Speed Up Your Database 300 Times

Transcript

1. @afilina Speed Up Your Database 300 Times OSCON - May

   11, 2017

2. Objectives • What is slow? • Investigate performance issues. •

   Indexes. • Better queries. • Checklist.

3. Anna Filina • Project rescue expert • Dev, trainer, speaker

   • 1 company • 2 conferences

4. What is Slow?

5. Multitude of Queries • Loops:
 
 
 • Reduce number

   of queries. • Check for lazy loading in your ORM.
 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.
 echo $picture->album->user->name;

7. Multitude of Queries • Deeply nested code. • Try get_first_thumb

   instead.


8. Count Queries • Manual: add custom code before queries. •

   Auto: ORMs have listeners or data collectors.


9. Slow Query log • Open my.cnf
 
 
 • Queries

   taking 0.2s or longer will be logged. • Open the log file and analyze the queries. slow_query_log=ON long_query_time=0.2 slow_query_log_file=/path/to/file

10. Example Schema 200
 users 100
 albums each 100
 pictures each

    2,000,000 pictures

11. Simple Query • Get all pictures from user #1
 


    
 
 • 38 seconds. 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 • 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 • 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. How do They Work? Flip through all pages of an

    address book... ... or pull on the letter tab.

17. Add an Index • 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 • 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 • 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 • 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 • 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 • 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

24. Indexes Used • Primary and foreign keys make for great

    indexes. • Also whatever you use in JOIN, WHERE, ORDER BY.

25. Index Criteria • Change frequency • Selectivity: how many different

    values? • Good examples: ◦ user.id ◦ picture.date • Bad examples: ◦ user.is_active ◦ picture.views


26. Should We Optimize Further? We’re quite fast already.

27. Airport Customs • 5 customs officers, 1 min to process

    a traveller. • 5 travellers / min = fluid. • 6 travellers / min = queue slowly builds. • 1000 travellers / min = 3h wait if you arrive on 2nd minute.

28. Smaller Indexes • BIGINT as index is slower than TINYINT.

    • Use UNSIGNED to double the maximum value.

29. Tips for Better Queries

30. Functions • Avoid functions on an indexed column: • The

    index will be ignored here.
 SELECT id, title FROM picture
 WHERE YEAR(create_date) >= 2011;

31. Limit • 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 • 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 • 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

36. How Did I Find the Problem? • 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? • 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 • MySQL can partition your tables transparently. ◦

    Creates multiple files on disk. ◦ Shows data as a single table.


39. Archives • MySQL ARCHIVE storage engine: ◦ Doesn’t support UPDATE

    or DELETE ◦ Very fast for SELECT • You can archive old transactions, news, etc.


40. Split Databases • Horizontal partitioning, sharding. • Users 1-1000 and

    their data in database #1 • Users 1001-2000 and their data in database #2

41. Checklist ✓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

43. Even More Fun Stuff • Setup read slaves • Denormalize

    where appropriate • SSD hard drive

44. @afilina afilina.com