Optimizer (1)
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Explaining the Postgres Query Optimizer
BRUCE MOMJIAN
January, 2012
The optimizer is the "brain" of the database, interpreting SQL
queries and determining the fastest method of execution. Thistalk uses the EXPLAIN command to show how the optimizerinterprets queries and determines optimal execution.Creative Commons Attribution License http://momjian.us/presentations
Explaining the Postgres Query Optimizer 1 / 56
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Postgres Query Execution
UserTerminal
CodeDatabase
Server
Application
Queries
Results
PostgreSQL
Libpq
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Postgres Query Execution
utility
Plan
Optimal Path
Query
Postmaster
Postgres Postgres
Libpq
Main
Generate Plan
Traffic Cop
Generate Paths
Execute Plan
e.g. CREATE TABLE, COPY
SELECT, INSERT, UPDATE, DELETE
Rewrite Query
Parse Statement
Utility
Command
Storage ManagersCatalogUtilities
Access Methods Nodes / Lists
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Postgres Query Execution
utility
Plan
Optimal Path
Query
Generate Plan
Traffic Cop
Generate Paths
Execute Plan
e.g. CREATE TABLE, COPYSELECT, INSERT, UPDATE, DELETE
Rewrite Query
Parse Statement
UtilityCommand
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The Optimizer Is the Brain
http://www.wsmanaging.com/
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What Decisions Does the Optimizer Have to Make?
◮ Scan Method
◮ Join Method◮ Join Order
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Which Scan Method?
◮ Sequential Scan
◮ Bitmap Index Scan◮ Index Scan
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A Simple Example Using pg_class.relname
SELECT relnameFROM pg_classORDER BY 1LIMIT 8;
relname-----------------------------------
_pg_foreign_data_wrappers_pg_foreign_servers_pg_user_mappingsadministrable_role_authorizationsapplicable_roles
attributescheck_constraint_routine_usagecheck_constraints
(8 rows)
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Let’s Use Just the First Letter of pg_class.relname
SELECT substring(relname, 1, 1)FROM pg_classORDER BY 1LIMIT 8;
substring-----------
___aa
acc
(8 rows)
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Create a Temporary Table with an Index
CREATE TEMPORARY TABLE sample (letter, junk) ASSELECT substring(relname, 1, 1), repeat(’x’, 250)FROM pg_classORDER BY random(); -- add rows in random order
SELECT 253CREATE INDEX i_sample on sample (letter);CREATE INDEX
All the queries used in this presentation are available athttp://momjian.us/main/writings/pgsql/optimizer.sql.
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Create an E XPLAIN Function
CREATE OR REPLACE FUNCTION lookup_letter(text) RETURNS SETOF text AS $$BEGINRETURN QUERY EXECUTE ’
EXPLAIN SELECT letterFROM sampleWHERE letter = ’’’ || $1 || ’’’’;
END$$ LANGUAGE plpgsql;CREATE FUNCTION
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What is the Distribution of the sample Table?
WITH letters (letter, count) AS (SELECT letter, COUNT(*)FROM sample
GROUP BY 1)SELECT letter, count, (count * 100.0 / (SUM(count) OVER ()))::numeric(4,1) AS "%"FROM lettersORDER BY 2 DESC;
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What is the Distribution of the sample Table?
letter | count | %--------+-------+------
p | 199 | 78.7s | 9 | 3.6c | 8 | 3.2r | 7 | 2.8t | 5 | 2.0v | 4 | 1.6f | 4 | 1.6d | 4 | 1.6u | 3 | 1.2a | 3 | 1.2_ | 3 | 1.2
e | 2 | 0.8i | 1 | 0.4k | 1 | 0.4
(14 rows)
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Is the Distribution Important?
EXPLAIN SELECT letterFROM sampleWHERE letter = ’p’;
QUERY PLAN------------------------------------------------------------------------
Index Scan using i_sample on sample (cost=0.00..8.27 rows=1 width=32)Index Cond: (letter = ’p’::text)
(2 rows)
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Is the Distribution Important?
EXPLAIN SELECT letterFROM sampleWHERE letter = ’d’;
QUERY PLAN------------------------------------------------------------------------
Index Scan using i_sample on sample (cost=0.00..8.27 rows=1 width=32)Index Cond: (letter = ’d’::text)
(2 rows)
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I h D b I ?
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Is the Distribution Important?
