Speed Up Your Database

Speed Up Your Database

Are your queries slow? Learn how to speed them up through better SQL crafting and use of meaningful indexes. You will understand what works well and what doesn't, and will walk away with a checklist for faster databases. Through examples and benchmarks, I will demonstrate how to go from almost a minute of SQL execution to less than a millisecond. I expect that you will all be itching to analyze queries to see how much you can shave off.

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Anna Filina

October 08, 2014
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  1. foolab.ca | @foolabca Speed up Your Database Tech4Africa - October

    8, 2014
  2. Objectives • What is slow? • Indexes. • Better queries.

    • Checklist. 2
  3. Anna Filina 3 • Developer • Problem solver • Coach

    • Advisor
  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 LEFT

    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. 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 total pics
  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. Indexes used 23 • Primary and foreign keys make for

    great indexes. • Also whatever you use in JOINs and WHEREs.
  24. Index criteria 24 • Change frequency • Selectivity: how many

    different values? • Good examples: • Bad examples: user.gender picture.views user.id picture.date
  25. Shall we optimize further? We’re quite fast already.

  26. Airport customs 26 • 5 customs officers, 1 min to

    process a traveler. • 5 travelers / min = fluid. • 20 travelers / min = queue slowly builds. • 1000 travellers / min = 3h wait if you arrive on 2nd minute.
  27. Smaller indexes 27 • BIGINT as index is slower than

    TINYINT. • Use UNSIGNED to double the maximum value.
  28. Tips for better queries. 28

  29. Functions 29 • Avoid functions on an indexed column: •

    The index will be ignored here. SELECT id, title FROM picture WHERE YEAR(create_date) >= 2011;
  30. Limit 30 • 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;
  31. Other constraints 31 • 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);
  32. Other constraints 32 • 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
  33. Split tables 33 • MySQL can partition your tables transparently.

    ◦ Creates multiple files on disk. ◦ Shows data as a single table.
  34. Archives 34 • MySQL ARCHIVE storage engine: ◦ Doesn’t support

    UPDATE or DELETE ◦ Very fast for SELECT • You can archive old transactions, news, etc.
  35. Split databases 35 • Horizontal partitionning, sharding. • Users 1-1000

    and their data in database #1 • Users 1001-2000 and their data in database #2
  36. Checklist 36 ✓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. ✓Limit number of results. ✓Split tables and databases.
  37. 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/ 37
  38. Even more fun stuff 38 • Setup read slaves •

    Denormalize where appropriate • SSD hard drive
  39. Services • Development: PHP, JS, etc. • Fix problems: bugs,

    performance, etc. • Coaching & workshops. • Advisor: testing strategy, architecture, etc. 39