Skip to content

shannonlowder.com

Menu
  • About
  • Biml Interrogator Demo
  • Latest Posts
Menu

SQL 101 – INSERT

Posted on February 15, 2006February 9, 2011 by slowder

In all the previous articles I’ve written on SQL I’ve showed you how to get data out of the database.  Now, we’re switching gears.  I’m going to show you how to put data into the database.  The command to put data into the database is INSERT.  There are four main ways to use this command, so let’s dive right in.

Grab the example file here, so you can follow along on your own database server.

If you wanted to put a record into the productSale table, you have to understand what columns it has, and what types of data it wants for each column.  So let’s take a look at the table definition for productSale.

CREATE TABLE productSale (
buyer VARCHAR(255)
, productName VARCHAR(255)
, purchaseDate DATETIME
, qtypurchased INT
, pricePaid DECIMAL(9,2)
)

Based on this script, you can see the different data types each column expect.  In case you are unfamiliar with the data types, search this site for articles on “data types” and you’ll see a lot more detail than I’ll go into here. A varchar is a variable length string.  Datetimes are dates, int is an integer, and decimal(9,2) is a decimal.  Given this definition we know that the following INSERT statement will work.

INSERT INTO productSale
VALUES('Shannon Lowder', 'paper', '1/1/2000', 2, 1.00)

There is a slightly different version of this statement that lets you use SELECT instead of VALUES.  I prefer this version, since you can only use VALUES with one record of data at a time.  If you use the SELECT version, you can also use UNION or UNION ALL and insert multiple rows at once.

INSERT INTO productSale
SELECT 'Shannon Lowder', 'paper', '1/1/2000', 2, 1.00

Either way, both of the previous statements would insert the same data into the table.  Both show you the bare minimum required to insert a record into a table.  INSERT into [table name], and list the values you want to put into each column.  As long as you list a value for each column, and the data type for each value is compatible with the one the table is expecting, you’re golden.  If you have too many or too few columns, or if any one column has an incompatible type for the column that value is going into… you’ll receive an error from your server telling so.  Also, if you don’t include a value for a column that has been marked required, you’d get an error for that to.

But what if you don’t want to give a value for a certain column.  What if that column is optional, and you don’t want to enter it?  Then you’ll have to tell the interpreter which columns you are passing.  To do so, you have to alter your command slightly

INSERT INTO productSale
(buyer, productName, qtyPurchased, pricePaid)
SELECT 'Shannon Lowder', 'paper', 2, 1.00

Now you can enter just the information you want into the table.  This leaves us with one last method for getting data into a table.  What if we want to take data from one table, and insert it into another table.  We can take the last statement we wrote and alter is again to accomplish this task.

INSERT INTO productSale_test
(buyer, productName, qtyPurchased, pricePaid)
SELECT
    buyer
  , productName
  , qtyPurchased
  , pricePaid
FROM productSale

This way you can select the values from one table, and insert them into another.  Please note I have included the column list between the INSERT INTO and the SELECT statements.  That way the interpreter knows which columns to put the selected data into.

Conclusion

This is your first step into loading data into the database.  No matter how complex the load seems, it will always reduce to a simple INSERT statement.  Practice using this statement and there’s nothing you won’t be able to load.

If you have any problems, questions, or concerns, let me know!  I’m here to help!

Previous: JOIN Next: UPDATE

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recent Posts

  • A New File Interrogator
  • Using Generative AI in Data Engineering
  • Getting started with Microsoft Fabric
  • Docker-based Spark
  • Network Infrastructure Updates

Recent Comments

  1. slowder on Data Engineering for Databricks
  2. Alex Ott on Data Engineering for Databricks

Archives

  • July 2023
  • June 2023
  • March 2023
  • February 2023
  • January 2023
  • December 2022
  • November 2022
  • October 2022
  • October 2018
  • August 2018
  • May 2018
  • February 2018
  • January 2018
  • November 2017
  • October 2017
  • September 2017
  • August 2017
  • June 2017
  • March 2017
  • February 2014
  • January 2014
  • December 2013
  • November 2013
  • October 2013
  • August 2013
  • July 2013
  • June 2013
  • February 2013
  • January 2013
  • August 2012
  • June 2012
  • May 2012
  • April 2012
  • March 2012
  • February 2012
  • January 2012
  • December 2011
  • November 2011
  • October 2011
  • September 2011
  • August 2011
  • July 2011
  • June 2011
  • May 2011
  • April 2011
  • March 2011
  • February 2011
  • January 2011
  • December 2010
  • November 2010
  • October 2010
  • September 2010
  • August 2010
  • July 2010
  • June 2010
  • May 2010
  • April 2010
  • March 2010
  • January 2010
  • December 2009
  • November 2009
  • October 2009
  • September 2009
  • August 2009
  • July 2009
  • June 2009
  • May 2009
  • April 2009
  • March 2009
  • February 2009
  • January 2009
  • December 2008
  • November 2008
  • October 2008
  • September 2008
  • August 2008
  • July 2008
  • June 2008
  • May 2008
  • April 2008
  • March 2008
  • February 2008
  • January 2008
  • November 2007
  • October 2007
  • September 2007
  • August 2007
  • July 2007
  • June 2007
  • May 2007
  • April 2007
  • March 2007
  • February 2007
  • January 2007
  • December 2006
  • November 2006
  • October 2006
  • September 2006
  • August 2006
  • July 2006
  • June 2006
  • May 2006
  • April 2006
  • March 2006
  • February 2006
  • January 2006
  • December 2005
  • November 2005
  • October 2005
  • September 2005
  • August 2005
  • July 2005
  • June 2005
  • May 2005
  • April 2005
  • March 2005
  • February 2005
  • January 2005
  • November 2004
  • September 2004
  • August 2004
  • July 2004
  • April 2004
  • March 2004
  • June 2002

Categories

  • Career Development
  • Data Engineering
  • Data Science
  • Infrastructure
  • Microsoft SQL
  • Modern Data Estate
  • Personal
  • Random Technology
  • uncategorized
© 2025 shannonlowder.com | Powered by Minimalist Blog WordPress Theme