EXPLAIN SELECT letterFROM sampleWHERE letter = ’k’;
QUERY PLAN------------------------------------------------------------------------Index Scan using i_sample on sample (cost=0.00..8.27 rows=1 width=32)
Index Cond: (letter = ’k’::text)(2 rows)
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R i A C
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Running A NALYZE Causesa Sequential Scan for a Common Value
ANALYZE sample;
ANALYZE
EXPLAIN SELECT letter
FROM sampleWHERE letter = ’p’;
QUERY PLAN---------------------------------------------------------
Seq Scan on sample (cost=0.00..13.16 rows=199 width=2)Filter: (letter = ’p’::text)
(2 rows)
Autovacuum cannot ANALYZE (or VACUUM) temporary tables becausethese tables are only visible to the creating session.
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S i l S
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Sequential Scan
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A
D
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8K
Heap
A
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A L C V l C Bit H S
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A Less Common Value Causes a Bitmap Heap Scan
EXPLAIN SELECT letterFROM sampleWHERE letter = ’d’;
QUERY PLAN
-----------------------------------------------------------------------Bitmap Heap Scan on sample (cost=4.28..12.74 rows=4 width=2)
Recheck Cond: (letter = ’d’::text)-> Bitmap Index Scan on i_sample (cost=0.00..4.28 rows=4 width=0)
Index Cond: (letter = ’d’::text)(4 rows)
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Bit I d S
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Bitmap Index Scan
=&
Combined
’A’ AND ’NS’
1
0
1
0
TableIndex 1
col1 = ’A’
Index 2
1
0
0
col2 = ’NS’
1 0
1
0
0
Index
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An E en Rarer Value Causes an Inde Scan
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An Even Rarer Value Causes an Index Scan
EXPLAIN SELECT letterFROM sampleWHERE letter = ’k’;
QUERY PLAN-----------------------------------------------------------------------Index Scan using i_sample on sample (cost=0.00..8.27 rows=1 width=2)
Index Cond: (letter = ’k’::text)(2 rows)
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Index Scan
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Index Scan
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< >=Key
< >=Key
Index
Heap
< >=Key
A
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D
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D
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T
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Let’s Look at All Values and their Effects
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Let s Look at All Values and their Effects
WITH letter (letter, count) AS (SELECT letter, COUNT(*)FROM sampleGROUP BY 1
)SELECT letter AS l, count, lookup_letter(letter)FROM letter
ORDER BY 2 DESC;
l | count | lookup_letter---+-------+-----------------------------------------------------------------------
p | 199 | Seq Scan on sample (cost=0.00..13.16 rows=199 width=2)p | 199 | Filter: (letter = ’p’::text)s | 9 | Seq Scan on sample (cost=0.00..13.16 rows=9 width=2)
s | 9 | Filter: (letter = ’s’::text)c | 8 | Seq Scan on sample (cost=0.00..13.16 rows=8 width=2)c | 8 | Filter: (letter = ’c’::text)r | 7 | Seq Scan on sample (cost=0.00..13.16 rows=7 width=2)r | 7 | Filter: (letter = ’r’::text)
…
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OK Just the First Lines
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OK, Just the First Lines
WITH letter (letter, count) AS (SELECT letter, COUNT(*)FROM sampleGROUP BY 1
)SELECT letter AS l, count,(SELECT *
FROM lookup_letter(letter) AS l2LIMIT 1) AS lookup_letter
FROM letter
ORDER BY 2 DESC;
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Just the First EXPLAIN Lines
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Just the First E XPLAIN Lines
l | count | lookup_letter---+-------+-----------------------------------------------------------------------
