SQL Nested subqueries
Nested subqueries
A subquery can be nested inside other subqueries. SQL has an ability to nest queries within one another. A subquery is a SELECT statement that is nested within another SELECT statement and which return intermediate results. SQL executes innermost subquery first, then next level. See the following examples :
Example -1 : Nested subqueries
If we want to retrieve that unique job_id and there average salary from the employees table which unique job_id have a salary is smaller than (the maximum of averages of min_salary of each unique job_id from the jobs table which job_id are in the list, picking from (the job_history table which is within the department_id 50 and 100)) the following SQL statement can be used :
Sample table: employeesemployee_id first_name last_name email phone_number hire_date job_id salary commission_pct manager_id department_id ----------- ---------- ---------- ---------- ------------ ---------- ---------- ---------- -------------- ---------- ------------- 100 Steven King SKING 515.123.4567 6/17/1987 AD_PRES 24000 90 101 Neena Kochhar NKOCHHAR 515.123.4568 6/18/1987 AD_VP 17000 100 90 102 Lex De Haan LDEHAAN 515.123.4569 6/19/1987 AD_VP 17000 100 90 103 Alexander Hunold AHUNOLD 590.423.4567 6/20/1987 IT_PROG 9000 102 60 104 Bruce Ernst BERNST 590.423.4568 6/21/1987 IT_PROG 6000 103 60 105 David Austin DAUSTIN 590.423.4569 6/22/1987 IT_PROG 4800 103 60 106 Valli Pataballa VPATABAL 590.423.4560 6/23/1987 IT_PROG 4800 103 60 107 Diana Lorentz DLORENTZ 590.423.5567 6/24/1987 IT_PROG 4200 103 60 108 Nancy Greenberg NGREENBE 515.124.4569 6/25/1987 FI_MGR 12000 101 100 109 Daniel Faviet DFAVIET 515.124.4169 6/26/1987 FI_ACCOUNT 9000 108 100 110 John Chen JCHEN 515.124.4269 6/27/1987 FI_ACCOUNT 8200 108 100 111 Ismael Sciarra ISCIARRA 515.124.4369 6/28/1987 FI_ACCOUNT 7700 108 100 112 Jose Manue Urman JMURMAN 515.124.4469 6/29/1987 FI_ACCOUNT 7800 108 100 113 Luis Popp LPOPP 515.124.4567 6/30/1987 FI_ACCOUNT 6900 108 100 114 Den Raphaely DRAPHEAL 515.127.4561 7/1/1987 PU_MAN 11000 100 30 115 Alexander Khoo AKHOO 515.127.4562 7/2/1987 PU_CLERK 3100 114 30 116 Shelli Baida SBAIDA 515.127.4563 7/3/1987 PU_CLERK 2900 114 30 117 Sigal Tobias STOBIAS 515.127.4564 7/4/1987 PU_CLERK 2800 114 30 118 Guy Himuro GHIMURO 515.127.4565 7/5/1987 PU_CLERK 2600 114 30 119 Karen Colmenares KCOLMENA 515.127.4566 7/6/1987 PU_CLERK 2500 114 30 120 Matthew Weiss MWEISS 650.123.1234 7/7/1987 ST_MAN 8000 100 50 121 Adam Fripp AFRIPP 650.123.2234 7/8/1987 ST_MAN 8200 100 50 122 Payam Kaufling PKAUFLIN 650.123.3234 7/9/1987 ST_MAN 7900 100 50 123 Shanta Vollman SVOLLMAN 650.123.4234 7/10/1987 ST_MAN 6500 100 50 124 Kevin Mourgos KMOURGOS 650.123.5234 7/11/1987 ST_MAN 5800 100 50 125 Julia Nayer JNAYER 650.124.1214 7/12/1987 ST_CLERK 3200 120 50 126 Irene Mikkilinen IMIKKILI 650.124.1224 7/13/1987 ST_CLERK 2700 120 50 127 James Landry JLANDRY 650.124.1334 7/14/1987 ST_CLERK 2400 120 50 128 Steven Markle SMARKLE 650.124.1434 7/15/1987 ST_CLERK 2200 120 50 129 Laura