If you are looking to learn everything about SQL you are watching the right video. This video is a 500 page book in one video! It contains over 100 queries that will teach everything from basic queries through Stored Procedures, Functions, Triggers and Database Design. You’ll learn about numerous SQL queries while building a real world sales transaction database.
All of the queries along with a transcript of the video follows below. Take your time and create the database and enter the queries to maximize your learning potential.
My Ultimate Python Course is available here for free until October the 3rd. If you sign up for it you’ll get all the current videos plus every video I upload in the future for free. I’ll cover everything from the basics through machine learning by the the time I finish it.
Transcript for my SQL Tutorial
SQL TUTORIAL
A database is data that is structured into rows and columns like a spreadsheet. To receive or change data in a database you send it commands called queries. The database in turn returns a result based on that request.
Databases contain many tables of data organized into rows and columns. Each column represents represents one type of data the database stores. Each row contains multiple pieces of data specific to each entity you are describing. For example we store information on customers here. Each individual value stored is called a cell.
Primary keys are used to define unique entities in your tables. You can also have non-unique keys which are just referred to as non-primary keys. Let’s say we want to store possible countries in a table with an associated key for each country. We could then refer to those countries in our tables, but because they aren’t unique for each customer they can’t be used as primary keys.
cust_id in the Sales table is called a foreign key because it refers to a row of data in a separate table being the Customer table. You can have multiple foreign keys in a table, but only one primary key. Primary keys can be made up of multiple columns and in that case they are called composite primary keys, but don’t worry about that for now. I’ll cover that later.
Here it is possible to have multiple references to the customer table in the sales table. You can see that customer 9 is listed twice. That is known as a one to many relationship. I’ll get more into this later as well.
Create a Database
To create a database click the icon that looks like 3 stacked cylinders with a plus. That icon is labeled as Create a new schema in the connected server. MySQL calls databases schemas.
Give your database a name and leave everything else as default
Hit Apply and you’ll be asked to review the SQL script for creating a database which is CREATE SCHEMA ‘your_db_name’;
Click Apply again
Click the Schemas tab and you’ll see your test database in the list
Create a Customer Table
Double click ‘your_db_name’ in the left sidebar under Schemas
Click the Query 1 tab and enter the following commands :
CREATE TABLE customer(
first_name VARCHAR(30) NOT NULL,
last_name VARCHAR(30) NOT NULL,
email VARCHAR(60) NOT NULL,
company VARCHAR(60) NULL,
street VARCHAR(50) NOT NULL,
city VARCHAR(40) NOT NULL,
state CHAR(2) NOT NULL DEFAULT "PA",
zip MEDIUMINT UNSIGNED NOT NULL,
phone VARCHAR(20) NOT NULL,
birth_date DATE NULL,
sex ENUM('M', 'F') NOT NULL,
date_entered TIMESTAMP NOT NULL,
id INT UNSIGNED NOT NULL
AUTO_INCREMENT PRIMARY KEY
);
3. Right click tables under ‘your_db_name’ and click Refresh all to see student
What’s Going On
Create table customer creates the table named customer
When defining what data goes in each cell you must define the type of data you plan to store
These are common numeric data types. You can see their minimum and maximum values. The Float type value is purely theoretical and its precision is dependent on your specific machine and deprecated. Double is similar to Float and also deprecated.
Decimal are exact numeric data values. When defining them you use DECIMAL(P,D) where P is the precision for your numbers significant digits with a max value of 65. D is the maximum precision of digits that follow the decimal point. You can see the maximum and minimum values you can assign to time data types.
You can define how many characters you wish to store in a Varchar as long as the maximum is 255 which is the max size for storing in a 8 bit number. A Blob can store up to 65,535 characters. MediumBlob can store 16,777,215. A LONGBLOB can store 4,294,967,295 but is dependent on a defined maximum packet size and memory. Enums are string values that can only be assigned one value from the list that is provided. You can have a max of 65,535 distinct elements. Sets are like Enums.
Some additional data constraints include Not Null. If you mark data as not null that means it must have a value when a new row of data is created. Default designates a default value when a row is created without data. Unsigned means you cannot use negative values. Primary keys are identified as unique values assigned to a row. If you mark data as auto_increment each time a row of data is created you would mark this as a null value and the database will increment this value automatically based off of the previous value assigned to the last id created. You can only use auto_increment on one column per table and that column must be either a primary or unique key.
How to Design a Database Slide 8
Each table should represent 1 real world object
Each column stores 1 piece of information about that object
Define how tables may relate to each other (Primary & Foreign Keys)
Reduce redundant data (Normalizing a Database)
Turning a Invoice into a Database Slide 9
I’ll take a company invoice and translate it into a database. First I create the customer table. I could have just had one field named Address, but when an element contains 2 or more pieces of information it is best to create separate elements. I'll add information that may be important like birthdate, sex, and the date the customer was added. (Create in Workbench)
It also makes sense to do much the same with an employee except we'll define hire date.
Duplication is a sign that the data should be in its own table.
Tables should contain similar types of data.
Tables shouldn't contain duplicate data. For example if we had all individual items ordered all in one table sales person, purchase order number, time of sale, and 4 different pieces of credit card data would be repeated an unknown number of times.
Primary & Foreign Keys
You define relationships between tables using primary and foreign keys. Primary keys are unique identifiers associated with a column in a table. Foreign keys are primary keys that are referenced by their ids in another table.
How Tables Relate
New Database with Multiple Tables and Foreign Keys
CREATE TABLE customer(
first_name VARCHAR(30) NOT NULL,
last_name VARCHAR(30) NOT NULL,
email VARCHAR(60) NOT NULL,
company VARCHAR(60) NULL,
street VARCHAR(50) NOT NULL,
city VARCHAR(40) NOT NULL,
state CHAR(2) NOT NULL DEFAULT "PA",
zip MEDIUMINT UNSIGNED NOT NULL,
phone VARCHAR(20) NOT NULL,
birth_date DATE NULL,
sex ENUM('M', 'F') NOT NULL,
date_entered TIMESTAMP NOT NULL,
id INT UNSIGNED NOT NULL
AUTO_INCREMENT PRIMARY KEY
);
CREATE TABLE sales_person(
first_name VARCHAR(30) NOT NULL,
last_name VARCHAR(30) NOT NULL,
email VARCHAR(60) NOT NULL,
street VARCHAR(50) NOT NULL,
city VARCHAR(40) NOT NULL,
state CHAR(2) NOT NULL DEFAULT "PA",
zip MEDIUMINT UNSIGNED NOT NULL,
phone VARCHAR(20) NOT NULL,
birth_date DATE NULL,
sex ENUM('M', 'F') NOT NULL,
date_hired TIMESTAMP NOT NULL,
id INT UNSIGNED NOT NULL
AUTO_INCREMENT PRIMARY KEY
);
// Will hold show types business, casual and athletic
CREATE TABLE product_type(
name VARCHAR(30) NOT NULL,
id INT UNSIGNED NOT NULL
AUTO_INCREMENT
PRIMARY KEY
);
// We define a foreign key constraint to define a relationship between tables
// Make sure the data constraints are exactly the same for the foreign key
// and the referenced id in the other table
CREATE TABLE product(
type_id INT UNSIGNED NOT NULL,
FOREIGN KEY (type_id)
REFERENCES product_type(id),
name VARCHAR(30) NOT NULL,
supplier VARCHAR(30) NOT NULL,
description BLOB NOT NULL,
id INT UNSIGNED NOT NULL
AUTO_INCREMENT PRIMARY KEY
);
// This table describes just the quality of an item. If I were to list quantity here it would
// make it hard to look at this as a single item. Quantity should be kept in a completely different
// table if needed.
// Anything that gets in the way of being able to model an individual object should be put in
// another table.