p | 199 | Seq Scan on sample (cost=0.00..13.16 rows=199 width=2)s | 9 | Seq Scan on sample (cost=0.00..13.16 rows=9 width=2)c | 8 | Seq Scan on sample (cost=0.00..13.16 rows=8 width=2)r | 7 | Seq Scan on sample (cost=0.00..13.16 rows=7 width=2)
t | 5 | Bitmap Heap Scan on sample (cost=4.29..12.76 rows=5 width=2)f | 4 | Bitmap Heap Scan on sample (cost=4.28..12.74 rows=4 width=2)v | 4 | Bitmap Heap Scan on sample (cost=4.28..12.74 rows=4 width=2)d | 4 | Bitmap Heap Scan on sample (cost=4.28..12.74 rows=4 width=2)a | 3 | Bitmap Heap Scan on sample (cost=4.27..11.38 rows=3 width=2)_ | 3 | Bitmap Heap Scan on sample (cost=4.27..11.38 rows=3 width=2)u | 3 | Bitmap Heap Scan on sample (cost=4.27..11.38 rows=3 width=2)
e | 2 | Index Scan using i_sample on sample (cost=0.00..8.27 rows=1 width=2)i | 1 | Index Scan using i_sample on sample (cost=0.00..8.27 rows=1 width=2)k | 1 | Index Scan using i_sample on sample (cost=0.00..8.27 rows=1 width=2)
(14 rows)
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We Can Force an Index Scan
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We Can Force an Index Scan
SET enable_seqscan = false;
SET enable_bitmapscan = false;
WITH letter (letter, count) AS (
SELECT letter, COUNT(*)FROM sampleGROUP BY 1
)SELECT letter AS l, count,
(SELECT *
FROM lookup_letter(letter) AS l2LIMIT 1) AS lookup_letterFROM letterORDER BY 2 DESC;
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Notice the High Cost for Common Values
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Notice the High Cost for Common Values
l | count | lookup_letter---+-------+-----------------------------------------------------------------------p | 199 | Index Scan using i_sample on sample (cost=0.00..39.33 rows=199 widths | 9 | Index Scan using i_sample on sample (cost=0.00..22.14 rows=9 width=2)c | 8 | Index Scan using i_sample on sample (cost=0.00..19.84 rows=8 width=2)r | 7 | Index Scan using i_sample on sample (cost=0.00..19.82 rows=7 width=2)t | 5 | Index Scan using i_sample on sample (cost=0.00..15.21 rows=5 width=2)
d | 4 | Index Scan using i_sample on sample (cost=0.00..15.19 rows=4 width=2)v | 4 | Index Scan using i_sample on sample (cost=0.00..15.19 rows=4 width=2)f | 4 | Index Scan using i_sample on sample (cost=0.00..15.19 rows=4 width=2)_ | 3 | Index Scan using i_sample on sample (cost=0.00..12.88 rows=3 width=2)a | 3 | Index Scan using i_sample on sample (cost=0.00..12.88 rows=3 width=2)u | 3 | Index Scan using i_sample on sample (cost=0.00..12.88 rows=3 width=2)e | 2 | Index Scan using i_sample on sample (cost=0.00..8.27 rows=1 width=2)
i | 1 | Index Scan using i_sample on sample (cost=0.00..8.27 rows=1 width=2)k | 1 | Index Scan using i_sample on sample (cost=0.00..8.27 rows=1 width=2)
(14 rows)RESET ALL;RESET
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This Was the Optimizer’s Preference
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This Was the Optimizer s Preference
l | count | lookup_letter---+-------+-----------------------------------------------------------------------
p | 199 | Seq Scan on sample (cost=0.00..13.16 rows=199 width=2)s | 9 | Seq Scan on sample (cost=0.00..13.16 rows=9 width=2)c | 8 | Seq Scan on sample (cost=0.00..13.16 rows=8 width=2)r | 7 | Seq Scan on sample (cost=0.00..13.16 rows=7 width=2)
t | 5 | Bitmap Heap Scan on sample (cost=4.29..12.76 rows=5 width=2)f | 4 | Bitmap Heap Scan on sample (cost=4.28..12.74 rows=4 width=2)v | 4 | Bitmap Heap Scan on sample (cost=4.28..12.74 rows=4 width=2)d | 4 | Bitmap Heap Scan on sample (cost=4.28..12.74 rows=4 width=2)a | 3 | Bitmap Heap Scan on sample (cost=4.27..11.38 rows=3 width=2)_ | 3 | Bitmap Heap Scan on sample (cost=4.27..11.38 rows=3 width=2)u | 3 | Bitmap Heap Scan on sample (cost=4.27..11.38 rows=3 width=2)
e | 2 | Index Scan using i_sample on sample (cost=0.00..8.27 rows=1 width=2)i | 1 | Index Scan using i_sample on sample (cost=0.00..8.27 rows=1 width=2)k | 1 | Index Scan using i_sample on sample (cost=0.00..8.27 rows=1 width=2)
(14 rows)
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Which Join Method?
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Which Join Method?
