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SQL inserting records using nested subqueries with any operator

In this page, we are going to discuss, how two or more subqueries can be implemented in an INSERT INTO statement to insert rows into a table.

Example:

Sample table: agent1
 
+------------+----------------------+--------------------+------------+-----------------+---------+
| 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    |         |
+------------+----------------------+--------------------+------------+-----------------+---------+
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    |         |
+------------+----------------------+--------------------+------------+-----------------+---------+
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: 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

To insert records into 'agent1' table from 'agents' table with following conditions -

1. 'agent_code' of agents table must be any 'agent_code' from 'customer' table which satisfies the condition bellow :

2. 'agent_code' of customer table must be any 'agent_code' from 'orders' table which satisfies the condition bellow :

3. 'advance_amount' of 'orders' table must be more than 600,

the following SQL statement can be used:

SQL Code:


-- This SQL code attempts to insert selected rows from the 'agents' table into the 'agent1' table, filtered by nested subqueries.
-- INSERT INTO statement begins
INSERT INTO agent1
-- Specifies the target table 'agent1' where the data will be inserted
SELECT * FROM  agents
-- Selects all columns and rows from the 'agents' table
WHERE agent_code=ANY(
-- Specifies a condition involving a subquery to filter the rows from the 'agents' table
SELECT agent_code FROM customer
-- Selects the 'agent_code' column from the 'customer' table in the subquery
WHERE agent_code =ANY(
-- Specifies another condition involving a nested subquery to further filter the rows
SELECT agent_code FROM orders
-- Selects the 'agent_code' column from the 'orders' table in the nested subquery
WHERE  advance_amount>600));
-- Filters the rows selected from the 'orders' table based on the condition that the 'advance_amount' is greater than 600
-- The intermediate subquery selects the 'agent_code' column from the 'customer' table, filtering the rows based on the condition that the 'agent_code' must match any agent code retrieved from the nested subquery result
-- The outer query filters the rows selected from the 'agents' table based on the condition that the 'agent_code' must be equal to any agent code retrieved from the intermediate subquery result

Explanation:

  • This SQL code aims to copy selected rows from the 'agents' table to the 'agent1' table, based on nested subqueries involving conditions from the 'customer' and 'orders' tables.
  • The INSERT INTO statement specifies the target table 'agent1' where the data will be inserted.
  • The SELECT statement retrieves all columns and rows from the 'agents' table.
  • The WHERE clause includes a condition involving nested subqueries. The innermost subquery selects the 'agent_code' column from the 'orders' table, filtering the rows based on the condition that the 'advance_amount' must be greater than 600.
  • The intermediate subquery selects the 'agent_code' column from the 'customer' table, filtering the rows based on the condition that the 'agent_code' must match any agent code retrieved from the nested subquery result.
  • The outer query filters the rows selected from the 'agents' table based on the condition that the 'agent_code' must be equal to any agent code retrieved from the intermediate subquery result.
  • As a result, only the rows from the 'agents' table corresponding to agents associated with customers having orders with an advance amount greater than 600 will be inserted into the 'agent1' table.

SQL insert using nested subqueries with any operator and group by

In the following we are going to discuss, how an ANY operator with GROUP BY can be used in an INSERT INTO statement to insert records into a table.

Example:

Sample table: agent1
 
+------------+----------------------+--------------------+------------+-----------------+---------+
| 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    |         |
+------------+----------------------+--------------------+------------+-----------------+---------+
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    |         |
+------------+----------------------+--------------------+------------+-----------------+---------+
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: 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

To insert records into 'agent1' table from 'agents' table with following conditions -

1. 'agent_code' of agents table must be any 'agent_code' from 'customer' table which satisfies the condition bellow :

2. 'agent_code' of customer table must be any 'agent_code' from 'orders' table which satisfies the condition bellow :

3. 'ord_amount' of 'orders' table must be more than 1000,

4. 'ord_amount' of 'orders' table should arrange in a group,

the following SQL statement can be used:

SQL Code:


