*
Select Everything
ORDER BY
Choose column which you would like your data ordered by. Has option ASCending or DESCending value.
LIMIT
Limit the amount of data you get back from your query.
SUM()
Finds the sum of the data. Access it like this: sum(COLUMN_NAME)
COUNT()
Finds the count, or total number of items, of the data we query for. Access it like this: count(COLUMN_NAME)
AVG()
Finds the average of the data we query for. Access it like this: avg(COLUMN_NAME). Remember average is the total amount divided by the total count
MAX()
Finds the max of the data we query for. Access it like this: max(COLUMN_NAME). Returns only one piece of data.
MIN()
Finds the minimum of the data we query for. Access it like this: min(COLUMN_NAME).
Returns only one piece of data.
LIKE
Underscore to place for single letter
WHERE name LIKE '_onathan'
Percent to match any number of letters before or after depending on placement.
WHERE name LIKE 'J%'
Select Empty Data
SELECT * FROM users
WHERE name IS NULL;
SELECT * FROM users
WHERE name IS NOT NULL
Add Rows
INSERT INTO users (
first_name,
last_name,
) VALUES (
'Bryan',
'Smith',
'bryan@devmountain.com'
);
Delete Rows
DELETE FROM users
WHERE name = 'Bryan';
Be careful with this one. It is always a good practice to do a select first to see what data you will be deleting.
Update Rows
UPDATE users
SET name = 'Jase'
WHERE user_id = 4;
Be careful with this one. It is always a good practice to do a select first to see what data you will be updating.
DISTINCT
SELECT DISTINCT name FROM users;
Find the unique values. No duplicates.
As your application grows, you will find a need for multiple tables. Each table will have data that another table may find useful. The process of splitting out our data into multiple tables is called normalization.
Please don't pile all of your data into one table. This would be like stuffing all of your clothes into one dresser drawer. You want a swimsuit? Too bad, here is that shirt you've had since 8th grade