Čtení explain planu (CSPUG 21.6.2011)

ˇ
Cteme EXPLAIN
CSPUG, Praha

Tom´ˇ Vondra (tv@fuzzy.cz)
as

Czech and Slovak PostgreSQL Users Group

21.6.2011

Agenda

K ˇemu slouˇ´ EXPLAIN a EXPLAIN ANALYZE?
c zı
Jak funguje plńovń´ jak se vyb´ a “optim´ln´ plń?
a a ı, ır´ a ı” a
Z´kladn´ fyzick´ oper´tory : scany, joiny, ...
a ı e a
Jak poznat ˇe je nˇco ˇpatnˇ?
z e s e
Dalˇ´ uˇiteˇn´ n´stroje.
sı z c e a

T. Vondra (CSPUG) ˇ
Cteme EXPLAIN

K ˇemu slouˇ´ EXPLAIN a EXPLAIN ANALYZE?
c zı

SQL je deklarativn´ jazyk
ı
SQL dotaz nen´ program, popisuje v´sledek (logick´ algebra).
ı y a
Existuje mnoho zp˚sob˚ jak dan´ dotaz vyhodnotit (fyzick´ algebra).
u u y a
Nalezen´ “optim´ln´
ı a ıho” zp˚sobu je starost´ datab´ze.
u ı a
Optim´ln´ = nejm´nˇ n´roˇn´ na zdroje (CPU, I/O, pamˇˇ, ...)
a ı e e a c y et
Z´vis´ na podm´ ach (poˇet uˇivatel˚, velikost work mem, ...).
a ı ınk´ c z u

stupnˇ volnosti
e
access strategy (sequential scan, index scan, ...)
join order
join strategy (merge join, hash join, nested loop)
aggregation strategy (plain, hash, sorted)

Cteme EXPLAIN

Stromov´ struktura exekuˇn´ pl´nu
a c ıho a

SELECT * FROM a JOIN b ON ( a . id = b . id ) LIMIT 100;

Cteme EXPLAIN

V´poˇet ceny
y c

chci porovnat nˇkolik variant ˇeˇen´ a vybrat tu “nejlevnˇjˇ´
e r s ı e sı”
pˇıstup obvykl´ v (ne)line´rn´ programovń´
r´ y a ım a ı
ze statistik se odhadne poˇet ˇ´dek
c ra
s vyuˇit´ “cost” promˇnnćh se spoˇte cena plńu
z ım e y c a
seq page cost = 1.0
random page cost = 4.0
cpu tuple cost = 0.01
cpu index tuple cost = 0.005
cpu operator cost = 0.0025
...
porovn´m ceny moˇnost´ vyberu tu s nejniˇˇ´ cenou
a z ı, zsı

Cteme EXPLAIN

Orientaˇn´ principy
c ı

I/O tradiˇnˇ dominuje - minimalizace I/O operac´
c e ı
n´hodn´ I/O je n´roˇnˇjˇ´ neˇ sekvenˇn´ I/O
a e a c e sı z c ı
minimalizace CPU operac´
ı
nepouˇ´
zıvat pˇıliˇ mnoho pamˇti
r´ s e
minimalizace toku dat
preferovat niˇˇ´ startup nebo celkovou cenu (?)
zsı

Cena je zhruba ˇas proporˇnˇ k sekvenˇn´
c c e c ımu naˇten´ str´nky z disku.
c ı a

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Z´kladn´ fyzick´ oper´tory / pˇıstup k tabulce
a ı e a r´

sequential scan
pˇeˇti vˇechny ˇ´dky tabulky (a aˇ pak ﬁltruj)
r c s ra z
data (bloky) se ˇtou sekvenˇnˇ, kaˇd´ pr´vˇ 1x
c c e z y a e

index scan
najdi v indexu odkazy na odpov´ ıc´ ˇ´dky
ıdaj´ ı ra
z tabulky naˇti jen ty potˇebn´ bloky (i opakovanˇ)
c r e e
kombinace sekvenˇn´ a n´hodn´ho I/O
c ıho a e

bitmap index scan
pˇeˇti listy indexu, vytvoˇ z nich bitmapu ˇ´dk˚
r c r ra u
naˇti jen ty bloky tabulky pro kter´ je v bitmapˇ “1”
c e e
sekvenˇn´ I/O ale “startup” cena (tvorba bitmapy)
c ı
moˇnost kombinace v´ index˚ (OR, AND)
z ıce u
ﬂexibilnˇjˇ´ neˇ multi-column indexy
e sı z

