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Postgres Performance for Humans (Ancient City R...

Postgres Performance for Humans (Ancient City Ruby)

Craig Kerstiens

April 07, 2014
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  1. Postgres It might help to explain that the pronunciation is

    "post-gres" or! "post-gres-cue-ell", not "post-gray-something".! ! I heard people making this same mistake in presentations at this! past weekend's Postgres Anniversary Conference :-( Arguably,! the 1996 decision to call it PostgreSQL instead of reverting to! plain Postgres was the single worst mistake this project ever made.! It seems far too late to change now, though.! ! ! ! ! regards, tom lane!
  2. 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 NoSQL inside SQL Momentum
  3. Postgres Setup/Config On Amazon Use Heroku 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/
  4. 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)
  5. 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; ! ! !
  6. 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
  7. Rough guidelines Cache hit rate >= 99% ! Index hit

    rate >= 95% where on > 10,000 rows
  8. $ 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
  9. $ 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
  10. Sequential Scanning Record 1 Record 2 Record 3 Record 4

    Record 5 Record … Record 1 Record 2 Record 3 Record 4 Record 5 Record …
  11. Index Scans A-F G-L M-R S-Z G H I J

    K L Record 57 Record … Record …
  12. Sequential Scans Good for large reports ! Computing over lots

    of data (1k + rows) Index Scans Good for small results ! Most common queries in your app
  13. 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)
  14. 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
  15. 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
  16. 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
  17. 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)
  18. 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 ...
  19. 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
  20. total | avg | query --------+--------+------------------------- 295.76 | 10.13 |

    SELECT id FROM users... 219.13 | 80.24 | SELECT * FROM ... (2 rows) ! pg_stat_statements
  21. Indexes B-Tree Generalized Inverted Index (GIN) Generalized Search Tree (GIST)

    K Nearest Neighbors (KNN) Space Partitioned GIST (SP-GIST)
  22. Indexes B-Tree Generalized Inverted Index (GIN) Generalized Search Tree (GIST)

    K Nearest Neighbors (KNN) Space Partitioned GIST (SP-GIST) VODKA (Coming soon)
  23. Conditional > SELECT * FROM places; ! name | population

    ----------------------------------- ACMAR | 6055 ARAB | 13650
  24. Conditional > SELECT * FROM places WHERE population > 10000;

    ! name | population ----------------------------------- ARAB | 13650
  25. > SELECT * FROM places WHERE get_numeric('pop', data) > 10000;

    ! data ----------------------------------- {"city": "ARAB", "pop": 13650} Functional
  26. hstore CREATE EXTENSION hstore; CREATE TABLE users ( id integer

    NOT NULL, email character varying(255), data hstore, created_at timestamp without time zone, last_login timestamp without time zone );
  27. hstore INSERT INTO users VALUES ( 1, '[email protected]', 'sex =>

    "M", state => “California”', now(), now() ); !
  28. 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
  29. OLAP ! Whole other talk Disk IO is important Order

    on disk is helpful (pg-reorg) MPP solutions on top of Postgres Recap
  30. 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