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create database test;
use test;
create table customers
(
id int,
name varchar(50)
);
create table orders
(
order_id int,
amount int,
cust_id int
);
insert into customers values(1,'John');
insert into customers values(2,'David');
insert into customers values(3,'Ronn');
insert into customers values(4,'Betty');
insert into orders values(1,100,10);
insert into orders values(2,500,3);
insert into orders values(3,300,6);
insert into orders values(4,800,2);
insert into orders values(5,350,1);
select * from customers;
select * from orders;
# Get the orders information along with customers full details
# if order amount were greater than 400
select c.*,o.*
from orders o
inner join customers c on o.cust_id=c.id
where o.amount >400;
select c.*,o.*
from orders o
inner join customers c on o.cust_id=c.id and o.amount >400;
# Window Functions
create table shop_sales_data
(
sales_date date,
shop_id varchar(5),
sales_amount int
);
insert into shop_sales_data values('2022-02-14','S1',200);
insert into shop_sales_data values('2022-02-15','S1',300);
insert into shop_sales_data values('2022-02-14','S2',600);
insert into shop_sales_data values('2022-02-15','S3',500);
insert into shop_sales_data values('2022-02-18','S1',400);
insert into shop_sales_data values('2022-02-17','S2',250);
insert into shop_sales_data values('2022-02-20','S3',300);
# Total count of sales for each shop using window function
# Working functions - SUM(), MIN(), MAX(), COUNT(), AVG()
# If we only use Order by In Over Clause
select *,
sum(sales_amount) over(order by sales_amount desc) as total_sum_of_sales
from shop_sales_data;
# If we only use Partition By
select *,
sum(sales_amount) over(partition by shop_id) as total_sum_of_sales
from shop_sales_data;
# If we only use Partition By & Order By together
select *,
sum(sales_amount) over(partition by shop_id order by sales_amount desc) as total_sum_of_sales
from shop_sales_data;
select shop_id, count(*) as total_sale_count_by_shops from shop_sales_data group by shop_id;
create table amazon_sales_data
(
sales_data date,
sales_amount int
);
insert into amazon_sales_data values('2022-08-21',500);
insert into amazon_sales_data values('2022-08-22',600);
insert into amazon_sales_data values('2022-08-19',300);
insert into amazon_sales_data values('2022-08-18',200);
insert into amazon_sales_data values('2022-08-25',800);
# Query - Calculate the date wise rolling average of amazon sales
select * from amazon_sales_data;
select *,
avg(sales_amount) over(order by sales_data) as rolling_avg
from amazon_sales_data;
select *,
avg(sales_amount) over(order by sales_data) as rolling_avg,
sum(sales_amount) over(order by sales_data) as rolling_sum
from amazon_sales_data;
# Rank(), Row_Number(), Dense_Rank() window functions
insert into shop_sales_data values('2022-02-19','S1',400);
insert into shop_sales_data values('2022-02-20','S1',400);
insert into shop_sales_data values('2022-02-22','S1',300);
insert into shop_sales_data values('2022-02-25','S1',200);
insert into shop_sales_data values('2022-02-15','S2',600);
insert into shop_sales_data values('2022-02-16','S2',600);
insert into shop_sales_data values('2022-02-16','S3',500);
insert into shop_sales_data values('2022-02-18','S3',500);
insert into shop_sales_data values('2022-02-19','S3',300);
select *,
row_number() over(partition by shop_id order by sales_amount desc) as row_num,
rank() over(partition by shop_id order by sales_amount desc) as rank_val,
dense_rank() over(partition by shop_id order by sales_amount desc) as dense_rank_val
from shop_sales_data;
create table employees
(
emp_id int,
salary int,
dept_name VARCHAR(30)
);
insert into employees values(1,10000,'Software');
insert into employees values(2,11000,'Software');
insert into employees values(3,11000,'Software');
insert into employees values(4,11000,'Software');
insert into employees values(5,15000,'Finance');
insert into employees values(6,15000,'Finance');
insert into employees values(7,15000,'IT');
insert into employees values(8,12000,'HR');
insert into employees values(9,12000,'HR');
insert into employees values(10,11000,'HR');
select * from employees;
# Query - get one employee from each department who is getting maximum salary (employee can be random if salary is same)
select
tmp.*
