Indexing Complex PostgreSQL Data Types
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The document is a presentation by Jonathan S. Katz on indexing complex PostgreSQL data types, covering various indexing methods including B-trees, GIST, and GIN. It explores their applications, performance characteristics, and practical examples using PostgreSQL for efficient data retrieval. The presentation also discusses production indexing strategies and the integration of full-text search functionalities with PostgreSQL through indexing techniques.
![Full-‐Text
Search:
Basics
53
SELECT title
FROM category
WHERE
to_tsvector('english', title) @@ to_tsquery('united & kingdom’);
!
title
-----
Lists of railway stations in the United Kingdom
Political history of the United Kingdom
Military of the United Kingdom
United Kingdom constitution
Television channels in the United Kingdom
United Kingdom
Roman Catholic secondary schools in the United Kingdom
[results truncated]
!
!
QUERY PLAN
------------
Seq Scan on category (cost=0.00..49262.77 rows=46 width=29) (actual time=21.900..16809.890
rows=8810 loops=1)
Filter: (to_tsvector('english'::regconfig, title) @@ to_tsquery('united & kingdom'::text))
Rows Removed by Filter: 1814834
!
Total runtime: 16811.108 ms](https://image.slidesharecdn.com/explain-postgresql-indexes-131101090758-phpapp02/85/Indexing-Complex-PostgreSQL-Data-Types-53-320.jpg)
![Regular
Expressions
-‐
No
Index
63
EXPLAIN ANALYZE SELECT title FROM category
WHERE title ~ '(([iI]sland(s)?)|([pP]eninsula))$';
!
QUERY PLAN
----------
Seq Scan on category (cost=0.00..40144.55 rows=182 width=29) (actual
time=2.509..4260.792 rows=5878 loops=1)
Filter: (title ~ '(([iI]sland(s)?)|([pP]eninsula))$'::text)
Rows Removed by Filter: 1817766
!
Total runtime: 4261.204 ms](https://image.slidesharecdn.com/explain-postgresql-indexes-131101090758-phpapp02/85/Indexing-Complex-PostgreSQL-Data-Types-63-320.jpg)
![Regular
Expressions
-‐
Indexed
64
CREATE INDEX category_title_regex_idx ON category
USING gin(title gin_trgm_ops);
!
EXPLAIN ANALYZE SELECT title FROM category
WHERE title ~ '(([iI]sland(s)?)|([pP]eninsula))$';
QUERY PLAN
-----------
Bitmap Heap Scan on category (cost=197.41..871.77 rows=182 width=29) (actual
time=107.445..146.713 rows=5878 loops=1)
Recheck Cond: (title ~ '(([iI]sland(s)?)|([pP]eninsula))$'::text)
Rows Removed by Index Recheck: 4712
-> Bitmap Index Scan on category_title_regex_idx (cost=0.00..197.37 rows=182 width=0) (actual
time=106.645..106.645 rows=10590 loops=1)
Index Cond: (title ~ '(([iI]sland(s)?)|([pP]eninsula))$'::text)
!
!
Total runtime: 147.026 ms](https://image.slidesharecdn.com/explain-postgresql-indexes-131101090758-phpapp02/85/Indexing-Complex-PostgreSQL-Data-Types-64-320.jpg)
![Range
Type
Examples
66
--find all ranges that overlap with [100, 200)
!
SELECT * FROM ranges WHERE int4range(100, 200) && range;
!
range
-----------
[10,102)
[13,102)
[18,108)
[32,101)
[34,134)
[37,123)
[43,111)
[46,132)
[48,107)
[results trunctated]
!QUERY PLAN
-----------
Seq Scan on ranges (cost=0.00..14239.86 rows=7073 width=32) (actual time=0.018..185.411 rows=143 loops
Filter: ('[100,200)'::int4range && range)
Rows Removed by Filter: 999857
!