Cteme EXPLAIN

Pˇıklad - vytvoˇen´ tabulky
r´ r ı

tabulka se 100.000 ˇ´dk˚
ra u
CREATE TABLE tab ( id INT );

INSERT INTO tab SELECT * FROM generate_series (1 ,100000);

ANALYZE tab ;

SELECT relpages , reltuples FROM pg_class
WHERE relname = ’ tab ’;

relpages | reltuples
-- - - - - - - - -+ - - - - - - - - - - -
393 | 100000
(1 row )

Cteme EXPLAIN

Pˇıklad - sequential vs. index scan
r´

sekvenˇn´ sken
c ı
EXPLAIN SELECT * FROM tab WHERE id BETWEEN 1000 AND 2000;

QUERY PLAN
-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Seq Scan on tab ( cost =0.00..1893.00 rows =927 width =4)
Filter : (( id >= 1000) AND ( id <= 2000))

index scan
CREATE INDEX idx ON tab ( id );
EXPLAIN ANALYZE SELECT * FROM tab WHERE id BETWEEN 1000 AND 2000;

QUERY PLAN
-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Index Scan using idx on tab ( cost =0.00..39.54 rows =1014 width =4)
( actual time =0.108..1.703 rows =1001 loops =1)
Index Cond : (( id >= 1000) AND ( id <= 2000))
Total runtime : 2.840 ms

Cteme EXPLAIN

Pˇıklad - bitmap index scan
r´

bitmap index scan
EXPLAIN SELECT * FROM tab WHERE ( id = 110 OR id = 130);

QUERY PLAN
-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Bitmap Heap Scan on tab ( cost =8.53..16.14 rows =2 width =4)
Recheck Cond : (( id = 110) OR ( id = 130))
-> BitmapOr ( cost =8.53..8.53 rows =2 width =0)
-> Bitmap Index Scan on idx ( cost =0.00..4.27 rows =1 width =0)
Index Cond : ( id = 110)
-> Bitmap Index Scan on idx ( cost =0.00..4.27 rows =1 width =0)

Cteme EXPLAIN

Join strategies

nested loop
hash join
merge join

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Nested loop

velice jednoduch´ - v principu dvˇ vnoˇen´ smyˇky
y e r e c
pro vˇtˇ´ relace pomal´, ale rychle produkuje prvn´ ˇ´dek
e sı y ı ra
jedin´ join pouˇiteln´ pro CROSS JOIN a non-equijoin podm´
y z y ınky
vˇtˇinou je k vidˇn´ v OLTP syst´mech (pr´ce s mal´mi poˇty ˇ´dek)
es e ı e a y c ra

FOR a IN vnejsi_relace
FOR b IN vnitrni_relace
RETURN (a,b) pokud splˇuje JOIN podm´nku
n ı

Cteme EXPLAIN

Nested Loop

CREATE TABLE vnejsi ( id INT , val INT UNIQUE );
CREATE TABLE vnitrni ( id INT PRIMARY KEY );

INSERT INTO vnejsi
SELECT i , i +1 FROM generate_series (1 ,1000) s ( i );

INSERT INTO vnitrni
SELECT i FROM generate_series (1 ,1000) s ( i );

EXPLAIN SELECT 1 FROM vnejsi , vnitrni
WHERE vnejsi . id = vnitrni . id
AND vnejsi . val = 10;

QUERY PLAN
-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Nested Loop ( cost =0.00..16.55 rows =1 width =0)
-> Index Scan using vnejsi_val_key on vnejsi ( cost =0.00..8.27 rows =1 width =4)
Index Cond : ( val = 10)
-> Index Scan using vnitrni_pkey on vnitrni ( cost =0.00..8.27 rows =1 width =4)
Index Cond : ( vnitrni . id = vnejsi . id )
(5 rows )

Cteme EXPLAIN

Merge Join

setˇıd´ obˇ relace dle joinovac´ podm´
r´ ı e ı ınky (jen equijoin)
potom ˇte ˇ´dek po ˇ´dku a posouv´ se kupˇedu
c ra ra a r
nˇkdy jsou potˇeba rescany (duplicity ve vnˇjˇ´ tabulce)
e r e sı
velmi rychl´ pro setˇıdˇn´ relace, jinak n´roˇn´ startup
y r´ e e a c y
vˇtˇinou k vidˇn´ v DSS/DWH syst´mech
es e ı e

