_metrics_view is morally

select investment_id, first(val) from (select * from contribution_metrics
UNION ALL select * from investment_metrics) group by id

Typically, querying this view is very fast since I have indexes in both
component queries, leading to a very tight plan:

Sort Key: "*SELECT* 1".metric
->  Subquery Scan on "*SELECT* 1"  (cost=14.68..14.68 rows=1 width=26)
(actual time=0.043..0.044 rows=2 loops=1)
  ->  Sort  (cost=14.68..14.68 rows=1 width=42) (actual
time=0.042..0.043 rows=2 loops=1)
Sort Key: cm.metric, cm.last_update_on DESC
Sort Method: quicksort  Memory: 25kB
->  Nested Loop  (cost=0.14..14.68 rows=1 width=42) (actual
time=0.032..0.034 rows=2 loops=1)
  ->  Index Scan using contributions_investment_id_idx on
contributions  (cost=0.08..4.77 rows=2 width=26) (actual time=0.026..0.027
rows=1 loops=1)
Index Cond: (investment_id = $1)
  ->  Index Only Scan using
contribution_metrics_contribution_id_metric_last_update_on_idx on
contribution_metrics cm  (cost=0.06..4.95 rows=2 width=34) (actual
time=0.005..0.006 r
Index Cond: (contribution_id = contributions.id)
Heap Fetches: 2
->  Subquery Scan on "*SELECT* 2"  (cost=0.08..5.86 rows=3 width=26)
(actual time=0.008..0.008 rows=3 loops=1)
  ->  Index Only Scan using
investment_metrics_investment_id_metric_last_updated_on_idx on
investment_metrics im  (cost=0.08..5.85 rows=3 width=42) (actual
time=0.008..0.008 rows=3 loops=1)
Index Cond: (investment_id = $1)
Heap Fetches: 3

Unfortunately, when I try to query this view in the larger query above, I
get a *much* worse plan for this view, leading to >1000x degradation in
performance:

->  Append  (cost=10329.18..26290.92 rows=482027 width=26) (actual
time=90.157..324.544 rows=482027 loops=1)
  ->  Subquery Scan on "*SELECT* 1"  (cost=10329.18..10349.44 rows=5788
width=26) (actual time=90.157..91.207 rows=5788 loops=1)
->  Sort  (cost=10329.18..10332.08 rows=5788 width=42) (actual
time=90.156..90.567 rows=5788 loops=1)
  Sort Key: contributions_1.investment_id, cm.metric,
cm.last_update_on DESC
  Sort Method: quicksort  Memory: 645kB
  ->  Hash Join  (cost=105.62..10256.84 rows=5788 width=42)
(actual time=1.924..85.913 rows=5788 loops=1)
Hash Cond: (contributions_1.id = cm.contribution_id)
->  Seq Scan on contributions contributions_1
(cost=0.00..9694.49 rows=351495 width=26) (actual time=0.003..38.794
rows=351495 loops=1)
->  Hash  (cost=85.36..85.36 rows=5788 width=34)
(actual time=1.907..1.907 rows=5788 loops=1)
  Buckets: 8192  Batches: 1  Memory Usage: 453kB
  ->  Seq Scan on contribution_metrics cm
(cost=0.00..85.36 rows=5788 width=34) (actual time=0.003..0.936 rows=5788
loops=1)
  ->  Subquery Scan on "*SELECT* 2"  (cost=0.08..15941.48 rows=476239
width=26) (actual time=0.017..203.006 rows=476239 loops=1)
->  Index Only Scan using
investment_metrics_investment_id_metric_last_updated_on_idx1 on
investment_metrics im  (cost=0.08..14512.76 rows=476239 width=42) (actual
time=0.016..160.410 rows=476239 l
  Heap Fetches: 476239

I've played around with a number of solutions (including lateral joins) and
the closest I can come is:

select investment_id
from contribution_investment_metrics
where investment_id = (
select investments.id
from investments
join contributions on investments.id = contributions.investment_id
where contributions.id = '\x58c9c0d3ee944c48b32f814d'
)

This doesn't really work for my purposes, since I want to project columns
from contributions and investments and I want to run this query on "up to a
handful" contributions at once (maybe more than one, never more than 100).

I'm on PostgreSQL 9.6.5.
Schema and full explain analyzes:
https://gist.github.com/awreece/28c359c6d834717ab299665022b19fd6
I don't think it's relevant, but since
https://wiki.postgresql.org/wiki/SlowQueryQuestions asks -- I'm running in
Heroku.

What are my options here? Currently, I'm planning to avoid these bad plans
by using a less straightforward query for the view:

SELECT
coalesce(contrib.id, cm.contribution_id) AS contribution_id,
coalesce(cm.yield, im.yield) AS yield,
coalesce(cm.term, im.term) AS term
FROM contributions contrib
JOIN investment_metrics_view im ON im.investment_id =