Total runtime: 185.439 ms](https://image.slidesharecdn.com/explain-postgresql-indexes-131101090758-phpapp02/85/Indexing-Complex-PostgreSQL-Data-Types-66-320.jpg)
![Integer
Arrays
74
CREATE UNLOGGED TABLE int_arrays AS
SELECT ARRAY[x, x + 1, x + 2] AS data
FROM generate_series(1,1000000) x;
!
CREATE INDEX int_arrays_data_idx ON
int_arrays (data);
!
CREATE INDEX int_arrays_data_gin_idx ON
int_arrays USING GIN(data);](https://image.slidesharecdn.com/explain-postgresql-indexes-131101090758-phpapp02/85/Indexing-Complex-PostgreSQL-Data-Types-74-320.jpg)
![GIN
+
Integer
Arrays
76
EXPLAIN ANALYZE
SELECT *
FROM int_arrays
WHERE ARRAY[5432] <@ data;
QUERY PLAN
-----------
Bitmap Heap Scan on int_arrays (cost=70.75..7680.14 rows=5000 width=33)
(actual time=0.020..0.021 rows=3 loops=1)
Recheck Cond: ('{5432}'::integer[] <@ data)
-> Bitmap Index Scan on int_arrays_data_gin_idx (cost=0.00..69.50
rows=5000 width=0) (actual time=0.014..0.014 rows=3 loops=1)
Index Cond: ('{5432}'::integer[] <@ data)
!
Total runtime: 0.045 ms](https://image.slidesharecdn.com/explain-postgresql-indexes-131101090758-phpapp02/85/Indexing-Complex-PostgreSQL-Data-Types-76-320.jpg)
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Indexing Complex PostgreSQL Data Types
- 1. Indexing Complex PostgreSQL Data Types Jonathan S. Katz -‐ PGDay UK 2014 -‐ July 9, 2014
- 2. About • Jonathan S. Katz – CTO, VenueBook – Co-‐Organizer, NYC PostgreSQL User Group – PGConf NYC 2015 • Mar 25 -‐ 27, 2015 • New York Marriott Downtown • http://nyc.pgconf.us – @jkatz05 2
- 3. Quick Overview • Introductory Talk with demos and fun • B-‐trees • GiST: Generalized Search Trees • GIN: Generalized Inverted Index • SP-‐GiST: Space Partitioned Generalized Search Trees 3
- 4. Assumptions • PostgreSQL 9.3+ • most will be 9.0+ • PostGIS 2.0+ • Believe it will work for most available versions • PostgreSQL 9.4 beta? 4
- 20. What We Learned • Without any data structure around search, we rely on "hope" • Assumed "unique values" and "equality" – would have to scan all rows otherwise • …and what about: – INSERT – UPDATE – DELETE 20
- 21. What We Need • Need a data structure for search that: – allows efficient lookups – plays nicely with disk I/O – does not take too long for updates 21
- 22. B-‐Trees • "default" index • quick traversal to leaf nodes • leaf nodes pre-‐sorted • node size designed to fit in disk block size – "degree" of nodes has max-‐size • theoretical performance – reads: O(log n) – writes: O(log n) – space: O(n) 22
- 23. B-‐Trees and PostgreSQL • supports – <=, <, =, >, >= – BETWEEN, IN – IS NOT NULL, IS NULL – LIKE in specific case of ‘plaintext%’ – ~ in specific case of ‘^plaintext’ – ILIKE and ~* if pattern starts with nonalpha characters • does not support • IS NOT DISTINCT FROM 23
- 24. B-‐Trees and PostgreSQL • data types supported – any data type with all the equality operators defined – number types • integer, numeric, decimal – text • char, varchar, text – date / times • timestamptz, timestamp, date, time, timetz, interval - arrays, ranges 24