Bissot LBISSOT 650.124.5234 7/16/1987 ST_CLERK 3300 121 50 130 Mozhe Atkinson MATKINSO 650.124.6234 7/17/1987 ST_CLERK 2800 121 50 131 James Marlow JAMRLOW 650.124.7234 7/18/1987 ST_CLERK 2500 121 50 132 TJ Olson TJOLSON 650.124.8234 7/19/1987 ST_CLERK 2100 121 50 133 Jason Mallin JMALLIN 650.127.1934 7/20/1987 ST_CLERK 3300 122 50 134 Michael Rogers MROGERS 650.127.1834 7/21/1987 ST_CLERK 2900 122 50 135 Ki Gee KGEE 650.127.1734 7/22/1987 ST_CLERK 2400 122 50 136 Hazel Philtanker HPHILTAN 650.127.1634 7/23/1987 ST_CLERK 2200 122 50 137 Renske Ladwig RLADWIG 650.121.1234 7/24/1987 ST_CLERK 3600 123 50 138 Stephen Stiles SSTILES 650.121.2034 7/25/1987 ST_CLERK 3200 123 50 139 John Seo JSEO 650.121.2019 7/26/1987 ST_CLERK 2700 123 50 140 Joshua Patel JPATEL 650.121.1834 7/27/1987 ST_CLERK 2500 123 50 141 Trenna Rajs TRAJS 650.121.8009 7/28/1987 ST_CLERK 3500 124 50 142 Curtis Davies CDAVIES 650.121.2994 7/29/1987 ST_CLERK 3100 124 50 143 Randall Matos RMATOS 650.121.2874 7/30/1987 ST_CLERK 2600 124 50 144 Peter Vargas PVARGAS 650.121.2004 7/31/1987 ST_CLERK 2500 124 50 145 John Russell JRUSSEL 011.44.1344. 8/1/1987 SA_MAN 14000 0.4 100 80 146 Karen Partners KPARTNER 011.44.1344. 8/2/1987 SA_MAN 13500 0.3 100 80 147 Alberto Errazuriz AERRAZUR 011.44.1344. 8/3/1987 SA_MAN 12000 0.3 100 80 148 Gerald Cambrault GCAMBRAU 011.44.1344. 8/4/1987 SA_MAN 11000 0.3 100 80 149 Eleni Zlotkey EZLOTKEY 011.44.1344. 8/5/1987 SA_MAN 10500 0.2 100 80 150 Peter Tucker PTUCKER 011.44.1344. 8/6/1987 SA_REP 10000 0.3 145 80 151 David Bernstein DBERNSTE 011.44.1344. 8/7/1987 SA_REP 9500 0.25 145 80 152 Peter Hall PHALL 011.44.1344. 8/8/1987 SA_REP 9000 0.25 145 80 153 Christophe Olsen COLSEN 011.44.1344. 8/9/1987 SA_REP 8000 0.2 145 80 154 Nanette Cambrault NCAMBRAU 011.44.1344. 8/10/1987 SA_REP 7500 0.2 145 80 155 Oliver Tuvault OTUVAULT 011.44.1344. 8/11/1987 SA_REP 7000 0.15 145 80 156 Janette King JKING 011.44.1345. 8/12/1987 SA_REP 10000 0.35 146 80 157 Patrick Sully PSULLY 011.44.1345. 8/13/1987 SA_REP 9500 0.35 146 80 158 Allan McEwen AMCEWEN 011.44.1345. 8/14/1987 SA_REP 9000 0.35 146 80 159 Lindsey Smith LSMITH 011.44.1345. 8/15/1987 SA_REP 8000 0.3 146 80 160 Louise Doran LDORAN 011.44.1345. 8/16/1987 SA_REP 7500 0.3 146 80 161 Sarath Sewall SSEWALL 011.44.1345. 8/17/1987 SA_REP 7000 0.25 146 80 162 Clara Vishney CVISHNEY 011.44.1346. 8/18/1987 SA_REP 10500 0.25 147 80 163 Danielle Greene DGREENE 011.44.1346. 8/19/1987 SA_REP 9500 0.15 147 80 164 Mattea Marvins MMARVINS 011.44.1346. 8/20/1987 SA_REP 7200 0.1 147 80 165 David Lee DLEE 011.44.1346. 8/21/1987 SA_REP 6800 0.1 147 80 166 Sundar Ande SANDE 011.44.1346. 8/22/1987 SA_REP 6400 0.1 147 80 167 Amit Banda ABANDA 011.44.1346. 8/23/1987 SA_REP 6200 0.1 147 80 168 Lisa Ozer LOZER 011.44.1343. 8/24/1987 SA_REP 11500 0.25 148 80 169 Harrison Bloom HBLOOM 011.44.1343. 8/25/1987 SA_REP 10000 0.2 148 80 170 Tayler Fox TFOX 011.44.1343. 8/26/1987 SA_REP 9600 0.2 148 80 171 William Smith WSMITH 011.44.1343. 8/27/1987 SA_REP 7400 0.15 148 80 172 Elizabeth Bates EBATES 011.44.1343. 