SQL For PHP Programmers

Tutorial Day: Nov 11th,
9:00a - 12:30p
Dave Stokes
MySQL Community Manager
David.Stokes@Oracle.com
@Stoker
SSQQLL FFoorr PPHHPP PPrrooggrraammmmeerrss

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SSaaffee HHaarrbboorr SSttaatteemmeenntt
The following is intended to outline our general
product direction. It is intended for information
purposes only, and may not be incorporated
into any contract. It is not a commitment to
deliver any material, code, or functionality, and
should not be relied upon in making purchasing
decision. The development, release, and timing
of any features or functionality described for
Oracle’s products remains at the sole discretion
of Oracle.

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The Problem wwiitthh PPHHPP PPrrooggrraammmmeerrss
Your are up to date on the latest version of PHP

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The latest version of Javascript – no problemo!

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Frameworks – you know two or three or more – plus the
ones you wrote yourself

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Frameworks – you know two or three or more – plus the
ones you wrote yourself
But roughly 2-3% have had any training in Structured
Query Language (SQL)

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So what is SQL?!?!??!?!??!??!
http://en.wikipedia.org/wiki/SQL
SQL (/ˈɛs kjuː ˈɛl/ or /ˈsiːkwəl/; Structured Query
Language) is a special-purpose programming language
designed for managing data held in a relational database
management system (RDBMS), or for stream processing
in a relational data stream management system
(RDSMS).
Originally based upon relational algebra and tuple
relational calculus, SQL consists of a data definition
language and a data manipulation language. The scope of
SQL includes data insert, query, update and delete,
schema creation and modification, and data access
control.

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Oh Crap!!!
He Said 'relational
algebra' and 'tuple
relational
calculus'!

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Relational algebra
http://en.wikipedia.org/wiki/Relational_algebra
Relational algebra is a family of algebra with a well-founded
semantics used for modelling the data stored in
relational databases, and defining queries on it.
To organize the data, first the redundant data and
repeating groups of data are removed, which we call
normalized. By doing this the data is organized or
normalized into what is called first normal form (1NF).
Typically a logical data model documents and
standardizes the relationships between data entities (with
its elements). A primary key uniquely identifies an
instance of an entity, also known as a record.

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Relation Algebra Continued
Once the data is normalized and in sets of data (entities
and tables), the main operations of the relational algebra
can be performed which are the set operations (such as
union, intersection, and cartesian product), selection
(keeping only some rows of a table) and the projection
(keeping only some columns). Set operations are
performed in the where statement in SQL, which is where
one set of data is related to another set of data.

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Database Normalization Forms
1nf
– No columns with repeated or similar data
– Each data item cannot be broken down further
– Each row is unique (has a primary key)
– Each filed has a unique name
2nf
– Move non-key attributes that only depend on part of the
key to a new table
● Ignore tables with simple keys or no no-key attributes
3nf
– Move any non-key attributes that are more dependent
on other non-key attributes than the table key to a
new table.
● Ignore tables with zero or only one non-key attribute

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In more better English, por favor!
3NF means there are no transitive dependencies.
A transitive dependency is when two columnar
relationships imply another relationship. For example,
person -> phone# and phone# -> ringtone, so person ->
ringtone
– A → B
– It is not the case that B → A
– B → C

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And the rarely seen 4nf & 5nf
You can break the information down further but very rarely
do you need to to 4nf or 5nf

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So why do all this normalization?
http://databases.about.com/od/specificproducts/a/normalization.htm
Normalization is the process of efficiently
organizing data in a database. There are two
goals of the normalization process: eliminating
redundant data (for example, storing the same
data in more than one table ) and ensuring data
dependencies make sense (only storing related
data in a table). Both of these are worthy goals as
they reduce the amount of space a database
consumes and ensure that data is logically
stored.

