Postgres Performance for Humans - PyCaribbean

Transcript

1. Postgres Performance
   for Humans @craigkerstiens
2. Shameless plugs http://www.postgresweekly.com http://www.craigkerstiens.com http://www.postgresguide.com http://www.postgresapp.com http://www.citusdata.com
3. Postgres - TLDR
4. Postgres - TLDR Datatypes Conditional Indexes Transactional DDL Foreign Data Wrappers Concurrent Index Creation Extensions Common Table Expressions Fast Column Addition Listen/Notify Table Inheritance Per Transaction sync replication Window functions JSONB Momentum
5. TLDR in a quote http://www.craigkerstiens.com/2012/04/30/why-postgres/ “It’s the emacs of databases”
6. OLTP vs OLAP
7. OLTP vs OLAP Web apps
8. OLTP vs OLAP BI/Reporting
9. Postgres Setup/Config On Amazon Use RDS, Heroku, Citus OR ‘postgresql when its not your dayjob’ Other clouds ‘postgresql when its not your dayjob’ Real hardware High performance PostgreSQL http://thebuild.com/blog/2012/06/04/postgresql-when-its-not-your-job-at-djangocon-europe/
11. Cache rules everything around me
12. Cache Hit Rate SELECT 'index hit rate' as name, (sum(idx_blks_hit) - sum(idx_blks_read)) / sum(idx_blks_hit + idx_blks_read) as ratio FROM pg_statio_user_indexes union all SELECT 'cache hit rate' as name, case sum(idx_blks_hit) when 0 then 'NaN'::numeric else to_char((sum(idx_blks_hit) - sum(idx_blks_read)) / sum(idx_blks_hit + idx_blks_read), '99.99')::numeric end as ratio FROM pg_statio_user_indexes)
13. Cache Hit Rate name | ratio ----------------+------------------------ cache hit rate | 0.99
14. Index Hit Rate SELECT relname, 100 * idx_scan / (seq_scan + idx_scan), n_live_tup FROM pg_stat_user_tables ORDER BY n_live_tup DESC;
15. Index Hit Rate relname | percent_of_times_index_used | rows_in_table ---------------------+-----------------------------+--------------- events | 0 | 669917 app_infos_user_info | 0 | 198218 app_infos | 50 | 175640 user_info | 3 | 46718 rollouts | 0 | 34078 favorites | 0 | 3059 schema_migrations | 0 | 2 authorizations | 0 | 0 delayed_jobs | 23 | 0
16. Rough guidelines Cache hit rate >= 99% Index hit rate >= 95% where on > 10,000 rows
17. Shortcuts
18. $ cat ~/.psqlrc \set ON_ERROR_ROLLBACK interactive -- automatically switch between extended and normal \x auto -- always show how long a query takes \timing \set show_slow_queries 'SELECT (total_time / 1000 / 60) as total_minutes, (total_time/calls) as average_time, query FROM pg_stat_statements ORDER BY 1 DESC LIMIT 100;' psql
19. $ cat ~/.psqlrc \set ON_ERROR_ROLLBACK interactive -- automatically switch between extended and normal \x auto -- always show how long a query takes \timing \set show_slow_queries 'SELECT (total_time / 1000 / 60) as total_minutes, (total_time/calls) as average_time, query FROM pg_stat_statements ORDER BY 1 DESC LIMIT 100;' psql
20. Understanding Specific Query Performance
21. Understanding Query Performance SELECT last_name FROM employees WHERE salary >= 50000;
22. Explain # EXPLAIN SELECT last_name FROM employees WHERE salary >= 50000; QUERY PLAN -------------------------------------------------- Seq Scan on employees (cost=0.00..35811.00 rows=1 width=6) Filter: (salary >= 50000) (3 rows)
23. Explain # EXPLAIN SELECT last_name FROM employees WHERE salary >= 50000; QUERY PLAN -------------------------------------------------- Seq Scan on employees width=6) Filter: (salary >= 50000) (3 rows) startup time max time rows return (cost=0.00..35811.00 rows=1
24. Explain Analyze # EXPLAIN ANALYZE SELECT last_name FROM employees WHERE salary >= 50000; QUERY PLAN -------------------------------------------------- Seq Scan on employees (cost=0.00..35811.00 rows=1 width=6) (actual time=2.401..295.247 rows=1428 loops=1) Filter: (salary >= 50000) Total runtime: 295.379 (3 rows) Filter: (salary >= 50000) (3 rows) startup time max time rows return actual time 2.401..295.247 rows=1428 295.379
25. Rough guidelines Page response times < 100 ms Common queries < 10ms Rare queries < 100ms
26. Explain Analyze # EXPLAIN ANALYZE SELECT last_name FROM employees WHERE salary >= 50000; QUERY PLAN -------------------------------------------------- Seq Scan on employees (cost=0.00..35811.00 rows=1 width=6) (actual time=2.401..295.247 rows=1428 loops=1) Filter: (salary >= 50000) Total runtime: 295.379 (3 rows) Filter: (salary >= 50000) (3 rows) startup time max time rows return actual time 2.401..295.247 rows=1428 295.379
27. # CREATE INDEX idx_emps ON employees (salary); Indexes!
