10 things you might
not know about
MySQL
Jorge Bernal <jbernal@warp.es>
Version 0.1

1
Query cache

Before
mysql> SELECT AVG(Population) FROM City_huge;
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| AVG(Population) |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| 354359.9948 |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
1 row in set (0.58 sec)
mysql> SELECT AVG(Population) FROM City_huge;
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| AVG(Population) |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| 354359.9948 |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
1 row in set (0.56 sec)

After
mysql> SELECT AVG(Population) FROM City_huge;
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| AVG(Population) |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| 354359.9948 |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
1 row in set (0.56 sec)
mysql> SELECT AVG(Population) FROM City_huge;
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| AVG(Population) |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| 354359.9948 |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
1 row in set (0.00 sec)

The magic
SET global query_cache_size=8 * 1024 * 1024;
mysql> SHOW GLOBAL VARIABLES LIKE 'query_cache%';
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+
| Variable_name | Value |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+
| query_cache_limit | 1048576 |
| query_cache_min_res_unit | 4096 |
| query_cache_size | 8388608 |
| query_cache_type | ON |
| query_cache_wlock_invalidate | OFF |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+
5 rows in set (0.00 sec)
mysql> SHOW GLOBAL STATUS LIKE 'Qc%';
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+
| Variable_name | Value |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+
| Qcache_free_blocks | 1 |
| Qcache_free_memory | 8378312 |
| Qcache_hits | 1 |
| Qcache_inserts | 1 |
| Qcache_lowmem_prunes | 0 |
| Qcache_not_cached | 0 |
| Qcache_queries_in_cache | 1 |
| Qcache_total_blocks | 4 |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+
8 rows in set (0.00 sec)

2
Life beyond MyISAM/
InnoDB

Archive
mysql> SELECT ENGINE,
(INDEX_LENGTH+DATA_LENGTH) AS Size
FROM INFORMATION_SCHEMA.TABLES
WHERE TABLE_NAME = 'City_huge';
+‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐+
| ENGINE | Size |
+‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐+
| MyISAM | 63203585 |
+‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐+
mysql> SELECT ENGINE,
(INDEX_LENGTH+DATA_LENGTH) AS Size
FROM INFORMATION_SCHEMA.TABLES
WHERE TABLE_NAME = 'City_huge';
+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐+
| ENGINE | Size |
+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐+
| ARCHIVE | 13520399 |
+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐+

ARCHIVE MyISAM
80% compression

But...
• Only INSERT and SELECT
• No indexing

CSV
mysql> ALTER TABLE City ENGINE=CSV;
Query OK, 0 rows affected (0.05 sec)
Records: 0 Duplicates: 0 Warnings: 0
root@warhol:/usr/local/mysql/data/world$ head City.CSV
1,\"Kabul\",\"AFG\",\"Kabol\",1780000
2,\"Qandahar\",\"AFG\",\"Qandahar\",237500
3,\"Herat\",\"AFG\",\"Herat\",186800
4,\"Mazar‐e‐Sharif\",\"AFG\",\"Balkh\",127800
5,\"Amsterdam\",\"NLD\",\"Noord‐Holland\",731200
6,\"Rotterdam\",\"NLD\",\"Zuid‐Holland\",593321
7,\"Haag\",\"NLD\",\"Zuid‐Holland\",440900
8,\"Utrecht\",\"NLD\",\"Utrecht\",234323
9,\"Eindhoven\",\"NLD\",\"Noord‐Brabant\",201843
10,\"Tilburg\",\"NLD\",\"Noord‐Brabant\",193238

Merge
mysql> SHOW CREATE TABLE allweek\\G
[...]
Create Table: CREATE TABLE `allweek` (
`ID` int(11) NOT NULL DEFAULT '0',
`Name` char(35) NOT NULL DEFAULT '',
`CountryCode` char(3) NOT NULL DEFAULT '',
`District` char(20) NOT NULL DEFAULT '',
`Population` int(11) NOT NULL DEFAULT '0',
`modtime` datetime DEFAULT NULL,
KEY `modtime` (`modtime`)
) ENGINE=MRG_MyISAM DEFAULT CHARSET=latin1
UNION=(`monday`,`tuesday`,`wednesday`,`thursday`,`city
date`)
1 row in set (0.00 sec)