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Example – Cars
Name Gender Color Model
Heather F Blue Mustang
Heather F White Challenger
Eli M Blue F-type
Oscar M Blue 911
Dave M Blue Mustang
There is redundant information
across multiple rows but each
row is unique

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2nf – split into tables
Name Gender
Heather F
Eli M
Oscar M
Dave M
Color Model Owner
Blue Mustang Heather
White Challenger Heather
Blue F-type Eli
Blue 911 Oscar
Blue Mustang Dave
Split data into
two tables –
one for owner
data and one
for car data

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3nf – split owner and car info into different tables
Car_ID Color Model Owner
_ID
1 Blue Mustang 1
2 White Challenger 1
3 Blue F-type 2
4 Blue 911 3
5 Blue Mustang 4
The car info is
separated from the
car info. Note that
the car table has a
column for the
owner's ID from the
owner table.
Owner_ID Name Gender
1 Heather F
2 Eli M
3 Oscar M
4 Dave M

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But what if White Mustang is shared or 4nf
Owner_ID Name Gender
1 Heather F
2 Eli M
3 Oscar M
4 Dave M
Car_id Model Color
1 Mustang Blue
2 Challenger White
3 F-type Blue
4 911 Blue
Car_id Owner_id
1 1
2 1
3 2
4 3
1 4
Tables for Owner,
Car, & Ownership
data
Now we have a flexible way to
search data about owners, cars, and
their relations.

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So now what!!!
By normalizing to 3nf (or 4th), we are storing the data with
no redundancies (or very, very few)
Now we need a way to define how the data is stored
And a way to manipulate it.

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SQL
SQL is a declarative language made up of
– DDL – Data Definition Language
– DML – Data Manipulation Language
SQL was one of the first commercial languages for Edgar
F. Codd's relational model, as described in his influential
1970 paper, "A Relational Model of Data for Large Shared
Data Banks." --Wikipedia
– Codd, Edgar F (June 1970). "A Relational Model of
Data for Large Shared Data Banks". Communications
of the ACM (Association for Computing Machinery)
13 (6): 377–87. doi:10.1145/362384.362685.
Retrieved 2007-06-09.

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SQL is declarative
Describe what you want, not how to process
Hard to look at a query to tell if it is efficient by just looks
Optimizer picks GPS-like best route
– Can pick wrong – traffic, new construction, washed out
roads, and road kill! Oh my!!

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SQL is made up of two parts
Data Definition Language (DDL)
– For defining data structures
●CREATE, DROP, ALTER, and
RENAME
Data Manipulation Language
– Used to SELECT, INSERT, DELETE, and
UPDATE data

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DDL
Useful commands
– DESC[ribe] table
– SHOW CREATE TABLE table
–

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The stuff in the parenthesis
CHAR(30) or VARCHAR(30) will hold strings up to 30
character long.
– SQL MODE (more later) tells server to truncate or
return error if value is longer that 30 characters
–
INT(5) tells the server to show five digits of data
DECIMAL(5,3) stores five digits with two decimals, i.e.
-99.999 to 99.999
FLOAT(7,4) -999.9999 to 999.9999

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NULL No Value
Null is used to indicate a lack of value or no data
– Gender : Male, Female, NULL
Nulls are very messy in B-tree Indexing, try to avoid
Math with NULLs is best avoided

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DESC City in detail
Describe table tells us the names of the columns (Fields),
the data type, if the column is NULLABLE, Keys, any
default value, and Extras.

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Data Types
Varies with vendor
Usually have types for text, integers, BLOBs, etc.
Refer to manual

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MySQL World Database
http://dev.mysql.com/doc/index-other.html
Used in MySQL documentation, books, on line tutorials,
etc.
Three tables
– City
– Country
– Country Language

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Simple SELECT
Query
Result

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Join two tables
To get a query that provides the names of the City and the
names of the countries, JOIN the two tables on a common
data between the two columns (that are hopefully
indexed!)

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http://i.imgur.com/hhRDO4d.png – Get a copy!!!

