1. Performance Tuning MySQL
Highload++, 11 October 2009
Morgan Tocker, firstname at percona dot com
Director of Training, Percona Inc.

2. Introduction
• So you’ve written/inherited an application.
• Usage has gone crazy.
• And you’ve diagnosed that the database is the
bottleneck.
• So how do we fix it......

3. What this talk is about.
• We’re going to be talking about tuning a system
that’s never been shown love.
– There’s more than one strategy to do this.
– I’m going to show you the three common paths people
take.

4. The three ways people take:
• Option 1: Upgrade the Hardware.
• Option 2: Change a configuration setting in MySQL.
• Option 3: Improve Indexing/Tune Queries.

5. Option 1: Add Hardware
• This “Kind of” works... but for how long?
• There’s a price point where big machines get
exponentially more expensive.

6. Add Hardware (cont.)
• If you are going to do this, beware:
– Bigger Machines will have the same speed drives.
– Check what your bottleneck is first!

7. Add Hardware (cont.)
• Potential Bottlenecks:
DISK RAM
This is the number one bottleneck for Use it to relieve pressure off our
a lot of people. disks, particularly with expensive
random reads.
CPU NETWORK
Not normally a bottleneck, but having We care about round trips, but we’re
fast CPUs can mean locking code is not usually limited by throughput.
blocking for less time.

8. The memory problem

9. When this technique really works:
• You had good performance when all of your working
set[1] of data fitted in main memory.
• As your working set increases, you just increase the
size of memory[2].
[1] Working set can be between 1% and 100% of database size. On poorly
indexed systems it is often a higher percentage - but the real value depends on
what hotspots your data has.
[2] The economical maximum for main memory is currently 128GB. This can be
done for less than $10K with a Dell server.

10. Add Hardware (cont.)
Pros:
Good if you have a large working set or an “excess money”
problem.
Cons:
• Not as easy to get many multiples better performance.
• Can get expensive once you get past a price point of
hardware.
• Still some features missing in MySQL hurting the very top end
of users (save contents of buffer pool for warm restarts)
Conclusion:
I wouldn’t normally recommend this be your first optimization
path if the system hasn’t ever been tuned.

11. Option 2: Change Configuration
• The “--run-faster
startup option”.
• This may work, but it
assumes misconfigured
setting to start with.
• There are no silver
bullets.
“Silver Bullets” kill Werewolf (Оборотни)

12. Changing Configuration
★ Most of this is based off running the command SHOW
GLOBAL STATUS first, then analyzing the result.
★ Be careful when running this - the period of time it
aggregates data for may cause skew.
✦
A very simple utility called ‘mext’ solves this - http://
www.xaprb.com/mext
12

13. Temporary Tables on Disk
★ You may be able to change tmp_table_size and
max_heap_table_size to end up with to increase the
threshold.
Created_tmp_disk_tables 0 0 0
Created_tmp_files 5 0 0
Created_tmp_tables 12550 421 417
13

14. Temporary Tables on Disk (cont.)
★ These are often caused by some internal GROUP BYs
and complex joins with an ORDER BY that can’t use an
index.
★ They default to memory unless they grow too big, but...
★ All temporary tables with text/blob columns will be
created on disk regardless!
14

15. Binary Log cache
★ Updates are buffered before being written to the binary
log. If they’re too big, the buffer creates a temporary file
on disk:
★ mysql> show global status like 'binlog%';
+-----------------------+-------+
| Variable_name | Value |
+-----------------------+-------+
| Binlog_cache_disk_use | 1082 |
| Binlog_cache_use | 78328 |
+-----------------------+-------+
2 rows in set (0.00 sec)
★ Corresponding Session Variable:
binlog_cache_size
15

16. Sorting Data
★ mysql> show global status like 'sort%';
+-------------------+---------+
| Variable_name | Value |
+-------------------+---------+
| Sort_merge_passes | 9924 |
| Sort_range | 234234 |
| Sort_rows | 9438998 |
| Sort_scan | 24333 |
+-------------------+---------+
4 rows in set (0.00 sec)
★ Corresponding Session Variable:
sort_buffer_size
16

17. Sorting Data (cont.)
★ Caused by:
✦
ORDER BY (and not being able to use an index for sorting).
✦
GROUP BY (instead of GROUP BY c ORDER BY NULL).
★ sort_merge_passes is incremented every time the
internal sort algorithm has to loop over more than once to
sort.
✦
A small number is healthy - be careful not to over set the
sort_buffer_size.
✦
Sometimes I look at how many sort_merge_passes occur per
second (run SHOW GLOBAL STATUS more than once).
17