3
AUTO_INCREMENT
woes

mysql> CREATE TABLE t (
‐> id INT NOT NULL PRIMARY KEY AUTO_INCREMENT
‐> );
Query OK, 0 rows affected (0.00 sec)
mysql> INSERT INTO t (id)
‐> VALUES (NULL);
Query OK, 1 row affected (0.04 sec)
mysql> SELECT *
‐> FROM t
‐> WHERE id IS NULL;
+‐‐‐‐+
| id |
+‐‐‐‐+
| 1 |
+‐‐‐‐+

But...
• id is AUTO_INCREMENT, so it’s 1
• let’s run that again
mysql> CREATE TABLE t (
‐> id INT NOT NULL PRIMARY KEY AUTO_INCREMENT
‐> );
Query OK, 0 rows affected (0.00 sec)
mysql> INSERT INTO t (id)
‐> VALUES (NULL);
Query OK, 1 row affected (0.04 sec)
mysql> SELECT *
‐> FROM t
‐> WHERE id IS NULL;
+‐‐‐‐+
| id |
+‐‐‐‐+
| 1 |
+‐‐‐‐+

mysql> CREATE TABLE t (
‐> id INT NOT NULL PRIMARY KEY AUTO_INCREMENT
‐> );
Query OK, 0 rows affected (0.00 sec)
mysql> INSERT INTO t (id)
‐> VALUES (NULL);
Query OK, 1 row affected (0.04 sec)
mysql> SELECT *
‐> FROM t
‐> WHERE id IS NULL;
+‐‐‐‐+
| id |
+‐‐‐‐+
| 1 |
+‐‐‐‐+
mysql> SELECT *
‐> FROM t
‐> WHERE id IS NULL;
Empty set (0.00 sec)
mysql> SELECT * FROM t;
+‐‐‐‐+
| id |
+‐‐‐‐+
| 1 |
+‐‐‐‐+
1 row in set (0.00 sec)

WTF???
• WHERE ID IS NULL acts as
WHERE ID = LAST_ISERT_ID()
• But only the first time
• Brought to you by some weird ODBC
compatibility decision

4
How to get random
data

SELECT name
FROM random
ORDER BY RAND()
LIMIT 1

X
SELECT name
FROM random
ORDER BY RAND()
LIMIT 1
Wrong!

There is a better way
SELECT name
FROM random JOIN
(
SELECT CEIL(
RAND() *
(SELECT MAX(id) FROM random))
AS id
) AS r2
USING (id);

Really?

Really?
ORDER BY RAND() Subquery
10.000,00
1.000,00
100,00
Seconds
10,00
1,00
0,10
100 1000 10000 100000
Table Size

So...
Every time you use ORDER BY RAND()...
God kills a kitten
Please, think of the kittens

5
Prefix indexes

There’s no need to
index the whole
column

Name is CHAR(52)
mysql> SHOW CREATE TABLE Country\\G
*************************** 1. row ***************************
Table: Country
Create Table: CREATE TABLE `Country` (
`Code` char(3) NOT NULL DEFAULT '',
`Name` char(52) NOT NULL DEFAULT '',
`Continent` enum('Asia','Europe','North
America','Africa','Oceania','Antarctica','South America') NOT NULL
DEFAULT 'Asia',
`Region` char(26) NOT NULL DEFAULT '',
`SurfaceArea` float(10,2) NOT NULL DEFAULT '0.00',
`IndepYear` smallint(6) DEFAULT NULL,
`Population` int(11) NOT NULL DEFAULT '0',
`LifeExpectancy` float(3,1) DEFAULT NULL,
`GNP` float(10,2) DEFAULT NULL,
`GNPOld` float(10,2) DEFAULT NULL,
`LocalName` char(45) NOT NULL DEFAULT '',
`GovernmentForm` char(45) NOT NULL DEFAULT '',
`HeadOfState` char(60) DEFAULT NULL,
`Capital` int(11) DEFAULT NULL,
`Code2` char(2) NOT NULL DEFAULT '',
PRIMARY KEY (`Code`)
) ENGINE=MyISAM DEFAULT CHARSET=latin1
1 row in set (0.02 sec)

mysql> SELECT COUNT(*) AS Total, COUNT(DISTINCT Name) AS Diff, COUNT(*)
‐ COUNT(DISTINCT Name) AS Dupes FROM Country;
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Total | Diff | Dupes |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| 239 | 239 | 0 |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
1 row in set (1.04 sec)
mysql> SELECT COUNT(*) AS Total, COUNT(DISTINCT LEFT(Name,50)) AS Diff,
COUNT(*) ‐ COUNT(DISTINCT LEFT(Name,50)) AS Dupes FROM Country;
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Total | Diff | Dupes |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| 239 | 239 | 0 |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
1 row in set (2.02 sec)
mysql> SELECT COUNT(*) AS Total, COUNT(DISTINCT LEFT(Name,20)) AS Diff,
COUNT(*) ‐ COUNT(DISTINCT LEFT(Name,20)) AS Dupes FROM Country;
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Total | Diff | Dupes |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| 239 | 239 | 0 |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
1 row in set (0.93 sec)

