Thread: BUG #13614: Optimizer choosing "Nested Loop" due to bad estimate of "rows=1"

The following bug has been logged on the website:

Bug reference:      13614
Logged by:          Matthew Bellew
Email address:      matthewb@labkey.com
PostgreSQL version: 9.3.9
Operating system:   Linux
Description:

There seems to be a weakness in the optimizer that lets the row estimator
predict "1 row" too easily.  The danger being, of course, that there are
strategies that work for 1 row that are disastrous if the estimate is even a
little off.  Like say 577.  It seems that the row estimator should be more
conservative (maybe when multiplying percentage of rows estimages? just
guesing), and/or make more conservative join choices to distinguish "maybe
1" from "exactly 1".

I believe this has gotten worse only recently in the 9.3 branch as things
have gone to hell on one customer server only running 9.3.9.


********* QUERY **********
EXPLAIN ANALYZE
SELECT *
FROM (
SELECT
study_temp_5.study_GridBase_SubjectId AS study_GridBase_SubjectId,
study_temp_5.study_GridBase_Study AS study_GridBase_Study,
study_temp_5.study_GridBase_TreatmentSummary AS
study_GridBase_TreatmentSummary,
study_temp_5.study_GridBase_SubjectVisit AS study_GridBase_SubjectVisit,
study_temp_5.study_BAMA_mfi_delta AS study_BAMA_mfi_delta
FROM (SELECT study_GridBase_1_study_BAMA_2_11.study_GridBase_Container AS
study_GridBase_Container,
study_GridBase_1_study_BAMA_2_11.study_BAMA_Container AS
study_BAMA_Container,
study_GridBase_1_study_BAMA_2_11.study_GridBase_SubjectId AS
study_GridBase_SubjectId,
study_GridBase_1_study_BAMA_2_11.study_BAMA_SubjectId AS
study_BAMA_SubjectId,
study_GridBase_1_study_BAMA_2_11.study_GridBase_SubjectVisit AS
study_GridBase_SubjectVisit,
study_GridBase_1_study_BAMA_2_11.study_GridBase_Study AS
study_GridBase_Study,
study_GridBase_1_study_BAMA_2_11.study_GridBase_TreatmentSummary AS
study_GridBase_TreatmentSummary,
study_GridBase_1_study_BAMA_2_11.study_GridBase_sequencenum AS
study_GridBase_sequencenum,
study_GridBase_1_study_BAMA_2_11.study_BAMA_mfi_delta AS
study_BAMA_mfi_delta,
study_GridBase_1_study_BAMA_2_11.study_BAMA_SubjectVisit_sequencenum AS
study_BAMA_SubjectVisit_sequencenum,
study_GridBase_1_study_BAMA_2_11.Folder AS Folder,
study_GridBase_1_study_BAMA_2_11.SubjectVisit_Container AS
SubjectVisit_Container
FROM (SELECT GridBase_4.container AS study_GridBase_Container,
BAMA_9.Container AS study_BAMA_Container,
GridBase_4.SubjectId AS study_GridBase_SubjectId,
BAMA_9.SubjectId AS study_BAMA_SubjectId,
GridBase_4.SubjectVisit AS study_GridBase_SubjectVisit,
GridBase_4.Study AS study_GridBase_Study,
GridBase_4.TreatmentSummary AS study_GridBase_TreatmentSummary,
GridBase_4.sequencenum AS study_GridBase_sequencenum,
BAMA_9.mfi_delta AS study_BAMA_mfi_delta,
BAMA_9.SubjectVisit_sequencenum AS study_BAMA_SubjectVisit_sequencenum,
BAMA_9.Folder AS Folder,
BAMA_9.SubjectVisit_Container AS SubjectVisit_Container
FROM (SELECT SV_3.SubjectId_SubjectId AS SubjectId,
study_4.label AS Study,
TASM_7.arm_id_coded_label AS TreatmentSummary,
SV_3.Visit_Label AS SubjectVisit,
SV_3.sequencenum AS sequencenum,
SV_3.Container AS container,
study_4.container AS container1,
SV_3.SubjectId_Container AS SubjectId_Container,
SV_3.Visit_Folder AS Visit_Folder,
TASM_7.arm_id_container AS arm_id_container
FROM (SELECT SubjectVisit_2.container AS Container,
SubjectVisit_2.participantid AS SubjectId, SubjectVisit_2.sequencenum AS
sequencenum, SubjectVisit_2.participantid AS SubjectId1,
SubjectVisit_2$SubjectId$.container AS SubjectId_Container,
SubjectVisit_2$SubjectId$.participantid AS SubjectId_SubjectId,
SubjectVisit_2$Visit$.container AS Visit_Folder, SubjectVisit_2$Visit$.label
AS Visit_Label
FROM (SELECT * FROM study.participantvisit
WHERE (Container IN (SELECT EntityId FROM (VALUES