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Simple join
Both City and Country
have columns that
can be used for JOINs
– Country.Code
– City.CountryCode

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What happened when you send a query
Server receives the query
The user is authenticated for permissions
– Database, table, and/or column level
Syntax
Optimizer
– Statistics on data
– Cost model
● Pick cheapest option (DISK I/O)
● Cardinality of indexes
Get the data
Sorting/Grouping/etc
Data returned

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EXPLAIN
EXPLAIN is pre pended to the query to show the results
from the optimizer

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VISUAL Explain
MySQL Workbench
MySQL 5.6/5.7
Uses JSON output from
EXPLAIN and turns it into
something more visually
appealing

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Optimizer Trace
{
"query_block": {
"select_id": 1,
"cost_info": {
"query_cost": "5779.32"
},
"nested_loop": [
{
"table": {
"table_name": "City",
"access_type": "ALL",
"rows_examined_per_scan": 4079,
"rows_produced_per_join": 4079,
"filtered": 100,
"cost_info": {
"read_cost": "68.72",
"eval_cost": "815.80",
"prefix_cost": "884.52",
"data_read_per_join": "286K"
},
"used_columns": [
"Name",
"CountryCode",
"Population"
]
}
},
{
"table": {
"table_name": "Country",
"access_type": "eq_ref",
"possible_keys": [
"PRIMARY"
],
"key": "PRIMARY",
"used_key_parts": [
"Code"
],
"key_length": "3",
"ref": [
"world.City.CountryCode"
],
"rows_examined_per_scan": 1,
"rows_produced_per_join": 4079,
"filtered": 100,
"cost_info": {
"read_cost": "4079.00",
"eval_cost": "815.80",
"prefix_cost": "5779.32",
"data_read_per_join": "1M"
},
"used_columns": [
"Code",
"Name"

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MySQL Internals Manual :: 7 The Optimizer :: 7.1 Code
and Concepts :: 7.1.2 The Optimizer Code
handle_select()
mysql_select()
JOIN::prepare()
setup_fields()
JOIN::optimize() /* optimizer is from here ... */
optimize_cond()
opt_sum_query()
make_join_statistics()
get_quick_record_count()
choose_plan()
/* Find the best way to access tables */
/* as specified by the user. */
optimize_straight_join()
best_access_path()
/* Find a (sub-)optimal plan among all or subset */
/* of all possible query plans where the user */
/* controls the exhaustiveness of the search. */
greedy_search()
best_extension_by_limited_search()
best_access_path()
/* Perform an exhaustive search for an optimal plan */
find_best()
make_join_select() /* ... to here */
JOIN::exec()

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Data and Data types
Use the smallest reasonable field
– BIGINT are not needed for customer id numbers
– Signed/unsigned
● Customer “negative seven four two four three”
– All those extra bits have to be moved disk → memory
→ buffer → Ether → buffer → program
CHAR versus VARCHAR
– Space and compression
– Overhead slight
ENUMs and BITs
– Have to plan ahead
– Sorting issues
● Value of the ENUM

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INTEGER, INT, SMALLINT, TINYINT, MEDIUMINT, BIGINT
Type Storage
(bytes)
Minimum
Signed
Maximum
Signed
Minimum
Unsigned
Maximum
Unsigned
TINYINT 1 -128 127 0 255
SMALLINT 2 -32768 32767 0 65535
MEDIUMINT 3 -8388608 8388607 0 16777215
INT 4 -
2147483648
2147483647 0 4294967295
BIGINT 8 -
9223372036
854775808
9223372036
854775807
0 1844674407
3709551615

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Creating a table with MySQL command line client
CREATE TABLE fooint (a INT(1), b INT(4), c iINT(10));
INSERT INTO fooint (a,b,c) VALUES (1,100,10000);
INSERT INTO fooint VALUES (777,777,0);
SELECT * from foointg
INSERT INTO fooint (a) values (1234567890);
SELECT * FROM foointG
Note that ; and g are equivalent and G is for vertical
output.
What happens with INSERT INTO fooint (a) values
(12345678900); ??

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ALTER, TRUNCATE, and DROP table
ALTER TABLE fooint ADD COLUMN id INT UNSIGNED
NOT NULL FIRST;
– Also BEFORE and AFTER
SELECT * FROM fooint;
TRUNCATE fooint;
– Used to remove data but not schema definition
DROP TABLE fooint;
– Goodbye to table and schema

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Another table
CREATE TABLE fooint (id INT UNSIGNED NOT NULL
PRIMARY KEY AUTO_INCREMENT, name Char(30));
DESC fooint; and SHOW CREATE TABLE foointl
INSERT INTO fooint (name) VALUES ('Alpha'),('Beta'),
('Gamma');
SELECT * FROM fooint;
– Note the id column incremented automatically