8/28/1987 SA_REP 7300 0.15 148 80 173 Sundita Kumar SKUMAR 011.44.1343. 8/29/1987 SA_REP 6100 0.1 148 80 174 Ellen Abel EABEL 011.44.1644. 8/30/1987 SA_REP 11000 0.3 149 80 175 Alyssa Hutton AHUTTON 011.44.1644. 8/31/1987 SA_REP 8800 0.25 149 80 176 Jonathon Taylor JTAYLOR 011.44.1644. 9/1/1987 SA_REP 8600 0.2 149 80 177 Jack Livingston JLIVINGS 011.44.1644. 9/2/1987 SA_REP 8400 0.2 149 80 178 Kimberely Grant KGRANT 011.44.1644. 9/3/1987 SA_REP 7000 0.15 149 179 Charles Johnson CJOHNSON 011.44.1644. 9/4/1987 SA_REP 6200 0.1 149 80 180 Winston Taylor WTAYLOR 650.507.9876 9/5/1987 SH_CLERK 3200 120 50 181 Jean Fleaur JFLEAUR 650.507.9877 9/6/1987 SH_CLERK 3100 120 50 182 Martha Sullivan MSULLIVA 650.507.9878 9/7/1987 SH_CLERK 2500 120 50 183 Girard Geoni GGEONI 650.507.9879 9/8/1987 SH_CLERK 2800 120 50 184 Nandita Sarchand NSARCHAN 650.509.1876 9/9/1987 SH_CLERK 4200 121 50 185 Alexis Bull ABULL 650.509.2876 9/10/1987 SH_CLERK 4100 121 50 186 Julia Dellinger JDELLING 650.509.3876 9/11/1987 SH_CLERK 3400 121 50 187 Anthony Cabrio ACABRIO 650.509.4876 9/12/1987 SH_CLERK 3000 121 50 188 Kelly Chung KCHUNG 650.505.1876 9/13/1987 SH_CLERK 3800 122 50 189 Jennifer Dilly JDILLY 650.505.2876 9/14/1987 SH_CLERK 3600 122 50 190 Timothy Gates TGATES 650.505.3876 9/15/1987 SH_CLERK 2900 122 50 191 Randall Perkins RPERKINS 650.505.4876 9/16/1987 SH_CLERK 2500 122 50 192 Sarah Bell SBELL 650.501.1876 9/17/1987 SH_CLERK 4000 123 50 193 Britney Everett BEVERETT 650.501.2876 9/18/1987 SH_CLERK 3900 123 50 194 Samuel McCain SMCCAIN 650.501.3876 9/19/1987 SH_CLERK 3200 123 50 195 Vance Jones VJONES 650.501.4876 9/20/1987 SH_CLERK 2800 123 50 196 Alana Walsh AWALSH 650.507.9811 9/21/1987 SH_CLERK 3100 124 50 197 Kevin Feeney KFEENEY 650.507.9822 9/22/1987 SH_CLERK 3000 124 50 198 Donald OConnell DOCONNEL 650.507.9833 9/23/1987 SH_CLERK 2600 124 50 199 Douglas Grant DGRANT 650.507.9844 9/24/1987 SH_CLERK 2600 124 50 200 Jennifer Whalen JWHALEN 515.123.4444 9/25/1987 AD_ASST 4400 101 10 201 Michael Hartstein MHARTSTE 515.123.5555 9/26/1987 MK_MAN 13000 100 20 202 Pat Fay PFAY 603.123.6666 9/27/1987 MK_REP 6000 201 20 203 Susan Mavris SMAVRIS 515.123.7777 9/28/1987 HR_REP 6500 101 40 204 Hermann Baer HBAER 515.123.8888 9/29/1987 PR_REP 10000 101 70 205 Shelley Higgins SHIGGINS 515.123.8080 9/30/1987 AC_MGR 12000 101 110 206 William Gietz WGIETZ 515.123.8181 10/1/1987 AC_ACCOUNT 8300 205 110Sample table: jobs
| JOB_ID | JOB_TITLE | MIN_SALARY | MAX_SALARY |
|---|---|---|---|
| AD_PRES | President | 20000 | 40000 |
| AD_VP | Administration Vice President | 15000 | 30000 |
| AD_ASST | Administration Assistant | 3000 | 6000 |
| FI_MGR | Finance Manager | 8200 | 16000 |
| FI_ACCOUNT | Accountant | 4200 | 9000 |
| AC_MGR | Accounting Manager | 8200 | 16000 |
| AC_ACCOUNT | Public Accountant | 4200 | 9000 |
| SA_MAN | Sales Manager | 10000 | 20000 |
| SA_REP | Sales Representative | 6000 | 12000 |
| PU_MAN | Purchasing Manager | 8000 | 15000 |
| PU_CLERK | Purchasing Clerk | 2500 | 5500 |