Cteme EXPLAIN

Merge Join

CREATE TABLE vnejsi ( id INT );
CREATE TABLE vnitrni ( id INT );

INSERT INTO vnejsi

INSERT INTO vnitrni

EXPLAIN SELECT 1 FROM vnejsi JOIN vnitrni USING ( id );

QUERY PLAN
-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Merge Join ( cost = 1 9 3 95 . 6 4 . . 2 1 3 95 . 6 4 rows =100000 width =0)
Merge Cond : ( vnejsi . id = vnitrni . id )
-> Sort ( cost =96 97. 82.. 994 7.8 2 rows =100000 width =4)
Sort Key : vnejsi . id
-> Seq Scan on vnejsi ( cost =0.00..1393.00 rows =100000 width =4)
-> Sort ( cost =96 97. 82.. 994 7.8 2 rows =100000 width =4)
Sort Key : vnitrni . id
-> Seq Scan on vnitrni ( cost =0.00..1393.00 rows =100000 width =4)

Cteme EXPLAIN

Hash Join

1 naˇti menˇ´ (vnitˇn´ relaci a vygeneruj z n´ hash tabulku (pˇes join kl´c)
c sı r ı) ı r ıˇ
2 ˇti vnˇjˇ´ tabulku a vyhled´vej v hash tabulce pˇed hash kl´ce
c e sı a r ıˇ

CREATE TABLE vnejsi ( id INT );
CREATE TABLE vnitrni ( id INT );

INSERT INTO vnejsi SELECT i FROM generate_series (1 ,100000) s ( i );
INSERT INTO vnitrni SELECT i FROM generate_series (1 ,100000) s ( i );

EXPLAIN SELECT 1 FROM vnejsi_tabulka JOIN vnitrni_tabulka USING ( id );

QUERY PLAN
-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Hash Join ( cost =29 85. 00.. 702 9.0 0 rows =100000 width =0)
Hash Cond : ( vnejsi . id = vnitrni . id )
-> Seq Scan on vnejsi ( cost =0.00..1393.00 rows =100000 width =4)
-> Hash ( cost =13 93. 00.. 139 3.0 0 rows =100000 width =4)
-> Seq Scan on vnitrni ( cost =0.00..1393.00 rows =100000 width =4)
(5 rows )

Cteme EXPLAIN

Hash Join / batches

Co kdyˇ se hash tabulka nevejde to pamˇti (work mem)?
z e
1 rozdˇl menˇ´ tabulku na ˇ´sti, aby se tabulka do pamˇti veˇla
e sı ca e s
2 pro kaˇdou ˇ´st sestav tabulku a proved
z ca ˇ join s “velkou” tabulkou
3 mńˇ efektivn´ (opakovan´ ˇten´ vnˇjˇ´ tabulky)
e e ı e c ı e sı
4 pozn´ se dle “batches” v plńu
a a

EXPLAIN ANALYZE SELECT 1 FROM vnejsi_tabulka JOIN vnitrni_tabulka USING ( id );

QUERY PLAN
-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ...
Hash Join ( cost =29 85. 00.. 702 9.0 0 rows =100000 width =0) ( actual time =277.886..792 ...
Hash Cond : ( vnejsi . id = vnitrni . id )
-> Seq Scan on vnejsi ( cost =0.00..1393.00 rows =100000 width =4) ( actual time = ...
-> Hash ( cost =13 93. 00.. 139 3.0 0 rows =100000 width =4) ( actual time =277.836..27 ...
Buckets : 8192 Batches : 4 Memory Usage : 589 kB
-> Seq Scan on vnitrni ( cost =0.00..1393.00 rows =100000 width =4) ( actua ...
(7 rows )

zvyˇte work mem (ˇ´ mńˇ batch˚, t´ vˇtˇinou l´pe)
s cım e e u ım e s e

Cteme EXPLAIN

Srovn´n´ join metod
a ı

Nested Loop
ˇpatnˇ funguje pro dvˇ velk´ relace
s e e e
ide´ln´ pro malou vnˇjˇ´ relaci + rychl´ dotaz do vnitˇn´ (index scan)
a ı e sı y r ı
jedin´ metoda pro non-equijoin :-(
a