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Foreign Keys
CREATE TABLE employee (
e_id INT NOT NULL,
name CHAR(20),
PRIMARY KEY (e_id)
);
CREATE TABLE building (
office_nbr INT NOT NULL,
description CHAR(20),
e_id INT NOT NULL,
PRIMARY KEY (office_nbr),
FOREIGN KEY (e_id)
REFERENCES employee
(e_id)
ON UPDATE CASCADE
ON DELETE CASCADE);

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More on foreign keys
mysql> INSERT INTO
employee VALUES (10,'Larry'),
(20,'Shemp'), (40,'Moe');
Query OK, 3 rows affected (0.04
sec)
Records: 3 Duplicates: 0
Warnings: 0
mysql> INSERT INTO building
VALUES (100,'Corner
Office',10), (101,'Lobby',40);
Query OK, 2 rows affected (0.04
sec)
Records: 2 Duplicates: 0
Warnings: 0
mysql> SELECT * FROM employee;
+------+-------+
| e_id | name |
+------+-------+
| 10 | Larry |
| 20 | Shemp |
| 40 | Moe |
+------+-------+
3 rows in set (0.00 sec)
mysql> SELECT * FROM building;
+------------+---------------+------+
| office_nbr | description | e_id |
+------------+---------------+------+
| 100 | Corner Office | 10 |
| 101 | Lobby | 40 |
+------------+---------------+------+

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Using foreign keys
mysql> SELECT * FROM employee JOIN building ON
(employee.e_id=building.e_id);
+------+-------+------------+---------------+------+
| e_id | name | office_nbr | description | e_id |
+------+-------+------------+---------------+------+
| 10 | Larry | 100 | Corner Office | 10 |
| 40 | Moe | 101 | Lobby | 40 |
+------+-------+------------+---------------+------+

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Left Join with foreign keys
mysql> SELECT * FROM employee LEFT JOIN building ON
+------+-------+------------+---------------+------+
+------+-------+------------+---------------+------+
| 40 | Moe | 101 | Lobby | 40 |
| 20 | Shemp | NULL | NULL | NULL |
+------+-------+------------+---------------+------+

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Foreign keys keep you from messy data
mysql> INSERT INTO building VALUES (120,'Cubicle',77);
ERROR 1452 (23000): Cannot add or update a child row: a
foreign key constraint fails (`test`.`building`, CONSTRAINT
`building_ibfk_1` FOREIGN KEY (è_id`) REFERENCES
èmployee` (è_id`) ON DELETE CASCADE ON UPDATE
CASCADE)
mysql>

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Taking advantage of CASCADE
mysql> DELETE FROM employee WHERE e_id=40;
Query OK, 1 row affected (0.08 sec)
+------+-------+------------+---------------+------+
+------+-------+------------+---------------+------+
+------+-------+------------+---------------+------+

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Cascade keeping foreign key data updated
mysql> UPDATE employee SET e_id=21 WHERE e_id=20;
Query OK, 1 row affected (0.04 sec)
Rows matched: 1 Changed: 1 Warnings: 0
+------+-------+------------+---------------+------+
+------+-------+------------+---------------+------+
+------+-------+------------+-------------

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Indexing
MySQL uses mainly B-trees for indexes
Great for =, <, >, <=, =>, BETWEEN, some LIKE 'string%'

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13.1.8 CREATE INDEX Syntax
CREATE [UNIQUE|FULLTEXT|SPATIAL] INDEX index_name
[index_type]
ON tbl_name (index_col_name,...)
[index_option]
[algorithm_option | lock_option] ...
index_col_name:
col_name [(length)] [ASC | DESC]
index_type:
USING {BTREE | HASH}
index_option:
KEY_BLOCK_SIZE [=] value
| index_type
| WITH PARSER parser_name
| COMMENT 'string'
algorithm_option:
ALGORITHM [=] {DEFAULT|INPLACE|COPY}
lock_option:
LOCK [=] {DEFAULT|NONE|SHARED|EXCLUSIVE}