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AS _ids(EntityId)))) SubjectVisit_2
    LEFT OUTER JOIN (SELECT * FROM study.participant
WHERE (Container IN (SELECT EntityId FROM (VALUES

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AS _ids(EntityId)))) SubjectVisit_2$SubjectId$ ON
(SubjectVisit_2.participantid = SubjectVisit_2$SubjectId$.participantid
     AND SubjectVisit_2.container = SubjectVisit_2$SubjectId$.container)
    LEFT OUTER JOIN (SELECT * FROM study.visit
WHERE (Container IN (SELECT EntityId FROM (VALUES

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AS _ids(EntityId)))) SubjectVisit_2$Visit$ ON (SubjectVisit_2.visitrowid =
SubjectVisit_2$Visit$.rowid)) SV_3
LEFT JOIN (SELECT * FROM cds.study
WHERE (container IN (SELECT EntityId FROM (VALUES

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AS _ids(EntityId)))) study_4 ON SV_3.Container=study_4.container
LEFT JOIN (SELECT treatmentarmsubjectmap_6.arm_id AS arm_id,
treatmentarmsubjectmap_6.container AS container,
treatmentarmsubjectmap_6.participantid AS participantid,
treatmentarmsubjectmap_6$arm_id$.coded_label AS arm_id_coded_label,
treatmentarmsubjectmap_6$arm_id$.container AS arm_id_container
FROM (SELECT * FROM cds.treatmentarmsubjectmap
WHERE (container IN (SELECT EntityId FROM (VALUES

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AS _ids(EntityId)))) treatmentarmsubjectmap_6
    LEFT OUTER JOIN (SELECT * FROM cds.treatmentarm
WHERE (container IN (SELECT EntityId FROM (VALUES

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AS _ids(EntityId)))) treatmentarmsubjectmap_6$arm_id$ ON
(treatmentarmsubjectmap_6.arm_id = treatmentarmsubjectmap_6$arm_id$.arm_id))
TASM_7 ON TASM_7.participantid=SV_3.SubjectId) GridBase_4
FULL JOIN (SELECT BAMA_8.container AS Container, BAMA_8.container AS Folder,
BAMA_8.lsid AS lsid, BAMA_8.mfi_delta AS mfi_delta, BAMA_8.participantid AS
SubjectId, BAMA_8$SubjectVisit$.container AS SubjectVisit_Container,
BAMA_8$SubjectVisit$.sequencenum AS SubjectVisit_sequencenum
FROM (SELECT * FROM studydataset.c222d2643_bama x
WHERE (Container IN (SELECT EntityId FROM (VALUES

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AS _ids(EntityId)))) BAMA_8
    LEFT OUTER JOIN (SELECT * FROM study.participantvisit
WHERE (Container IN (SELECT EntityId FROM (VALUES