Merge Join
ide´ln´ pro jiˇ setˇıdˇn´ relace (napˇ. CLUSTER + index scan)
a ı z r´ e e r
pokud vyˇaduje extra tˇıdˇn´ probl´m (hlavnˇ velk´ on-disk tˇıdˇn´
z r´ e ı, e e e r´ e ı)

Hash Join
nevyˇaduje tˇıdˇn´ mus´ ale vytvoˇit hash tabulku
z r´ e ı, ı r
vyˇaduje ale dostatek pamˇti (work mem pro hash tabulku)
z e
pokud je hash tabulka moc velk´, dˇl´ se do batch˚ (pomalejˇ´
a eı u sı)

Cteme EXPLAIN

Sort & Limit

ORDER BY ale i spousta dalˇ´ (DISTINCT, GROUP BY, UNION)
sıch
tˇi moˇnosti
r z
quicksort (v pamˇti, omezeno work mem)
e
merge sort (na disku)
index scan (dostateˇnˇ korelovan´ index, napˇ. CLUSTERED)
c e y r
LIMIT ˇık´ “chci jenom p´r ˇ´dek, preferuj rychle startuj´ ı pl´ny”
r´ a a ra ıc´ a
vˇtˇinou mal´ startovn´ ˇas znamen´ velk´ celkov´ ˇas
es y ıc a y yc

EXPLAIN ANALYZE SELECT * FROM tab ORDER BY id ;

Sort (...) ( actual time =44 6.08 9.. 591 .71 4 rows =100000 loops =1)
Sort Key : id
Sort Method : external sort Disk : 1368 kB
-> Seq Scan on tab (...) ( actual time =0.016..129.756 rows =100000 loops =1)

Cteme EXPLAIN

Sort

v pamˇti
e
SET work_mem = ’8 MB ’;


QUERY PLAN
-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Sort (...) ( actual time =31 2.7 09. .432 .41 0 rows =100000 loops =1)
Sort Key : id
Sort Method : quicksort Memory : 4392 kB
-> Seq Scan on tab (...) ( actual time =0.020..146.975 rows =100000 loops =1)

s dobˇe korelovan´m indexem
r y


QUERY PLAN
-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Cteme EXPLAIN

Typy uzl˚ - ostatn´
u ı

agregace (GROUP BY, DISTINCT)
LIMIT
modiﬁkace tabulky (INSERT, UPDATE, DELETE)
mnoˇinov´ operace (INTERSECT, EXCEPT)
z e
subplan (pro korelovan´ subselecty), initplan (nekorelovan´)
e e
CTE, window functions
materializace
zamyk´n´ ˇ´dek
a ı ra
append (inheritance)
...

Cteme EXPLAIN

Chybn´ odhad poˇtu ˇ´dk˚ (relace resp. vyhovuj´ ıch podm´
y c ra u ıc´ ınce).

V ˇem spoˇ´ a probl´m?
c cıv´ e
Plńovaˇ si mysl´ ˇe tabulka je mal´ ale ve skuteˇnosti je velk´.
a c ız a c a
Plńovaˇ si mysl´ ˇe podm´
a c ız ınce vyhovuje p´r ˇ´dek, ve skuteˇnosti mnoho.
a ra c
nebo naopak ...

Jak se projevuje?
vol´ se nevhodn´ zp˚sob pˇıstupu k tabulce (index vs. sekvenˇn´ sken)
ı y u r´ c ı
vol´ se nevhodn´ zp˚sob joinovń´ (nested loop nam´ hash/merge joinu)
ı y u a ı ısto

Co je pˇıˇinou?
rć
zastaral´ statistiky (napˇ. hned po loadu)
e r
chybn´ statistiky - obˇas poˇet distinct hodnot, nevhodn´ formulace podm´
e c c a ınek
podm´
ınky na korelovanćh sloupc´ (cross-column statistiky zat´ nejsou)
y ıch ım
LIMIT situaci vˇtˇinou v´raznˇ zhorˇuje (preferuje plńy s levn´m startem)
es y e s a y

Cteme EXPLAIN

Pˇıklad - zd´nlivˇ velk´ selektivita
r´ a e a

zaloˇeno na “race condition” - spust´ dotaz jeˇtˇ neˇ se staˇ´ pˇepoˇ´ statistiky
z ım se z cı r cıtat
CREATE TABLE tab ( id INT );
INSERT INTO tab SELECT * FROM generate_series (1 ,100000);
ANALYZE tab ;

DELETE FROM tab ;
INSERT INTO tab SELECT 1111 FROM generate_series (1 ,100000);

EXPLAIN ANALYZE SELECT * FROM tab WHERE id = 1111;
QUERY PLAN
-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
(3 rows )

... wait ....