CREATE TABLE item(
product_id INT UNSIGNED NOT NULL,
FOREIGN KEY (product_id)
REFERENCES product(id),
size INT NOT NULL,
color VARCHAR(30) NOT NULL,
picture VARCHAR(256) NOT NULL,
price DECIMAL(6,2) NOT NULL,
id INT UNSIGNED NOT NULL
AUTO_INCREMENT PRIMARY KEY
);
// Only information pertaining to the order is here aside from products and prices
// It simulates 2 people agreeing to do business, the time of that event, a purchase order and
// the means of payment
CREATE TABLE sales_order(
cust_id INT UNSIGNED NOT NULL,
sales_person_id INT UNSIGNED NOT NULL,
FOREIGN KEY (cust_id)
REFERENCES customer(id),
FOREIGN KEY (sales_person_id)
REFERENCES sales_person(id),
time_order_taken TIMESTAMP NOT NULL,
purchase_order_number INT NOT NULL,
credit_card_number VARCHAR(16) NOT NULL,
credit_card_exper_month TINYINT NOT NULL,
credit_card_exper_day TINYINT NOT NULL,
credit_card_secret_code TINYINT NOT NULL,
name_on_card VARCHAR(50) NOT NULL,
id INT UNSIGNED NOT NULL
AUTO_INCREMENT PRIMARY KEY
);
// Each item that is part of an order goes in its own table. It is linked to the order with
// sales_order_id
// This simulates picking up a quantity of an individual item, with a certain discount and tax rate
// The item itself is hidden in the item table and 100% defined specifically there in a way that
// makes it easy to refer to it just by its id
// If I were to list color, size or anything else here we would break that ability to consider
// items in a self contained way
CREATE TABLE sales_item(
item_id INT UNSIGNED NOT NULL,
sales_order_id INT UNSIGNED NOT NULL,
FOREIGN KEY (item_id)
REFERENCES item(id),
FOREIGN KEY (sales_order_id)
REFERENCES sales_order(id),
quantity INT NOT NULL,
discount DECIMAL(3,2) NULL DEFAULT 0,
taxable BOOL NOT NULL DEFAULT 0,
sales_tax_rate DECIMAL(5,2) NOT NULL
DEFAULT 0,
id INT UNSIGNED NOT NULL
AUTO_INCREMENT PRIMARY KEY
);
// Do your best to consider the data in a database to be just that, data. It is often better
// to perform calculations outside of the database versus storing those calculations such as
// total sales amounts in a database.
Altering Tables
// Add a new column
ALTER TABLE sales_item ADD day_of_week VARCHAR(8)
// Modify a column (Change any Constraint this Way)
ALTER TABLE sales_item MODIFY day_of_week VARCHAR(9) NOT NULL
// Change name of a column
ALTER TABLE sales_item CHANGE COLUMN day_of_week weekday
// Drop a column
ALTER TABLE sales_item DROP COLUMN weekday
// Add a new table
CREATE TABLE transaction_type(
name VARCHAR(30) NOT NULL,
payment_type VARCHAR(30) NOT NULL,
id INT UNSIGNED NOT NULL
AUTO_INCREMENT
PRIMARY KEY
);
// Rename table
RENAME TABLE transaction_type TO transaction
// Create index based on a single column (Use UNIQUE INDEX for a unique index)
CREATE INDEX transaction_id ON transaction(name)
// Create an index based on 2 columns
CREATE INDEX transaction_id_2 ON transaction(name, payment_type)
// Delete data in a table
TRUNCATE TABLE transaction
// Drop a table
DROP TABLE transaction
Altering Tables in Workbench
You can view and alter table columns by Right clicking them in the left column and clicking on Alter Table. Click on name or data type to rename. Change constraints in this order : Primary Key, Not Null, Unique, Binary, Unsigned, Zerofill (Pads displayed field with zeroes), Auto Increment, Generated (Contain Functions that auto create values) and default values.
Double click <click to edit> to add a column. Right click Delete Selected to delete. Click Apply to set changes.
Insert Update and Delete Database Values
Use INSERT INTO to insert values into your tables
INSERT INTO product_type (name, id) VALUES ('Business', NULL);
INSERT INTO product_type (name, id) VALUES ('Casual', NULL);
INSERT INTO product_type (name, id) VALUES ('Athletic', NULL);
You can also insert multiple rows without defining column names if you put the values in the same order as the table data.
INSERT INTO product VALUES
(1, 'Grandview', 'Allen Edmonds', 'Classic broguing adds texture to a charming longwing derby crafted in America from lustrous leather', NULL),
(1, 'Clarkston', 'Allen Edmonds', 'Sharp broguing touches up a charming, American-made derby fashioned from finely textured leather', NULL),
(1, 'Derby', 'John Varvatos', 'Leather upper, manmade sole', NULL),
(1, 'Ramsey', 'Johnston & Murphy', 'Leather upper, manmade sole', NULL),
(1, 'Hollis', 'Johnston & Murphy', 'Leather upper, manmade sole', NULL),
(2, 'Venetian Loafer', 'Mezlan', 'Suede upper, leather sole', NULL),
(2, 'Malek', 'Johnston & Murphy', 'Contrast insets at the toe and sides bring updated attitude to a retro-inspired sneaker set on a sporty foam sole and triangle-lugged tread.', NULL),
(3, 'Air Max 270 React', 'Nike', 'The reggae inspired Nike Air 270 React fuses forest green with shades of tan to reveal your righteous spirit', NULL),
(3, 'Joyride', 'Nike', 'Tiny foam beads underfoot conform to your foot for cushioning that stands up to your mileage', NULL),
(2, 'Air Force 1', 'Nike', 'A modern take on the icon that blends classic style and fresh, crisp details', NULL),
(3, 'Ghost 12', 'Brooks', 'Just know that it still strikes a just-right balance of DNA LOFT softness and BioMoGo DNA responsiveness', NULL),
(3, 'Revel 3', 'Brooks', 'Style to spare, now even softer.', NULL),
(3, 'Glycerin 17', 'Brooks', 'A plush fit and super soft transitions make every stride luxurious', NULL);
Insert Customers
INSERT INTO customer (first_name, last_name, email, company, street, city, state, zip, phone, birth_date, sex, date_entered, id) VALUES
('Christopher', 'Jones', 'christopherjones@bp.com', 'BP', '347 Cedar St', 'Lawrenceville', 'GA', '30044', '348-848-8291', '1938-09-11', 'M', '2015-07-21 11:27:02', NULL),
('Matthew', 'Martinez', 'matthewmartinez@ge.com', 'GE', '602 Main Place', 'Fontana', 'CA', '92336', '117-997-7764', '1931-09-04', 'M', '2015-01-01 22:39:28', NULL),
('Melissa', 'Moore', 'melissamoore@aramark.com', 'Aramark', '463 Park Rd', 'Lakewood', 'NJ', '08701', '269-720-7259', '1967-08-27', 'M', '2017-10-20 21:59:29', NULL),
('Melissa', 'Brown', 'melissabrown@verizon.com', 'Verizon', '712 View Ave', 'Houston', 'TX', '77084', '280-570-5166', '1948-06-14', 'F', '2016-07-16 12:26:45', NULL),
('Jennifer', 'Thomas', 'jenniferthomas@aramark.com', 'Aramark', '231 Elm St', 'Mission', 'TX', '78572', '976-147-9254', '1998-03-14', 'F', '2018-01-08 09:27:55', NULL),
('Stephanie', 'Martinez', 'stephaniemartinez@albertsons.com', 'Albertsons', '386 Second St', 'Lakewood', 'NJ', '08701', '820-131-6053', '1998-01-24', 'M', '2016-06-18 13:27:34', NULL),