-- This SQL code attempts to insert selected rows from the 'agents' table into the 'agent1' table, filtered by nested subqueries.
-- INSERT INTO statement begins
INSERT INTO agent1
-- Specifies the target table 'agent1' where the data will be inserted
SELECT * FROM  agents
-- Selects all columns and rows from the 'agents' table
WHERE agent_code=ANY(
-- Specifies a condition involving a subquery to filter the rows from the 'agents' table
SELECT agent_code FROM customer
-- Selects the 'agent_code' column from the 'customer' table in the subquery
WHERE agent_code =ANY(
-- Specifies another condition involving a nested subquery to further filter the rows
SELECT agent_code FROM orders
-- Selects the 'agent_code' column from the 'orders' table in the nested subquery
WHERE  ord_amount>1000
-- Filters the rows selected from the 'orders' table based on the condition that the 'ord_amount' is greater than 1000
GROUP BY ord_amount));
-- Groups the rows selected from the 'orders' table by 'ord_amount' and retrieves the unique 'agent_code' values
-- The intermediate subquery selects the 'agent_code' column from the 'customer' table, filtering the rows based on the condition that the 'agent_code' must match any agent code retrieved from the nested subquery result
-- The outer query filters the rows selected from the 'agents' table based on the condition that the 'agent_code' must be equal to any agent code retrieved from the intermediate subquery result

Explanation:

  • This SQL code aims to copy selected rows from the 'agents' table to the 'agent1' table, based on nested subqueries involving conditions from the 'customer' and 'orders' tables.
  • The INSERT INTO statement specifies the target table 'agent1' where the data will be inserted.
  • The SELECT statement retrieves all columns and rows from the 'agents' table.
  • The WHERE clause includes a condition involving nested subqueries. The innermost subquery selects the 'agent_code' column from the 'orders' table, filtering the rows based on the condition that the 'ord_amount' must be greater than 1000.
  • The intermediate subquery selects the 'agent_code' column from the 'customer' table, filtering the rows based on the condition that the 'agent_code' must match any agent code retrieved from the nested subquery result.
  • The outer query filters the rows selected from the 'agents' table based on the condition that the 'agent_code' must be equal to any agent code retrieved from the intermediate subquery result.
  • As a result, only the rows from the 'agents' table corresponding to agents associated with customers having orders with an order amount greater than 1000 will be inserted into the 'agent1' table.

SQL insert using nested subqueries with any operator and group by and order by

In the following we are going to discuss, how an ANY operator with ORDER BY and GROUP BY can be used in an INSERT INTO statement to insert records into a table.

Example:

Sample table: agent1
 
+------------+----------------------+--------------------+------------+-----------------+---------+
| 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    |         |
+------------+----------------------+--------------------+------------+-----------------+---------+
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    |         |
+------------+----------------------+--------------------+------------+-----------------+---------+
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: 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

To insert records into 'agent1' table from 'agents' table with following conditions -

1. 'agent_code' of agents table must be any 'agent_code' from 'customer' table which satisfies the condition bellow :

2. 'agent_code' of customer table must be any 'agent_code' from 'orders' table which satisfies the condition bellow :

3. 'advance_amount' of 'orders' table must be more than 600,

4. same 'advance_amount' of 'orders' table should come in a group,

5. 'advance_amount' of 'orders' table should be arranged in ascending order,

the following SQL statement can be used:

SQL Code:


