@afilina Speed Up Your Database 300 Times PHP Serbia - May 28, 2017 (Beginner Talk) 

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

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

What is Slow? 

Multitude of Queries • Loops:
 
 
 • Reduce number of queries. • Check for lazy loading in your ORM.
 foreach ($pictures as $id) {
 query_pic_details($id);
 } 

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

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

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

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 

Example Schema 200
 users 100
 albums each 100
 pictures each 2,000,000 pictures 

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; 

Why is it so Slow? 

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

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 

Indexes 

How do They Work? 

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); 

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 

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); 

Under the Hood • Details:
 
 
 
 • Total 200,000 rows scanned.
 table : album key : NULL rows : 20,000 table : picture key : album_id rows : 100 

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); 

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 

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 

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

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

Indexing Strings • Useful for LIKE searches. • InnoDB
 
 
 ALTER TABLE photo ADD FULLTEXT INDEX(description); 

Should We Optimize Further? 

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. 

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

Tips for Better Queries 

Functions • Avoid functions on an indexed column: • The index will be ignored here.
 SELECT id, title FROM picture
 WHERE YEAR(created_date) >= 2011; 

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; 

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); 

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 

Story 

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

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 | 

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 

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

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

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

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. 

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 

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

@afilina afilina.com