from (select *,
row_number() over(partition by dept_name order by salary desc) as row_num
from employees) tmp
where tmp.row_num = 1;
# Query - get one employee from each department who is getting maximum salary (employee can be random if salary is same)
select
tmp.*
from (select *,
row_number() over(partition by dept_name order by salary desc) as row_num
from employees) tmp
where tmp.row_num = 1;
# Query - get all employees from each department who are getting maximum salary
select
tmp.*
from (select *,
rank() over(partition by dept_name order by salary desc) as rank_num
from employees) tmp
where tmp.rank_num = 1;
# Query - get all top 2 ranked employees from each department who are getting maximum salary
select
tmp.*
from (select *,
dense_rank() over(partition by dept_name order by salary desc) as dense_rank_num
from employees) tmp
where tmp.dense_rank_num <= 2;
# Example for lag and lead
create table daily_sales
(
sales_date date,
sales_amount int
);
insert into daily_sales values('2022-03-11',400);
insert into daily_sales values('2022-03-12',500);
insert into daily_sales values('2022-03-13',300);
insert into daily_sales values('2022-03-14',600);
insert into daily_sales values('2022-03-15',500);
insert into daily_sales values('2022-03-16',200);
select * from daily_sales;
select *,
lag(sales_amount, 1) over(order by sales_date) as pre_day_sales
from daily_sales;
# Query - Calculate the differnce of sales with previous day sales
# Here null will be derived
select sales_date,
sales_amount as curr_day_sales,
lag(sales_amount, 1) over(order by sales_date) as prev_day_sales,
sales_amount - lag(sales_amount, 1) over(order by sales_date) as sales_diff
from daily_sales;
# Here we can replace null with 0
select sales_date,
sales_amount as curr_day_sales,
lag(sales_amount, 1, 0) over(order by sales_date) as prev_day_sales,
sales_amount - lag(sales_amount, 1, 0) over(order by sales_date) as sales_diff
from daily_sales;
# Diff between lead and lag
select *,
lag(sales_amount, 1) over(order by sales_date) as pre_day_sales
from daily_sales;
select *,
lead(sales_amount, 1) over(order by sales_date) as next_day_sales
from daily_sales;
# Diff between lead and lag
select *,
lag(sales_amount, 1) over(order by sales_date) as pre_day_sales
from daily_sales;
select *,
lead(sales_amount, 1) over(order by sales_date) as next_day_sales
from daily_sales;
# How to use Frame Clause - Rows BETWEEN
select * from daily_sales;
select *,
sum(sales_amount) over(order by sales_date rows between 1 preceding and 1 following) as prev_plus_next_sales_sum
from daily_sales;
select *,
sum(sales_amount) over(order by sales_date rows between 1 preceding and current row) as prev_plus_next_sales_sum
from daily_sales;
select *,
sum(sales_amount) over(order by sales_date rows between current row and 1 following) as prev_plus_next_sales_sum
from daily_sales;
select *,
sum(sales_amount) over(order by sales_date rows between 2 preceding and 1 following) as prev_plus_next_sales_sum
from daily_sales;
select *,
sum(sales_amount) over(order by sales_date rows between unbounded preceding and current row) as prev_plus_next_sales_sum
from daily_sales;
select *,
sum(sales_amount) over(order by sales_date rows between current row and unbounded following) as prev_plus_next_sales_sum
from daily_sales;
select *,
sum(sales_amount) over(order by sales_date rows between unbounded preceding and unbounded following) as prev_plus_next_sales_sum
from daily_sales;
# Alternate way to esclude computation of current row
select *,
sum(sales_amount) over(order by sales_date rows between unbounded preceding and unbounded following) - sales_amount as prev_plus_next_sales_sum
from daily_sales;
# How to work with Range Between
select *,
sum(sales_amount) over(order by sales_amount range between 100 preceding and 200 following) as prev_plus_next_sales_sum
from daily_sales;
# Calculate the running sum for a week
# Calculate the running sum for a month
insert into daily_sales values('2022-03-20',900);
insert into daily_sales values('2022-03-23',200);
insert into daily_sales values('2022-03-25',300);
insert into daily_sales values('2022-03-29',250);
select * from daily_sales;
select *,
sum(sales_amount) over(order by sales_date range between interval '6' day preceding and current row) as running_weekly_sum
from daily_sales;