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You may not know
Can create multi-column indexes
– Year,month,day index can be used for
● Year,Month,Day searches
● Year,Month searches
● Year searches
– But not day or month, day searches
Can index on part of a column
– CREATE INDEX ndx1 ON customer (name(10));
NULLs are to be avoided
– Extra optimizer steps
UNIQUE indexes enforce no duplicates
InnoDB will create an index if YOU DO NOT

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13.2.5.2 INSERT … ON DUPLICATE KEY UPDATE
If you specify ON DUPLICATE KEY UPDATE, and a row
is inserted that would cause a duplicate value in a
UNIQUE index or PRIMARY KEY, an UPDATE of the old
row is performed. For example, if column a is declared as
UNIQUE and contains the value 1, the following two
statements have identical effect:
INSERT INTO table (a,b,c) VALUES (1,2,3)
ON DUPLICATE KEY UPDATE c=c+1;

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FIND Asian countries with Population > 5 million
EXPLAIN SELECT * FROM Country
WHERE Continent = 'Asia'
AND Population > 5000000;

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Will indexes make query faster?
SELECT * FROM Country
WHERE Continent = 'Asia'
AND Population > 5000000;
Index columns
– Population
– Continent
– Population and Continent
– Continent and Population
● Which to pick? Optimizer will look at option that takes
the least CPU and I/O
● Statistics from storage engine
– Indexes to use, order to join tables, avoiding sorts,
is sorting expensive

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Index on Population
Alter TABLE Country ADD INDEX p (Population)
Down from 239!!

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A reading from the Book of MySQL – Chapter 8 Optimization
READ this section of the MySQL Manual
– Hardware
– Configuration
– Queries
– Locking
– Benchmarkings
Set InnoDb Buffer Cache Size to 75-80% of system RAM
Move Logs off disks/controllers with data
Use bigger stronger machines for replication slaves
Looking at SSDs, FusionIO cards
Monitor! Monitor! Monitor!

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ALTER TABLE Country ADD INDEX C (continent)
42 is better than
239 or 54

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CREATE INDEX cpidx ON Country (continent,
Population)
Event better still!

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CREATE INDEX pcidx on Country (Population,continent)
FORCING the use of the
new index shows
it is not optimal.

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Index-es
Indexes need maintenance
– Run OPTIMIZE TABLE when system quiet
– Each INSERT/DELETE takes overhead
● Slows you down
– Therefore remove unused indexes
– MySQL Utilities mysqlindexcheck to look for
unused index on long running systems (do not
use AFTER a restart)
● Use good naming convention, document
– Statistics can be saved/reloaded at shutdown/reboot
● After a reboot w/o saving, the system is going to
need to rebuild stats from scratch, run slower
– Log queries not using Indexes
● Not all of these are bad, just recognize them

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Transactions
You will need to use a
transactional storage
engine like InnoDB, NDB
You need to START a
transaction, do the work
and COMMIT to record the
changes or ROLLBACK to
cancel the recording.
To avoid using ROLLBACK, you can
employ the following strategy:
– Use LOCK TABLES to lock
all the tables you want to
access.
– Test the conditions that must
be true before performing
the update.
– Update if the conditions are
satisfied.
– Use UNLOCK TABLES to
release your locks.
Note
– This solution does not
handle the situation when
someone kills the threads
in the middle of an
update. In that case, all
locks are released but
some of the updates may
not have been executed.

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CREATE TABLE account (id int not null, balance
decimal(6,2) default 0)
INSERT INTO account VALUES (1,1000.10), (2,400),
(3,0), (15,.99);
START TRANSACTION;
UPDATE account SET balance = 1010.10 WHERE id = 1;
UPDATE account SET balance = 300 WHERE id=2;
COMMIT;
START TRANSACTION
UPDATE account SET balance=1000 WHERE ID=3;
ROLLBACK;

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AUTOCOMMIT
AUTOCOMMIT is set to 1 by default or 'do things as I type
them mode'.
– START TRANSACTION overrides
– Some APIs like JDBC have own way to handle
transactions (see chapter 23 in MySQL docs)
AUTOCOMMIT set to 0 requires a COMMIT to store
changes
Some statements cannot be rolled back. In general, these
include data definition language (DDL) statements, such
as those that create or drop databases, those that create,
drop, or alter tables or stored routines
Some statements cause an implicit commit – DDL, user
account changes, transaction control or locking
statements, data loading, and replication control
statements,