18. Query Cache
★ mysql> show global status like 'Qcache%';
+-------------------------+--------+
| Variable_name | Value |
+-------------------------+--------+
| Qcache_free_memory | 99812 |
| Qcache_hits | 210213 |
| Qcache_inserts | 82333 |
| Qcache_not_cached | 2032 |
| Qcache_queries_in_cache | 5322 |
+-------------------------+--------+
8 rows in set (0.00 sec)
18

19. Query Cache (cont.)
★ You really need to have at least as many hits as inserts,
but query cache discussion is not-that-simple a
discussion.
★ The query cache does not scale well on SMP machines.
✦
Unless you get large multiples of hits over inserts you may choose
to disable it with query_cache_type = 0
19

20. Table Locks
★ Some storage engines (MyISAM, Memory) have table
level locking. Under concurrency this can be a real
contention point:
★ mysql> show global status like 'table_locks%';
+-----------------------+-------+
| Variable_name | Value |
+-----------------------+-------+
| Table_locks_immediate | 52323 |
| Table_locks_waited | 3293 |
+-----------------------+-------+
2 rows in set (0.00 sec)
★ Tip: You really need to watch this one in particular in
mext. Locking problems tend to snowball.
20

21. Table Cache
★ MySQL requires a copy of a each table open per
connection. The default table_cache is lower than
the default max_connections!
★ mysql> show global status like 'Open%tables';
+---------------+--------+
| Variable_name | Value |
+---------------+--------+
| Open_tables | 64 |
| Opened_tables | 532432 |
+---------------+--------+
2 rows in set (0.00 sec)
★ Corresponding Global Variable:
table_cache_size
21

22. Thread Cache
★ Each connection in MySQL is a thread. You can
reduce Operating System thread creation/destruction
with a small thread_cache:
★ mysql> show global status like 'threads%';
+-------------------+-------+
| Variable_name | Value |
+-------------------+-------+
| Threads_cached | 16 |
| Threads_connected | 67 |
| Threads_created | 4141 |
| Threads_running | 6 |
+-------------------+-------+
4 rows in set (0.00 sec)
★ Corresponding Global Variable:
thread_cache_size
22

23. Max Connections
★ Seeing max_used_connections equal to
max_connections indicates that a connection was
likely refused at some point:
★ mysql> show global status like 'max%';
+----------------------+-------+
| Variable_name | Value |
+----------------------+-------+
| Max_used_connections | 401 |
+----------------------+-------+
1 row in set (0.00 sec)
★ Corresponding Global Variable:
max_connections
23

24. Cartesian Products?
★ Joining two tables without an index on either can often
mean you’re doing something wrong. You can see this if
Select_full_join > 0:
★ mysql> show global status like 'Select_full_join';
+------------------+-------+
| Variable_name | Value |
+------------------+-------+
| Select_full_join | 0 |
+------------------+-------+
1 row in set (0.00 sec)
24

25. InnoDB Buffer Pool
mysql> pager grep -B1 -A12 'BUFFER POOL AND MEMORY'
mysql> show innodb status;
----------------------
BUFFER POOL AND MEMORY
----------------------
Total memory allocated 1205671692; in additional pool
allocated 1029120
Buffer pool size 65536
Free buffers 56480
Database pages 8489
Modified db pages 0
Pending reads 0
Pending writes: LRU 0, flush list 0, single page 0
Pages read 8028, created 485, written 96654
0.00 reads/s, 0.00 creates/s, 0.00 writes/s
Buffer pool hit rate 1000 / 1000
--------------

26. InnoDB Log Buffer Size
mysql> show global status like 'innodb_log_waits';
+------------------+-------+
| Variable_name | Value |
+------------------+-------+
| Innodb_log_waits | 0 |
+------------------+-------+
1 row in set (0.00 sec)
Corresponding Global Variable:
innodb_log_buffer_size

27. Best way to review global status?
★ Trained Eye helps, but you still miss things sometimes.
★ Most of this can be automated. The tool I like the most
(for simplicity) is this one:
✦
Matthew Montgomery’s Tuning Primer:
http://forge.mysql.com/projects/project.php?id=44
27

28. Every setting has a range!
★ You really can have too much of a good thing.
★ It takes more resources to allocate larger chunks of
memory, and in some cases you’ll miss valuable CPU
caches.
★ We’ve blogged about this with sort_buffer_size
here:
✦
http://www.mysqlperformanceblog.com/2007/08/18/how-fast-
can-you-sort-data-with-mysql/
28

29. Change Configuration (cont.)
Pros:
Can get some quick wins, sometimes.
Cons:
Assumes a setting is misconfigured in the first place.
Over-tuning can cause negative effects. Try setting
your sort_buffer_size to 400M to find out how!
Conclusions:
Not a bad approach - since it is easy to apply without
changing your application.