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),('d723e374-32f7-1033-aa71-2e7eaed2baf3'),('d723d50f-32f7-1033-aa71-2e7eaed2baf3'),('fe1e86c1-326e-1033-aa71-2e7eaed2baf3'),('d723dc49-32f7-1033-aa71-2e7eaed2baf3'),('fe1e829d-326e-1033-aa71-2e7eaed2baf3'),('fe1e8c70-326e-1033-aa71-2e7eaed2baf3'),('5fcb2b51-32f8-1033-aa71-2e7eaed2baf3'),('d723d5cd-32f7-1033-aa71-2e7eaed2baf3'),('d723e00e-32f7-1033-aa71-2e7eaed2baf3'),('5fcb2f84-32f8-1033-aa71-2e7eaed2baf3'),('fe1e7e0b-326e-1033-aa71-2e7eaed2baf3'),('d723dd66-32f7-1033-aa71-2e7eaed2baf3'),('5fcb2d9a-32f8-1033-aa71-2e7eaed2baf3'),('fe1e75b4-326e-1033-aa71-2e7eaed2baf3'),('d723d68b-32f7-1033-aa71-2e7eaed2baf3'),('fe1e8603-326e-1033-aa71-2e7eaed2baf3'),('5fcb30a1-32f8-1033-aa71-2e7eaed2baf3'),('e1e05c39-3560-1033-aa71-2e7eaed2baf3'),('fe1e872f-326e-1033-aa71-2e7eaed2baf3'),('fe1e73ca-326e-1033-aa71-2e7eaed2baf3'),('d723c759-32f7-1033-aa71-2e7eaed2baf3'),('e1e05c98-3560-1033-aa71-2e7eaed2baf3'),('d723d451-32f7-1033-aa71-2e7eaed2baf3'),('d723d62c-32f7-1033-aa71-2e7eaed2baf3'),('fe1e7061-326e-1033-aa71-2e7eaed2baf3'),('fe1e8054-326e-1033-aa71-2e7eaed2baf3'),('d723d8d4-32f7-1033-aa71-2e7eaed2baf3'),('d723e6e9-32f7-1033-aa71-2e7eaed2baf3'),('5fcb2eb7-32f8-1033-aa71-2e7eaed2baf3'),('fe1e791a-326e-1033-aa71-2e7eaed2baf3'),('d723cb2d-32f7-1033-aa71-2e7eaed2baf3'),('fe1e8bb2-326e-1033-aa71-2e7eaed2baf3'),('d723c57e-32f7-1033-aa71-2e7eaed2baf3'),('fe1e81df-326e-1033-aa71-2e7eaed2baf3'),('fe1e7613-326e-1033-aa71-2e7eaed2baf3'),('fe1e7672-326e-1033-aa71-2e7eaed2baf3'),('d723e2b6-32f7-1033-aa71-2e7eaed2baf3'),('d723e12b-32f7-1033-aa71-2e7eaed2baf3'),('d723e3d3-32f7-1033-aa71-2e7eaed2baf3'),('d723d933-32f7-1033-aa71-2e7eaed2baf3'),('d723e4ff-32f7-1033-aa71-2e7eaed2baf3'),('5fcb2e58-32f8-1033-aa71-2e7eaed2baf3'),('fe1e77fd-326e-1033-aa71-2e7eaed2baf3'),('fe1e7c8f-326e-1033-aa71-2e7eaed2baf3'),('d723e991-32f7-1033-aa71-2e7eaed2baf3'),('d723c69b-32f7-1033-aa71-2e7eaed2baf3'),('fe1e89c8-326e-1033-aa71-2e7eaed2baf3'),('fe1e8af4-326e-1033-aa71-2e7eaed2baf3'),('fe1e80b3-326e-1033-aa71-2e7eaed2baf3'),('5fcb2cdc-32f8-1033-aa71-2e7eaed2baf3'),('d723e315-32f7-1033-aa71-2e7eaed2baf3'),('e1e05bd3-3560-1033-aa71-2e7eaed2baf3'),('5fcb328b-32f8-1033-aa71-2e7eaed2baf3'),('d723c885-32f7-1033-aa71-2e7eaed2baf3'),('5fcb2359-32f8-1033-aa71-2e7eaed2baf3'),('fe1e6818-326e-1033-aa71-2e7eaed2baf3'),('d723cace-32f7-1033-aa71-2e7eaed2baf3'),('d723ca01-32f7-1033-aa71-2e7eaed2