Speed Up Your Database 300 Times

Are your queries slow? Learn how to speed them up through better SQL crafting and use of meaningful indices. You will understand what works well and what doesn’t, and will walk away with a checklist for faster databases. I expect that you will all be itching to analyze MySQL queries to see how much you can shave off.

Transcript

1. foolab.ca | @foolabca Speed Up Your Database 300 Times PHPBenelux - January 30, 2016
2. Objectives • What is slow? • Investigate performance issues. • Indexes. • Better queries. • Checklist. 2
3. Anna Filina • Developer • Problem solver • Teacher • Advisor • FooLab + ConFoo 3
4. What is slow?
5. Multitude of queries • Loops:
 
 
 • Reduce number of queries. • Check for lazy loading in your ORM.
 5 foreach ($pictures as $id) {
 query_pic_details($id); }
6. Lazy loading • Data loaded on demand.
 • Use JOIN to get all related data in one go.
 6 echo $picture->album->user->name;
7. Multitude of queries • Deeply nested code. • Try get_first_thumb instead.
 7
8. Count queries • Manual: add custom code before queries. • Auto: ORMs (like Doctrine) have listeners or data collectors.
 8
9. Slow query log • Open my.cnf
 
 
 • Queries taking 0.5s or longer will be logged. • Open the log file and analyze the queries. 9 slow_query_log=ON long_query_time=0.5 slow_query_log_file=/path/to/file
10. Example schema 10 200
 users 100
 albums each 100
 pictures each 2,000,000 pictures
11. Simple query • Get all pictures from user #1
 
 
 
 • 38 seconds. 11 SELECT picture.id, picture.title
 FROM picture
 LEFT JOIN album ON picture.album_id = album.id
 WHERE album.user_id = 1;
12. Why is it so slow?
13. Explain 13 • Prefix with EXPLAIN:
 
 
 
 • Each row = table scanned:
 
 
 EXPLAIN SELECT picture.id, picture.title
 FROM picture
 LEFT JOIN album ON picture.album_id = album.id
 WHERE album.user_id = 1; +----+-------------+---------+------+---------------+------+---------+------+---------+--------------------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+---------+------+---------------+------+---------+------+---------+--------------------------------+ | 1 | SIMPLE | album | ALL | NULL | NULL | NULL | NULL | 20000 | Using where | | 1 | SIMPLE | picture | ALL | NULL | NULL | NULL | NULL | 2000000 | Using where; Using join buffer | +----+-------------+---------+------+---------------+------+---------+------+---------+--------------------------------+
14. Explain 14 • Details:
 
 
 
 • Because of the join,
 we’re scanning all albums for each picture. • Total 40 billion rows scanned.
 table : album key : NULL rows : 20,000 table : picture key : NULL rows : 2,000,000
15. Indexes
16. Metaphor 16 Flip through all pages of a address book... ... or pull on the letter tab.
17. Add an index 17 • album.id
 • Same query down to 2.38 sec. • We just saved 36 sec on every request!
 ALTER TABLE album ADD PRIMARY KEY(id);
18. Under the hood 18 • Details:
 
 
 
 • With the index,
 we find our album right away. • Total 2,000,000 rows scanned.
 table : album key : PRIMARY rows : 1 table : picture key : NULL rows : 2,000,000
19. Add a better index 19 • picture.album_id
 
 • Now down to 0.12 sec. • 317 times faster than original. ALTER TABLE picture ADD INDEX(album_id);
20. Under the hood 20 • Details:
 
 
 
 • Total 200,000 rows scanned.
 table : album key : NULL rows : 20,000 table : picture key : album_id rows : 100
21. Add a better index 21 • album.user_id
 
 • Now down to 0.10 sec. • Gained another 17% in speed.
 ALTER TABLE album ADD INDEX(user_id);
22. Under the hood 22 • Details:
 
 
 
 • With the second index,
 we’re scanning 100 pics. • Total 10,000 rows scanned. table : album key : user_id rows : 100 table : picture key : album_id rows : 100
23. Don't index everything! There is an index on each field And all selects take long to yield. Try finding which ones you need, Remove the rest and feel the speed. -- Anna Filina 23
24. Indexes used 24 • Primary and foreign keys make for great indexes. • Also whatever you use in JOIN, WHERE, ORDER BY.
25. Index criteria 25 • Change frequency • Selectivity: how many different values? • Good examples: ◦ user.id ◦ picture.date • Bad examples: ◦ user.is_active ◦ picture.views