28. Indexes! EXPLAIN ANALYZE SELECT last_name FROM employees WHERE salary >= 50000; QUERY PLAN -------------------------------------------------- Index Scan using idx_emps on employees (cost=0.00..8.49 rows=1 width=6) (actual time = 0.047..1.603 rows=1428 loops=1) Index Cond: (salary >= 50000) Total runtime: 1.771 ms (3 rows)
29. pg_stat_statements $ select * from pg_stat_statements where query ~ 'from users where email'; userid │ 16384 dbid │ 16388 query │ select * from users where email = ?; calls │ 2 total_time │ 0.000268 rows │ 2 shared_blks_hit │ 16 shared_blks_read │ 0 shared_blks_dirtied │ 0 shared_blks_written │ 0 local_blks_hit │ 0 local_blks_read │ 0 local_blks_dirtied │ 0 local_blks_written │ 0 ...
30. SELECT (total_time / 1000 / 60) as total, (total_time/calls) as avg, query FROM pg_stat_statements ORDER BY 1 DESC LIMIT 100; pg_stat_statements
31. total | avg | query --------+--------+------------------------- 295.76 | 10.13 | SELECT id FROM users... 219.13 | 80.24 | SELECT * FROM ... (2 rows) pg_stat_statements
32. Indexes
33. Indexes B-Tree Generalized Inverted Index (GIN) Generalized Search Tree (GIST) K Nearest Neighbors (KNN) Space Partitioned GIST (SP-GIST)
34. Indexes Which do I use?
35. BTree This is what you usually want
36. Generalized Inverted Index (GIN) Use with multiple values in 1 column Array/hStore
37. Generalized Search Tree (GIST) Full text search Shapes
38. Indexes B-Tree Generalized Inverted Index (GIN) Generalized Search Tree (GIST) K Nearest Neighbors (KNN) Space Partitioned GIST (SP-GIST) VODKA (Coming soon)
39. More indexes
40. Indexes Conditional Functional Concurrent creation
41. Conditional > SELECT * FROM places WHERE population > 10000; name | population ----------------------------------- ARAB | 13650
42. Conditional > CREATE INDEX idx_large_population ON places(name) where population > 10000;
43. Functional > SELECT * FROM places; data ----------------------------------- {"city": "ACMAR", "pop": 6055} {"city": "ARAB", "pop": 13650}
44. > SELECT * FROM places WHERE get_numeric('pop', data) > 10000; data ----------------------------------- {"city": "ARAB", "pop": 13650} Functional
45. > CREATE INDEX idx_large_population ON places(get_numeric('pop', data)); Functional
46. CREATE INDEX CONCURRENTLY ... roughly 2-3x slower Doesn’t lock table One more thing
47. hstore / JSON / JSONB
48. hstore / JSON / JSONB
49. JSONB CREATE TABLE users ( id integer NOT NULL, email character varying(255), data jsonb, created_at timestamp without time zone, last_login timestamp without time zone );
50. SELECT '{"id":1,"email": “craig.kerstiens@gmail.com",}'::jsonb; SELECT * FROM users WHERE data->’conference’ = ‘pycaribbean’; JSONB
51. JSONB Indexes work gin gist
52. Pooling
53. Application/Framework layer Stand alone daemon Options
54. pgbouncer pgpool PG options
55. Adding Cache
56. Increase cache with cash
57. Vertical scaling
58. Horizontal scaling Manually shard your data (don’t use schemas) Use something like Citus
59. Backups
60. Logical pg_dump can be human readable, is portable
61. Physical The bytes on disk Base backup
62. Logical Good across architectures Good for portability Has load on DB Works < 50 GB Physical More initial setup Less portability Limited load on system Use above 50 GB
63. Recap
64. OLAP Whole other talk Disk IO is important Order on disk is helpful (pg-reorg) MPP solutions on top of Postgres Recap
65. OLTP (webapps) Ensure bulk of data is cache Optimize overall query load with pg_stat_statements Efficient use of indexes When cache sucks, throw more at it Recap
66. Questions http://www.speakerdeck.com/u/ craigkerstiens/