Speed Up Your Database 300 Times

Are your queries slow? Learn how to speed them up through better SQL 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. @afilina
   Speed Up Your Database
   300 Times
   PHP Serbia - May 28, 2017
   (Beginner Talk)
   

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

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

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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.

   foreach ($pictures as $id) {

   query_pic_details($id);

   }
   

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6. Lazy Loading
   • Data loaded on demand.

   • Use JOIN to get all related data in one go.

   echo $picture->album->user->name;
   

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

   

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8. Count Queries
   • Manual: add custom code before queries.
   • Auto: ORMs have listeners or data
   collectors.

   

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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
   

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10. Example Schema
    200

    users
    100

    albums each
    100

    pictures each
    2,000,000 pictures
    

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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;
    

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12. Why is it so Slow?
    

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

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14. Explain
    • Details:

    

    

    

    • Because of the join,

    we’re scanning all albums for each
    picture.
    • 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. How do They Work?
    

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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);
    

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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
    

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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);
    

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20. Under the Hood
    • 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
    • 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
    • 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. 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
    

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24. Indexes Used
    • Primary and foreign keys make for great
    indexes.
    • Also whatever you use in JOIN, WHERE,
    ORDER BY.
    

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25. Index Criteria
    • Change frequency
    ◦ Good: picture.date
    ◦ Bad: picture.views
    • Selectivity: how many different values?
    ◦ Good: user.id
    ◦ Bad: user.is_active
    

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26. Indexing Strings
    • Useful for LIKE searches.
    • InnoDB

    

    

    ALTER TABLE photo ADD FULLTEXT INDEX(description);
    

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27. Should We Optimize
    Further?
    

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28. 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.
    

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

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30. Tips for Better
    Queries
    

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31. Functions
    • Avoid functions on an indexed column:
    • The index will be ignored here.

    SELECT id, title FROM picture

    WHERE YEAR(created_date) >= 2011;
    

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32. Limit
    • Now down to 0.0009 sec.
    • 100 times faster than the previous query.
    • 42,000 times faster than original.
    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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33. 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);
    

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34. 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
    

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35. Story
    

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36. Problem
    • New features released before sale
    • Index was not used
    • Creating too many tmp tables on disk
    • Slow queries
    • Long queue
    

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37. 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 |
    

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38. 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
    

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

    

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40. Archives
    • 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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41. Split Databases
    • Horizontal partitioning, sharding.
    • Users 1-1000 and their data in database
    #1
    • Users 1001-2000 and their data in
    database #2
    

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42. 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.
    ✓Different storage
    engines.
    ✓Split tables and
    databases.
    

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43. 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
    

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44. Even More Fun Stuff
    • Setup read slaves
    • Denormalize where appropriate
    ◦ picture.views
    • SSD hard drive
    

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45. @afilina afilina.com
    

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