('Daniel', 'Williams', 'danielwilliams@tjx.com', 'TJX', '107 Pine St', 'Katy', 'TX', '77449', '744-906-9837', '1985-07-20', 'F', '2015-07-03 10:40:18', NULL),
('Lauren', 'Anderson', 'laurenanderson@pepsi.com', 'Pepsi', '13 Maple Ave', 'Riverside', 'CA', '92503', '747-993-2446', '1973-09-09', 'F', '2018-02-01 16:43:51', NULL),
('Michael', 'Jackson', 'michaeljackson@disney.com', 'Disney', '818 Pine Ave', 'Mission', 'TX', '78572', '126-423-3144', '1951-03-03', 'F', '2017-04-02 21:57:36', NULL),
('Ashley', 'Johnson', 'ashleyjohnson@boeing.com', 'Boeing', '874 Oak Ave', 'Pacoima', 'CA', '91331', '127-475-1658', '1937-05-10', 'F', '2015-01-04 08:58:56', NULL),
('Brittany', 'Thomas', 'brittanythomas@walmart.com', 'Walmart', '187 Maple Ave', 'Brownsville', 'TX', '78521', '447-788-4913', '1986-10-22', 'F', '2018-05-23 08:04:32', NULL),
('Matthew', 'Smith', 'matthewsmith@ups.com', 'UPS', '123 Lake St', 'Brownsville', 'TX', '78521', '961-108-3758', '1950-06-16', 'F', '2018-03-15 10:08:54', NULL),
('Lauren', 'Wilson', 'laurenwilson@target.com', 'Target', '942 Fifth Ave', 'Mission', 'TX', '78572', '475-578-8519', '1965-12-26', 'M', '2017-07-16 11:01:01', NULL),
('Justin', 'Smith', 'justinsmith@boeing.com', 'Boeing', '844 Lake Ave', 'Lawrenceville', 'GA', '30044', '671-957-1492', '1956-03-16', 'F', '2017-10-07 10:50:08', NULL),
('Jessica', 'Garcia', 'jessicagarcia@toyota.com', 'Toyota', '123 Pine Place', 'Fontana', 'CA', '92336', '744-647-2359', '1996-08-05', 'F', '2016-09-14 12:33:05', NULL),
('Matthew', 'Jackson', 'matthewjackson@bp.com', 'BP', '538 Cedar Ave', 'Katy', 'TX', '77449', '363-430-1813', '1966-02-26', 'F', '2016-05-01 19:25:17', NULL),
('Stephanie', 'Thomas', 'stephaniethomas@apple.com', 'Apple', '804 Fourth Place', 'Brownsville', 'TX', '78521', '869-582-9955', '1988-08-26', 'F', '2018-10-21 22:01:57', NULL),
('Jessica', 'Jackson', 'jessicajackson@aramark.com', 'Aramark', '235 Pine Place', 'Chicago', 'IL', '60629', '587-334-1054', '1991-07-22', 'F', '2015-08-28 03:11:35', NULL),
('James', 'Martinez', 'jamesmartinez@kroger.com', 'Kroger', '831 Oak St', 'Brownsville', 'TX', '78521', '381-428-3119', '1927-12-22', 'F', '2018-01-27 07:41:48', NULL),
('Christopher', 'Robinson', 'christopherrobinson@ibm.com', 'IBM', '754 Cedar St', 'Pharr', 'TX', '78577', '488-694-7677', '1932-06-25', 'F', '2016-08-19 16:11:31', NULL);
// See list of customers
SELECT * FROM customer;
Insert Sales Persons
INSERT INTO sales_person (first_name, last_name, email, street, city, state, zip, phone, birth_date, sex, date_hired, id) VALUES
('Jennifer', 'Smith', 'jennifersmith@volkswagen.com', '610 Maple Place', 'Hawthorne', 'CA', '90250', '215-901-2287', '1941-08-09', 'F', '2014-02-06 12:22:48', NULL),
('Michael', 'Robinson', 'michaelrobinson@walmart.com', '164 Maple St', 'Pacoima', 'CA', '91331', '521-377-4462', '1956-04-23', 'M', '2014-09-12 17:27:23', NULL),
('Brittany', 'Jackson', 'brittanyjackson@disney.com', '263 Park Rd', 'Riverside', 'CA', '92503', '672-708-7601', '1934-07-05', 'F', '2015-01-17 02:51:55', NULL),
('Samantha', 'Moore', 'samanthamoore@ge.com', '107 Pine Place', 'Houston', 'TX', '77084', '893-423-2899', '1926-05-05', 'M', '2015-11-14 22:26:21', NULL),
('Jessica', 'Thompson', 'jessicathompson@fedex.com', '691 Third Place', 'Sylmar', 'CA', '91342', '349-203-4736', '1938-12-18', 'M', '2014-12-13 06:54:39', NULL)
// See all sales persons
SELECT * FROM sales_person;
Correct Purchase Order Number
// Make a unique number that can accommodate a large number
ALTER TABLE sales_order MODIFY purchase_order_number BIGINT UNSIGNED NOT NULL
UNIQUE;
// Change secret code to accommodate numbers up to 999
ALTER TABLE sales_order MODIFY credit_card_secret_code SMALLINT UNSIGNED NOT NULL;
Insert Items
INSERT INTO item VALUES
(2, 10, 'Gray', 'Coming Soon', 199.60, NULL),
(11, 12, 'Red', 'Coming Soon', 155.65, NULL),
(2, 11, 'Red', 'Coming Soon', 128.87, NULL),
(11, 11, 'Green', 'Coming Soon', 117.52, NULL),
(5, 8, 'Black', 'Coming Soon', 165.39, NULL),
(7, 11, 'Brown', 'Coming Soon', 168.15, NULL),
(5, 8, 'Gray', 'Coming Soon', 139.48, NULL),
(5, 11, 'Blue', 'Coming Soon', 100.14, NULL),
(4, 10, 'Brown', 'Coming Soon', 117.66, NULL),
(8, 10, 'Brown', 'Coming Soon', 193.53, NULL),
(7, 8, 'Light Brown', 'Coming Soon', 154.62, NULL),
(12, 10, 'Green', 'Coming Soon', 188.32, NULL),
(3, 12, 'Green', 'Coming Soon', 101.49, NULL),
(7, 9, 'Black', 'Coming Soon', 106.39, NULL),
(8, 12, 'Red', 'Coming Soon', 124.77, NULL),
(5, 8, 'Black', 'Coming Soon', 86.19, NULL),
(8, 12, 'Blue', 'Coming Soon', 196.86, NULL),
(8, 8, 'Blue', 'Coming Soon', 123.27, NULL),
(7, 11, 'Red', 'Coming Soon', 130.76, NULL),
(9, 12, 'Black', 'Coming Soon', 152.98, NULL),
(11, 8, 'Blue', 'Coming Soon', 175.58, NULL),
(7, 11, 'Light Brown', 'Coming Soon', 146.83, NULL),
(4, 8, 'Green', 'Coming Soon', 159.82, NULL),
(12, 8, 'Light Brown', 'Coming Soon', 171.92, NULL),
(1, 12, 'Light Brown', 'Coming Soon', 128.77, NULL),
(2, 10, 'Gray', 'Coming Soon', 102.45, NULL),
(10, 8, 'Green', 'Coming Soon', 186.86, NULL),
(1, 8, 'Blue', 'Coming Soon', 139.73, NULL),
(9, 8, 'Light Brown', 'Coming Soon', 151.57, NULL),
(2, 10, 'Green', 'Coming Soon', 177.16, NULL),
(3, 9, 'Gray', 'Coming Soon', 124.87, NULL),
(8, 8, 'Black', 'Coming Soon', 129.40, NULL),
(5, 9, 'Black', 'Coming Soon', 107.55, NULL),
(5, 8, 'Light Brown', 'Coming Soon', 103.71, NULL),
(11, 10, 'Green', 'Coming Soon', 152.31, NULL),
(6, 12, 'Red', 'Coming Soon', 108.96, NULL),
(7, 12, 'Blue', 'Coming Soon', 173.14, NULL),
(3, 10, 'Green', 'Coming Soon', 198.44, NULL),
(1, 9, 'Light Brown', 'Coming Soon', 119.61, NULL),
(1, 10, 'Black', 'Coming Soon', 114.36, NULL),
(7, 9, 'Light Brown', 'Coming Soon', 181.93, NULL),
(5, 10, 'Black', 'Coming Soon', 108.32, NULL),
(1, 12, 'Black', 'Coming Soon', 153.97, NULL),
(2, 12, 'Gray', 'Coming Soon', 184.27, NULL),
(2, 9, 'Blue', 'Coming Soon', 151.63, NULL),
(6, 8, 'Brown', 'Coming Soon', 159.39, NULL),
(11, 9, 'Red', 'Coming Soon', 150.49, NULL),
(9, 10, 'Gray', 'Coming Soon', 139.26, NULL),
(4, 8, 'Gray', 'Coming Soon', 166.87, NULL),
(12, 9, 'Red', 'Coming Soon', 110.77, NULL);
Insert into Sales Order
INSERT INTO sales_order VALUES
(1, 2, '2018-03-23 10:26:23', 20183231026, 5440314057399014, 3, 5, 415, 'Ashley Martin', NULL),
(8, 2, '2017-01-09 18:58:15', 2017191858, 6298551651340835, 10, 27, 962, 'Michael Smith', NULL),
(9, 3, '2018-12-21 21:26:57', 201812212126, 3194084144609442, 7, 16, 220, 'Lauren Garcia', NULL),