EXPLAIN ANALYZE SELECT * FROM tab WHERE id = 1111;
QUERY PLAN
-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Filter : ( id = 1111)

Cteme EXPLAIN

Pˇıklad - korelovan´ sloupce
r´ e

CREATE TABLE tab ( a INT , b INT );
INSERT INTO tab SELECT i , i FROM generate_series (1 ,100000) s ( i );
ANALYZE tab ;

EXPLAIN ANALYZE SELECT * FROM tab WHERE a >= 50000 AND b <= 50000;

QUERY PLAN
-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Filter : (( a >= 50000) AND ( b <= 50000))
(3 rows )

Cteme EXPLAIN

Dalˇ´ problematick´ m´
sı a ısta

Nevhodn´ nastaven´ “cost” promˇnnćh
e ı e y
vćhoz´ hodnoty vych´z´ z “typick´ho” syst´mu
y ı a ı e e
nemus´ nutnˇ odpov´
ı e ıdat tomu vaˇemu
s
napˇ. pokud m´te SSD, st´ a se rozd´ mezi n´hodn´m a sekvenˇn´ I/O
r a ır´ ıl a y c ım
pokud m´te rychl´ disky (15k SAS) tak ˇ´steˇnˇ tak´, byˇ ne tak markantnˇ
a e ca c e e t e
mal´ effective cache size znev´hodˇuje indexy
a y n

ˇ
Cern´ d´
e ıry
triggery
referenˇn´ integrita (ciz´ klće bez index˚)
c ı ı ıˇ u

Cteme EXPLAIN

EXPLAIN kuchaˇka
r

Zkontrolujte uzly kde nesed´ odhad poˇtu ˇ´dek.
ı c ra
Mal´ rozd´ nevad´ ˇ´dov´ rozd´ uˇ jsou probl´m.
e ıly ı, ra e ıly z e
Pokud je ˇpatnˇ odhad, nem˚ˇe b´t volba pl´nu spolehliv´.
s e uz y a a
Zkuste aktualizovat statistiky, pˇeformulovat podm´
r ınky, ...

Pod´
ıvejte se na na uzly s nejvˇtˇım proporˇn´ rozd´
e s´ c ım ılem mezi cenou a ˇasem.
c
Jste si jisti ˇe m´te rozumnˇ nastaveny promˇnn´?
z a e e e
Zmˇnte nastaven´ (v session) a sledujte jak se zmˇn´ pl´n a v´kon dotazu.
eˇ ı e ı a y

r r ˇ
Pˇi optimalizaci se soustˇedte na uzly s nejvyˇˇı cenou / skuteˇn´m ˇasem.
ss´ c y c
Tam kde se tr´v´ nejv´ ˇasu m˚ˇete optimalizac´ nejv´ z´
a ı ıc c uz ı ıce ıskat.
Nelze napˇ. pˇidat index nebo zv´ˇit work mem?
r r ys

Cteme EXPLAIN

explain.depesz.com

http://explain.depesz.com
v´born´ n´stroj pro vizualizaci a anal´zu explain planu
y y a y
skvˇl´ pro pos´ an´ pl´nu napˇ. do e-mailov´ch konferenc´ (nezmrˇ´ se)
ee ıl´ ı a r y ı sı

Cteme EXPLAIN

explain.depesz.com

Jak dlouho trval dan´ krok (samostatnˇ / vˇetnˇ podˇızen´ch)?
y e c e r´ y
Jak pˇesn´ byl odhad poˇtu ˇ´dek?
r y c ra
Kolik ˇ´dek se vyprodukovalo?
ra