26. Shall we optimize further? We’re quite fast already.
27. Airport customs 27 • 5 customs officers, 1 min to process a traveller. • 5 travellers / min = fluid. • 20 travellers / min = queue slowly builds. • 1000 travellers / min = 3h wait if you arrive on 2nd minute.
28. Smaller indexes 28 • BIGINT as index is slower than TINYINT. • Use UNSIGNED to double the maximum value.
29. Tips for Better Queries
30. Functions 30 • Avoid functions on an indexed column: • The index will be ignored here.
 SELECT id, title FROM picture
 WHERE YEAR(create_date) >= 2011;
31. Limit 31 • Now down to 0.0009 sec. • 100 times faster than the last query. SELECT picture.id, picture.title
 FROM picture
 LEFT JOIN album ON picture.album.id = album.id
 WHERE album.user_id = 1
 LIMIT 25;
32. Other constraints 32 • Range:
 
 
 
 
 • Only makes sense if that column is indexed:
 
 
 SELECT picture.id, picture.title
 FROM picture
 LEFT JOIN album ON picture.album.id = album.id
 WHERE album.user_id = 1
 AND picture.id BETWEEN 26 AND 50; # 0.023 sec ALTER TABLE picture ADD INDEX(id);
33. Other constraints 33 • IN clause:
 
 
 
 
 
 
 
 
 
 SELECT picture.id, picture.title
 FROM picture
 LEFT JOIN album ON picture.album.id = album.id
 WHERE album.user_id = 1
 AND picture.id IN (15,26,29,32,45); # 0.048 sec
34. Story
35. Problem • New features released before sale • Index was not used • Creating too many tmp tables on disk • Slow queries • Long queue 35
36. How did I find the problem? 36 • Status:
 
 
 
 
 
 
 
 
 
 SHOW GLOBAL STATUS; +------------------------------------------+-------------+ | Variable_name | Value | +------------------------------------------+-------------+ | Aborted_clients | 0 | | Aborted_connects | 0 | | Binlog_cache_disk_use | 0 | | Binlog_cache_use | 0 | | Binlog_stmt_cache_disk_use | 0 | | Binlog_stmt_cache_use | 0 | | Bytes_received | 23378012 | | Bytes_sent | 2707328 |
37. How did I find the problem? 37 • Created_tmp_disk_tables ◦ Temporary tables created on order by, group by, etc. ◦ Stored on disk when can't fit in RAM ◦ Maybe too big resultset. ◦ Are we using index? • Handler_read_rnd_next ◦ Full or partial table scan
38. Split tables 38 • MySQL can partition your tables transparently. ◦ Creates multiple files on disk. ◦ Shows data as a single table.

39. Archives 39 • MySQL ARCHIVE storage engine: ◦ Doesn’t support UPDATE or DELETE ◦ Very fast for SELECT • You can archive old transactions, news, etc.

40. Split databases 40 • Horizontal partitioning, sharding. • Users 1-1000 and their data in database #1 • Users 1001-2000 and their data in database #2
41. Checklist 41 ✓Count queries. ✓Log the slow ones. ✓Use EXPLAIN and check number of rows scanned. ✓Try different indexes, remove unused ones. ✓Use small indexes, preferably numeric. ✓Don’t use functions on indexed columns. ✓Make sure indexes are used. ✓Limit number of results. ✓Split tables and databases.
42. Further reading • Storage engines: http://www.linux.org/article/view/an- introduction-to-mysql-storage-engines • Sharding: http://www.jurriaanpersyn.com/archives/ 2009/02/12/database-sharding-at-netlog-with-mysql- and-php/ • Optimization manual: http://dev.mysql.com/doc/refman/ 5.7/en/optimization.html 42
43. Even more fun stuff 43 • Setup read slaves • Denormalize where appropriate • SSD hard drive
44. Services • Development: PHP, JS, etc. • Fix problems: bugs, performance, etc. • Coaching & workshops. • Advisor: testing strategy, architecture, etc. 44
45. @afilina afilina.com joind.in