(8, 2, '2017-08-20 15:33:17', 20178201533, 2704487907300646, 7, 10, 430, 'Jessica Robinson', NULL),
(3, 4, '2017-09-19 13:28:35', 20179191328, 8102877849444788, 4, 15, 529, 'Melissa Jones', NULL),
(14, 1, '2016-10-02 18:30:13', 20161021830, 7294221943676784, 10, 22, 323, 'Lauren Moore', NULL),
(4, 2, '2016-03-21 07:24:30', 2016321724, 1791316080799942, 1, 24, 693, 'Joshua Wilson', NULL),
(1, 1, '2018-08-04 12:22:06', 2018841222, 4205390666512184, 5, 16, 758, 'Jennifer Garcia', NULL),
(8, 4, '2016-08-25 10:36:09', 20168251036, 3925972513042074, 1, 10, 587, 'Michael Thomas', NULL),
(8, 4, '2018-08-10 20:24:52', 20188102024, 2515001187633555, 10, 7, 354, 'David Martin', NULL),
(5, 2, '2016-11-28 15:21:48', 201611281521, 6715538212478349, 5, 25, 565, 'Jennifer Johnson', NULL),
(5, 3, '2016-12-07 10:20:05', 20161271020, 5125085038984547, 10, 27, 565, 'Brittany Garcia', NULL),
(13, 3, '2018-10-11 16:27:04', 201810111627, 5559881213107031, 7, 14, 593, 'Sarah Jackson', NULL),
(14, 1, '2018-04-26 20:35:34', 20184262035, 2170089500922701, 7, 26, 105, 'Daniel Harris', NULL),
(3, 2, '2016-11-14 04:32:50', 20161114432, 6389550669359545, 7, 19, 431, 'Brittany Williams', NULL),
(18, 3, '2016-07-10 17:55:01', 20167101755, 7693323933630220, 4, 22, 335, 'Christopher Thomas', NULL),
(12, 2, '2018-05-13 06:20:56', 2018513620, 1634255384507587, 1, 4, 364, 'Megan Garcia', NULL),
(3, 4, '2016-03-04 20:52:36', 2016342052, 7720584466409961, 2, 7, 546, 'Justin Taylor', NULL),
(17, 1, '2017-02-16 15:44:27', 20172161544, 7573753924723630, 3, 15, 148, 'Michael White', NULL),
(19, 3, '2017-08-04 07:24:30', 201784724, 9670036242643402, 10, 24, 803, 'Melissa Taylor', NULL),
(8, 2, '2018-07-08 15:51:11', 2018781551, 5865443195522495, 2, 2, 793, 'James Thompson', NULL),
(18, 1, '2017-03-02 03:08:03', 20173238, 9500873657482557, 6, 22, 793, 'Daniel Williams', NULL),
(7, 1, '2018-03-19 10:54:30', 20183191054, 7685678049357511, 2, 9, 311, 'Joshua Martinez', NULL),
(18, 1, '2017-07-04 18:48:02', 2017741848, 2254223828631172, 6, 18, 621, 'Justin Taylor', NULL),
(16, 1, '2018-07-23 21:44:51', 20187232144, 8669971462260333, 10, 3, 404, 'Ashley Garcia', NULL),
(8, 4, '2016-05-21 16:26:49', 20165211626, 9485792104395686, 2, 4, 270, 'Andrew Taylor', NULL),
(19, 4, '2018-09-04 18:24:36', 2018941824, 5293753403622328, 8, 4, 362, 'Matthew Miller', NULL),
(9, 2, '2018-07-01 18:19:10', 2018711819, 7480694928317516, 10, 5, 547, 'Justin Thompson', NULL),
(8, 4, '2018-09-10 20:15:06', 20189102015, 7284020879927491, 4, 15, 418, 'Samantha Anderson', NULL),
(17, 2, '2016-07-13 16:30:53', 20167131630, 7769197595493852, 1, 19, 404, 'Jessica Thomas', NULL),
(17, 4, '2016-09-22 22:58:11', 20169222258, 1394443435119786, 7, 5, 955, 'James Wilson', NULL),
(17, 4, '2017-10-28 11:35:05', 201710281135, 6788591532433513, 8, 13, 512, 'Michael Williams', NULL),
(12, 4, '2018-11-11 04:55:50', 20181111455, 1854718494260005, 3, 26, 928, 'Melissa Jones', NULL),
(15, 4, '2016-08-11 23:05:58', 2016811235, 7502173302686796, 3, 11, 836, 'Michael Thompson', NULL),
(2, 3, '2018-07-13 07:50:24', 2018713750, 5243198834590551, 10, 12, 725, 'Joseph Thomas', NULL),
(9, 3, '2017-09-28 11:42:16', 20179281142, 7221309687109696, 2, 5, 845, 'James Martinez', NULL),
(7, 1, '2016-01-09 18:15:08', 2016191815, 9202139348760334, 4, 4, 339, 'Samantha Wilson', NULL),
(18, 1, '2016-03-14 17:33:26', 20163141733, 3066530074499665, 6, 23, 835, 'David Garcia', NULL),
(12, 3, '2017-08-21 18:14:01', 20178211814, 1160849457958425, 8, 19, 568, 'Samantha Miller', NULL),
(8, 1, '2018-09-12 19:25:25', 20189121925, 6032844702934349, 8, 13, 662, 'Justin Brown', NULL),
(19, 2, '2016-11-06 03:07:33', 201611637, 1369214097312715, 9, 23, 330, 'Joseph Jones', NULL),
(3, 4, '2016-06-06 01:07:15', 20166617, 7103644598069058, 1, 5, 608, 'Brittany Thomas', NULL),
(13, 4, '2017-05-15 01:02:57', 201751512, 2920333635602602, 11, 14, 139, 'Stephanie Smith', NULL),
(15, 4, '2016-03-27 02:18:18', 2016327218, 7798214190926405, 5, 13, 809, 'Stephanie Taylor', NULL),
(9, 2, '2018-01-25 14:43:01', 20181251443, 4196223548846892, 10, 17, 115, 'Melissa Martin', NULL),
(6, 3, '2017-01-08 13:54:49', 2017181354, 8095784052038731, 8, 23, 416, 'Amanda White', NULL),
(12, 2, '2017-09-24 15:24:44', 20179241524, 6319974420646022, 2, 4, 755, 'Megan Anderson', NULL),
(11, 2, '2018-04-09 18:53:22', 2018491853, 3258192259182097, 11, 22, 730, 'Samantha Thompson', NULL),
(10, 2, '2018-01-11 22:20:29', 20181112220, 8336712415869878, 3, 18, 872, 'Melissa Wilson', NULL),
(14, 3, '2018-11-10 03:08:36', 2018111038, 6942550153605236, 9, 18, 250, 'Jessica Johnson', NULL),
(6, 4, '2016-06-26 16:48:19', 20166261648, 5789348928562200, 2, 7, 458, 'Christopher Jones', NULL),
(5, 1, '2018-06-23 02:25:16', 2018623225, 8550095429571317, 9, 25, 590, 'Samantha Wilson', NULL),
(18, 2, '2017-07-01 01:16:04', 201771116, 2651011719468438, 11, 11, 107, 'Andrew Miller', NULL),
(12, 4, '2017-01-17 21:42:51', 20171172142, 7354378345646144, 3, 14, 772, 'Andrew Moore', NULL),
(7, 3, '2016-01-07 22:56:31', 2016172256, 3429850164043973, 2, 6, 295, 'Joseph Taylor', NULL),
(10, 1, '2016-01-27 01:14:53', 2016127114, 2480926933843246, 7, 3, 704, 'Ashley Taylor', NULL),
(13, 1, '2018-09-15 08:15:17', 2018915815, 6626319262681476, 4, 8, 837, 'Stephanie Thomas', NULL),
(9, 1, '2018-04-06 15:40:28', 2018461540, 4226037621059886, 10, 26, 896, 'Stephanie Jones', NULL),
(17, 3, '2016-10-17 21:31:09', 201610172131, 7862008338119027, 10, 25, 767, 'Amanda Robinson', NULL),
(12, 2, '2016-06-04 22:27:57', 2016642227, 4472081783581101, 10, 9, 279, 'Justin Williams', NULL),
(9, 3, '2018-01-27 06:57:23', 2018127657, 2384491606066483, 11, 23, 417, 'Joshua Garcia', NULL),
(14, 2, '2018-07-19 22:11:23', 20187192211, 2680467440231722, 10, 8, 545, 'Ashley Wilson', NULL),
(19, 4, '2018-11-06 03:12:35', 2018116312, 3973342791188144, 10, 9, 749, 'Megan Martinez', NULL),
(11, 2, '2017-01-15 14:11:54', 20171151411, 3042008865691398, 8, 3, 695, 'Brittany White', NULL),
(10, 4, '2018-10-07 01:26:57', 2018107126, 7226038495242154, 8, 7, 516, 'Stephanie White', NULL),
(12, 3, '2018-10-02 16:13:23', 20181021613, 7474287104417454, 11, 1, 184, 'Daniel Davis', NULL),
(8, 1, '2018-08-12 23:54:52', 20188122354, 6454271840792089, 1, 19, 914, 'Michael Robinson', NULL),
(11, 2, '2016-07-06 04:57:33', 201676457, 6767948287515839, 8, 7, 127, 'Samantha Anderson', NULL),
(12, 2, '2018-09-06 10:34:03', 2018961034, 2724397042248973, 11, 11, 686, 'Ashley Harris', NULL),
(16, 1, '2017-11-12 07:05:38', 2017111275, 4832060124173185, 11, 27, 697, 'Brittany White', NULL),