baf3'),('d723db7c-32f7-1033-aa71-2e7eaed2baf3'),('fe1e7555-326e-1033-aa71-2e7eaed2baf3'),('fe1e7ff5-326e-1033-aa71-2e7eaed2baf3'),('5fcb2c6e-32f8-1033-aa71-2e7eaed2baf3'),('5fcb2f16-32f8-1033-aa71-2e7eaed2baf3'),('d723d9a1-32f7-1033-aa71-2e7eaed2baf3'),('d723e806-32f7-1033-aa71-2e7eaed2baf3'),('d723cdd5-32f7-1033-aa71-2e7eaed2baf3'),('5fcb25a2-32f8-1033-aa71-2e7eaed2baf3'),('d723d267-32f7-1033-aa71-2e7eaed2baf3'),('fe1e7e6a-326e-1033-aa71-2e7eaed2baf3'),('d723e06d-32f7-1033-aa71-2e7eaed2baf3'),('d723ea4f-32f7-1033-aa71-2e7eaed2baf3'),('d723df50-32f7-1033-aa71-2e7eaed2baf3'),('d723dca8-32f7-1033-aa71-2e7eaed2baf3'),('5fcb2fe3-32f8-1033-aa71-2e7eaed2baf3'),('fe1e7f96-326e-1033-aa71-2e7eaed2baf3'),('d723d3e3-32f7-1033-aa71-2e7eaed2baf3'),('fe1e7f37-326e-1033-aa71-2e7eaed2baf3'),('fe1e730c-326e-1033-aa71-2e7eaed2baf3'),('fe1e7497-326e-1033-aa71-2e7eaed2baf3'),('d723ca6f-32f7-1033-aa71-2e7eaed2baf3'),('d723e68a-32f7-1033-aa71-2e7eaed2baf3'),('d723d875-32f7-1033-aa71-2e7eaed2baf3'),('fe1e87ed-326e-1033-aa71-2e7eaed2baf3'),('fe1e7438-326e-1033-aa71-2e7eaed2baf3'),('5fcb26bf-32f8-1033-aa71-2e7eaed2baf3'),('fe1e736b-326e-1033-aa71-2e7eaed2baf3'),('d723eb0d-32f7-1033-aa71-2e7eaed2baf3'),('fe1e7c30-326e-1033-aa71-2e7eaed2baf3'),('d723e9f0-32f7-1033-aa71-2e7eaed2baf3'),('d723eb6c-32f7-1033-aa71-2e7eaed2baf3'),('5fcb2417-32f8-1033-aa71-2e7eaed2baf3'),('fe1e7d4d-326e-1033-aa71-2e7eaed2baf3'),('d723cf60-32f7-1033-aa71-2e7eaed2baf3'),('d723d816-32f7-1033-aa71-2e7eaed2baf3'),('d723c9a2-32f7-1033-aa71-2e7eaed2baf3'),('5fcb2476-32f8-1033-aa71-2e7eaed2baf3'),('d723d07d-32f7-1033-aa71-2e7eaed2baf3'),('d723ddc5-32f7-1033-aa71-2e7eaed2baf3'),('fe1e8545-326e-1033-aa71-2e7eaed2baf3'),('d723da5f-32f7-1033-aa71-2e7eaed2baf3'),('d723d01e-32f7-1033-aa71-2e7eaed2baf3'),('d723ebda-32f7-1033-aa71-2e7eaed2baf3'),('d723e257-32f7-1033-aa71-2e7eaed2baf3'),('d723cbeb-32f7-1033-aa71-2e7eaed2baf3'),('d723ccb8-32f7-1033-aa71-2e7eaed2baf3'),('d723cf01-32f7-1033-aa71-2e7eaed2baf3'))
AS _ids(EntityId)))) BAMA_8$SubjectVisit$ ON (BAMA_8.participantsequencenum
= BAMA_8$SubjectVisit$.participantsequencenum
     AND BAMA_8.container = BAMA_8$SubjectVisit$.container)) BAMA_9 ON
BAMA_9.SubjectId=GridBase_4.SubjectId AND
BAMA_9.SubjectVisit_sequencenum=GridBase_4.sequencenum AND
BAMA_9.Container=GridBase_4.container) study_GridBase_1_study_BAMA_2_11
WHERE (study_GridBase_1_study_BAMA_2_11.study_BAMA_mfi_delta IS NOT NULL)
ORDER BY