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SET autocommit=0;
LOCK TABLES t1 WRITE, t2 READ, ...;
... do something with tables t1 and t2 here ...
COMMIT;
UNLOCK TABLES;
Note ROLLBACKS do not release locks

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SET autocommit=0;
LOCK TABLES t1 WRITE, t2 READ, ...;
... do something with tables t1 and t2 here ...
COMMIT;
UNLOCK TABLES;
Note ROLLBACKS do not release locks

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First steps of scaling
✔ Upgrade MySQL versions
✔ 5.5 is 25% faster than 5.1
✔ 5.6 is 20% faster
✔ 5.7 will be faster still
✔ Memory
✔ DISKS
✔ Move logs to different platters, controllers
Move large databases to own disks
✔ TUNE queries
✔ Scale horizontally especially is server is old
✔ Use bigger, more powerful boxes for replicants
✔ Optimize network

74
Read/Write Splitting – Scale READS

Why does that query take so LONG!!!
● I only want the top N
customers!
● And I want them sorted
by last name, first
name, middle name,
postal code, and
average purchase!
The stuff on the right
hand side of the
WHERE clause may
seem like a great way
to prune down the
number of records for a
query BUT that is not
always the case.

Query we want to 'prune'
Select City.name AS 'City',
Country.name AS 'Country',
City.Population AS 'Pop'
FROM City
JOIN Country ON
(City.CountryCode = Country.Code)

The Stats!
Total cost is about
5780. Bad news is
that we have a Full
Table Scan on City
but that was expected.
Good news is that we
have 1 read on
Country using the
Primary key to get
record we need to
match on the join.

Now we only want the top ten!
LIMIT 10
But the cost is still
5780!?!?!
The server has to run
through ALL the
records to get just the
first ten. So LIMIT
does not buy us any
speed in this case.

ORDER BY Country
Oh no! The
cost jumps to
9858! And
we have to
open a temp
table and
then sort that
file
(expensive)

WHERE City.Population > 1000000
Nope. The
server is doing
the join first
and then
tossing away
the records
that are <=
1000000

SELECT a.name FROM
(select City.name, CountryCode,
Population from City where Population
> 1000000) as a

SELECT a.name FROM (select
City.name, CountryCode, Population
from City where Population >
1000000) as a
JOIN Country ON (a.CountryCode =
Country.code)
That is an awful
lot of work to try
to save a few
hundred and
not very pretty.

The World is too small
● The World database is too small and already too
optimized to gain much more performance.
● So lets create a new database and copy the date to
get 100K+ records;
– CREATE TABLE bigcity LIKE city;
– INSERT INTO bigcity SELECT * FROM city;
INSERT INTO bigcity SELECT
NULL,Name,CountryCode,District,Population FROM
bigcity; <many times>

SELECT countrycode, sum(Population)
FROM bigcity
GROUP BY CountryCode;
● Takes .343 seconds
● ALTER TABLE bigcity ADD INDEX
cc_pop (CountryCode,Population);
● Now takes .156 seconds

Zipcode – from a free zipcode database
SELECT city, state, zipcode
FROM zip
WHERE zipcode = '76262';
Pretend out app provides the us
postal code given the city and state
data. Here no indexes.

Indexing city and state only.
● CREATE INDEX csidx ON
zip (city(8),state(2)); /* only use first 8 on name and 2
for state*/
● SELECT city, state, zipcode
FROM zip
WHERE city='Roanoke' and state='TX';

Good optimization
The optimizer uses the
city and state from the
index we created to
know where to dive into
the data. Some indexes
will be able to supply all
the data so there will be
no need to dive into the
data.

Composite and Compound Indexes
● Composite index covers all the columns in the filter,
join and select criteria. All of these columns are
stored on all of the index pages accordingly in the
index B-tree.
● Compound index covers all the filter and join key
columns in the B-tree, keeping the select columns
only on the leaf pages as they will not be searched,
only extracted! This saves space and creates
smaller index pages (and is faster).