(16, 4, '2016-06-08 17:38:18', 2016681738, 2187337846675221, 5, 9, 895, 'Megan Wilson', NULL),
(3, 3, '2016-02-08 21:46:46', 2016282146, 8361948319742012, 6, 26, 157, 'Jessica Taylor', NULL),
(8, 1, '2016-10-22 03:01:13', 2016102231, 1748352966511490, 8, 7, 815, 'Justin Davis', NULL),
(5, 4, '2018-12-06 12:51:24', 20181261251, 3987075017699453, 7, 18, 557, 'Andrew Martinez', NULL),
(4, 1, '2017-09-23 07:14:32', 2017923714, 4497706297852239, 2, 12, 756, 'Justin Moore', NULL),
(5, 3, '2016-02-28 23:16:42', 20162282316, 9406399694013062, 1, 26, 853, 'Joseph Moore', NULL),
(11, 4, '2016-05-24 14:37:36', 20165241437, 4754563147105980, 8, 8, 742, 'Amanda Brown', NULL),
(1, 2, '2018-04-08 09:35:58', 201848935, 5031182534686567, 2, 11, 760, 'Andrew Thompson', NULL),
(11, 1, '2017-10-07 20:45:13', 20171072045, 9736660892936088, 5, 19, 240, 'Megan Robinson', NULL),
(19, 2, '2017-03-19 23:03:38', 2017319233, 1154891936822433, 2, 14, 554, 'Christopher Davis', NULL),
(1, 1, '2018-04-26 11:58:53', 20184261158, 5672494499371853, 11, 18, 692, 'James Thomas', NULL),
(1, 3, '2018-07-20 10:05:17', 2018720105, 9695318985866569, 2, 12, 107, 'Jennifer Martin', NULL),
(7, 3, '2018-06-21 18:41:12', 20186211841, 2824438494479373, 1, 12, 296, 'Joseph Miller', NULL),
(6, 1, '2016-04-07 08:47:40', 201647847, 5608599820055114, 7, 2, 163, 'Brittany Brown', NULL),
(15, 3, '2016-07-22 19:25:23', 20167221925, 3011298350076480, 1, 9, 352, 'Jessica Jackson', NULL),
(16, 4, '2016-10-14 10:17:30', 201610141017, 5250543218399397, 9, 16, 975, 'David Wilson', NULL),
(3, 4, '2018-05-15 03:51:28', 2018515351, 8835896606865589, 11, 4, 675, 'Andrew Garcia', NULL),
(19, 3, '2017-05-25 07:44:57', 2017525744, 9159566098395188, 6, 23, 112, 'Ashley Brown', NULL),
(11, 2, '2017-12-02 19:07:39', 2017122197, 9920715756046783, 2, 25, 490, 'Joshua Garcia', NULL),
(7, 4, '2016-05-01 04:50:28', 201651450, 8393790616940265, 9, 22, 490, 'Matthew White', NULL),
(15, 3, '2018-01-21 19:54:46', 20181211954, 8078408967493993, 6, 18, 316, 'Jessica Thomas', NULL),
(6, 1, '2018-04-11 11:23:58', 20184111123, 3921559263693643, 11, 17, 221, 'Andrew Jackson', NULL),
(13, 3, '2018-03-05 10:26:27', 2018351026, 4739593984654108, 10, 18, 925, 'Samantha White', NULL),
(8, 4, '2018-11-15 14:53:55', 201811151453, 8752393645304583, 4, 14, 554, 'Daniel Jackson', NULL),
(10, 1, '2017-09-03 12:57:29', 2017931257, 3434269111389638, 6, 18, 360, 'Megan Johnson', NULL),
(7, 1, '2018-06-28 12:10:58', 20186281210, 6543388006451934, 5, 19, 491, 'Megan Thomas', NULL),
(15, 3, '2018-07-13 12:21:29', 20187131221, 4717498129166869, 5, 21, 386, 'Megan Davis', NULL),
(4, 1, '2016-08-01 16:26:39', 2016811626, 1822404586758111, 3, 2, 346, 'Joseph Davis', NULL),
(3, 2, '2016-10-27 10:53:05', 201610271053, 8446943405552052, 11, 17, 266, 'Daniel Smith', NULL),
(18, 3, '2018-10-20 15:28:54', 201810201528, 6433477195769821, 8, 26, 723, 'Lauren Smith', NULL);
Insert Sales Item
INSERT INTO sales_item VALUES
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(8, 237, 2, 0.16, 0, 0.0, NULL),
(24, 290, 2, 0.06, 0, 0.0, NULL),
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(26, 255, 2, 0.13, 0, 0.0, NULL),
(19, 226, 1, 0.19, 0, 0.0, NULL),
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(48, 224, 2, 0.15, 0, 0.0, NULL),
(30, 211, 2, 0.06, 0, 0.0, NULL),
(1, 260, 2, 0.18, 0, 0.0, NULL),
(48, 202, 2, 0.12, 0, 0.0, NULL),
(35, 234, 2, 0.07, 0, 0.0, NULL),
(29, 213, 1, 0.15, 0, 0.0, NULL),
(15, 298, 2, 0.13, 0, 0.0, NULL),
(27, 235, 2, 0.07, 0, 0.0, NULL),
(30, 205, 1, 0.05, 0, 0.0, NULL),
(45, 233, 1, 0.09, 0, 0.0, NULL),
(31, 220, 1, 0.18, 0, 0.0, NULL),
(32, 288, 1, 0.13, 0, 0.0, NULL),
(47, 243, 1, 0.09, 0, 0.0, NULL),
(23, 220, 2, 0.16, 0, 0.0, NULL),
(44, 286, 2, 0.18, 0, 0.0, NULL),
(35, 275, 2, 0.12, 0, 0.0, NULL),
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(31, 237, 1, 0.14, 0, 0.0, NULL),
(21, 211, 2, 0.14, 0, 0.0, NULL),
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(48, 201, 1, 0.06, 0, 0.0, NULL),
(37, 287, 1, 0.11, 0, 0.0, NULL),
(38, 266, 1, 0.13, 0, 0.0, NULL),
(14, 207, 2, 0.13, 0, 0.0, NULL),
(26, 285, 2, 0.2, 0, 0.0, NULL),
(21, 283, 2, 0.16, 0, 0.0, NULL),
(8, 215, 2, 0.18, 0, 0.0, NULL),
(40, 232, 1, 0.19, 0, 0.0, NULL),
(49, 238, 1, 0.15, 0, 0.0, NULL),
(41, 213, 2, 0.06, 0, 0.0, NULL),
(36, 259, 1, 0.1, 0, 0.0, NULL),
(14, 246, 2, 0.14, 0, 0.0, NULL),
(30, 277, 2, 0.19, 0, 0.0, NULL),
(12, 278, 2, 0.18, 0, 0.0, NULL),
(5, 221, 1, 0.18, 0, 0.0, NULL),
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(42, 240, 2, 0.15, 0, 0.0, NULL),
(8, 248, 2, 0.18, 0, 0.0, NULL),
(27, 282, 2, 0.08, 0, 0.0, NULL),
(21, 263, 1, 0.1, 0, 0.0, NULL),
(42, 221, 2, 0.08, 0, 0.0, NULL),
(31, 223, 2, 0.18, 0, 0.0, NULL),
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(48, 229, 2, 0.14, 0, 0.0, NULL),
(15, 249, 2, 0.15, 0, 0.0, NULL),
(34, 237, 1, 0.16, 0, 0.0, NULL),
(22, 235, 1, 0.19, 0, 0.0, NULL),
(22, 229, 2, 0.11, 0, 0.0, NULL),
(38, 292, 2, 0.08, 0, 0.0, NULL),
(21, 211, 2, 0.17, 0, 0.0, NULL),
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(12, 207, 1, 0.17, 0, 0.0, NULL),
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(39, 282, 2, 0.2, 0, 0.0, NULL),
(27, 272, 2, 0.17, 0, 0.0, NULL),
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(36, 285, 2, 0.18, 0, 0.0, NULL),
(42, 257, 2, 0.18, 0, 0.0, NULL),
(27, 271, 1, 0.19, 0, 0.0, NULL),
(20, 240, 1, 0.18, 0, 0.0, NULL),
(14, 223, 2, 0.16, 0, 0.0, NULL),
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(25, 260, 1, 0.12, 0, 0.0, NULL),
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(18, 290, 2, 0.09, 0, 0.0, NULL),
(17, 206, 2, 0.13, 0, 0.0, NULL),
(43, 217, 1, 0.08, 0, 0.0, NULL),
(20, 233, 2, 0.11, 0, 0.0, NULL),
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(47, 281, 1, 0.16, 0, 0.0, NULL),
(39, 239, 2, 0.17, 0, 0.0, NULL),
(47, 279, 1, 0.12, 0, 0.0, NULL),
(6, 208, 1, 0.17, 0, 0.0, NULL),
(15, 260, 2, 0.11, 0, 0.0, NULL),
(49, 266, 1, 0.15, 0, 0.0, NULL),
(34, 244, 2, 0.09, 0, 0.0, NULL),
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(32, 247, 2, 0.11, 0, 0.0, NULL),
(49, 272, 2, 0.17, 0, 0.0, NULL),
(37, 259, 2, 0.11, 0, 0.0, NULL),
(43, 298, 1, 0.16, 0, 0.0, NULL),
(26, 228, 1, 0.15, 0, 0.0, NULL),
(16, 287, 1, 0.16, 0, 0.0, NULL),
(6, 249, 2, 0.07, 0, 0.0, NULL),
(6, 214, 2, 0.2, 0, 0.0, NULL),
(27, 288, 1, 0.19, 0, 0.0, NULL),
(37, 238, 1, 0.13, 0, 0.0, NULL),
(44, 208, 1, 0.18, 0, 0.0, NULL),
(49, 213, 1, 0.11, 0, 0.0, NULL),
(30, 261, 2, 0.09, 0, 0.0, NULL),