mysql> SELECT COUNT(*) AS Total, COUNT(DISTINCT Name) AS Diff, COUNT(*)
‐ COUNT(DISTINCT Name) AS Dupes FROM Country;
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Total | Diff | Dupes |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| 239 | 239 | 0 |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
1 row in set (1.04 sec)
mysql> SELECT COUNT(*) AS Total, COUNT(DISTINCT LEFT(Name,50)) AS Diff,
COUNT(*) ‐ COUNT(DISTINCT LEFT(Name,50)) AS Dupes FROM Country;
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Total | Diff | Dupes |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| 239 | 239 | 0 |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
1 row in set (2.02 sec)
mysql> SELECT COUNT(*) AS Total, COUNT(DISTINCT LEFT(Name,20)) AS Diff,
COUNT(*) ‐ COUNT(DISTINCT LEFT(Name,20)) AS Dupes FROM Country;
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Total | Diff | Dupes |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
Got it? Cool! Now...
| 239 | 239 | 0 |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
1 row in set (0.93 sec)

There is a better way
CREATE DEFINER=`root`@`localhost` PROCEDURE `pref_index`(t_name CHAR(255), c_name CHAR(255))
BEGIN
DECLARE plength INT DEFAULT 1;
SET @q = CONCAT('SELECT CHARACTER_MAXIMUM_LENGTH INTO @maxlen
FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_SCHEMA = \"world2\" AND
TABLE_NAME = \"',
t_name,'\" AND
COLUMN_NAME = \"',
c_name, '\"');
PREPARE q FROM @q;
EXECUTE q;
DEALLOCATE PREPARE q;
REPEAT
SET @qq = CONCAT('SELECT COUNT(*) ‐ COUNT(DISTINCT ',c_name,')
into @dupe FROM ',t_name);
SET @pq = CONCAT('SELECT COUNT(*) ‐ COUNT(DISTINCT LEFT(',c_name,',',plength,'))
into @pdupe FROM ',t_name);
PREPARE qs FROM @qq;
EXECUTE qs;
DEALLOCATE PREPARE qs;
PREPARE ps FROM @pq;
EXECUTE ps;
DEALLOCATE PREPARE ps;
SET plength = plength + 1;
UNTIL plength >= @maxlen OR @pdupe = @dupe
END REPEAT;
SELECT plength, @pdupe, @dupe;
END

mysql> CALL pref_index('Country', 'Name');
+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| plength | @pdupe | @dupe |
+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| 18 | 0 | 0 |
+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
1 row in set (1.18 sec)
Query OK, 0 rows affected (1.18 sec)
mysql> SELECT COUNT(*) AS Total, COUNT(DISTINCT LEFT(Name,18)) AS
Diff, COUNT(*) ‐ COUNT(DISTINCT LEFT(Name,18)) AS Dupes FROM Country;
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Total | Diff | Dupes |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| 239 | 239 | 0 |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
1 row in set (0.00 sec)
mysql> ALTER TABLE Country ADD KEY (Name(18));
Query OK, 239 rows affected (1.48 sec)
Records: 239 Duplicates: 0 Warnings: 0

mysql> CALL pref_index('Country', 'Name');
+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| plength | @pdupe | @dupe |
+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| 18 | 0 | 0 |
+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
1 row in set (1.18 sec)
Query OK, 0 rows affected (1.18 sec)
mysql> SELECT COUNT(*) AS Total, COUNT(DISTINCT LEFT(Name,18)) AS
Diff, COUNT(*) ‐ COUNT(DISTINCT LEFT(Name,18)) AS Dupes FROM Country;
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Total | Diff | Dupes |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| 239 | 239 | 0 |
We just saved 34 bytes per row!
+‐‐‐‐‐‐‐+‐‐‐‐‐‐+‐‐‐‐‐‐‐+
1 row in set (0.00 sec)
mysql> ALTER TABLE Country ADD KEY (Name(18));
Query OK, 239 rows affected (1.48 sec)
Records: 239 Duplicates: 0 Warnings: 0

6
InnoDB clustered index

Data stored in PK order
Primary key Index
Leaf nodes
Indexes point to PK instead of actual data

So what?