30. Option 3: Add an index
• Should really be called “Add an index, or slightly
rewrite a query”.
• This is the least “fun” approach.
• It delivers the most value for money though!

31. The EXPLAIN Command
mysql> EXPLAIN SELECT Name FROM Country WHERE continent =
'Asia' AND population > 5000000 ORDER BY NameG
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: Country
type: ALL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: 239
Extra: Using where; Using filesort
1 row in set (0.00 sec)

32. Explain (cont.)
mysql> ALTER TABLE Country ADD INDEX p (Population);
Query OK, 239 rows affected (0.01 sec)
Records: 239 Duplicates: 0 Warnings: 0
mysql> EXPLAIN SELECT Name FROM Country WHERE Continent =
'Asia' AND population > 5000000 ORDER BY NameG
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: Country
type: ALL
possible_keys: p
key: NULL
key_len: NULL
ref: NULL
rows: 239
Extra: Using where; Using filesort
1 row in set (0.06 sec)

33. Now it is...
mysql> EXPLAIN SELECT Name FROM Country WHERE Continent = 'Asia'
AND population > 50000000 ORDER BY NameG
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: Country
type: range
possible_keys: p
key: p
key_len: 4
ref: NULL
rows: 54
Extra: Using where; Using filesort
1 row in set (0.00 sec)

34. Another Index..
mysql> ALTER TABLE Country ADD INDEX c (Continent);
Query OK, 239 rows affected (0.01 sec)
Records: 239 Duplicates: 0 Warnings: 0
mysql> EXPLAIN SELECT Name FROM Country WHERE Continent = 'Asia'
AND population > 50000000 ORDER BY NameG
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: Country
type: ref
possible_keys: p,c
key: c
key_len: 1
ref: const
rows: 42
Extra: Using where; Using filesort
1 row in set (0.01 sec)

35. Changes back to p at 500M!
mysql> EXPLAIN SELECT Name FROM Country WHERE Continent = 'Asia'
AND population > 500000000 ORDER BY NameG
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: Country
type: range
possible_keys: p,c
key: p
key_len: 4
ref: NULL
rows: 4
Extra: Using where; Using filesort
1 row in set (0.00 sec)

36. Try another index...
mysql> ALTER TABLE Country ADD INDEX p_c (Population, Continent);
Query OK, 239 rows affected (0.01 sec)
Records: 239 Duplicates: 0 Warnings: 0
mysql> EXPLAIN SELECT Name FROM Country WHERE Continent = 'Asia'
AND population > 50000000 ORDER BY NameG
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: Country
type: ref
possible_keys: p,c,p_c
key: c
key_len: 1
ref: const
rows: 42
Extra: Using where; Using filesort
1 row in set (0.01 sec)

37. How about this one?
mysql> ALTER TABLE Country ADD INDEX c_p (Continent,Population);
Query OK, 239 rows affected (0.01 sec)
Records: 239 Duplicates: 0 Warnings: 0
mysql> EXPLAIN SELECT Name FROM Country WHERE Continent = 'Asia'
AND population > 50000000 ORDER BY NameG
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: Country
type: range
possible_keys: p,c,p_c,c_p
key: c_p
key_len: 5
ref: NULL
rows: 7
Extra: Using where; Using filesort
1 row in set (0.00 sec)

38. The Best...
mysql> ALTER TABLE Country ADD INDEX c_p_n
(Continent,Population,Name);
Query OK, 239 rows affected (0.02 sec)
Records: 239 Duplicates: 0 Warnings: 0
mysql> EXPLAIN SELECT Name FROM Country WHERE Continent = 'Asia'
AND population > 50000000 ORDER BY NameG
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: Country
type: range
possible_keys: p,c,p_c,c_p,c_p_n
key: c_p_n
key_len: 5
ref: NULL
rows: 7
Extra: Using where; Using index; Using filesort
1 row in set (0.00 sec)

39. So what’s the end result?
• We’re looking at 9 rows, not the whole table.
– We’re returning those rows from the index - bypassing the
table.
• A simple example - but easy to demonstrate how to
reduce table scans.
• You wouldn’t add all these indexes - I’m just doing it
as a demonstration.
– Indexes (generally) hurt write performance.