(33, 245, 2, 0.09, 0, 0.0, NULL),
(24, 270, 2, 0.05, 0, 0.0, NULL),
(42, 249, 2, 0.14, 0, 0.0, NULL),
(43, 283, 1, 0.16, 0, 0.0, NULL),
(39, 277, 2, 0.12, 0, 0.0, NULL),
(1, 265, 1, 0.19, 0, 0.0, NULL),
(42, 277, 1, 0.1, 0, 0.0, NULL),
(2, 237, 2, 0.11, 0, 0.0, NULL),
(24, 259, 2, 0.07, 0, 0.0, NULL),
(42, 288, 1, 0.17, 0, 0.0, NULL),
(45, 221, 1, 0.18, 0, 0.0, NULL),
(10, 275, 2, 0.05, 0, 0.0, NULL),
(15, 209, 2, 0.15, 0, 0.0, NULL),
(24, 282, 2, 0.09, 0, 0.0, NULL),
(30, 287, 1, 0.15, 0, 0.0, NULL),
(22, 257, 1, 0.19, 0, 0.0, NULL)
-------- Getting Data from One Table --------
Here we will learn about SELECT, FROM, WHERE, ORDER BY and LIMIT. You've seen a few of these already. Here I'll retrieve all data from from the table sales_item.
SELECT * FROM sales_item;
WHERE is used to define which rows are included in the results based on a condition. Show all sales with a discount greater than 15%
Conditional Operators
= : Equal
< : Less than
> : Greater than
<= : Less than or Equal
>= : Greater than or Equal
<> : Not Equal
!= : Not Equal
SELECT * FROM sales_item WHERE discount > .15;
Logical Operators
AND, OR and NOT are logical operators. Use them to combine conditions. Find the order dates for all orders in December, 2018.
SELECT time_order_taken
FROM sales_order
WHERE time_order_taken > '2018-12-01' AND time_order_taken < '2018-12-31';
You can use BETWEEN to get the same results
SELECT time_order_taken
FROM sales_order
WHERE time_order_taken BETWEEN '2018-12-01' AND '2018-12-31';
ORDER BY determines which column is used to define the order of results. The default order is from low to high.
SELECT * FROM sales_item WHERE discount > .15 ORDER BY discount;
The following gives results from high to low
SELECT * FROM sales_item WHERE discount > .15 ORDER BY discount DESC;
LIMIT limits the number of rows in the result. Get just the top 5. You could use LIMIT 5, 10 to get the next 5
SELECT * FROM sales_item WHERE discount > .15 ORDER BY discount DESC LIMIT 5;
You can limit the results. Get the name, phone number and state where state is Texas. We can use CONCAT to merge to columns. We can then use AS to define a new column name.
SELECT CONCAT(first_name, ' ', last_name) AS Name, phone, state FROM customer WHERE state = 'TX';
You can perform calculations. Get the total value of all business shoes in inventory.
SELECT product_id, SUM(price) AS Value FROM item WHERE product_id = 1;
You can use distinct to eliminate duplicates in results. Get a list of states we have customers in.
SELECT DISTINCT state
FROM customer
ORDER BY state;
Find all states where we have customers not including 'CA'
SELECT DISTINCT state
FROM customer
WHERE state != 'CA'
ORDER BY state;
The IN phrase can be used to test if a value is in a list. Find customer states that are in my list. You can also use NOT IN.
SELECT DISTINCT state
FROM customer
WHERE state IN ('CA', 'NJ')
ORDER BY state;
-------- Getting Data from Multiple Tables --------
We can get results from multiple tables with either inner joins, outer joins, or unions. The most common join is the inner join. You join data from 2 tables in the FROM clause with the JOIN keyword. The ON keyword is used to define the join condition. Get all items ordered ever and sort them by id while listing their price :
SELECT item_id, price
FROM item INNER JOIN sales_item
ON item.id = sales_item.item_id
ORDER BY item_id;
We use the join condition to find ids that are equal in the tables item and sales_item. These joins are normally done using the primary and foreign keys in the tables as we did here. When we join tables while checking for equality between a common column this is called a equijoin.
You can define multiple join conditions with logical operators :
SELECT item_id, price
FROM item INNER JOIN sales_item
ON item.id = sales_item.item_id
AND price > 120.00
ORDER BY item_id;
Now let's join 3 tables. Get the orders, quantity and the total sale.
SELECT sales_order.id, sales_item.quantity, item.price,
(sales_item.quantity * item.price) AS Total
FROM sales_order
JOIN sales_item
ON sales_item.sales_order_id = sales_order.id
JOIN item
ON item.id = sales_item.item_id
ORDER BY sales_order.id;
Other arithmetic operators include :
Addition : +
Subtraction : -
Division : /
Integer Division : DIV
Modulus : %
You can also define the join conditions using WHERE, but this is not considered to be a best practice.
SELECT item_id, price
FROM item, sales_item
WHERE item.id = sales_item.item_id
AND price > 120.00
ORDER BY item_id;
-------- Outer Joins --------
Outer joins return all of the rows from one of the tables being joined even if no matches are found.
A Left Outer Join returns all rows from the table being joined on the left. The Right Outer Join returns all rows from the table on the right. It's common practice to avoid Right Outer joins though.
Here I'll get product information from 2 tables
SELECT name, supplier, price
FROM product LEFT JOIN item
ON item.product_id = product.id
ORDER BY name;
-------- Cross Joins --------
Cross joins include data from each row in both tables. I'll grab information from the item and sales_item table. This will produce many results. Since there are no join conditions in a Cross Join you will rarely use them.
SELECT sales_order_id, quantity, product_id
FROM item CROSS JOIN sales_item
ORDER BY sales_order_id;
-------- Unions --------
Unions combine the results of 2 or more select statements in one result. Each result must return the same number of columns and data in each column must have the same data type.
Let's say we want to send birthday cards to all customers and sales persons for the month of December we could do this. Always put the Order By statement last. The column names are taken from those provided in the 1st select statement.
SELECT first_name, last_name, street, city, zip, birth_date
FROM customer
WHERE MONTH(birth_date) = 12
UNION
SELECT first_name, last_name, street, city, zip, birth_date
FROM sales_person
WHERE MONTH(birth_date) = 12
ORDER BY birth_date;
-------- IS NULL --------
Null is used when a value is not known. IS NULL can be used to search for potential problems.
Search for items with NULL prices
SELECT product_id, price
FROM item
WHERE price = NULL;
You can also use IS NOT NULL
-------- LIKE & REGEXP --------
Like is used to search for simple string matches. Match any customers whose name begins with M
SELECT first_name, last_name
FROM customer
WHERE first_name LIKE "M%";
% matches for zero or more characters.
_ Matches any single character.