◮ Nested Loop
◮ With Inner Sequential Scan
◮ With Inner Index Scan◮ Hash Join
◮ Merge Join
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What Is in pg proc.oid?
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What Is in pg_proc.oid?
SELECT oidFROM pg_procORDER BY 1LIMIT 8;
oid-----
313334353839
4041
(8 rows)
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Create Temporary Tables
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C eate e po a y ab esfrom pg_proc and pg_class
CREATE TEMPORARY TABLE sample1 (id, junk) ASSELECT oid, repeat(’x’, 250)FROM pg_procORDER BY random(); -- add rows in random order
SELECT 2256CREATE TEMPORARY TABLE sample2 (id, junk) AS
SELECT oid, repeat(’x’, 250)FROM pg_classORDER BY random(); -- add rows in random order
SELECT 260These tables have no indexes and no optimizer statistics.
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Join the Two Tables
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J with a Tight Restriction
EXPLAIN SELECT sample2.junkFROM sample1 JOIN sample2 ON (sample1.id = sample2.id)WHERE sample1.id = 33;
QUERY PLAN
---------------------------------------------------------------------Nested Loop (cost=0.00..234.68 rows=300 width=32)
-> Seq Scan on sample1 (cost=0.00..205.54 rows=50 width=4)Filter: (id = 33::oid)
-> Materialize (cost=0.00..25.41 rows=6 width=36)-> Seq Scan on sample2 (cost=0.00..25.38 rows=6 width=36)
Filter: (id = 33::oid)(6 rows)
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Nested Loop Join
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p J with Inner Sequential Scan
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No Setup Required
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Used For Small Tables
Outer Inner
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Pseudocode for Nested Loop Join
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p J with Inner Sequential Scan
for (i = 0; i < length(outer); i++)
for (j = 0; j < length(inner); j++)if (outer[i] == inner[j])
output(outer[i], inner[j]);
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Join the Two Tables with a Looser Restriction
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EXPLAIN SELECT sample1.junkFROM sample1 JOIN sample2 ON (sample1.id = sample2.id)WHERE sample2.id > 33;
QUERY PLAN----------------------------------------------------------------------
Hash Join (cost=30.50..950.88 rows=20424 width=32)Hash Cond: (sample1.id = sample2.id)-> Seq Scan on sample1 (cost=0.00..180.63 rows=9963 width=36)-> Hash (cost=25.38..25.38 rows=410 width=4)
-> Seq Scan on sample2 (cost=0.00..25.38 rows=410 width=4)Filter: (id > 33::oid)
(6 rows)
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Hash Join
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Hashed
Must fit in Main Memory
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Outer Inner
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Pseudocode for Hash Join
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for (j = 0; j < length(inner); j++)hash_key = hash(inner[j]);append(hash_store[hash_key], inner[j]);
for (i = 0; i < length(outer); i++)hash_key = hash(outer[i]);for (j = 0; j < length(hash_store[hash_key]); j++)
if (outer[i] == hash_store[hash_key][j])output(outer[i], inner[j]);
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Join the Two Tables with No Restriction
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EXPLAIN SELECT sample1.junkFROM sample1 JOIN sample2 ON (sample1.id = sample2.id);
QUERY PLAN-------------------------------------------------------------------------
Merge Join (cost=927.72..1852.95 rows=61272 width=32)
Merge Cond: (sample2.id = sample1.id)-> Sort (cost=85.43..88.50 rows=1230 width=4)Sort Key: sample2.id-> Seq Scan on sample2 (cost=0.00..22.30 rows=1230 width=4)
-> Sort (cost=842.29..867.20 rows=9963 width=36)Sort Key: sample1.id
-> Seq Scan on sample1 (cost=0.00..180.63 rows=9963 width=36)(8 rows)
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Merge Join
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Sorted
Sorted
Ideal for Large Tables
An Index Can Be Used to Eliminate the Sort
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Outer Inner
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Pseudocode for Merge Join
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sort(outer);sort(inner);i = 0;j = 0;save_j = 0;while (i < length(outer))
if (outer[i] == inner[j])output(outer[i], inner[j]);if (outer[i] <= inner[j] && j < length(inner))
j++;if (outer[i] < inner[j])
save_j = j;
elsei++;j = save_j;
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Order of Joined Relations Is Insignificant
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EXPLAIN SELECT sample2.junkFROM sample2 JOIN sample1 ON (sample2.id = sample1.id);
QUERY PLAN------------------------------------------------------------------------
Merge Join (cost=927.72..1852.95 rows=61272 width=32)Merge Cond: (sample2.id = sample1.id)
-> Sort (cost=85.43..88.50 rows=1230 width=36)Sort Key: sample2.id-> Seq Scan on sample2 (cost=0.00..22.30 rows=1230 width=36)
-> Sort (cost=842.29..867.20 rows=9963 width=4)Sort Key: sample1.id-> Seq Scan on sample1 (cost=0.00..180.63 rows=9963 width=4)
(8 rows)The most restrictive relation, e.g. sample2, is always on the outer side of merge joins. All previous merge joins also had sample2 in outer position.