- 25. Demo Specs + Configuration • Hardware specs – 2.3GHz Intel i7 2x4 core – 16GB RAM DDR3 1600MHz – Apple SSD 6Gbps • postgresql.conf (9.3, 9.4 beta 1) – shared_buffers = 1GB – work_mem = 64MB – maintenance_work_mem = 1024MB – effective_cache_size = 8GB 25
- 26. Demo #1: Basic Indexing Plans 26
- 27. Demo #1 Notes • Index maintenance • VACUUM – "cleans up" after writes on table / indexes – ANALYZE – keeps statistics up-‐to-‐date for planner ! VACUUM ANALYZE tablename; ! • Good idea to leave autovacuum on 27
- 28. Indexing in Production • CREATE INDEX CONCURRENTLY • REINDEX – corruption, bloat, invalid • FILLFACTOR – 10 – 100 – default: 90 – strategy: lower % :: write-‐activity • TABLESPACE • NULLS LAST, NULLS FIRST 28
- 29. Demo #2: Partial Indexes 29 CREATE INDEX indexname ON tablename (columnname) WHERE somecondition;
- 30. Demo #2 Notes • Partial Indexes are – good if known to query limited subset of table – take up less space – allow for much quicker writes • Like all good things, do not overuse and saturate your I/O 30
- 31. Unique Indexes • only for B-‐trees • NULL not unique • use UNIQUE constraints – automatically create indexes ! CREATE TABLE foo (bar int UNIQUE); -- or CREATE UNIQUE INDEX foo_bar_idx ON foo (bar); ALTER TABLE foo ADD CONSTRAINT a_unique USING INDEX a_idx; 31
- 32. Multi-‐Column Indexes • Useful for – querying two columns that are frequently queried together – enforcing UNIQUEness across columns • n.b. creating UNIQUE constraint on table creates UNIQUE INDEX • PostgreSQL supports – up to 32 columns – B-‐tree, GiST, GIN • Be careful of how you choose initial column order! 32
- 33. Multi-‐Column Indexes 33 CREATE INDEX multicolumn_idx ON tablename (col1, col2); ! ! ! CREATE UNIQUE INDEX pn_idx ON phone_numbers (country_code, national_number) WHERE extension IS NULL
- 34. Demo #3 Notes • Multi-‐column indexes can be – efficient for speed + space – inefficient with performance • Usage depends on your application needs 34
- 35. Expression Indexes • can index on expressions to speed up lookups – e.g. case insensitive email addresses – can use functions or scalars • (x * y) / 100 • COALESCE(first_name, '') || ' ' || COALESCE(last_name, '') • LOWER(email_address) • tradeoff: slower writes 35
- 36. Demo #4: Expression Indexes 36
- 37. Demo #4 Notes • fast lookups, slow updates • size on disk • cool data types – geometric – JSON 37
- 39. Geometric Data Types CREATE TABLE points (coord point); ! CREATE INDEX points_idx ON points (coord); ERROR: data type point has no default operator class for access method "btree" HINT: You must specify an operator class for the index or define a default operator class for the data type. 39
- 40. GiST • "generalized search tree" • infrastructure that provides template to create arbitrary indexing schemes – supports concurrency, logging, searching – only have to define behavior – user-‐defined operator class • <<, &<, &>, >>, <<|, &<|, |&>, |>>, @>, <@, ~=, && – have to implement functions in interface • supports lossless + lossy indexes • provides support for "nearest-‐neighbor" queries – "KNN-‐Gist" 40 CREATE INDEX points_coord_gist_idx ON points USING gist(coord)