SELECT first_name, last_name
FROM customer
WHERE first_name LIKE 'A_____';
Return all customers whose 1st name begins with D, or whose last name ends with an n
SELECT first_name, last_name
FROM customer
WHERE first_name LIKE 'D%' OR last_name LIKE '%n';
REGEXP SLIDE
REGEXP is used to search for complex patterns using regular expressions. Match 1st name that starts with Ma
SELECT first_name, last_name
FROM customer
WHERE first_name REGEXP '^Ma';
Match names that end with ez
SELECT first_name, last_name
FROM customer
WHERE last_name REGEXP 'ez$';
Match last names that contain ez or son
SELECT first_name, last_name
FROM customer
WHERE last_name REGEXP 'ez|son';
Match last names that contain w, x, y or z
SELECT first_name, last_name
FROM customer
WHERE last_name REGEXP '[w-z]';
-------- SUMMARIZING RESULTS --------
GROUP BY defines how the results are grouped. COUNT returns the total number of records that match.
We'll use GROUP BY to return a single row for each unique value. How many customers have birthdays in certain months
SELECT MONTH(birth_date) AS Month, COUNT(*) AS Amount
FROM customer
GROUP BY Month
ORDER BY Month;
HAVING narrows the results based on a condition. Let's only get months if more than 1 person has a birthday that month
SELECT MONTH(birth_date) AS Month, COUNT(*) AS Amount
FROM customer
GROUP BY Month
HAVING Amount > 1
ORDER BY Month;
Get total purchases from each company
SELECT cust_id, company, COUNT(*) AS Purchases
FROM sales_order
JOIN customer
ON customer.id = cust_id
GROUP BY cust_id;
-------- AGGREGATE FUNCTIONS --------
Aggregate functions return a single value from multiple parameters. For example sum all our inventory
SELECT SUM(price)
FROM item;
Get count, sum, min, max and average value of our items
SELECT COUNT(*) AS Items,
SUM(price) AS Value,
ROUND(AVG(price), 2) AS Avg,
MIN(price) AS Min,
MAX(price) AS Max
FROM item;
SLIDE
-------- WORKING WITH VIEWS --------
Views are select statements thats result is stored in your database. Let's create a view that contains our main purchase order info.
CREATE VIEW purchase_order_overview AS
SELECT sales_order.purchase_order_number, customer.company,
sales_item.quantity, product.supplier, product.name, item.price,
(sales_item.quantity * item.price) AS Total,
CONCAT(sales_person.first_name, ' ', sales_person.last_name) AS Salesperson
FROM sales_order
JOIN sales_item
ON sales_item.sales_order_id = sales_order.id
JOIN item
ON item.id = sales_item.item_id
JOIN customer
ON sales_order.cust_id = customer.id
JOIN product
ON product.id = item.product_id
JOIN sales_person
ON sales_person.id = sales_order.sales_person_id
ORDER BY purchase_order_number;
When data in the database is updated so is the view. You can use the view in all the same ways you can a regular table.
SELECT *
FROM purchase_order_overview;
You can drop this view with DROP VIEW purchase_order_overview. Views allow you to hide, or change data in other tables while keeping the view the same. This is beneficial in hiding data from users who shouldn't have access to it.
If you want a view to be updatable it can't include :
DISTINCT
UNION
Aggregate Functions
GROUP BY or HAVING
To make my view updatable I'd change it to
CREATE VIEW purchase_order_overview AS
SELECT sales_order.purchase_order_number, customer.company,
sales_item.quantity, product.supplier, product.name, item.price,
sales_person.first_name AS Sales F Name,
sales_person.last_name AS Sales L Name
FROM sales_order
JOIN sales_item
ON sales_item.sales_order_id = sales_order.id
JOIN item
ON item.id = sales_item.item_id
JOIN customer
ON sales_order.cust_id = customer.id
JOIN product
ON product.id = item.product_id
JOIN sales_person
ON sales_person.id = sales_order.sales_person_id
ORDER BY purchase_order_number;
Recalculate Total
SELECT *, (quantity * price) AS Total
FROM purchase_order_overview;
-------- STORED PROGRAMS --------
You can write programs that are similar to traditional programming languages. There are different types of stored programs.
Stored Procedures can be executed by an application that has access to your database. For example PHP could call for this code to execute.
Stored Functions can be executed by SQL statements.
Triggers execute when Insert, Update or Delete commands are executed.
Events execute at scheduled times.
Create a Stored Procedure that gets a list of customer company names
-- Define that we are using the customer_db DB
USE customer_db;
-- If I already created a procedure with this name
-- delete the previous and update it with new code
DROP PROCEDURE IF EXISTS get_customers;
-- Switch delimiter from ; to // because we use ; to
-- define the end of our statements in our procedure
DELIMITER //
-- Create stored procedure named sample
CREATE PROCEDURE get_customers()
-- Code assigned to the procedure starts at BEGIN
BEGIN
SELECT customer.company
FROM customer;
END //
-- Change delimiter back to ;
DELIMITER ;
-- Execute stored procedure
CALL get_customers();
Create a procedure that returns inventory value based on supplier
-- Define that we are using the customer_db DB
USE customer_db;
DROP PROCEDURE IF EXISTS get_supplier_value;
DELIMITER //
-- Accept an argument being the suppliers name
CREATE PROCEDURE get_supplier_value(IN the_supplier VARCHAR(30))
BEGIN
-- Declare 2 variables to hold results of query
DECLARE supplier_name VARCHAR(30);
DECLARE price_sum DECIMAL(8, 2);
-- Get results of query and store them in variables
SELECT product.supplier, SUM(item.price)
INTO supplier_name, price_sum
FROM product, item
WHERE product.supplier = the_supplier;
-- Output results of query
SELECT CONCAT(supplier_name, ' Inventory Value : $', price_sum) AS output;
END //
DELIMITER ;
-- Pass Nike to the procedure
CALL get_supplier_value('Nike');
Accept multiple values using IN and OUT parameters
-- Define that we are using the customer_db DB
USE customer_db;
DROP PROCEDURE IF EXISTS get_customer_birthday;
DELIMITER //
-- To receive multiple values you must use OUT parameters
-- IN designates a parameter expected to be passed to a procedure
-- it is pass by value and its value is only affected in the procedure
-- OUT designates a variable whose value will be passed back to
-- the calling program. The procedure can't access an OUT parameters
-- value when it's passed.
-- INOUT parameters can both be passed in and returned
CREATE PROCEDURE get_customer_birthday(IN the_month INT,
OUT bd_month INT,
OUT bd_day INT,
OUT f_name VARCHAR(20),
OUT l_name VARCHAR(20),
OUT p_num VARCHAR(20)
)
BEGIN
-- Get results of query and store them in variables
SELECT MONTH(birth_date), DAY(birth_date), first_name, last_name, phone
FROM customer
WHERE MONTH(birth_date) = the_month;
END //
DELIMITER ;
-- Pass the month and the OUT parameters
CALL get_customer_birthday(9, @bd_month, @bd_day, @f_name, @l_name, @p_num);
Check order status with IF ELSEIF and ELSE
USE customer_db;
DROP PROCEDURE IF EXISTS check_month_orders;
DELIMITER //
CREATE PROCEDURE check_month_orders(IN the_month INT)
BEGIN
DECLARE total_orders INT;
SELECT COUNT(purchase_order_number)
INTO total_orders
FROM sales_order
WHERE MONTH(time_order_taken) = the_month;
-- Different output using if
IF total_orders > 5 THEN
SELECT CONCAT(total_orders, ' Orders : Doing Good') AS output;
ELSEIF total_orders < 5 THEN
SELECT CONCAT(total_orders, ' Orders : Doing Bad') AS output;
ELSE
SELECT CONCAT(total_orders, ' Orders : On Target') AS output;
END IF;
END //
DELIMITER ;
-- Pass Nike to the procedure
CALL check_month_orders(9);
Doing the same with CASE
-- Define that we are using the customer_db DB
USE customer_db;
DROP PROCEDURE IF EXISTS check_month_orders;
DELIMITER //
CREATE PROCEDURE check_month_orders(IN the_month INT)
BEGIN
DECLARE total_orders INT;
SELECT COUNT(purchase_order_number)
INTO total_orders
FROM sales_order
WHERE MONTH(time_order_taken) = the_month;
-- Case executes different code depending on an exact value
-- for total_orders or a range of values
CASE
WHEN total_orders < 1 THEN
SELECT CONCAT(total_orders, ' Orders : Terrible') AS output;
WHEN total_orders > 1 < 5 THEN