study_GridBase_1_study_BAMA_2_11.study_GridBase_Container,study_GridBase_1_study_BAMA_2_11.study_GridBase_SubjectId,study_GridBase_1_study_BAMA_2_11.study_GridBase_SubjectVisit)
study_temp_5 ) x
WHERE (((study_GridBase_SubjectId = ANY ('{cvd259 P530,cvd259 P531,cvd259
P532,cvd259 P533,cvd259 P534,cvd259 P535}'))))
LIMIT 26
**************************


                               QUERY PLAN


-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Limit  (cost=105.23..105.25 rows=1 width=51) (actual
time=3819.879..3819.890 rows=26 loops=1)
   ->  Subquery Scan on study_temp_5  (cost=105.23..105.25 rows=1 width=51)
(actual time=3819.877..3819.885 rows=26 loops=1)
         ->  Sort  (cost=105.23..105.24 rows=1 width=185) (actual
time=3819.875..3819.875 rows=26 loops=1)
               Sort Key: participantvisit.container,
participant.participantid, visit.label
               Sort Method: quicksort  Memory: 178kB
               ->  Hash Semi Join  (cost=65.79..105.22 rows=1 width=185)
(actual time=338.564..3807.957 rows=577 loops=1)
                     Hash Cond: ((x.container)::text =
"*VALUES*_7".column1)
                     ->  Nested Loop  (cost=60.09..99.52 rows=1 width=318)
(actual time=338.433..3807.331 rows=577 loops=1)
                           Join Filter: (((x.container)::text =
(participantvisit_1.container)::text) AND ((x.participantsequencenum)::text
= (participantvisit_1.participantsequencenum)::text))
                           Rows Removed by Join Filter: 448665
                           ->  Hash Semi Join  (cost=59.67..97.86 rows=1
width=297) (actual time=1.794..51.016 rows=8006 loops=1)
                                 Hash Cond: ((x.container)::text =
"*VALUES*_6".column1)
                                 ->  Nested Loop  (cost=53.97..92.16 rows=1
width=265) (actual time=1.657..44.411 rows=8006 loops=1)
                                       ->  Nested Loop Left Join
(cost=53.56..91.20 rows=1 width=193) (actual time=1.516..23.837 rows=83
loops=1)
                                             ->  Nested Loop Left Join
(cost=41.62..79.17 rows=1 width=175) (actual time=1.178..20.964 rows=83
loops=1)
                                                   ->  Nested Loop Left Join
 (cost=35.64..45.30 rows=1 width=171) (actual time=0.830..3.485 rows=83
loops=1)
                                                         ->  Nested Loop
(cost=29.67..39.30 rows=1 width=162) (actual time=0.690..1.551 rows=83
loops=1)
                                                               Join Filter:
("*VALUES*".column1 = (participantvisit.container)::text)
                                                               ->  Hash Join
 (cost=29.25..32.79 rows=2 width=109) (actual time=0.545..0.603 rows=6
loops=1)
                                                                     Hash
Cond: ("*VALUES*".column1 = (participant.container)::text)
                                                                     ->
HashAggregate  (cost=3.42..5.42 rows=200 width=32) (actual time=0.104..0.148
rows=228 loops=1)

->  Values Scan on "*VALUES*"  (cost=0.00..2.85 rows=228 width=32) (actual
time=0.003..0.038 rows=228 loops=1)
                                                                     ->
Hash  (cost=25.78..25.78 rows=4 width=77) (actual time=0.396..0.396 rows=6
loops=1)

Buckets: 1024  Batches: 1  Memory Usage: 1kB

->  Hash Semi Join  (cost=6.12..25.78 rows=4 width=77) (actual
time=0.309..0.391 rows=6 loops=1)

    Hash Cond: ((participant.container)::text = "*VALUES*_1".column1)

    ->  Index Scan using ix_participant_participantid on participant
(cost=0.41..19.96 rows=7 width=45) (actual time=0.151..0.230 rows=6
loops=1)

          Index Cond: ((participantid)::text = ANY ('{"cvd259 P530","cvd259
P531","cvd259 P532","cvd259 P533","cvd259 P534","cvd259 P535"}'::text[]))

    ->  Hash  (cost=2.85..2.85 rows=228 width=32) (actual time=0.139..0.139
rows=228 loops=1)