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Add Optimizer Statistics
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ANALYZE sample1;
ANALYZE sample2;
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This Was a Merge Join without Optimizer Statistics
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EXPLAIN SELECT sample2.junkFROM sample1 JOIN sample2 ON (sample1.id = sample2.id);
QUERY PLAN------------------------------------------------------------------------
Hash Join (cost=15.85..130.47 rows=260 width=254)Hash Cond: (sample1.id = sample2.id)-> Seq Scan on sample1 (cost=0.00..103.56 rows=2256 width=4)-> Hash (cost=12.60..12.60 rows=260 width=258)
-> Seq Scan on sample2 (cost=0.00..12.60 rows=260 width=258)(5 rows)
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Outer Joins Can Affect Optimizer Join Usage
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EXPLAIN SELECT sample1.junkFROM sample1 RIGHT OUTER JOIN sample2 ON (sample1.id = sample2.id);
QUERY PLAN-------------------------------------------------------------------------
Hash Left Join (cost=131.76..148.26 rows=260 width=254)
Hash Cond: (sample2.id = sample1.id)-> Seq Scan on sample2 (cost=0.00..12.60 rows=260 width=4)-> Hash (cost=103.56..103.56 rows=2256 width=258)
-> Seq Scan on sample1 (cost=0.00..103.56 rows=2256 width=258)(5 rows)
Use of hashes for outer joins was added in Postgres 9.1.
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Cross Joins Are Nested Loop Joinsith t J i R t i ti
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without Join Restriction
EXPLAIN SELECT sample1.junkFROM sample1 CROSS JOIN sample2;
QUERY PLAN
----------------------------------------------------------------------Nested Loop (cost=0.00..7448.81 rows=586560 width=254)
-> Seq Scan on sample1 (cost=0.00..103.56 rows=2256 width=254)-> Materialize (cost=0.00..13.90 rows=260 width=0)
-> Seq Scan on sample2 (cost=0.00..12.60 rows=260 width=0)(4 rows)
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Create Indexes
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CREATE INDEX i_sample1 on sample1 (id);
CREATE INDEX i_sample2 on sample2 (id);
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Nested Loop with Inner Index Scan Now Possible
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EXPLAIN SELECT sample2.junkFROM sample1 JOIN sample2 ON (sample1.id = sample2.id)WHERE sample1.id = 33;
QUERY PLAN---------------------------------------------------------------------------------
Nested Loop (cost=0.00..16.55 rows=1 width=254)-> Index Scan using i_sample1 on sample1 (cost=0.00..8.27 rows=1 width=4)
Index Cond: (id = 33::oid)-> Index Scan using i_sample2 on sample2 (cost=0.00..8.27 rows=1 width=258)
Index Cond: (sample2.id = 33::oid)(5 rows)
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Nested Loop Join with Inner Index Scan
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aaa
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No Setup Required
Index Lookup
Index Must Already Exist
Outer Inner
Explaining the Postgres Query Optimizer 48 / 56
Pseudocode for Nested Loop Joinwith Inner Index Scan
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with Inner Index Scan
for (i = 0; i < length(outer); i++)
index_entry = get_first_match(outer[j])while (index_entry)output(outer[i], inner[index_entry]);index_entry = get_next_match(index_entry);
Explaining the Postgres Query Optimizer 49 / 56
Query Restrictions Affect Join Usage
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EXPLAIN SELECT sample2.junkFROM sample1 JOIN sample2 ON (sample1.id = sample2.id)WHERE sample2.junk ˜ ’^aaa’;
QUERY PLAN-------------------------------------------------------------------------------
Nested Loop (cost=0.00..21.53 rows=1 width=254)-> Seq Scan on sample2 (cost=0.00..13.25 rows=1 width=258)
Filter: (junk ˜ ’^aaa’::text)-> Index Scan using i_sample1 on sample1 (cost=0.00..8.27 rows=1 width=4)
Index Cond: (sample1.id = sample2.id)(5 rows)
No junk rows begin with ’aaa’.