40. Example 2: Join Analysis
mysql> EXPLAIN SELECT * FROM city WHERE countrycode IN
(SELECT code FROM country WHERE name='Australia')G
*************************** 1. row ***************************
id: 1
select_type: PRIMARY
table: city
type: ALL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: 4079
Extra: Using where
*************************** 2. row ***************************
id: 2
select_type: DEPENDENT SUBQUERY
table: country
type: unique_subquery
possible_keys: PRIMARY
key: PRIMARY
key_len: 3
ref: func
rows: 1
Extra: Using where

41. Join analysis (cont.)
mysql> EXPLAIN SELECT city.* FROM city, country WHERE
city.countrycode=country.code AND country.name='Australia'G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: city
type: ALL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: 4079
Extra:
*************************** 2. row ***************************
id: 1
select_type: SIMPLE
table: country
type: eq_ref
possible_keys: PRIMARY
key: PRIMARY
key_len: 3
ref: world.city.CountryCode
rows: 1
Extra: Using where

42. Try an index...
mysql> ALTER TABLE city ADD INDEX (countrycode);
Query OK, 4079 rows affected (0.03 sec)
Records: 4079 Duplicates: 0 Warnings: 0

43. Is that any better?
mysql> EXPLAIN SELECT city.* FROM city, country WHERE
city.countrycode=country.code AND country.name='Australia'G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: city
type: ALL
possible_keys: CountryCode
key: NULL
key_len: NULL
ref: NULL
rows: 4079
Extra:
*************************** 2. row ***************************
id: 1
select_type: SIMPLE
table: country
type: eq_ref
possible_keys: PRIMARY
key: PRIMARY
key_len: 3
ref: world.city.CountryCode
rows: 1
Extra: Using where
2 rows in set (0.01 sec)

44. Try Again
mysql> ALTER TABLE country ADD INDEX (name);
Query OK, 239 rows affected (0.01 sec)
Records: 239 Duplicates: 0 Warnings: 0

45. Looking good...
mysql> EXPLAIN SELECT city.* FROM city, country WHERE
city.countrycode=country.code AND country.name='Australia'G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: country
type: ref
possible_keys: PRIMARY,Name
key: Name
key_len: 52
ref: const
rows: 1
Extra: Using where
*************************** 2. row ***************************
id: 1
select_type: SIMPLE
table: city
type: ref
possible_keys: CountryCode
key: CountryCode
key_len: 3
ref: world.country.Code
rows: 18
Extra:
2 rows in set (0.00 sec)

46. My Advice
• Focus on components of the WHERE clause.
• The optimizer does cool things - don’t make
assumptions. For Example:
– EXPLAIN SELECT * FROM City WHERE id =
1810;
– EXPLAIN SELECT * FROM City WHERE id = 1810
LIMIT 1;
– EXPLAIN SELECT * FROM City WHERE id
BETWEEN 100 and 200;
– EXPLAIN SELECT * FROM City WHERE id >= 100
and id <= 200;

47. The answer...
mysql> EXPLAIN SELECT * FROM City WHERE id = 1810;
+----+-------------+-------+-------+---------------+---------+---------+-------+------+-------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+-------+---------------+---------+---------+-------+------+-------+
| 1 | SIMPLE | City | const | PRIMARY | PRIMARY | 4 | const | 1 | |
+----+-------------+-------+-------+---------------+---------+---------+-------+------+-------+
1 row in set (0.00 sec)
mysql> EXPLAIN SELECT * FROM City WHERE id = 1810 LIMIT 1;
+----+-------------+-------+-------+---------------+---------+---------+-------+------+-------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+-------+---------------+---------+---------+-------+------+-------+
| 1 | SIMPLE | City | const | PRIMARY | PRIMARY | 4 | const | 1 | |
+----+-------------+-------+-------+---------------+---------+---------+-------+------+-------+
1 row in set (0.00 sec)