          Buckets: 1024  Batches: 1  Memory Usage: 16kB

          ->  Values Scan on "*VALUES*_1"  (cost=0.00..2.85 rows=228
width=32) (actual time=0.003..0.060 rows=228 loops=1)
                                                               ->  Index
Scan using pk_participantvisit on participantvisit  (cost=0.42..3.24 rows=1
width=53) (actual time=0.079..0.124 rows=14 loops=6)
                                                                     Index
Cond: (((container)::text = (participant.container)::text) AND
((participantid)::text = (participant.participantid)::text))
                                                         ->  Hash Semi Join
(cost=5.97..5.99 rows=1 width=46) (actual time=0.019..0.021 rows=1
loops=83)
                                                               Hash Cond:
((study.container)::text = "*VALUES*_3".column1)
                                                               ->  Index
Scan using study_container_key on study  (cost=0.27..0.29 rows=1 width=46)
(actual time=0.015..0.016 rows=1 loops=83)
                                                                     Index
Cond: ((participantvisit.container)::text = (container)::text)
                                                               ->  Hash
(cost=2.85..2.85 rows=228 width=32) (actual time=0.101..0.101 rows=228
loops=1)

Buckets: 1024  Batches: 1  Memory Usage: 16kB
                                                                     ->
Values Scan on "*VALUES*_3"  (cost=0.00..2.85 rows=228 width=32) (actual
time=0.003..0.044 rows=228 loops=1)
                                                   ->  Hash Semi Join
(cost=5.98..33.85 rows=1 width=12) (actual time=0.094..0.209 rows=1
loops=83)
                                                         Hash Cond:
((visit.container)::text = "*VALUES*_2".column1)
                                                         ->  Index Scan
using pk_visit on visit  (cost=0.28..28.15 rows=1 width=49) (actual
time=0.090..0.204 rows=1 loops=83)
                                                               Index Cond:
(participantvisit.visitrowid = rowid)
                                                         ->  Hash
(cost=2.85..2.85 rows=228 width=32) (actual time=0.124..0.124 rows=228
loops=1)
                                                               Buckets: 1024
 Batches: 1  Memory Usage: 16kB
                                                               ->  Values
Scan on "*VALUES*_2"  (cost=0.00..2.85 rows=228 width=32) (actual
time=0.004..0.057 rows=228 loops=1)
                                             ->  Hash Semi Join
(cost=11.94..12.03 rows=1 width=31) (actual time=0.029..0.033 rows=1
loops=83)
                                                   Hash Cond:
((treatmentarmsubjectmap.container)::text = "*VALUES*_4".column1)
                                                   ->  Nested Loop Left Join
 (cost=6.24..6.33 rows=1 width=68) (actual time=0.026..0.029 rows=1
loops=83)
                                                         ->  Index Only Scan
using pk_treatmentarmsubjectmap on treatmentarmsubjectmap  (cost=0.27..0.29
rows=1 width=64) (actual time=0.011..0.013 rows=1 loops=83)
                                                               Index Cond:
(participantid = (participantvisit.participantid)::text)
                                                               Heap Fetches:
0
                                                         ->  Hash Semi Join
(cost=5.97..6.02 rows=1 width=32) (actual time=0.012..0.014 rows=1
loops=83)
                                                               Hash Cond:
((treatmentarm.container)::text = "*VALUES*_5".column1)
                                                               ->  Index
Scan using pk_treatmentarm on treatmentarm  (cost=0.27..0.32 rows=1
width=69) (actual time=0.008..0.009 rows=1 loops=83)
                                                                     Index
Cond: ((treatmentarmsubjectmap.arm_id)::text = (arm_id)::text)
                                                               ->  Hash
(cost=2.85..2.85 rows=228 width=32) (actual time=0.110..0.110 rows=228
loops=1)