Explaining the Postgres Query Optimizer 50 / 56
All ’junk’ Columns Begin with ’xxx’
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EXPLAIN SELECT sample2.junkFROM sample1 JOIN sample2 ON (sample1.id = sample2.id)WHERE sample2.junk ˜ ’^xxx’;
QUERY PLAN------------------------------------------------------------------------
Hash Join (cost=16.50..131.12 rows=260 width=254)
Hash Cond: (sample1.id = sample2.id)-> Seq Scan on sample1 (cost=0.00..103.56 rows=2256 width=4)-> Hash (cost=13.25..13.25 rows=260 width=258)
-> Seq Scan on sample2 (cost=0.00..13.25 rows=260 width=258)Filter: (junk ˜ ’^xxx’::text)
(6 rows)
Hash join was chosen because many more rows are expected. Thesmaller table, e.g. sample2, is always hashed.
Explaining the Postgres Query Optimizer 51 / 56
Without LIMIT, Hash Is Usedfor this Unrestricted Join
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for this Unrestricted Join
EXPLAIN SELECT sample2.junkFROM sample1 JOIN sample2 ON (sample1.id = sample2.id);
QUERY PLAN
------------------------------------------------------------------------Hash Join (cost=15.85..130.47 rows=260 width=254)Hash Cond: (sample1.id = sample2.id)-> Seq Scan on sample1 (cost=0.00..103.56 rows=2256 width=4)-> Hash (cost=12.60..12.60 rows=260 width=258)
-> Seq Scan on sample2 (cost=0.00..12.60 rows=260 width=258)
(5 rows)
Explaining the Postgres Query Optimizer 52 / 56
LIMIT Can Affect Join Usage
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EXPLAIN SELECT sample2.id, sample2.junkFROM sample1 JOIN sample2 ON (sample1.id = sample2.id)ORDER BY 1LIMIT 1;
QUERY PLAN------------------------------------------------------------------------------------------
Limit (cost=0.00..1.83 rows=1 width=258)-> Nested Loop (cost=0.00..477.02 rows=260 width=258)
-> Index Scan using i_sample2 on sample2 (cost=0.00..52.15 rows=260 width=258)-> Index Scan using i_sample1 on sample1 (cost=0.00..1.62 rows=1 width=4)
Index Cond: (sample1.id = sample2.id)(5 rows)
Explaining the Postgres Query Optimizer 53 / 56
LIMIT 10
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EXPLAIN SELECT sample2.id, sample2.junkFROM sample1 JOIN sample2 ON (sample1.id = sample2.id)ORDER BY 1LIMIT 10;
QUERY PLAN------------------------------------------------------------------------------------------
Limit (cost=0.00..18.35 rows=10 width=258)-> Nested Loop (cost=0.00..477.02 rows=260 width=258)
-> Index Scan using i_sample2 on sample2 (cost=0.00..52.15 rows=260 width=258)-> Index Scan using i_sample1 on sample1 (cost=0.00..1.62 rows=1 width=4)
Index Cond: (sample1.id = sample2.id)(5 rows)
Explaining the Postgres Query Optimizer 54 / 56
LIMIT 100 Switches to Hash Join
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EXPLAIN SELECT sample2.id, sample2.junkFROM sample1 JOIN sample2 ON (sample1.id = sample2.id)ORDER BY 1LIMIT 100;
QUERY PLAN------------------------------------------------------------------------------------
Limit (cost=140.41..140.66 rows=100 width=258)
-> Sort (cost=140.41..141.06 rows=260 width=258)Sort Key: sample2.id-> Hash Join (cost=15.85..130.47 rows=260 width=258)
Hash Cond: (sample1.id = sample2.id)-> Seq Scan on sample1 (cost=0.00..103.56 rows=2256 width=4)-> Hash (cost=12.60..12.60 rows=260 width=258)
-> Seq Scan on sample2 (cost=0.00..12.60 rows=260 width=258)(8 rows)
Explaining the Postgres Query Optimizer 55 / 56
Conclusion