NoSQL
● No Schema
– Teenager with all belongings in center of room
– Does not scale easily
– ACID compliance not important
– Effective for some jobs but watch out if you need an
AND or an OR

http://en.wikipedia.org/wiki/NoSQL
A NoSQL (often interpreted as Not Only SQL) database
provides a mechanism for storage and retrieval of data
that is modeled in means other than the tabular relations
used in relational databases. Motivations for this
approach include simplicity of design, horizontal scaling
and finer control over availability. The data structure (e.g.
key-value, graph, or document) differs from the RDBMS,
and therefore some operations are faster in NoSQL and
some in RDBMS. There are differences though, and the
particular suitability of a given NoSQL DB depends on
the problem it must solve (e.g., does the solution use
graph algorithms?).

Key/value pair
● A key-value pair (KVP) is a set of two linked data
items: a key, which is a unique identifier for some
item of data, and the value, which is either the data
that is identified or a pointer to the location of that
data.
http://searchenterprisedesktop.techtarget.com/definition/key-value-pair
● Berkeley Database (Sleepycat) 1986
– Hash table

Graph Databases
● In computing, a graph database is a database that
uses graph structures with nodes, edges, and
properties to represent and store data. A graph
database is any storage system that provides index-free
adjacency. This means that every element
contains a direct pointer to its adjacent elements
and no index lookups are necessary
http://en.wikipedia.org/wiki/Graph_database

Document Databases
A document-oriented database is a computer program
designed for storing, retrieving, and managing
document-oriented information, also known as semi-structured
data. Document-oriented databases are one
of the main categories of NoSQL databases and the
popularity of the term "document-oriented database"
(or "document store") has grown with the use of the
term NoSQL itself. In contrast to relational databases
and their notions of"Tables" and "Relations" (between
Tables), these systems are designed around an
abstract notion of a "Document".
http://en.wikipedia.org/wiki/Document-oriented_database

Hybrids

Bypassing syntax checking and
optimizer upto 9x faster than SQL

Paradigm Shifting w/o a clutch
● SQL is a declarative language
● PHP is procedural/object orientated
● Square Pegs for Round Holes

Object Relation Mapper
● Adds another layer
– Easier to learn SQL
– Not as expressive as SQL
– ORMs are a hammer in a world of nails and blasting
caps
– If you can not solve problem with an object, adding
another object is not going to help

Query Rewrite plugin or How To Fix
your ORM's lousy SQL
mysql < <plugin directory>/install_rewriter_plugin.sql
INSERT INTO query_rewrite.rewrite_rules( pattern,
replacement )
VALUES ( 'SELECT ?', 'SELECT ? + 1' );

Better Example
● SELECT patientId, time
FROM patient_records
WHERE patientId > 1000 AND patientId < 2000
AND time < “2014-09-08” AND illness = ”Headache”
ORDER BY time LIMIT 1;
● SELECT patiendId, time
FROM patient_records
WHERE patientId > 1000 AND patientID < 2000
AND time < “2014-09-08” AND illness =
”Acrocephalosyndactylia” *
ORDER BY time LIMIT 1;

The Rules for the rewrite
INSERT INTO query_rewrite.rewrite_rules ( pattern, pattern_database, replacement )
VALUES
(
# Pattern
'SELECT patientid, time ' ||
'FROM patient_records ' ||
'WHERE patientid > ? AND patientid < ? ' ||
'AND time < ? AND illness = ? ' ||
'ORDER BY time LIMIT 1',
# Database
'health_clinic',
# Replacement
'SELECT patientid, time ' ||
'FROM patient_records FORCE INDEX(patientIdIdx) ' ||
'WHERE patientid > ? AND patientid < ? ' ||
'AND time < ? AND illness = ? ' ||
'ORDER BY time LIMIT 1');

Recommended Books
● High Performance MySQL, 3rd Edition
Baron Schwartz, Peter Zaitsev & Vadim
Tkachenko
● SQL Antipatterns
– Bill Karwin
● SQL and Relational Theory
– CJ Date
● Effective MySQL Replication Techniques in Depth
– Ronald Bradford

Q/A
● Slideshare.net/davestokes
● @stoker
● David.Stokes@Oracle.com

SQL For PHP Programmers

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