Buckets: 1024  Batches: 1  Memory Usage: 16kB
                                                                     ->
Values Scan on "*VALUES*_5"  (cost=0.00..2.85 rows=228 width=32) (actual
time=0.002..0.027 rows=228 loops=1)
                                                   ->  Hash
(cost=2.85..2.85 rows=228 width=32) (actual time=0.115..0.115 rows=228
loops=1)
                                                         Buckets: 1024
Batches: 1  Memory Usage: 16kB
                                                         ->  Values Scan on
"*VALUES*_4"  (cost=0.00..2.85 rows=228 width=32) (actual time=0.002..0.035
rows=228 loops=1)
                                       ->  Index Scan using
c222d2643_bama_container_participantid_sequencenum__key on c222d2643_bama x
(cost=0.41..0.86 rows=9 width=79) (actual time=0.087..0.222 rows=96
loops=83)
                                             Index Cond: (((container)::text
= (participantvisit.container)::text) AND ((participantid)::text =
(participantvisit.participantid)::text))
                                             Filter: (mfi_delta IS NOT
NULL)
                                 ->  Hash  (cost=2.85..2.85 rows=228
width=32) (actual time=0.126..0.126 rows=228 loops=1)
                                       Buckets: 1024  Batches: 1  Memory
Usage: 16kB
                                       ->  Values Scan on "*VALUES*_6"
(cost=0.00..2.85 rows=228 width=32) (actual time=0.005..0.040 rows=228
loops=1)
                           ->  Index Scan using
ix_participantvisit_sequencenum on participantvisit participantvisit_1
(cost=0.42..1.65 rows=1 width=59) (actual time=0.017..0.456 rows=56
loops=8006)
                                 Index Cond: (sequencenum =
participantvisit.sequencenum)
                                 Filter: ((participantvisit.container)::text
= (container)::text)
                                 Rows Removed by Filter: 1303
                     ->  Hash  (cost=2.85..2.85 rows=228 width=32) (actual
time=0.111..0.111 rows=228 loops=1)
                           Buckets: 1024  Batches: 1  Memory Usage: 16kB
                           ->  Values Scan on "*VALUES*_7"  (cost=0.00..2.85
rows=228 width=32) (actual time=0.003..0.042 rows=228 loops=1)
 Total runtime: 3820.475 ms
(77 rows)

This issue has been affecting a lot of our customers lately, (perhaps it has
become  even more common to use nested loop joins in 9.4.5?).  I have created
a repro to show  the problem.

Given two table A joined to B and a filter of say selectivity Asel% on A and
Bsel% on B.   It is very optimistic to assume that the selectivity of the rows
in A join B  is Asel * Bsel.   But this seems to be what Postgres assumes.  A
very conservative estimate would be  that the join selectivity is
min(Asel,Bsel).  The conservative estimate is probably not  ideal, but is less
likely to lead to a pathological plan (one that is orders of magnitude  worse
than if no filters had been applied at all), than using the very optimistic
estimate.

Just guessing that the geometric mean of the optimistic and pessimistic
estimate.   sqrt(min(Asel,Bsel) * (Asel*Bsel)) might be close to a good
compromise.

Regardless, or the exact estimation it seems clear that

a) the current estimate for joins over filtered tables it way too optmistic b)
the estimate should not be allowed to float down to 1, 1 should only be used
to  mean exactly 1, any other estimate should be capped at some sort of
minimum to help  avoid choosing joins that degrade badly if the estimate is
wrong.


SAMPLE 1 - no UNIQUE indexes, on my machine despite estimates, the optimizer
chooses MERGE JOINS and results are very fast.

DROP TABLE IF EXISTS  A;
DROP TABLE IF EXISTS  B;
DROP TABLE IF EXISTS  C;
DROP TABLE IF EXISTS  D;
DROP TABLE IF EXISTS  E;

SELECT * INTO A
FROM
(
  SELECT seq, 100000-seq as rev, (seq+111) % 100000 as rot,
  rand, rand%10 as ten, rand%100 as hundred, rand%1000 as thousand,
  md5(random()::text) AS descr
  FROM (SELECT generate_series(1,100000) AS seq, cast(floor(random()*10000) as
int4) as rand) _
) _;
--CREATE UNIQUE INDEX ON A (seq);
--CREATE UNIQUE INDEX ON A (rev);
--CREATE UNIQUE INDEX ON A (rot);
CREATE INDEX ON A (hundred);

SELECT * INTO B FROM A;
--CREATE UNIQUE INDEX ON B (seq);
--CREATE UNIQUE INDEX ON B (rev);
--CREATE UNIQUE INDEX ON B (rot);
CREATE INDEX ON B (hundred);