- 41. Demo #5: Geometric GiST + KNN-‐Gist 41
- 42. Demo #5 Notes • GiST indexes on geometric types radically speedup reads • Writes are slower due to distance calculation • Index size can be very big 42
- 43. PostGIS • For when you are doing real things with shapes 43• (and geographic information systems)
- 44. PostGIS + Indexes • B-‐Tree? • R-‐Tree? • PostGIS docs do not recommend using just an R-‐Tree index • GiST • overlaps! containment! • uses a combination of GiST + R-‐Tree 44
- 45. PostGIS + GiST 45 2-‐D CREATE INDEX zipcodes_geom_gist_idx ON zipcodes USING gist(geom); N-‐D (PostGIS 2.0+ CREATE INDEX zipcodes_geom_gist_idx ON zipcodes USING gist(geom gist_geometry_ops_nd);
- 46. Example -‐ USA Zipcode Boundaries • 33,120 rows • geom: MultiPolygon • 52MB without indexes • With geometry GiST + integer B-‐Tree: 869MB 46
- 47. What Zipcode Is My Office In? • Geocoded Address • Lat,Long = 40.7356197,-‐73.9891102 • PostGIS: POINT(-‐73.9891102 40.7356197) • 4269 -‐ “SRID” -‐ unique ID for coordinate system definitions 47 SELECT zcta5ce10 AS zipcode FROM zipcodes WHERE ST_Contains( geom, --MultiPolygon ST_GeomFromText('POINT(-73.9891102 40.7356197)', 4269) );
- 48. What Zipcode Is My Office In? • No Index 48 Seq Scan on zipcodes (cost=0.00..15382.00 rows=1 width=6) (actual time=64.780..5153.485 rows=1 loops=1) Filter: ((geom && '0101000020AD100000F648DE944D7F52C08CE54CC9285E4440'::geometry) AND _st_contains(geom, '0101000020AD100000F648DE944D7F52C08CE54CC9285E4440'::geometry)) Rows Removed by Filter: 33119 Total runtime: 5153.505 ms
- 49. What Zipcode Is My Office In? • Here’s the GiST: 49 Index Scan using zipcodes_geom_gist on zipcodes (cost=0.28..8.54 rows=1 width=6) (actual time=0.120..0.207 rows=1 loops=1) Index Cond: (geom && '0101000020AD100000F648DE944D7F52C08CE54CC9285E4440'::geometry) Filter: _st_contains(geom, '0101000020AD100000F648DE944D7F52C08CE54CC9285E4440'::geometry) Rows Removed by Filter: 1 ! Total runtime: 0.235 ms
- 50. PostGIS + GiST Conclusion • 5153ms => 0.235ms = 21,900x speedup :-‐) 50 SELECT zcta5ce10 AS zipcode FROM zipcodes WHERE ST_Contains( geom, ST_GeomFromText('POINT(-73.9891102 40.7356197)', 4269) ); ! zipcode --------- 10003
- 51. Full Text Search • PostgreSQL offers full text search with the tsearch2 engine – algorithms for performing FTS – to_tsvector('english', content) @@ to_tsquery('irish & conference | meeting') – provides indexing capabilities for efficient search 51
- 52. Test Data Set • Wikipedia English category titles – all 1,823,644 that I downloaded 52
- 53. Full-‐Text Search: Basics 53 SELECT title FROM category WHERE to_tsvector('english', title) @@ to_tsquery('united & kingdom’); ! title ----- Lists of railway stations in the United Kingdom Political history of the United Kingdom Military of the United Kingdom United Kingdom constitution Television channels in the United Kingdom United Kingdom Roman Catholic secondary schools in the United Kingdom [results truncated] ! ! QUERY PLAN ------------ Seq Scan on category (cost=0.00..49262.77 rows=46 width=29) (actual time=21.900..16809.890 rows=8810 loops=1) Filter: (to_tsvector('english'::regconfig, title) @@ to_tsquery('united & kingdom'::text)) Rows Removed by Filter: 1814834 ! Total runtime: 16811.108 ms