-- This SQL code attempts to insert selected rows from the 'agents' table into the 'agent1' table, filtered by nested subqueries.
-- INSERT INTO statement begins
INSERT INTO agent1
-- Specifies the target table 'agent1' where the data will be inserted
SELECT * FROM  agents
-- Selects all columns and rows from the 'agents' table
WHERE agent_code=ANY(
-- Specifies a condition involving a subquery to filter the rows from the 'agents' table
SELECT agent_code FROM customer
-- Selects the 'agent_code' column from the 'customer' table in the subquery
WHERE agent_code =ANY(
-- Specifies another condition involving a nested subquery to further filter the rows
SELECT agent_code FROM orders
-- Selects the 'agent_code' column from the 'orders' table in the nested subquery
WHERE  advance_amount>600
-- Filters the rows selected from the 'orders' table based on the condition that the 'advance_amount' is greater than 600
GROUP BY advance_amount
-- Groups the rows selected from the 'orders' table by 'advance_amount'
ORDER BY advance_amount));
-- Orders the rows selected from the 'orders' table in ascending order based on the 'advance_amount'
-- The intermediate subquery selects the 'agent_code' column from the 'customer' table, filtering the rows based on the condition that the 'agent_code' must match any agent code retrieved from the nested subquery result
-- The outer query filters the rows selected from the 'agents' table based on the condition that the 'agent_code' must be equal to any agent code retrieved from the intermediate subquery result

Explanation:

  • This SQL code aims to copy selected rows from the 'agents' table to the 'agent1' table, based on nested subqueries involving conditions from the 'customer' and 'orders' tables.
  • The INSERT INTO statement specifies the target table 'agent1' where the data will be inserted.
  • The SELECT statement retrieves all columns and rows from the 'agents' table.
  • The WHERE clause includes a condition involving nested subqueries. The innermost subquery selects the 'agent_code' column from the 'orders' table, filtering the rows based on the condition that the 'advance_amount' must be greater than 600.
  • The intermediate subquery selects the 'agent_code' column from the 'customer' table, filtering the rows based on the condition that the 'agent_code' must match any agent code retrieved from the nested subquery result.
  • The outer query filters the rows selected from the 'agents' table based on the condition that the 'agent_code' must be equal to any agent code retrieved from the intermediate subquery result.
  • The rows selected from the 'orders' table are also grouped by 'advance_amount' and ordered in ascending order based on 'advance_amount'.

SQL insert using subqueries with max() function

In the following we are going to discuss, how an MAX() function in a subquery can be used in an INSERT INTO statement to insert records into a table.

Example:

Sample table: highorder
   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: 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

To add values against 'ord_amount','ord_date','cust_code' columns in 'highorder' table from 'orders' table with following conditions -

1. 'orders' table have defined as alias 'a' and alias 'b',

2. 'ord_amount' of alias 'a' must equal to maximum 'ord_amount' of alias 'b' which satisfies the condition bellow :

3. 'ord_date' of alias 'a' and alias 'b' must be same,

the following SQL statement can be used:

SQL Code:


-- This SQL code attempts to insert selected rows into the 'highorder' table based on a subquery.
-- INSERT INTO statement begins
INSERT INTO highorder
-- Specifies the target table 'highorder' where the data will be inserted
SELECT ord_amount,ord_date,cust_code
-- Selects specific columns from the 'orders' table
FROM orders a
-- Alias 'a' is assigned to the 'orders' table
WHERE ord_amount=
-- Specifies a condition to filter rows from the 'orders' table
(SELECT MAX(ord_amount)
-- Selects the maximum 'ord_amount' from the 'orders' table
FROM orders b
-- Alias 'b' is assigned to the 'orders' table (used in the subquery)
WHERE a.ord_date=b.ord_date);
-- Filters rows from the 'orders' table where the 'ord_amount' matches the maximum 'ord_amount' for each 'ord_date'

Explanation:

  • This SQL code inserts selected rows into the 'highorder' table from the 'orders' table based on a subquery.
  • The INSERT INTO statement specifies the target table 'highorder' where the data will be inserted.
  • The SELECT statement selects specific columns ('ord_amount', 'ord_date', and 'cust_code') from the 'orders' table.
  • The alias 'a' is assigned to the 'orders' table in the outer query to differentiate it from the 'orders' table in the subquery.
  • The WHERE clause includes a condition that filters rows from the 'orders' table. It selects rows where the 'ord_amount' is equal to the maximum 'ord_amount' for each 'ord_date'.
  • The subquery within the WHERE clause selects the maximum 'ord_amount' for each 'ord_date' from the 'orders' table. It uses alias 'b' for the 'orders' table to distinguish it from the outer query.

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