Choose your PK
wisely

Load data in order
mysql> load data infile '/tmp/city_order.txt' into
table City_huge;
Query OK, 818027 rows affected (15.86 sec)
Records: 818027 Deleted: 0 Skipped: 0 Warnings: 0
mysql> load data infile '/tmp/city_rand.txt' into
table City_huge;
Query OK, 818027 rows affected (33 min 23.57 sec)
Records: 818027 Deleted: 0 Skipped: 0 Warnings: 0

7
Profiling

mysql> select * from v_client_portfolio_high;
+‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| client_id | client_first_name | client_last_name | portfolio_value |
+‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| 5 | ABNER | ROSSELLETT | 1252115.50 |
| 500 | CANDICE | BARTLETT | 1384877.50 |
+‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
2 rows in set (4.01 sec)

mysql> select * from v_client_portfolio_high;
+‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| client_id | client_first_name | client_last_name | portfolio_value |
+‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| 5 | ABNER | ROSSELLETT | 1252115.50 |
| 500 | CANDICE | BARTLETT | 1384877.50 |
+‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
W hy?
2 rows in set (4.01 sec)

mysql> set profiling=1;
Query OK, 0 rows affected (0.00 sec)
mysql> select * from v_client_portfolio_high;
+‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| client_id | client_first_name | client_last_name | portfolio_value |
+‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| 5 | ABNER | ROSSELLETT | 1252115.50 |
| 500 | CANDICE | BARTLETT | 1384877.50 |
+‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
2 rows in set (4.01 sec)
mysql> show profiles;
+‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| Query_ID | Duration | Query |
+‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+
| 1 | 0.00007600 | set profiling=1 |
| 2 | 4.01965600 | select * from v_client_portfolio_high |
+‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+

mysql> select min(seq) seq,state,count(*) numb_ops,
‐> round(sum(duration),5) sum_dur, round(avg(duration),5) avg_dur,
‐> round(sum(cpu_user),5) sum_cpu, round(avg(cpu_user),5) avg_cpu
‐> from information_schema.profiling
‐> where query_id = 2
‐> group by state
‐> order by seq;
+‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+
| seq | state | numb_ops | sum_dur | avg_dur | sum_cpu | avg_cpu |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+
| 0 | (initialization) | 1 | 0.00004 | 0.00004 | 0.00000 | 0.00000 |
| 1 | Opening tables | 1 | 0.00023 | 0.00023 | 0.00000 | 0.00000 |
| 2 | System lock | 1 | 0.00001 | 0.00001 | 0.00000 | 0.00000 |
| 3 | Table lock | 1 | 0.00001 | 0.00001 | 0.00000 | 0.00000 |
| 4 | checking permissions | 1 | 0.00010 | 0.00010 | 0.00000 | 0.00000 |
| 5 | optimizing | 4 | 0.00004 | 0.00001 | 0.00000 | 0.00000 |
| 6 | statistics | 4 | 0.00007 | 0.00002 | 0.00100 | 0.00025 |
| 7 | preparing | 4 | 0.00005 | 0.00001 | 0.00000 | 0.00000 |
| 8 | Creating tmp table | 1 | 0.00003 | 0.00003 | 0.00000 | 0.00000 |
| 9 | executing | 37352 | 0.16631 | 0.00000 | 0.05899 | 0.00000 |
| 10 | Copying to tmp table | 1 | 0.00006 | 0.00006 | 0.00000 | 0.00000 |
| 15 | Sending data | 37353 | 3.85151 | 0.00010 | 3.72943 | 0.00010 |
| 74717 | Sorting result | 1 | 0.00112 | 0.00112 | 0.00100 | 0.00100 |
| 74719 | removing tmp table | 2 | 0.00003 | 0.00001 | 0.00000 | 0.00000 |
| 74721 | init | 1 | 0.00002 | 0.00002 | 0.00000 | 0.00000 |
| 74727 | end | 1 | 0.00001 | 0.00001 | 0.00000 | 0.00000 |
| 74728 | query end | 1 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| 74729 | freeing items | 1 | 0.00002 | 0.00002 | 0.00000 | 0.00000 |
| 74730 | closing tables | 2 | 0.00001 | 0.00001 | 0.00000 | 0.00000 |
| 74733 | logging slow query | 1 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
+‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐‐‐+

