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.

Transcript

1. foolab.ca | @foolabca
   Speed up Your Database
   Tech4Africa - October 8, 2014
   

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2. Objectives
   • What is slow?
   • Indexes.
   • Better queries.
   • Checklist.
   2
   

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3. Anna Filina
   3
   • Developer
   • Problem solver
   • Coach
   • Advisor
   

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4. What is slow?
   

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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);
   }
   

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6. Lazy loading
   • Data loaded on demand.
   • Use LEFT JOIN to get all related data in one go.
   6
   echo $picture->album->user->name;
   

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7. Multitude of queries
   • Deeply nested code.
   • Try get_first_thumb instead.
   7
   

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8. Count queries
   • Manual: add custom code before queries.
   • Auto: ORMs (like Doctrine) have listeners.
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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
   

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10. Example schema
    10
    200
    users
    100
    albums each
    100
    pictures each
    2,000,000 total pics
    

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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;
    

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12. Why is it so slow?
    

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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 |
    +----+-------------+---------+------+---------------+------+---------+------+---------+--------------------------------+
    

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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
    

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15. Indexes
    

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16. Metaphor
    16
    Flip through all pages of a address book...
    ... or pull on the letter tab.
    

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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);
    

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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
    

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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);
    

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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
    

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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);
    

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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
    

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23. Indexes used
    23
    • Primary and foreign keys make for great indexes.
    • Also whatever you use in JOINs and WHEREs.
    

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24. Index criteria
    24
    • Change frequency
    • Selectivity: how many different values?
    • Good examples:
    • Bad examples:
    user.gender
    picture.views
    user.id
    picture.date
    

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25. Shall we optimize
    further?
    We’re quite fast already.
    

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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.
    

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27. Smaller indexes
    27
    • BIGINT as index is slower than TINYINT.
    • Use UNSIGNED to double the maximum value.
    

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28. Tips for better queries.
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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;
    

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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;
    

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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);
    

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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
    

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33. Split tables
    33
    • MySQL can partition your tables transparently.
    ◦ Creates multiple files on disk.
    ◦ Shows data as a single table.
    

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34. Archives
    34
    • MySQL ARCHIVE storage engine:
    ◦ Doesn’t support UPDATE or DELETE
    ◦ Very fast for SELECT
    • You can archive old transactions, news, etc.
    

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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
    

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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.
    

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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/
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38. Even more fun stuff
    38
    • Setup read slaves
    • Denormalize where appropriate
    • SSD hard drive
    

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39. Services
    • Development: PHP, JS, etc.
    • Fix problems: bugs, performance, etc.
    • Coaching & workshops.
    • Advisor: testing strategy, architecture, etc.
    39
    

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