Q:

From the following tables, write a SQL query to find those orders where every order amount less than the maximum order amount of a customer lives in London City

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From the following tables, write a SQL query to find those orders where every order amount less than the maximum order amount of a customer lives in London City. Return ord_no, purch_amt, ord_date, customer_id and salesman_id.

Sample table: orders

ord_no      purch_amt   ord_date    customer_id  salesman_id
----------  ----------  ----------  -----------  -----------
70001       150.5       2012-10-05  3005         5002
70009       270.65      2012-09-10  3001         5005
70002       65.26       2012-10-05  3002         5001
70004       110.5       2012-08-17  3009         5003
70007       948.5       2012-09-10  3005         5002
70005       2400.6      2012-07-27  3007         5001
70008       5760        2012-09-10  3002         5001
70010       1983.43     2012-10-10  3004         5006
70003       2480.4      2012-10-10  3009         5003
70012       250.45      2012-06-27  3008         5002
70011       75.29       2012-08-17  3003         5007
70013       3045.6      2012-04-25  3002         5001
 customer_id |   cust_name    |    city    | grade | salesman_id 
-------------+----------------+------------+-------+-------------
        3002 | Nick Rimando   | New York   |   100 |        5001
        3007 | Brad Davis     | New York   |   200 |        5001
        3005 | Graham Zusi    | California |   200 |        5002
        3008 | Julian Green   | London     |   300 |        5002
        3004 | Fabian Johnson | Paris      |   300 |        5006
        3009 | Geoff Cameron  | Berlin     |   100 |        5003
        3003 | Jozy Altidor   | Moscow     |   200 |        5007
        3001 | Brad Guzan     | London     |       |        5005

All Answers

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SELECT *
FROM orders
WHERE purch_amt < 
   (SELECT MAX (purch_amt)
	FROM orders a, customer b
	WHERE  a.customer_id=b.customer_id
	AND b.city='London');

Output of the Query:

ord_no	purch_amt	ord_date	customer_id	salesman_id
70002	65.26		2012-10-05	3002		5001
70004	110.50		2012-08-17	3009		5003
70011	75.29		2012-08-17	3003		5007
70001	150.50		2012-10-05	3005		5002
70012	250.45		2012-06-27	3008		5002

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