- 54. Full-‐Text Search + GiST 54 CREATE INDEX category_title_gist_idx ON category USING gist(to_tsvector('english', title)); ! SELECT title FROM category WHERE to_tsvector('english', title) @@ to_tsquery('united & kingdom'); QUERY PLAN ------------- Bitmap Heap Scan on category (cost=4.77..182.47 rows=46 width=29) (actual time=75.517..180.650 rows=8810 loops=1) Recheck Cond: (to_tsvector('english'::regconfig, title) @@ to_tsquery('united & kingdom'::text)) -> Bitmap Index Scan on category_title_gist_idx (cost=0.00..4.76 rows=46 width=0) (actual time=74.687..74.687 rows=8810 loops=1) Index Cond: (to_tsvector('english'::regconfig, title) @@ to_tsquery('united & kingdom’::text)) ! Total runtime: 181.354 ms
- 55. Full Text Search + GiST • GiST indexes can produce false positives – "documents" represented by fixed length signature • words are hashed into single bits and concatenated – when false positive occurs, row is returned and checked to see if false match • Extra validations = performance degradation 55
- 56. Performance Summary with GiST • initial index build takes awhile => slow writes • reads are quick • Table size: 271MB • Index size: 83MB 56
- 57. GIN Index • "generalized inverted index" • supports searching within composite data – arrays, full-‐text documents, hstore • key is stored once and points to composites it is contained in • like GiST, provides index infrastructure to extend GIN based on behavior – supports operators <@, @>, =, && • GIN performance ⬄ log(# unique things) 57
- 58. Full Text Search + GIN 58 CREATE INDEX category_title_gin_idx ON category USING gin(to_tsvector('english', title)); ! EXPLAIN ANALYZE SELECT title FROM category WHERE to_tsvector('english', title) @@ to_tsquery('united & kingdom'); ! QUERY PLAN ------- Bitmap Heap Scan on category (cost=28.36..206.06 rows=46 width=29) (actual time=8.864..14.674 rows=8810 loops=1) Recheck Cond: (to_tsvector('english'::regconfig, title) @@ to_tsquery('united & kingdom'::text)) -> Bitmap Index Scan on category_title_gin_idx (cost=0.00..28.35 rows=46 width=0) (actual time=7.905..7.905 rows=8810 loops=1) Index Cond: (to_tsvector('english'::regconfig, title) @@ to_tsquery('united & kingdom'::text)) ! ! Total runtime: 15.157 ms
- 59. Performance Summary with GIN • index build was much quicker • significant speedup from no index – (12,000ms => 15ms) • significant speedup from GiST – (181ms => 15ms) • Table size: 271MB • Index size: • 9.3: 71MB • 9.4 beta 1: 40MB 59
- 60. What Was Not Discussed • Word density – prior to 9.3, performance issues with greater word density • Type of text data – phrases vs paragraphs 60
- 61. Full Text Search – GiST vs GIN • Reads – overall, GIN should win • Writes – traditionally, GiST has better performance for writes – GIN • FASTUPDATE • 9.4: compression 61