| ST_MAN | Stock Manager | 5500 | 8500 |
| ST_CLERK | Stock Clerk | 2000 | 5000 |
| SH_CLERK | Shipping Clerk | 2500 | 5500 |
| IT_PROG | Programmer | 4000 | 10000 |
| MK_MAN | Marketing Manager | 9000 | 15000 |
| MK_REP | Marketing Representative | 4000 | 9000 |
| HR_REP | Human Resources Representative | 4000 | 9000 |
| PR_REP | Public Relations Representative | 4500 | 10500 |
SQL Code:
-- Selecting job_id and the average salary from the employees table
SELECT job_id, AVG(salary)
-- Grouping the results by job_id
FROM employees
GROUP BY job_id
-- Filtering the grouped results based on a condition
HAVING AVG(salary) <
(
-- Selecting the maximum of the average of min_salary from the jobs table
SELECT MAX(AVG(min_salary))
-- From the jobs table
FROM jobs
-- Filtering jobs where the job_id is in the subquery result
WHERE job_id IN
(
-- Selecting job_id from job_history table
SELECT job_id
-- Filtering job_history where department_id is between 50 and 100
FROM job_history
WHERE department_id BETWEEN 50 AND 100
)
-- Grouping the results by job_id from the subquery
GROUP BY job_id
);
The above code is executed in Oracle 11g Express Edition.
Explanation:
- This SQL query calculates the average salary for each job_id from the "employees" table.
- It groups the results by job_id.
- The HAVING clause filters the grouped results based on a condition.
- The condition compares the average salary for each job_id with the maximum average of the minimum salary for jobs within specific department_ids.
- The subquery selects the maximum of the average of min_salary from the "jobs" table, filtering it based on the job_ids present in the subquery result.
- The subquery within the WHERE clause selects job_ids from the "job_history" table where the department_id falls between 50 and 100.
- The main query then selects job_ids and their corresponding average salaries where the average salary is less than the maximum average of the minimum salary for jobs within specific department_ids.
or
-- Selecting job_id and the average salary from the employees table
SELECT job_id, AVG(salary)
-- Grouping the results by job_id
FROM employees
GROUP BY job_id
-- Filtering the grouped results based on a condition
HAVING AVG(salary) <
(
-- Selecting the maximum of the myavg column from the subquery
SELECT MAX(myavg)
-- Selecting job_id and average minimum salary as myavg from the jobs table
FROM (SELECT job_id, AVG(min_salary) as myavg
-- From the jobs table
FROM jobs
-- Filtering jobs where the job_id is in the subquery result
WHERE job_id IN
(
-- Selecting job_id from job_history table
SELECT job_id
-- Filtering job_history where department_id is between 50 and 100
FROM job_history
WHERE department_id BETWEEN 50 AND 100
)
-- Grouping the results by job_id
GROUP BY job_id) ss
);
The above code is executed in PostgreSQL 9.3
Explanation:
- This SQL query calculates the average salary for each job_id from the "employees" table.
- It groups the results by job_id.
- The HAVING clause filters the grouped results based on a condition.