SELECT CONCAT(total_orders, ' Orders : Get Better') AS output;
WHEN total_orders = 5 THEN
SELECT CONCAT(total_orders, ' Orders : On Target') AS output;
ELSE
SELECT CONCAT(total_orders, ' Orders : Doing Good') AS output;
END CASE;
END //
DELIMITER ;
-- Pass Nike to the procedure
CALL check_month_orders(9);
You can also
CASE total_orders
WHEN 1 THEN
SELECT CONCAT(total_orders, ' Orders : Terrible') AS output;
Sum values up to a max number using while
USE customer_db;
DROP PROCEDURE IF EXISTS loop_test;
DELIMITER //
CREATE PROCEDURE loop_test(IN i INT)
BEGIN
DECLARE j INT DEFAULT 1;
DECLARE tot_sum INT DEFAULT 0;
WHILE j <= i DO
SET tot_sum = tot_sum + j;
SET j = j + 1;
END WHILE;
SELECT CONCAT('sum = ', tot_sum) AS sum;
END //
DELIMITER ;
CALL loop_test(5);
Replace While with Repeat for same results
REPEAT
SET tot_sum = tot_sum + j;
SET j = j + 1;
UNTIL j > i
END REPEAT;
Cursors
Cursors allow you iterate through rows and work with them individually. Get a comma separated list of companies using a cursor
USE customer_db;
DROP PROCEDURE IF EXISTS get_companies;
DELIMITER //
-- We will receive the variable and pass the company list back
CREATE PROCEDURE get_companies(INOUT company_list VARCHAR(1000))
BEGIN
-- When set to 1 we know we are at the end of the list
DECLARE finished INTEGER DEFAULT 0;
-- Will hold each comapny name from the list
DECLARE company_name VARCHAR(30) DEFAULT "";
-- declare cursor for customer list which is populated
-- by SELECT
DECLARE cur_company
CURSOR FOR
SELECT company FROM customer;
-- Handles when no more companies were found
DECLARE CONTINUE HANDLER
FOR NOT FOUND SET finished = 1;
-- Initializes the result set
OPEN cur_company;
-- Will continue looping until LEAVE is called
get_company: LOOP
-- Retrieves the next row of data
FETCH cur_company INTO company_name;
-- Leave loop when no more rows to process
IF finished = 1 THEN
LEAVE get_company;
END IF;
-- Add company to result
SET company_list = CONCAT(company_name, ', ', company_list);
END LOOP get_company;
-- Release memory assigned for the cursor
CLOSE cur_company;
END //
DELIMITER ;
-- Generate a list of companies using a cursor
SET @company_list = '';
CALL get_companies(@company_list);
SELECT @company_list;
-------- ERROR HANDLING --------
You can handle errors in your procedures to avoid errors and provide understandable error messages. We'll create an error where the user tries to use a key already in use and provide a custom error message
-- Define that we are using the customer_db DB
USE customer_db;
DROP PROCEDURE IF EXISTS create_product_type;
DELIMITER //
CREATE PROCEDURE create_product_type(IN prod_name VARCHAR(30),
IN prod_id INT)
BEGIN
-- You are going to block the Duplicate Key error code 1062
-- This is thrown when you try to use a key already in use.
DECLARE EXIT HANDLER FOR 1062
BEGIN
SELECT CONCAT('You tried to use an Key ID that already exists. You can\'t use ID : ', prod_id);
END;
-- This will catch any other errors not caught by the above
DECLARE EXIT HANDLER FOR SQLSTATE '23000'
BEGIN
SELECT CONCAT('An Unknown Error Occurred');
END;
INSERT INTO product_type(name, id)
VALUES(prod_name, prod_id);
-- This isn't executed because you have DECLARE EXIT HANDLER above
-- If you had EXIT CONTINUE it would execute but your error message wouldn't
SELECT *
FROM product_type;
END //
DELIMITER ;
CALL create_product_type('Business Casual', 1);
There is a complete list of error messages here https://dev.mysql.com/doc/refman/5.7/en/server-error-reference.html
-------- RAISING USER DEFINED ERRORS --------
You can catch your own custom errors. Here I'll block a user from entering a duplicate value in the product_type table
-- Define that we are using the customer_db DB
USE customer_db;
DROP PROCEDURE IF EXISTS insert_product_type;
DELIMITER //
CREATE PROCEDURE insert_product_type(IN prod_name VARCHAR(30),
IN prod_id INT)
BEGIN
-- Will hold the result of checking for a name in product_type
DECLARE name_in_table INT;
-- Define the name for our custom error event
DECLARE duplicate_prod_name CONDITION FOR SQLSTATE '50000';
-- This is called if the custom error is thrown
DECLARE EXIT HANDLER FOR duplicate_prod_name
BEGIN
SELECT CONCAT('You can\'t enter a product name already in the table');
END;
-- Check if the name passed is already in the table
SET name_in_table = (SELECT COUNT(*) FROM product_type WHERE product_type.name = prod_name);
-- If name is already in the table trigger an error
-- SIGNAL is used to trigger an error
IF name_in_table = 1 THEN
SIGNAL duplicate_prod_name;
ELSE
INSERT INTO product_type(name, id)
VALUES(prod_name, prod_id);
END IF;
END //
DELIMITER ;
CALL insert_product_type('Athletic', 4);
-------- TRANSACTIONS --------
You can combine multiple SQL statements using a transaction. Within the transaction you can define when statements are committed and roll back statement execution if something goes wrong.
-- Define that we are using the customer_db DB
USE customer_db;
DROP PROCEDURE IF EXISTS insert_sales_item;
DELIMITER //
CREATE PROCEDURE insert_sales_item()
BEGIN
-- Will be true or false based on if errors occur
DECLARE sql_error TINYINT DEFAULT FALSE;
DECLARE CONTINUE HANDLER FOR SQLEXCEPTION
SET sql_error = TRUE;
START TRANSACTION;
INSERT INTO sales_item
VALUES (3, 263, 1, 0.09, 0, 0.0, NULL);
INSERT INTO sales_item
VALUES (26, 283, 1, 0.07, 0, 0.0, NULL);
INSERT INTO sales_item
VALUES (23, 253, 1, 0.05, 0, 0.0, NULL);
IF sql_error = FALSE THEN
COMMIT;
SELECT 'Sales Items Inserted';
ELSE
ROLLBACK;
SELECT 'Transaction Rolled Back';
END IF;
END //
DELIMITER ;
CALL insert_sales_item();
-------- STORED FUNCTIONS --------
You can create your own functions called stored functions. Your functions can only return a single value however. Since you know how to create stored procedures this will be easy to understand.
Here I'll create a function that adds up and order you define.
DELIMITER //
-- Create function and define argument it receives
CREATE FUNCTION get_order_total(sales_order_id INT)
-- Define the return type
RETURNS DECIMAL
-- Deterministic means it returns the same result with same argument passed
-- We define we read data, but don't insert
DETERMINISTIC READS SQL DATA
BEGIN
-- Will store result
DECLARE order_total DECIMAL(8, 2);
-- Sum all totals based on sales_order.id and put the result in order_total
SELECT SUM(sales_item.quantity * item.price) AS Total
INTO order_total
FROM sales_order
JOIN sales_item
ON sales_item.sales_order_id = sales_order.id
JOIN item
ON item.id = sales_item.item_id
WHERE sales_order.id = sales_order_id
GROUP BY sales_order.id
ORDER BY sales_order.id;
-- Return the result
RETURN(order_total);
END//
DELIMITER ;
Call for the function to execute
SELECT sales_order.id, get_order_total(sales_order.id) AS Order_Total
FROM sales_order
ORDER BY sales_order.id;
-------- TRIGGERS --------
Triggers can be executed automatically when a SQL query is executed.
I'll create a procedure that checks if a customer with the new first and last name exists and if it does I throw an error
DROP PROCEDURE IF EXISTS customer_in_db;
DELIMITER //
CREATE PROCEDURE customer_in_db(
f_name VARCHAR(30),
l_name VARCHAR(30)
)
BEGIN
DECLARE match_found INT;
SELECT COUNT(*)
INTO match_found
FROM customer
WHERE first_name = f_name AND last_name = l_name;
IF match_found > 0 THEN
SIGNAL SQLSTATE '50000'
SET MESSAGE_TEXT = 'Customer already exists';
END IF;
END//
DELIMITER ;
Now create a trigger that is fired if an insert is issued on customer
DELIMITER //
CREATE TRIGGER customers_before_insert
-- Call Trigger if customer is updated
BEFORE INSERT ON customer
FOR EACH ROW
BEGIN
CALL customer_in_db(NEW.first_name, NEW.last_name);
END//
DELIMITER ;
Now I try to insert a duplicate customer and it is blocked.
INSERT INTO customer (first_name, last_name, email, company, street, city, state, zip, phone, birth_date, sex, date_entered, id) VALUES
('Melissa', 'Moore', 'laurenthomas@aramark.com', 'Aramark', '322 Pine St', 'Mission', 'TX', '78572', '236-237-1929', '1967-07-23', 'M', '2016-08-15 11:37:14', NULL);