But...
• Only in 5.0
• 5.1 implementation under review
• http://forge.mysql.com/wiki/
Testing_Show_Profiles_5_1

8
Usage statistics
Know what’s going on

SHOW STATUS
mysql> SHOW STATUS LIKE 'Com_select';
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Variable_name | Value |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Com_select | 13 |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
1 row in set (1.65 sec)
mysql> SELECT 1;
+‐‐‐+
| 1 |
+‐‐‐+
| 1 |
+‐‐‐+
1 row in set (0.00 sec)
mysql> SHOW STATUS LIKE 'Com_select';
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Variable_name | Value |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Com_select | 14 |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
1 row in set (0.00 sec)

SHOW STATUS
mysql> SHOW STATUS LIKE 'Com_select';
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Variable_name | Value |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Com_select | 14 |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
1 row in set (0.00 sec)
mysql> FLUSH STATUS;
Query OK, 0 rows affected (0.00 sec)
mysql> SHOW STATUS LIKE 'Com_select';
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Variable_name | Value |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
| Com_select | 0 |
+‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐+‐‐‐‐‐‐‐+
1 row in set (0.00 sec)

External tools
• mysqlsla
http://hackmysql.com/mysqlsla
mysqlsla parses, filters, analyzes and
sorts MySQL slow, general, binary and
microslow patched logs in order to
create a customizable report of the
queries and their meta-property values.

External tools
• mysqlidxchk
http://hackmysql.com/mysqlidxchk
mysqlidxchk (MySQL Index Checker)
checks MySQL databases/tables for
unused indexes. Given one or more
slow, general, or \"raw\" log files,
mysqlidxchk reports which indexes in
the database schema are not used by
the queries in the log files.

9
Understanding
REPLACE

mysql> CREATE TABLE fk_relations (
‐> key1 INT NOT NULL PRIMARY KEY,
‐> key2 INT NOT NULL UNIQUE
‐> );
Query OK, 0 rows affected (0.02 sec)
mysql> INSERT INTO fk_relations VALUES (1,1), (2,2);
Query OK, 2 rows affected (0.00 sec)
Records: 2 Duplicates: 0 Warnings: 0
mysql> SELECT * FROM fk_relations;
+‐‐‐‐‐‐+‐‐‐‐‐‐+
| key1 | key2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
| 1 | 1 |
| 2 | 2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
2 rows in set (0.00 sec)

mysql> REPLACE INTO fk_relations VALUES (1,3);
Query OK, 2 rows affected (0.03 sec)

mysql> REPLACE INTO fk_relations VALUES (1,3);
Query OK, 2 rows affected (0.03 sec)
mysql> SELECT * FROM fk_relations;
+‐‐‐‐‐‐+‐‐‐‐‐‐+
| key1 | key2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
| 1 | 3 |
| 2 | 2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
2 rows in set (0.00 sec)
mysql> REPLACE INTO fk_relations VALUES (1,2);
Query OK, 3 rows affected (0.06 sec)
???

mysql> REPLACE INTO fk_relations VALUES (1,2);
Query OK, 3 rows affected (0.06 sec)
mysql> SELECT * FROM fk_relations;
+‐‐‐‐‐‐+‐‐‐‐‐‐+
| key1 | key2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
| 1 | 2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
1 row in set (0.00 sec)

+‐‐‐‐‐‐+‐‐‐‐‐‐+
| key1 | key2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
| 1 | 3 |
| 2 | 2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
mysql> REPLACE INTO fk_relations VALUES (1,2);
‐‐ Equals...

+‐‐‐‐‐‐+‐‐‐‐‐‐+
| key1 | key2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
| 1 | 3 |
| 2 | 2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
mysql> REPLACE INTO fk_relations VALUES (1,2);
‐‐ Equals...
‐‐ key1 is PK
mysql> DELETE FROM fk_relations WHERE key1 = 1;

+‐‐‐‐‐‐+‐‐‐‐‐‐+
| key1 | key2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
| 2 | 2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
mysql> REPLACE INTO fk_relations VALUES (1,2);
‐‐ Equals...
‐‐ key1 is PK
mysql> DELETE FROM fk_relations WHERE key1 = 1;
‐‐ key2 is Unique
mysql> DELETE FROM fk_relations WHERE key2 = 2;