- 62. Regular Expression Indexes • Added in 9.3 • Support for LIKE/ILIKE wildcard indexes in 9.1 – title LIKE '%ab%e' • Uses pg_trgm extension + GIN ! CREATE EXTENSION IF NOT EXISTS pg_trgm; CREATE INDEX category_title_regex_idx ON category USING GIN(title gin_trgm_ops); 62
- 63. Regular Expressions -‐ No Index 63 EXPLAIN ANALYZE SELECT title FROM category WHERE title ~ '(([iI]sland(s)?)|([pP]eninsula))$'; ! QUERY PLAN ---------- Seq Scan on category (cost=0.00..40144.55 rows=182 width=29) (actual time=2.509..4260.792 rows=5878 loops=1) Filter: (title ~ '(([iI]sland(s)?)|([pP]eninsula))$'::text) Rows Removed by Filter: 1817766 ! Total runtime: 4261.204 ms
- 64. Regular Expressions -‐ Indexed 64 CREATE INDEX category_title_regex_idx ON category USING gin(title gin_trgm_ops); ! EXPLAIN ANALYZE SELECT title FROM category WHERE title ~ '(([iI]sland(s)?)|([pP]eninsula))$'; QUERY PLAN ----------- Bitmap Heap Scan on category (cost=197.41..871.77 rows=182 width=29) (actual time=107.445..146.713 rows=5878 loops=1) Recheck Cond: (title ~ '(([iI]sland(s)?)|([pP]eninsula))$'::text) Rows Removed by Index Recheck: 4712 -> Bitmap Index Scan on category_title_regex_idx (cost=0.00..197.37 rows=182 width=0) (actual time=106.645..106.645 rows=10590 loops=1) Index Cond: (title ~ '(([iI]sland(s)?)|([pP]eninsula))$'::text) ! ! Total runtime: 147.026 ms
- 65. Range Types • stores range data – 1 to 6 – 2013-‐10-‐29 – 2013-‐11-‐2 • easy-‐to-‐use operators to check inclusion, overlaps • built-‐in types: integers, numerics, dates, timestamps • extensible 65
- 66. Range Type Examples 66 --find all ranges that overlap with [100, 200) ! SELECT * FROM ranges WHERE int4range(100, 200) && range; ! range ----------- [10,102) [13,102) [18,108) [32,101) [34,134) [37,123) [43,111) [46,132) [48,107) [results trunctated] !QUERY PLAN ----------- Seq Scan on ranges (cost=0.00..14239.86 rows=7073 width=32) (actual time=0.018..185.411 rows=143 loops Filter: ('[100,200)'::int4range && range) Rows Removed by Filter: 999857 ! Total runtime: 185.439 ms
- 67. Range Types + GiST 67 CREATE INDEX ranges_range_gist_idx ON ranges USING gist(range); ! EXPLAIN ANALYZE SELECT * FROM ranges WHERE int4range(100, 200) && range; ! QUERY PLAN ------------ Bitmap Heap Scan on ranges (cost=5.29..463.10 rows=130 width=13) (actual time=0.120..0.135 rows=144 loops=1) Recheck Cond: ('[100,200)'::int4range && range) -> Bitmap Index Scan on ranges_range_gist_idx (cost=0.00..5.26 rows=130 width=0) (actual time=0.109..0.109 rows=144 loops=1) Index Cond: ('[100,200)'::int4range && range) ! ! Total runtime: 0.168 ms
- 68. SP-‐GiST • space-‐partitioned generalized search tree • ideal for non-‐balanced data structures – k-‐d trees, quad-‐trees, suffix trees – divides search space into partitions of unequal size • matching partitioning rule = fast search • traditionally for "in-‐memory" transactions, converted to play nicely with I/O 68
- 69. Range Types: GiST vs SP-‐Gist CREATE TABLE ranges AS SELECT int4range( (random()*5)::int, (random()*5)::int + 5 ) AS range FROM generate_series(1,<N>) x; ! SELECT * FROM ranges WHERE range <operator> int4range(3,6); 69