- The condition compares the average salary for each job_id with the maximum average of the minimum salary for jobs within specific department_ids.
- The subquery selects the maximum value from the "myavg" column.
- This subquery selects job_ids and their corresponding average minimum salaries, filtering it based on the job_ids present in the subquery result.
- The subquery within the WHERE clause selects job_ids from the "job_history" table where the department_id falls between 50 and 100.
- The main query then selects job_ids and their corresponding average salaries where the average salary is less than the maximum average of the minimum salary for jobs within specific department_ids.
Output
JOB_ID AVG(SALARY) ---------- ----------- IT_PROG 5760 AC_ACCOUNT 8300 ST_MAN 7280 AD_ASST 4400 SH_CLERK 3215 FI_ACCOUNT 7920 PU_CLERK 2780 SA_REP 8350 MK_REP 6000 ST_CLERK 2785 HR_REP 6500
Let's break the example down into three parts and observes the results returned.
Atfirst the nested subquery as follows:
SQL Code:
-- Selecting job_id from the job_history table
SELECT job_id
-- Filtering job_history records based on department_id
WHERE department_id
-- Specifying the range for department_id filtering
BETWEEN 50 AND 100;
Explanation:
- This SQL query retrieves job_ids from the "job_history" table.
- It filters the records based on the department_id.
- The BETWEEN operator is used to specify a range for department_id filtering, including values between 50 and 100.
- The query returns job_ids associated with department_ids falling within the specified range.
This nested subquery retrieves the job_id(s) from job_history table which is within the department_id 50 and 100.
Here is the output.
Output:
JOB_ID ---------- ST_CLERK ST_CLERK IT_PROG SA_REP SA_MAN AD_ASST AC_ACCOUNT
Here is the visual representation of how the above output comes.
Now the subquery that receives output from the nested subquery stated above.
SELECT MAX(AVG(min_salary))
FROM jobs WHERE job_id
IN(.....output from the nested subquery......)
GROUP BY job_id
The subquery internally works as follows:
SQL Code:
-- Selecting the maximum of the average of min_salary from the jobs table
SELECT MAX(AVG(min_salary))
-- From the jobs table
FROM jobs
-- Filtering jobs based on job_id
WHERE job_id
-- Specifying the list of job_ids for filtering
IN (
'ST_CLERK', 'ST_CLERK', 'IT_PROG', 'SA_REP', 'SA_MAN', 'AD_ASST', 'AC_ACCOUNT'
)
-- Grouping the results by job_id
GROUP BY job_id;
Explanation:
- This SQL query calculates the average minimum salary for each job_id from the "jobs" table.
- It first filters the jobs based on specific job_ids listed.
- The MAX() function is then applied to get the maximum value of the average minimum salary across all the job_ids.
- The GROUP BY clause groups the results by job_id, allowing the calculation of the average minimum salary for each job_id separately.
- The query returns the maximum value among these averages.
Here is the output:
Output:
MAX(AVG(MIN_SALARY))
--------------------
10000
Here is the Visual representation of how the above output returns.
Now the outer query that receives output from the subquery and which also receives the output from the nested subquery stated above.
SELECT job_id,AVG(salary)
FROM employees
GROUP BY job_id
HAVING AVG(salary)<
(.....output from the subquery(
output from the nested subquery)......)
The outer query internally works as follows:
SQL Code:
-- Selecting job_id and the average salary from the employees table
SELECT job_id, AVG(salary)
-- Grouping the results by job_id
FROM employees
GROUP BY job_id
-- Filtering the grouped results based on a condition
HAVING AVG(salary) < 10000;
Explanation:
- This SQL query calculates the average salary for each job_id from the "employees" table.
- It groups the results by job_id.
- The HAVING clause filters the grouped results based on a condition.
- The condition specifies that only those records should be selected where the average salary for each job_id is less than 10000.
- The query returns job_ids and their corresponding average salaries where the average salary is less than 10000.
Output:
JOB_ID AVG(SALARY) ---------- ----------- IT_PROG 5760 AC_ACCOUNT 8300 ST_MAN 7280 AD_ASST 4400 SH_CLERK 3215 FI_ACCOUNT 7920 PU_CLERK 2780 SA_REP 8350 MK_REP 6000 ST_CLERK 2785 HR_REP 6500
Example -2 : Nested subqueries
Here is an another nested subquery example.