48. The answer (2)
mysql> EXPLAIN SELECT * FROM City WHERE id BETWEEN 100 and 200;
+----+-------------+-------+-------+---------------+---------+---------+------+------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+-------+---------------+---------+---------+------+------+-------------+
| 1 | SIMPLE | City | range | PRIMARY | PRIMARY | 4 | NULL | 101 | Using where |
+----+-------------+-------+-------+---------------+---------+---------+------+------+-------------+
1 row in set (0.01 sec)
mysql> EXPLAIN SELECT * FROM City WHERE id >= 100 and id <= 200;
+----+-------------+-------+-------+---------------+---------+---------+------+------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+-------+---------------+---------+---------+------+------+-------------+
| 1 | SIMPLE | City | range | PRIMARY | PRIMARY | 4 | NULL | 101 | Using where |
+----+-------------+-------+-------+---------------+---------+---------+------+------+-------------+
1 row in set (0.00 sec)

49. More information
• http://dev.mysql.com/
EXPLAIN
• Some very good examples are also
in “High Performance MySQL” 2nd
Ed.

50. Add an index (conclusion)
Pros:
The biggest wins. Seriously.
Cons:
Takes a bit of time for analysis. If you need to rewrite
a query - you need to go inside the application (not
everyone can).
Conclusion:
My #1 Recommendation.

51. Finding bad queries
• MySQL has a feature called the slow query log.
• We can enable it, and then set the long_query_time
to zero[1] seconds to find a selection of our queries.
[1] Requires MySQL 5.1 or patches MySQL 5.0 release.

52. root@ubuntu:~# perl mk-query-digest /bench/mysqldata/ubuntu-slow.log
# 1461.1s user time, 39.2s system time, 22.20M rss, 57.52M vsz
# Overall: 7.26M total, 38 unique, 17.28k QPS, 18.88x concurrency ________
# total min max avg 95% stddev median
# Exec time 7929s 12us 918ms 1ms 4ms 10ms 138us
# Lock time 154s 0 17ms 21us 36us 33us 18us
# Rows sent 5.90M 0 246 0.85 0.99 6.71 0.99
# Rows exam 6.90M 0 495 1.00 0.99 13.48 0
# Time range 2009-09-13 17:26:54 to 2009-09-13 17:33:54
# bytes 765.14M 6 599 110.56 202.40 65.01 80.10
# Rows read 0 0 0 0 0 0 0
52

53. ..
# Query 1: 655.60 QPS, 4.28x concurrency, ID 0x813031B8BBC3B329 at byte 518466
# This item is included in the report because it matches --limit.
# pct total min max avg 95% stddev median
# Count 3 274698
# Exec time 22 1794s 12us 918ms 7ms 2ms 43ms 332us
# Lock time 0 0 0 0 0 0 0 0
# Rows sent 0 0 0 0 0 0 0 0
# Rows exam 0 0 0 0 0 0 0 0
# Users 1 [root]
# Hosts 1 localhost
# Databases 1 tpcc
# Time range 2009-09-13 17:26:55 to 2009-09-13 17:33:54
# bytes 0 1.57M 6 6 6 6 0 6
# Query_time distribution
# 1us
# 10us ###
# 100us ################################################################
# 1ms ##
# 10ms ##
# 100ms #
# 1s
# 10s+
commitG
53

54. ..
# Query 2: 2.05k QPS, 4.20x concurrency, ID 0x10BEBFE721A275F6 at byte 17398977
# This item is included in the report because it matches --limit.
# pct total min max avg 95% stddev median
# Count 11 859757
# Exec time 22 1758s 64us 812ms 2ms 9ms 9ms 224us
# Lock time 17 27s 13us 9ms 31us 44us 26us 28us
# Rows sent 0 0 0 0 0 0 0 0
# Rows exam 0 0 0 0 0 0 0 0
# Users 1 [root]
# Hosts 1 localhost
# Databases 1 tpcc
# Time range 2009-09-13 17:26:55 to 2009-09-13 17:33:54
# bytes 22 170.52M 192 213 207.97 202.40 0.58 202.40
# Query_time distribution
# 1us
# 10us #
# 100us ################################################################
# 1ms ############
# 10ms ###
# 100ms #
# 1s
# 10s+
# Tables
# SHOW TABLE STATUS FROM `tpcc` LIKE 'order_line'G
# SHOW CREATE TABLE `tpcc`.`order_line`G
INSERT INTO order_line (ol_o_id, ol_d_id, ol_w_id, ol_number, ol_i_id, ol_supply_w_id,
ol_quantity, ol_amount, ol_dist_info) VALUES (3669, 4, 65, 1, 6144, 38, 5, 286.943756103516,
'sRgq28BFdht7nemW14opejRj')G
54