SELECT * INTO C FROM A;
--CREATE UNIQUE INDEX ON C (seq);
--CREATE UNIQUE INDEX ON C (rev);
--CREATE UNIQUE INDEX ON C (rot);
CREATE INDEX ON C (hundred);

SELECT * INTO D FROM A;
--CREATE UNIQUE INDEX ON D (seq);
--CREATE UNIQUE INDEX ON D (rev);
--CREATE UNIQUE INDEX ON D (rot);
CREATE INDEX ON D (hundred);


SELECT * INTO E FROM A;
--CREATE UNIQUE INDEX ON E (seq);
--CREATE UNIQUE INDEX ON E (rev);
--CREATE UNIQUE INDEX ON E (rot);
CREATE INDEX ON E (hundred);


SELECT _a.seq, _b.rand, _c.ten, _d.hundred, _e.descr
FROM
   (SELECT * FROM A WHERE hundred in (1,2,3,4,5)) _a
   JOIN (SELECT * FROM B WHERE hundred in (1,2,3,4,5)) _b ON  _a.seq = _b.seq
   JOIN (SELECT * FROM C WHERE hundred in (1,2,3,4,5)) _c ON _b.rev = _c.rev
   JOIN (SELECT * FROM D WHERE hundred in (1,2,3,4,5)) _d ON _c.seq = _d.seq
   JOIN (SELECT * FROM E  WHERE hundred in (1,2,3,4,5)) _e ON _d.rot = _e.rot



SAMPLE 2- with UNIQUE indexes, on my machine the optimizer starts using NESTED
LOOPS once the row estimate gets to 1.  This case isn't tragically bad (worse
than  SAMPLE 1), but we hit pathological cases in the wild that are
exceedingly hard to  avoid.


 DROP TABLE IF EXISTS  A;
DROP TABLE IF EXISTS  B;
DROP TABLE IF EXISTS  C;
DROP TABLE IF EXISTS  D;
DROP TABLE IF EXISTS  E;

SELECT * INTO A
FROM
(
  SELECT seq, 100000-seq as rev, (seq+111) % 100000 as rot,
  rand, rand%10 as ten, rand%100 as hundred, rand%1000 as thousand,
  md5(random()::text) AS descr
  FROM (SELECT generate_series(1,100000) AS seq, cast(floor(random()*10000) as
int4) as rand) _
) _;
CREATE UNIQUE INDEX ON A (seq);
CREATE UNIQUE INDEX ON A (rev);
CREATE UNIQUE INDEX ON A (rot);
CREATE INDEX ON A (hundred);

SELECT * INTO B FROM A;
CREATE UNIQUE INDEX ON B (seq);
CREATE UNIQUE INDEX ON B (rev);
CREATE UNIQUE INDEX ON B (rot);
CREATE INDEX ON B (hundred);

SELECT * INTO C FROM A;
CREATE UNIQUE INDEX ON C (seq);
CREATE UNIQUE INDEX ON C (rev);
CREATE UNIQUE INDEX ON C (rot);
CREATE INDEX ON C (hundred);

SELECT * INTO D FROM A;
CREATE UNIQUE INDEX ON D (seq);
CREATE UNIQUE INDEX ON D (rev);
CREATE UNIQUE INDEX ON D (rot);
CREATE INDEX ON D (hundred);


SELECT * INTO E FROM A;
CREATE UNIQUE INDEX ON E (seq);
CREATE UNIQUE INDEX ON E (rev);
CREATE UNIQUE INDEX ON E (rot);
CREATE INDEX ON E (hundred);


SELECT _a.seq, _b.rand, _c.ten, _d.hundred, _e.descr
FROM
   (SELECT * FROM A WHERE hundred in (1,2,3,4,5)) _a
   JOIN (SELECT * FROM B WHERE hundred in (1,2,3,4,5)) _b ON  _a.seq = _b.seq
   JOIN (SELECT * FROM C WHERE hundred in (1,2,3,4,5)) _c ON _b.rev = _c.rev
   JOIN (SELECT * FROM D WHERE hundred in (1,2,3,4,5)) _d ON _c.seq = _d.seq
   JOIN (SELECT * FROM E  WHERE hundred in (1,2,3,4,5)) _e ON _d.rot = _e.rot