Sample table: orders
ORD_NUM ORD_AMOUNT ADVANCE_AMOUNT ORD_DATE CUST_CODE AGENT_CODE ORD_DESCRIPTION
---------- ---------- -------------- --------- --------------- --------------- -----------------
200114 3500 2000 15-AUG-08 C00002 A008
200122 2500 400 16-SEP-08 C00003 A004
200118 500 100 20-JUL-08 C00023 A006
200119 4000 700 16-SEP-08 C00007 A010
200121 1500 600 23-SEP-08 C00008 A004
200130 2500 400 30-JUL-08 C00025 A011
200134 4200 1800 25-SEP-08 C00004 A005
200108 4000 600 15-FEB-08 C00008 A004
200103 1500 700 15-MAY-08 C00021 A005
200105 2500 500 18-JUL-08 C00025 A011
200109 3500 800 30-JUL-08 C00011 A010
200101 3000 1000 15-JUL-08 C00001 A008
200111 1000 300 10-JUL-08 C00020 A008
200104 1500 500 13-MAR-08 C00006 A004
200106 2500 700 20-APR-08 C00005 A002
200125 2000 600 10-OCT-08 C00018 A005
200117 800 200 20-OCT-08 C00014 A001
200123 500 100 16-SEP-08 C00022 A002
200120 500 100 20-JUL-08 C00009 A002
200116 500 100 13-JUL-08 C00010 A009
200124 500 100 20-JUN-08 C00017 A007
200126 500 100 24-JUN-08 C00022 A002
200129 2500 500 20-JUL-08 C00024 A006
200127 2500 400 20-JUL-08 C00015 A003
200128 3500 1500 20-JUL-08 C00009 A002
200135 2000 800 16-SEP-08 C00007 A010
200131 900 150 26-AUG-08 C00012 A012
200133 1200 400 29-JUN-08 C00009 A002
200100 1000 600 08-JAN-08 C00015 A003
200110 3000 500 15-APR-08 C00019 A010
200107 4500 900 30-AUG-08 C00007 A010
200112 2000 400 30-MAY-08 C00016 A007
200113 4000 600 10-JUN-08 C00022 A002
200102 2000 300 25-MAY-08 C00012 A012
Sample table : customer
+-----------+-------------+-------------+--------------+--------------+-------+-------------+-------------+-------------+---------------+--------------+------------+ |CUST_CODE | CUST_NAME | CUST_CITY | WORKING_AREA | CUST_COUNTRY | GRADE | OPENING_AMT | RECEIVE_AMT | PAYMENT_AMT |OUTSTANDING_AMT| PHONE_NO | AGENT_CODE | +-----------+-------------+-------------+--------------+--------------+-------+-------------+-------------+-------------+---------------+--------------+------------+ | C00013 | Holmes | London | London | UK | 2 | 6000.00 | 5000.00 | 7000.00 | 4000.00 | BBBBBBB | A003 | | C00001 | Micheal | New York | New York | USA | 2 | 3000.00 | 5000.00 | 2000.00 | 6000.00 | CCCCCCC | A008 | | C00020 | Albert | New York | New York | USA | 3 | 5000.00 | 7000.00 | 6000.00 | 6000.00 | BBBBSBB | A008 | | C00025 | Ravindran | Bangalore | Bangalore | India | 2 | 5000.00 | 7000.00 | 4000.00 | 8000.00 | AVAVAVA | A011 | | C00024 | Cook | London | London | UK | 2 | 4000.00 | 9000.00 | 7000.00 | 6000.00 | FSDDSDF | A006 | | C00015 | Stuart | London | London | UK | 1 | 6000.00 | 8000.00 | 3000.00 | 11000.00 | GFSGERS | A003 | | C00002 | Bolt | New York | New York | USA | 3 | 5000.00 | 7000.00 | 9000.00 | 3000.00 | DDNRDRH | A008 | | C00018 | Fleming | Brisban | Brisban | Australia | 2 | 7000.00 | 7000.00 | 9000.00 | 5000.00 | NHBGVFC | A005 | | C00021 | Jacks | Brisban | Brisban | Australia | 1 | 7000.00 | 7000.00 | 7000.00 | 7000.00 | WERTGDF | A005 | | C00019 | Yearannaidu | Chennai | Chennai | India | 1 | 8000.00 | 7000.00 | 7000.00 | 8000.00 | ZZZZBFV | A010 | | C00005 | Sasikant | Mumbai | Mumbai | India | 1 | 7000.00 | 11000.00 | 7000.00 | 11000.00 | 147-25896312 | A002 | | C00007 | Ramanathan | Chennai | Chennai | India | 1 | 7000.00 | 11000.00 | 9000.00 | 9000.00 | GHRDWSD | A010 | | C00022 | Avinash | Mumbai | Mumbai | India | 2 | 7000.00 | 11000.00 | 9000.00 | 9000.00 | 113-12345678 | A002 | | C00004 | Winston | Brisban | Brisban | Australia | 1 | 5000.00 | 8000.00 | 7000.00 | 6000.00 | AAAAAAA | A005 | | C00023 | Karl | London | London | UK | 0 | 4000.00 | 6000.00 | 7000.00 | 3000.00 | AAAABAA | A006 | | C00006 | Shilton | Torento | Torento | Canada | 1 | 10000.00 | 7000.00 | 6000.00 | 11000.00 | DDDDDDD | A004 | | C00010 | Charles | Hampshair | Hampshair | UK | 3 | 6000.00 | 4000.00 | 5000.00 | 5000.00 | MMMMMMM | A009 | | C00017 | Srinivas | Bangalore | Bangalore | India | 2 | 8000.00 | 4000.00 | 3000.00 | 9000.00 | AAAAAAB | A007 | | C00012 | Steven | San Jose | San Jose | USA | 1 | 5000.00 | 7000.00 | 9000.00 | 3000.00 | KRFYGJK | A012 | | C00008 | Karolina | Torento | Torento | Canada | 1 | 7000.00 | 7000.00 | 9000.00 | 5000.00 | HJKORED | A004 | | C00003 | Martin | Torento | Torento | Canada | 2 | 8000.00 | 7000.00 | 7000.00 | 8000.00 | MJYURFD | A004 | | C00009 | Ramesh | Mumbai | Mumbai | India | 3 | 8000.00 | 7000.00 | 3000.00 | 12000.00 | Phone No | A002 | | C00014 | Rangarappa | Bangalore | Bangalore | India | 2 | 8000.00 | 11000.00 | 7000.00 | 12000.00 | AAAATGF | A001 | | C00016 | Venkatpati | Bangalore | Bangalore | India | 2 | 8000.00 | 11000.00 | 7000.00 | 12000.00 | JRTVFDD | A007 | | C00011 | Sundariya | Chennai | Chennai | India | 3 | 7000.00 | 11000.00 | 7000.00 | 11000.00 | PPHGRTS | A010 | +-----------+-------------+-------------+--------------+--------------+-------+-------------+-------------+-------------+---------------+--------------+------------+Sample table : agents
+------------+----------------------+--------------------+------------+-----------------+---------+ | AGENT_CODE | AGENT_NAME | WORKING_AREA | COMMISSION | PHONE_NO | COUNTRY | +------------+----------------------+--------------------+------------+-----------------+---------+ | A007 | Ramasundar | Bangalore | 0.15 | 077-25814763 | | | A003 | Alex | London | 0.13 | 075-12458969 | | | A008 | Alford | New York | 0.12 | 044-25874365 | | | A011 | Ravi Kumar | Bangalore | 0.15 | 077-45625874 | | | A010 | Santakumar | Chennai | 0.14 | 007-22388644 | | | A012 | Lucida | San Jose | 0.12 | 044-52981425 | | | A005 | Anderson | Brisban | 0.13 | 045-21447739 | | | A001 | Subbarao | Bangalore | 0.14 | 077-12346674 | | | A002 | Mukesh | Mumbai | 0.11 | 029-12358964 | | | A006 | McDen | London | 0.15 | 078-22255588 | | | A004 | Ivan | Torento | 0.15 | 008-22544166 | | | A009 | Benjamin | Hampshair | 0.11 | 008-22536178 | | +------------+----------------------+--------------------+------------+-----------------+---------+
SQL Code:
-- Selecting specific columns from the orders table
SELECT ord_num, ord_date, ord_amount, advance_amount
-- Filtering orders based on multiple conditions
FROM orders
-- Specifying conditions for filtering
WHERE ord_amount > 2000
-- Filtering based on order amount greater than 2000
AND ord_date < '01-SEP-08'
-- Filtering based on order date before 01-SEP-08
AND ADVANCE_AMOUNT <
-- Comparing advance_amount with values from a subquery result
ANY(
SELECT OUTSTANDING_AMT
-- Selecting outstanding amounts from the CUSTOMER table
FROM CUSTOMER
-- Specifying conditions for filtering customers
WHERE GRADE = 3
-- Filtering based on customer grade being 3
AND CUST_COUNTRY <> 'India'
-- Filtering based on customer country not being India
AND opening_amt < 7000
-- Filtering based on opening amount less than 7000
AND EXISTS
-- Checking for existence of records in a subquery
(
SELECT *
-- Selecting all columns from the agents table
FROM agents
-- Specifying conditions for filtering agents
WHERE commission < .12
-- Filtering based on commission less than 0.12
)
);
Explanation:
- This SQL query retrieves specific columns from the "orders" table.