55. ..
# Query 4: 2.05k QPS, 1.42x concurrency, ID 0x6E70441DF63ACD21 at byte 192769443
# This item is included in the report because it matches --limit.
# pct total min max avg 95% stddev median
# Count 11 859769
# Exec time 7 597s 67us 794ms 693us 467us 6ms 159us
# Lock time 12 19s 9us 10ms 21us 31us 25us 19us
# Rows sent 0 0 0 0 0 0 0 0
# Rows exam 0 0 0 0 0 0 0 0
# Users 1 [root]
# Hosts 1 localhost
# Databases 1 tpcc
# Time range 2009-09-13 17:26:55 to 2009-09-13 17:33:54
# bytes 7 56.36M 64 70 68.73 65.89 0.30 65.89
# Query_time distribution
# 1us
# 10us #
# 100us ################################################################
# 1ms #
# 10ms #
# 100ms #
# 1s
# 10s+
# Tables
# SHOW TABLE STATUS FROM `tpcc` LIKE 'stock'G
# SHOW CREATE TABLE `tpcc`.`stock`G
UPDATE stock SET s_quantity = 79 WHERE s_i_id = 89277 AND s_w_id = 51G
# Converted for EXPLAIN
# EXPLAIN
select s_quantity = 79 from stock where s_i_id = 89277 AND s_w_id = 51G
55

56. ..
# Rank Query ID Response time Calls R/Call Item
# ==== ================== ================ ======= ========== ====
# 1 0x813031B8BBC3B329 1793.7763 23.9% 274698 0.006530 COMMIT
# 2 0x10BEBFE721A275F6 1758.1369 23.5% 859757 0.002045 INSERT order_line
# 3 0xBD195A4F9D50914F 924.4553 12.3% 859770 0.001075 SELECT UPDATE stock
# 4 0x6E70441DF63ACD21 596.6281 8.0% 859769 0.000694 UPDATE stock
# 5 0x5E61FF668A8E8456 448.0148 6.0% 1709675 0.000262 SELECT stock
# 6 0x0C3504CBDCA1EC89 308.9468 4.1% 86102 0.003588 UPDATE customer
# 7 0xA0352AA54FDD5DF2 307.4916 4.1% 86103 0.003571 UPDATE order_line
# 8 0xFFDA79BA14F0A223 192.8587 2.6% 86122 0.002239 SELECT customer warehouse
# 9 0x0C3DA99DF6138EB1 191.9911 2.6% 86120 0.002229 SELECT UPDATE customer
# 10 0xBF40A4C7016F2BAE 109.6601 1.5% 860614 0.000127 SELECT item
# 11 0x8B2716B5B486F6AA 107.9319 1.4% 86120 0.001253 INSERT history
# 12 0x255C57D761A899A9 103.9751 1.4% 86120 0.001207 UPDATE warehouse
# 13 0xF078A9E73D7A8520 102.8506 1.4% 86120 0.001194 UPDATE district
# 14 0x9577D48F480A1260 91.3182 1.2% 56947 0.001604 SELECT customer
# 15 0xE5E8C12332AD11C5 87.2532 1.2% 86122 0.001013 SELECT UPDATE district
# 16 0x2276F0D2E8CC6E22 86.1945 1.1% 86122 0.001001 UPDATE district
# 17 0x9EB8F1110813B80D 83.1471 1.1% 86106 0.000966 UPDATE orders
# 18 0x0BF7CEAD5D1D2D7E 80.5878 1.1% 86122 0.000936 INSERT orders
# 19 0xAC36DBE122042A66 74.5417 1.0% 8612 0.008656 SELECT order_line
# 20 0xF8A4D3E71E066ABA 46.7978 0.6% 8612 0.005434 SELECT orders
56

57. Advanced mk-query-digest
• Query Review - the best feature ever.
• Saves the fingerprint of your slow query, and only
shows you what you haven’t already looked at:
$ mk-query-digest --review h=host1,D=test,t=query_review
/path/to/slow.log

58. Audience Question:
• How do you find unused indexes in MySQL?

59. Finding unused indexes (cont.)
• You have to come to my talk tomorrow for
the answer:
– 11 AM - Quick Wins with Third Party Patches

60. The Scoreboard
Option Effort Wins
Add Hardware
* 1/2
Tweak Settings
** **
Add an Index
*** *****

61. The End.
• Questions?
Photo Credits:
http://www.flickr.com/photos/7954439@N06/2535687572/