- 70. N = 1,000,000 70 CREATE INDEX ranges_range_spgist_idx ON ranges USING spgist(range); ERROR: unexpected spgdoinsert() failure Fixed in 9.3.2 GiST Used GiST Time SP-Gist Used SP-GiST Time= Yes 121 Yes 37 && No 257 No 260 @> No 223 No 223 <@ Yes 163 Yes 111 << Yes 95 Yes 5 >> Yes 95 Yes 25 &< No 184 No 185 &> No 203 No 203
- 71. Range Types: GiST vs SP-‐GiST CREATE TABLE ranges AS SELECT int4range(x, x + (random()*5)::int + 5) AS range FROM generate_series(1,<N>) x; 71
- 72. N = 250,000 72 GiST Used GiST Time SP-‐GiST Used SP-‐GiST Time = Yes 0.5 Yes 0.7 && Yes 0.3 Yes 0.3 @> Yes 0.3 Yes 0.3 <@ Yes 0.06 Yes 0.25 << No 40 Yes 0.2 >> No 60 No 60 &< Yes 0.3 Yes 0.2 &> No 74 No 61
- 73. GiST vs SP-‐GiST: Space 73 GiST Clustered SP-‐GiST Clustered GiST Sparse SP-‐GiST Sparse 100K Size 6MB 5MB 6MB 11MB 100K Time 0.5s .4s 2.5s 7.8s 250K Size 15MB 12MB 15MB 28MB 250K Time 1.5s 1.1s 6.3s 47.2s 500K Size 30MB 25MB 30MB 55MB 500K Time 3.1s 3.0s 13.9s 192s 1MM Size 59MB 52MB! 60MB 110MB 1MM Time 5.1s 5.7s 29.2 777s
- 74. Integer Arrays 74 CREATE UNLOGGED TABLE int_arrays AS SELECT ARRAY[x, x + 1, x + 2] AS data FROM generate_series(1,1000000) x; ! CREATE INDEX int_arrays_data_idx ON int_arrays (data); ! CREATE INDEX int_arrays_data_gin_idx ON int_arrays USING GIN(data);
- 75. B-‐Tree(?) + Integer Arrays 75 EXPLAIN ANALYZE SELECT * FROM int_arrays WHERE 5432 = ANY (data); QUERY PLAN ----------- Seq Scan on int_arrays (cost=0.00..30834.00 rows=5000 width=33) (actual time=1.260..159.197 rows=3 loops=1) Filter: (5432 = ANY (data)) Rows Removed by Filter: 999997 ! Total runtime: 159.222 ms
- 76. GIN + Integer Arrays 76 EXPLAIN ANALYZE SELECT * FROM int_arrays WHERE ARRAY[5432] <@ data; QUERY PLAN ----------- Bitmap Heap Scan on int_arrays (cost=70.75..7680.14 rows=5000 width=33) (actual time=0.020..0.021 rows=3 loops=1) Recheck Cond: ('{5432}'::integer[] <@ data) -> Bitmap Index Scan on int_arrays_data_gin_idx (cost=0.00..69.50 rows=5000 width=0) (actual time=0.014..0.014 rows=3 loops=1) Index Cond: ('{5432}'::integer[] <@ data) ! Total runtime: 0.045 ms
- 77. Hash Indexes • only work with "=" operator • are still not WAL logged as of 9.4 beta 1 – not crash safe – not replicated 77
- 78. btree_gin 78 CREATE EXTENSION IF NOT EXISTS btree_gin; ! CREATE UNLOGGED TABLE numbers AS SELECT (random() * 2000)::int AS a FROM generate_series(1, 2000000) x; ! CREATE INDEX numbers_gin_idx ON numbers USING gin(a); ! EXPLAIN ANALYZE SELECT * FROM numbers WHERE a = 1000; ! QUERY PLAN ------------ Bitmap Heap Scan on numbers (cost=113.50..9509.26 rows=10000 width=4) (actual time=0.388..1.459 rows=991 loops=1) Recheck Cond: (a = 1000) -> Bitmap Index Scan on numbers_gin_idx (cost=0.00..111.00 rows=10000 width=0) (actual time=0.232..0.232 rows=991 loops=1) Index Cond: (a = 1000) ! Total runtime: 1.563 ms
- 79. btree_gin vs btree 79 -- btree SELECT pg_size_pretty(pg_total_relation_size('numbers_idx')); pg_size_pretty ---------------- 43 MB ! ! ! -- GIN SELECT pg_size_pretty(pg_total_relation_size('numbers_gin_idx')); pg_size_pretty ---------------- 16 MB • Only use GIN over btree if you have a lot of duplicate entries