- It filters the orders based on multiple conditions:
- Orders with ord_amount greater than 2000.
- Orders with ord_date before 01-SEP-08.
- Orders with advance_amount less than any of the values retrieved from a subquery.
- The subquery retrieves outstanding amounts from the CUSTOMER table based on several conditions.
- The conditions include GRADE being 3, CUST_COUNTRY not being 'India', and opening_amt less than 7000.
- The subquery also includes an EXISTS clause to check for the existence of records in another subquery.
- This inner subquery selects all columns from the agents table and filters them based on the commission being less than 0.12.
- Overall, the query selects orders that meet the specified conditions on ord_amount, ord_date, advance_amount, and the results of the subquery.
Output:
ORD_NUM ORD_DATE ORD_AMOUNT ADVANCE_AMOUNT
---------- --------- ---------- --------------
200130 30-JUL-08 2500 400
200127 20-JUL-08 2500 400
200110 15-APR-08 3000 500
200105 18-JUL-08 2500 500
200129 20-JUL-08 2500 500
200108 15-FEB-08 4000 600
200113 10-JUN-08 4000 600
200106 20-APR-08 2500 700
200109 30-JUL-08 3500 800
200107 30-AUG-08 4500 900
200101 15-JUL-08 3000 1000
200128 20-JUL-08 3500 1500
200114 15-AUG-08 3500 2000
Let's break the code and analyze what's going on in inner query. Here is the first code of inner query with output :
SQL Code:
SELECT *
FROM agents
WHERE commission<.12;
Output:
AGENT_CODE AGENT_NAME WORKING_AREA COMMISSION PHONE_NO COUNTRY ---------- --------------- ----------------- ---------- --------------- --------- A009 Benjamin Hampshair .11 008-22536178 A002 Mukesh Mumbai .11 029-12358964
Here is the second code of inner query (including first one) with output :
SQL Code:
-- Selecting outstanding amounts from the CUSTOMER table
SELECT OUTSTANDING_AMT
-- Filtering customers based on multiple conditions
FROM CUSTOMER
-- Specifying conditions for filtering customers
WHERE GRADE = 3
-- Filtering based on customer grade being 3
AND CUST_COUNTRY <> 'India'
-- Filtering based on customer country not being India
AND opening_amt < 7000
-- Filtering based on opening amount less than 7000
AND EXISTS(
-- Checking for existence of records in a subquery
SELECT *
-- Selecting all columns from the agents table
FROM agents
-- Specifying conditions for filtering agents
WHERE commission < .12
-- Filtering based on commission less than 0.12
);
Explanation:
- This SQL query retrieves outstanding amounts from the CUSTOMER table.
- It filters the customers based on multiple conditions:
- Customers with GRADE equal to 3.
- Customers with CUST_COUNTRY not equal to 'India'.
- Customers with opening_amt less than 7000.
- The query also includes an EXISTS clause to check for the existence of records in a subquery.
- The subquery selects all columns from the agents table and filters them based on the commission being less than 0.12.
- Overall, the query selects outstanding amounts from customers who meet the specified conditions on GRADE, CUST_COUNTRY, opening_amt, and the existence of agents with a commission less than 0.12.
Output:
OUTSTANDING_AMT
---------------
6000
3000
5000
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