+‐‐‐‐‐‐+‐‐‐‐‐‐+
| key1 | key2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
| 1 | 2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
mysql> REPLACE INTO fk_relations VALUES (1,2);
‐‐ Equals...
‐‐ key1 is PK
mysql> DELETE FROM fk_relations WHERE key1 = 1;
‐‐ key2 is Unique
mysql> DELETE FROM fk_relations WHERE key2 = 2;
mysql> INSERT INTO fk_relations VALUES (1,2);

That’s why
mysql> REPLACE INTO fk_relations VALUES (1,2);
Query OK, 3 rows affected (0.06 sec)
mysql> SELECT * FROM fk_relations;
+‐‐‐‐‐‐+‐‐‐‐‐‐+
| key1 | key2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
| 1 | 2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
1 row in set (0.00 sec)

That’s why
mysql> REPLACE INTO fk_relations VALUES (1,2);
Query OK, 3 rows affected (0.06 sec)
mysql> SELECT * FROM fk_relations;
+‐‐‐‐‐‐+‐‐‐‐‐‐+ + 1 insert
2 deletes
| key1 | key2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
| 1 | 2 |
+‐‐‐‐‐‐+‐‐‐‐‐‐+
1 row in set (0.00 sec)

10
The good and bad
about temporary tables

Performance
• Be careful with complex queries
• Extremely slow on large datasets
• Use summary tables instead

Too complex
select d.dept_name, SUM(salary) from departments d
LEFT JOIN dept_emp de USING (dept_no) LEFT JOIN
salaries s USING (emp_no) where s.from_date >=
'2000‐01‐01' and s.to_date < '2001‐01‐01' group by
dept_no;

Instead, split in two
select d.dept_name, SUM(salary) from departments d
LEFT JOIN dept_emp de USING (dept_no) LEFT JOIN
salaries s USING (emp_no) where s.from_date >=
'2000‐01‐01' and s.to_date < '2001‐01‐01' group by
dept_no;
CREATE TEMPORARY TABLE salaries2000
SELECT * FROM salaries s
WHERE s.from_date >= '2000‐01‐01'
AND s.to_date < '2001‐01‐01';
SELECT d.dept_name, SUM(salary)
FROM departments d
LEFT JOIN dept_emp de USING (dept_no)
LEFT JOIN salaries2000 s USING (emp_no)
GROUP BY dept_no;

Big performance gain
(If indexes are in their place)

Big performance gain
(If indexes are in their place)
I’ve seen 10X!

one more thing...

one more thing...
I had to do it ;-)

Shit happens
mysql> select * from t;
+‐‐‐‐‐‐+
| id |
+‐‐‐‐‐‐+
| 2 |
+‐‐‐‐‐‐+
1 row in set (0.00 sec)
mysql> drop table t;
Query OK, 0 rows affected (0.04 sec)
WTF?!?
mysql> drop table t;
Query OK, 0 rows affected (0.00 sec)
mysql> drop table t;
ERROR 1051 (42S02): Unknown table 't'

Some seconds ago...

mysql> select * from t;
+‐‐‐‐+
| id |
+‐‐‐‐+
| 1 |
+‐‐‐‐+
1 row in set (0.00 sec)
WTF?!?
mysql> create temporary table t(id int);
...again
Query OK, 0 rows affected (0.08 sec)
mysql> select * from t;
Empty set (0.00 sec)
mysql> insert into t values (2);
Query OK, 1 row affected (0.05 sec)
mysql> select * from t;
+‐‐‐‐‐‐+
| id |
+‐‐‐‐‐‐+
| 2 |
+‐‐‐‐‐‐+
1 row in set (0.00 sec)

• Temporary tables exist per session
• Be careful using connection pools
• They overlap current tables

There’s a reason for everything
mysql> select * from t;
+‐‐‐‐‐‐+
| id |
+‐‐‐‐‐‐+
| 2 |
+‐‐‐‐‐‐+
1 row in set (0.00 sec)
temporary
mysql> drop table t;
Query OK, 0 rows affected (0.04 sec)
mysql> select * from t;
+‐‐‐‐+
| id |
+‐‐‐‐+
| 1 |
+‐‐‐‐+
regular
1 row in set (0.00 sec)
mysql> drop table t;
Query OK, 0 rows affected (0.00 sec)
mysql> drop table t;
ERROR 1051 (42S02): Unknown table 't'

What else?
• MySQL Proxy
https://launchpad.net/mysql-proxy
• MySQL Sandbox
https://launchpad.net/mysql-sandbox
• MySQL Random Query Generator
https://launchpad.net/randgen

Questions?
Probably out of time at this point, but it’s
the standard

Thanks!