Cteme EXPLAIN

explain.depesz.com

Unique (cost=30938464.86..31166982.10 rows=30468966 width=89) (actual
time=249353.521..250273.108 rows=342107 loops=1)
-> Sort (cost=30938464.86..31014637.27 rows=30468966 width=89) (actual
time=249353.518..250155.187 rows=342108 loops=1)
Sort Key: (lower(u.samaccountname[1])), (g.cn[1])
Sort Method: external merge Disk: 13176kB
-> Append (cost=0.00..19340392.34 rows=30468966 width=89) (actual
time=44.687..242695.135 rows=342108 loops=1)
-> Nested Loop (cost=0.00..19031015.08 rows=30385836 width=89) (actual
time=44.685..240132.584 rows=2535 loops=1)
Join Filter: ((u.primarygroupid[1] = ANY (tmp_g.primarygrouptoken)) OR
(u.gidnumber[1] = ANY (tmp_g.gidnumber)) OR (tmp_g.dn = ANY (u.memberof)) OR
(tmp_g.cn[1] = ANY (u.memberof)) OR (tmp_g.dn = ANY (u.groupmembership)) OR
(tmp_g.cn[1] = ANY (u.groupmembership)) OR (u.samaccountname[1] = ANY
(tmp_g.memberuid)) OR (u.dn = ANY (tmp_g.member)) OR (u.cn[1] = ANY
(tmp_g.member)))
time=0.054..116.528 rows=1350 loops=1)
time=0.038..76.647 rows=1350 loops=1)
-> Seq Scan on ldap_group_inheritance i (cost=0.00..46.50 rows=1350 width=166)
(actual time=0.015..1.633 rows=1350 loops=1)
-> Index Scan using ldap_import_groups_dn_key on ldap_import_groups tmp_g
(cost=0.00..0.50 rows=1 width=940) (actual time=0.048..0.049 rows=1 loops=1350)
Index Cond: (tmp_g.dn = i.groupdn)
-> Index Scan using ldap_import_groups_dn_key on ldap_import_groups g
(cost=0.00..0.50 rows=1 width=129) (actual time=0.022..0.026 rows=1 loops=1350)
Index Cond: (g.dn = i.parentdn)
-> Seq Scan on ldap_import_users u (cost=0.00..3856.30 rows=83130 width=372)
-> Seq Scan on ldap_import_users u (cost=0.00..4687.60 rows=83130 width=126)
Total runtime: 250301.001 ms
(17 rows)

Cteme EXPLAIN

pgadmin3

http://www.pgadmin.org/
GUI umoˇnuj´ ı mimo jin´ i vizualizaci SQL dotaz˚
zˇ ıc´ e u

Cteme EXPLAIN

auto explain & explanation

auto explain
jak´si doplnˇk k log min duration statement
y e
umoˇnuje logovat EXPLAIN (ˇi EXPLAIN ANALYZE) pro dlouh´ dotazy
zˇ c e
http://developer.postgresql.org/pgdocs/postgres/auto-explain.html

explanation
flexibilnˇjˇ´ prće s informacemi o plńu pˇımo v SQL
e sı a a r´
http://www.pgxn.org/dist/explanation/doc/explanation.html

SELECT node_type , strategy , actual_startup_time , ac tual _to tal _ti me
FROM explanation (
query := $$ SELECT * FROM pg_class WHERE relname = ’ users ’ $$ ,
analyzed := true
);

node_type | strategy | a c t u al _ s t a r t u p _t i m e | act ual _tot al_ tim e
-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Index Scan | | 00:00:00.000017 | 00:00:00.000017

Cteme EXPLAIN

Odkazy

Query Execution Techniques in PostgreSQL, Neil Conway, 2007
http://neilconway.org/talks/executor.pdf

ˇ ı
Cten´ prov´dˇc´ pl´n˚ v PostgreSQL, Pavel Stˇhule, 2008
a e ıch a u e
http://www.root.cz/clanky/cteni-provadecich-planu-v-postgresql/

Using EXPLAIN @ wiki
http://wiki.postgresql.org/wiki/Using_EXPLAIN

Introduction to VACUUM, ANALYZE, EXPLAIN, and COUNT @ wiki
http://wiki.postgresql.org/wiki/Introduction_to_VACUUM,_ANALYZE,
_EXPLAIN,_and_COUNT

Explaining EXPLAIN, R. Treat, G. S. Mullane, AndrewSN, Magniﬁkus, B. Encina,
N. Conway, 2008
http://wiki.postgresql.org/images/4/45/Explaining_EXPLAIN.pdf

Cteme EXPLAIN

Čtení explain planu (CSPUG 21.6.2011)

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