- 80. hstore -‐ the PostgreSQL Key-‐Value Store 80 CREATE EXTENSION IF NOT EXISTS hstore; ! CREATE UNLOGGED TABLE keypairs AS SELECT (x || ' => ' || (x + (random() * 5)::int))::hstore AS data FROM generate_series(1,1000000) x; SELECT pg_size_pretty(pg_relation_size('keypairs')); ! ! SELECT * FROM keypairs WHERE data ? ‘3'; data ---------- "3"=>"4" ! EXPLAIN ANALYZE SELECT * FROM keypairs WHERE data ? ‘3'; QUERY PLAN ----------- Seq Scan on keypairs (cost=0.00..19135.06 rows=950 width=32) (actual time=0.065..208.808 rows=1 loops=1) Filter: (data ? '3'::text) Rows Removed by Filter: 999999 ! Total runtime: 208.825 ms
- 81. hstore -‐ the PostgreSQL Key-‐Value Store 81 CREATE INDEX keypairs_data_gin_idx ON keypairs USING gin(data); ! EXPLAIN ANALYZE SELECT * FROM keypairs WHERE data ? ‘3'; ! QUERY PLAN ----------- Bitmap Heap Scan on keypairs (cost=27.75..2775.66 rows=1000 width=24) (actual time=0.044..0.045 rows=1 loops=1) Recheck Cond: (data ? '3'::text) -> Bitmap Index Scan on keypairs_data_gin_idx (cost=0.00..27.50 rows=1000 width=0) (actual time=0.039..0.039 rows=1 loops=1) Index Cond: (data ? '3'::text) ! Total runtime: 0.071 ms
- 82. JSONB: Coming in 9.4 82 INSERT INTO documents SELECT row_to_json(ROW(x, x + 2, x + 3))::jsonb FROM generate_series(1,1000000) x; ! ! CREATE INDEX documents_data_gin_idx ON documents USING gin(data jsonb_path_ops); ! ! ! SELECT * FROM documents WHERE data @> '{ "f1": 10 }'; data -------------------------------- {"f1": 10, "f2": 12, "f3": 13} ! ! Execution time: 0.084 ms
- 83. Awesome vs WTF: A Note On Operator Indexability 83 EXPLAIN ANALYZE SELECT * FROM documents WHERE data @> '{ "f1": 10 }'; ! QUERY PLAN ----------- Bitmap Heap Scan on documents (cost=27.75..3082.65 rows=1000 width=66) (actual time=0.029..0.030 rows=1 loops=1) Recheck Cond: (data @> '{"f1": 10}'::jsonb) Heap Blocks: exact=1 -> Bitmap Index Scan on documents_data_gin_idx (cost=0.00..27.50 rows=1000 width=0) (actual time=0.014..0.014 rows=1 loops=1) Index Cond: (data @> '{"f1": 10}'::jsonb) ! Execution time: 0.084 ms ! EXPLAIN ANALYZE SELECT * FROM documents WHERE '{ "f1": 10 }' <@ data; ! QUERY PLAN ----------- Seq Scan on documents (cost=0.00..24846.00 rows=1000 width=66) (actual time=0.015..245.924 rows=1 loops=1) Filter: ('{"f1": 10}'::jsonb <@ data) Rows Removed by Filter: 999999 ! Execution time: 245.947 ms
- 84. For More Information… • http://www.postgresql.org/docs/current/static/ indexes.html • http://www.postgresql.org/docs/current/static/ gist.html • http://www.postgresql.org/docs/current/static/ gin.html • http://www.postgresql.org/docs/current/static/ spgist.html • GiST + GIN + Full Text Search: – http://www.postgresql.org/docs/current/static/textsearch-‐ indexes.html 84
- 85. Conclusion • Postgres has *a lot* of different types of indexes, and variations on each of its engines • Extensions make use of PostgreSQL indexes – PostGIS • Need to understand where index usage is appropriate in your application 85
- 86. Thanks! • http://nyc.pgconf.us • Twitter: @jkatz05 86
Editor's Notes
- #48: 001.sql